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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024; 81:456-467. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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Dintica CS, Habes M, Schreiner PJ, Launer LJ, Yaffe K. Trajectories in depressive symptoms and midlife brain health. Transl Psychiatry 2024; 14:169. [PMID: 38553474 PMCID: PMC10980805 DOI: 10.1038/s41398-024-02883-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/02/2024] Open
Abstract
Depressive symptoms may either be a risk factor or prodromal to dementia. Investigating this association in midlife may help clarify the role of depression in cognitive aging. We aimed to identify trajectories in depressive symptoms in early to mid-life and related cognitive and brain outcomes in midlife. This study includes 3944 Black and White participants (ages 26-45 years at baseline) from the Coronary Artery Risk Development in Young Adults (CARDIA) study with 20 years of follow-up. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression scale at five time points over 20 years. Growth mixture modeling (GMM) was used to identify depressive symptom trajectories. Participants completed a neuropsychological battery 20 years after baseline, including the Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment (MoCA), and category and letter fluency tests. A sub-sample of participants (n = 662) underwent brain magnetic resonance imaging (MRI) to characterize gray matter volumes and white matter hyperintensities (WMHs). We identified four classes of depressive symptom trajectories: a "declining" class (n = 286, 7.3%) with initially high symptoms and subsequent decline, a class with consistently high symptoms ("steady high"; n = 264, 6.7%), a class with late increases in symptoms ("increasing"; n = 277, 7%), and a class with consistently low symptoms ("steady low"; n = 3117, 79.0%). The steady high and the increasing classes had poorer performance on all cognitive tests, while the declining class had poorer performance on the DSST, verbal fluency, and MoCA. Compared to the steady low symptom class, the steady high class had lower volumes in the entorhinal cortex (β: -180.80, 95% CI: -336.69 to -24.91) and the amygdala (β: -40.97, 95% CI: -74.09 to -7.85), the increasing class had more WMHs (β: 0.55, 95% CI: 0.22 to 0.89), and the declining class was not significantly different in any brain measures. Trajectories in depressive symptoms in young to mid-adulthood show distinct cognitive and brain phenotypes in midlife. Steady high depressive symptoms may represent a group that is at risk for dementia, whereas increasing symptoms in midlife may be associated with white matter damage.
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Affiliation(s)
- Christina S Dintica
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California, CA, USA.
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Pamela J Schreiner
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA
| | | | - Kristine Yaffe
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California, CA, USA
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Heckbert SR, Jensen PN, Erus G, Nasrallah IM, Rashid T, Habes M, Austin TR, Floyd JS, Schaich CL, Redline S, Bryan RN, Costa MD. Heart rate fragmentation and brain MRI markers of small vessel disease in MESA. Alzheimers Dement 2024; 20:1397-1405. [PMID: 38009395 PMCID: PMC10917025 DOI: 10.1002/alz.13554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 11/28/2023]
Abstract
INTRODUCTION Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. METHODS In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. RESULTS In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03). DISCUSSION HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.
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Affiliation(s)
- Susan R. Heckbert
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Paul N. Jensen
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Guray Erus
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging CoreGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center San AntonioSan AntonioTexasUSA
| | - Mohamad Habes
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging CoreGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center San AntonioSan AntonioTexasUSA
| | - Thomas R. Austin
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - James S. Floyd
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Christopher L. Schaich
- Department of SurgeryHypertension and Vascular Research CenterWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Susan Redline
- Brigham and Women's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - R. Nick Bryan
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Madalena D. Costa
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
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Nance RM, Fohner AE, McClelland RL, Redline S, Nick Bryan R, Desiderio L, Habes M, Longstreth WT, Schwab RJ, Wiemken AS, Heckbert SR. The Association of Upper Airway Anatomy with Brain Structure: The Multi-Ethnic Study of Atherosclerosis. Brain Imaging Behav 2024:10.1007/s11682-023-00843-w. [PMID: 38194040 DOI: 10.1007/s11682-023-00843-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 01/10/2024]
Abstract
Sleep apnea, affecting an estimated 1 in 4 American adults, has been reported to be associated with both brain structural abnormality and impaired cognitive function. Obstructive sleep apnea is known to be affected by upper airway anatomy. To better understand the contribution of upper airway anatomy to pathways linking sleep apnea with impaired cognitive function, we investigated the association of upper airway anatomy with structural brain abnormalities. Based in the Multi-Ethnic Study of Atherosclerosis, a longitudinal cohort study of community-dwelling adults, a comprehensive sleep study and an MRI of the upper airway and brain were performed on 578 participants. Machine learning models were used to select from 74 upper airway measures those measures most associated with selected regional brain volumes and white matter hyperintensity volume. Linear regression assessed associations between the selected upper airway measures, sleep measures, and brain structure. Maxillary divergence was positively associated with hippocampus volume, and mandible length was negatively associated with total white and gray matter volume. Both coefficients were small (coefficients per standard deviation 0.063 mL, p = 0.04, and - 7.0 mL, p < 0.001 respectively), and not affected by adjustment for sleep study measures. Self-reported snoring >2 times per week was associated with larger hippocampus volume (coefficient 0.164 mL, p = 0.007), and higher percentage of time in the N3 sleep stage was associated with larger total white and gray matter volume (4.8 mL, p = 0.004). Despite associations of two upper airway anatomy measures with brain volume, the evidence did not suggest that these upper airway and brain structure associations were acting primarily through the pathway of sleep disturbance.
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Affiliation(s)
- Robin M Nance
- University of Washington, Seattle, WA, USA.
- , 325 9th Ave, Box 359931, Seattle, WA, 98104, USA.
| | - Alison E Fohner
- Department of Epidemiology & Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | | | - Susan Redline
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Richard J Schwab
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew S Wiemken
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn DC, Fox P, Habes M. Riemannian frameworks for the harmonization of resting-state functional MRI scans. Med Image Anal 2024; 91:103043. [PMID: 38029722 DOI: 10.1016/j.media.2023.103043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023]
Abstract
Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.
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Affiliation(s)
- Nicolas Honnorat
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Sudha Seshadri
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ron Killiany
- Center for Biomedical Imaging, Boston University Medical School, Boston, MA, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Fox
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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7
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TL, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga A, O’Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Bryan NR, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. medRxiv 2023:2023.12.29.23300642. [PMID: 38234857 PMCID: PMC10793523 DOI: 10.1101/2023.12.29.23300642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T. Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B. Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L. Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid O’Bryant
- Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
| | - Mallar M. Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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8
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Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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9
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Nance RM, Fohner AE, McClelland RL, Redline S, Bryan RN, Fitzpatrick A, Habes M, Longstreth WT, Schwab RJ, Wiemken AS, Heckbert SR. The association of upper airway anatomy with cognitive test performance: the Multi-Ethnic Study of Atherosclerosis. BMC Neurol 2023; 23:394. [PMID: 37907860 PMCID: PMC10617161 DOI: 10.1186/s12883-023-03443-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Numerous upper airway anatomy characteristics are risk factors for sleep apnea, which affects 26% of older Americans, and more severe sleep apnea is associated with cognitive impairment. This study explores the pathophysiology and links between upper airway anatomy, sleep, and cognition. METHODS Participants in the Multi-Ethnic Study of Atherosclerosis underwent an upper airway MRI, polysomnography to assess sleep measures including the apnea-hypopnea index (AHI) and completed the Cognitive Abilities Screening Instrument (CASI). Two model selection techniques selected from among 67 upper airway measures those that are most strongly associated with CASI score. The associations of selected upper airway measures with AHI, AHI with CASI score, and selected upper airway anatomy measures with CASI score, both alone and after adjustment for AHI, were assessed using linear regression. RESULTS Soft palate volume, maxillary divergence, and upper facial height were significantly positively associated with higher CASI score, indicating better cognition. The coefficients were small, with a 1 standard deviation (SD) increase in these variables being associated with a 0.83, 0.75, and 0.70 point higher CASI score, respectively. Additional adjustment for AHI very slightly attenuated these associations. Larger soft palate volume was significantly associated with higher AHI (15% higher AHI (95% CI 2%,28%) per SD). Higher AHI was marginally associated with higher CASI score (0.43 (95% CI 0.01,0.85) per AHI doubling). CONCLUSIONS Three upper airway measures were weakly but significantly associated with higher global cognitive test performance. Sleep apnea did not appear to be the mechanism through which these upper airway and cognition associations were acting. Further research on the selected upper airway measures is recommended.
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Affiliation(s)
- Robin M Nance
- University of Washington, 325 9th Ave, Box 359931, Seattle, 98104, USA.
| | - Alison E Fohner
- Department of Epidemiology & Cardiovascular Health Research Unit, University of Washington, Seattle, USA
| | | | - Susan Redline
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, USA
| | - Richard J Schwab
- Department of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Andrew S Wiemken
- Department of Medicine, University of Pennsylvania, Philadelphia, USA
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10
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Gonzales MM, Garbarino VR, Kautz TF, Palavicini JP, Lopez-Cruzan M, Dehkordi SK, Mathews JJ, Zare H, Xu P, Zhang B, Franklin C, Habes M, Craft S, Petersen RC, Tchkonia T, Kirkland JL, Salardini A, Seshadri S, Musi N, Orr ME. Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial. Nat Med 2023; 29:2481-2488. [PMID: 37679434 PMCID: PMC10875739 DOI: 10.1038/s41591-023-02543-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/14/2023] [Indexed: 09/09/2023]
Abstract
Cellular senescence contributes to Alzheimer's disease (AD) pathogenesis. An open-label, proof-of-concept, phase I clinical trial of orally delivered senolytic therapy, dasatinib (D) and quercetin (Q), was conducted in early-stage symptomatic patients with AD to assess central nervous system (CNS) penetrance, safety, feasibility and efficacy. Five participants (mean age = 76 + 5 years; 40% female) completed the 12-week pilot study. D and Q levels in blood increased in all participants (12.7-73.5 ng ml-1 for D and 3.29-26.3 ng ml-1 for Q). In cerebrospinal fluid (CSF), D levels were detected in four participants (80%) ranging from 0.281 to 0.536 ml-1 with a CSF to plasma ratio of 0.422-0.919%; Q was not detected. The treatment was well-tolerated, with no early discontinuation. Secondary cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment further supporting a favorable safety profile. CSF levels of interleukin-6 (IL-6) and glial fibrillary acidic protein (GFAP) increased (t(4) = 3.913, P = 0.008 and t(4) = 3.354, P = 0.028, respectively) with trending decreases in senescence-related cytokines and chemokines, and a trend toward higher Aβ42 levels (t(4) = -2.338, P = 0.079). In summary, CNS penetrance of D was observed with outcomes supporting safety, tolerability and feasibility in patients with AD. Biomarker data provided mechanistic insights of senolytic effects that need to be confirmed in fully powered, placebo-controlled studies. ClinicalTrials.gov identifier: NCT04063124 .
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Affiliation(s)
- Mitzi M Gonzales
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Valentina R Garbarino
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tiffany F Kautz
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Juan Pablo Palavicini
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Marisa Lopez-Cruzan
- Department of Psychiatry & Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Shiva Kazempour Dehkordi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Julia J Mathews
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Crystal Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Suzanne Craft
- Department of Internal Medicine Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | - Tamara Tchkonia
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - James L Kirkland
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Arash Salardini
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Nicolas Musi
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Miranda E Orr
- Department of Internal Medicine Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
- Salisbury VA Medical Center, Salisbury, NC, USA.
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11
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Rodriguez Lara F, Toro AR, Pinheiro A, Demissie S, Ekenze O, Martinez O, Parva P, Charidimou A, Ghosh S, DeCarli C, Seshadri S, Habes M, Maillard P, Romero JR. Relation of MRI-Visible Perivascular Spaces and Other MRI Markers of Cerebral Small Vessel Disease. Brain Sci 2023; 13:1323. [PMID: 37759924 PMCID: PMC10527297 DOI: 10.3390/brainsci13091323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Perivascular spaces (PVS) visible on brain MRI signal cerebral small vessel disease (CSVD). The coexistence of PVS with other CSVD manifestations likely increases the risk of adverse neurological outcomes. We related PVS to other CSVD manifestations and brain volumes that are markers of vascular brain injury and neurodegeneration. Framingham Heart Study (FHS) participants with CSVD ratings on brain MRI were included. PVS were rated in the basal ganglia (BG) and centrum semiovale (CSO) into grades I-IV and a category reflecting high burden in single or mixed CSO-BG regions. We related PVS to covert brain infarcts (CBI), white matter hyperintensities (WMH), cerebral microbleeds (CMB), total brain, hippocampal, and cortical gray matter volumes using adjusted multivariable regression analyses. In 2454 participants (mean age 54 ± 12 years), we observed that higher PVS burden in both BG and CSO was related to CMB in lobar and deep brain regions and increased WMH. Greater CSO PVS burden was associated with decreased total cortical gray volumes. PVS are associated with ischemic markers of CSVD and neurodegeneration markers. Further studies should elucidate the causality between PVS and other CSVD manifestations.
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Affiliation(s)
- Frances Rodriguez Lara
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA; (F.R.L.); (A.R.T.)
| | - Arturo Ruben Toro
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA; (F.R.L.); (A.R.T.)
| | - Adlin Pinheiro
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA; (A.P.); (S.D.)
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
| | - Serkalem Demissie
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA; (A.P.); (S.D.)
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
| | - Oluchi Ekenze
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Graduate Medical Sciences, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Oliver Martinez
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Pedram Parva
- Department of Radiology, Veterans Affairs Boston Healthcare System, Boston, MA 02118, USA;
- Department of Radiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Andreas Charidimou
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
| | - Saptaparni Ghosh
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
| | - Charles DeCarli
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Sudha Seshadri
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA;
| | - Mohamad Habes
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA;
| | - Pauline Maillard
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Jose Rafael Romero
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
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12
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Jensen PN, Rashid T, Ware JB, Cui Y, Sitlani CM, Austin TR, Longstreth WT, Bertoni AG, Mamourian E, Bryan RN, Nasrallah IM, Habes M, Heckbert SR. Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA. Alzheimers Dement 2023; 19:4139-4149. [PMID: 37289978 DOI: 10.1002/alz.13346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.
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Affiliation(s)
- Paul N Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
| | - Jeffrey B Ware
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yuhan Cui
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Thomas R Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Department of Neurology, University of Washington, Seattle, Washington, USA
| | - Alain G Bertoni
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Elizabeth Mamourian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - R Nick Bryan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ilya M Nasrallah
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
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13
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Li M, Habes M, Grabe H, Kang Y, Qi S, Detre JA. Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study. Front Aging Neurosci 2023; 15:1237198. [PMID: 37719871 PMCID: PMC10500060 DOI: 10.3389/fnagi.2023.1237198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/04/2023] [Indexed: 09/19/2023] Open
Abstract
Objective White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach. Methods High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction. Results Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's. Conclusion Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.
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Affiliation(s)
- Meng Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio, San Antonio, TX, United States
| | - Hans Grabe
- Department of Psychiatry and Psychotherapy, University of Greifswald, Stralsund, Germany
| | - Yan Kang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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14
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Habes M, Jacobson AM, Braffett BH, Rashid T, Ryan CM, Shou H, Cui Y, Davatzikos C, Luchsinger JA, Biessels GJ, Bebu I, Gubitosi-Klug RA, Bryan RN, Nasrallah IM. Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes. JAMA Netw Open 2023; 6:e2316182. [PMID: 37261829 PMCID: PMC10236234 DOI: 10.1001/jamanetworkopen.2023.16182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/09/2023] [Indexed: 06/02/2023] Open
Abstract
Importance Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging. Objective To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices. Design, Setting, and Participants This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022. Exposure T1D diagnosis. Main Outcomes and Measures Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy. Results This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: β, 6.16; SE, 0.71; control participants: β, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (β, -0.04; SE, 0.01; P < .001), but not among controls. Conclusions and Relevance The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.
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Affiliation(s)
- Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Alan M. Jacobson
- NYU Long Island School of Medicine, NYU Langone Hospital-Long Island, Mineola, New York
| | | | - Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Geert J. Biessels
- Department of Neurology, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ionut Bebu
- George Washington University, Biostatistics Center, Rockville, Maryland
| | - Rose A. Gubitosi-Klug
- Case Western Reserve University School of Medicine, Rainbow Babies and Children's Hospital, Cleveland, Ohio
| | - R. Nick Bryan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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15
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Liu X, Barisano G, Shao X, Jann K, Ringman JM, Lu H, Arfanakis K, Caprihan A, DeCarli C, Gold BT, Maillard P, Satizabal CL, Fadaee E, Habes M, Stables L, Singh H, Fischl B, Kouwe AVD, Schwab K, Helmer KG, Greenberg SM, Wang DJJ. Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function. Aging Dis 2023:AD.2023.0321-2. [PMID: 37307817 DOI: 10.14336/ad.2023.0321-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/21/2023] [Indexed: 06/14/2023] Open
Abstract
The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was proposed to evaluate glymphatic system (GS) function. However, few studies have validated its reliability and reproducibility. Fifty participants' DTI data from the MarkVCID consortium were included in this study. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation. The ALPS index was obtained by the average of bilateral ALPS index and was used for testing the cross-vendor, inter-rater and test-retest reliability by using R studio software. The ALPS index demonstrated favorable inter-scanner reproducibility (ICC=0.77 to 0.95, P < 0.001), inter-rater reliability (ICC=0.96 to 1, P< 0.001) and test-retest repeatability (ICC=0.89 to 0.95, P< 0.001), offering a potential biomarker for in vivo evaluation of GS function.
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Affiliation(s)
- Xiaodan Liu
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | | | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - John M Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University, Chicago, IL, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | | | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA, USA
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, Davis, CA, USA
| | - Claudia L Satizabal
- Population Health Sciences and Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Elyas Fadaee
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Lara Stables
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Computer Science and AI Lab, Cambridge, Massachusetts, USA
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven M Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
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Shah C, Srinivasan D, Erus G, Kurella Tamura M, Habes M, Detre JA, Haley WE, Lerner AJ, Wright CB, Wright JT, Oparil S, Kritchevsky SB, Punzi HA, Rastogi A, Malhotra R, Still CH, Williamson JD, Bryan RN, Fan Y, Nasrallah IM. Intensive Blood Pressure Management Preserves Functional Connectivity in Patients with Hypertension from the Systolic Blood Pressure Intervention Randomized Trial. AJNR Am J Neuroradiol 2023; 44:582-588. [PMID: 37105682 PMCID: PMC10171386 DOI: 10.3174/ajnr.a7852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 03/19/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND AND PURPOSE The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure. MATERIALS AND METHODS Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics. RESULTS Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013). CONCLUSIONS Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.
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Affiliation(s)
- C Shah
- From the Department of Radiology (C.S.), Imaging Institute, Cleveland Clinic, Cleveland, Ohio
| | - D Srinivasan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - G Erus
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - M Kurella Tamura
- Division of Nephrology (M.K.T.), Stanford University, and VA Palo Alto Geriatric Research and Education Clinical Center, Palo Alto, California
| | - M Habes
- Biggs Institute, University of Texas San Antonio (M.H.), San Antonio, Texas
| | - J A Detre
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - W E Haley
- Department of Nephrology and Hypertension (W.E.H.), Mayo Clinic, Jacksonville, Florida
| | | | - C B Wright
- National Institute of Neurological Disorders and Stroke (C.B.W.), National Institutes of Health, Bethesda, Maryland
| | - J T Wright
- Medicine (J.T.W.), Case Western Reserve University, and University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - S Oparil
- Division of Cardiovascular Disease (S.O.), Department of Medicine, University of Alabama, Birmingham, Alabama
| | - S B Kritchevsky
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine (S.B.K., J.D.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - H A Punzi
- Punzi Medical Center (H.A.P.), Carrollton, Texas
| | - A Rastogi
- Division of Nephrology (A.R.), Department of Medicine, University of California Los Angeles, Los Angeles, California
| | - R Malhotra
- Division of Nephrology (R.M.), University of California San Diego, San Diego, California
| | - C H Still
- Frances Payne Bolton School of Nursing (C.H.S.), Case Western Reserve University, Cleveland, Ohio
| | - J D Williamson
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine (S.B.K., J.D.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - R N Bryan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Y Fan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - I M Nasrallah
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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Gonzales MM, Garbarino VR, Kautz T, Palavicini JP, Lopez-Cruzan M, Dehkordi SK, Mathews J, Zare H, Xu P, Zhang B, Franklin C, Habes M, Craft S, Petersen RC, Tchkonia T, Kirkland J, Salardini A, Seshadri S, Musi N, Orr ME. Senolytic therapy to modulate the progression of Alzheimer's Disease (SToMP-AD) - Outcomes from the first clinical trial of senolytic therapy for Alzheimer's disease. Res Sq 2023:rs.3.rs-2809973. [PMID: 37162971 PMCID: PMC10168460 DOI: 10.21203/rs.3.rs-2809973/v1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Cellular senescence has been identified as a pathological mechanism linked to tau and amyloid beta (Aβ) accumulation in mouse models of Alzheimer's disease (AD). Clearance of senescent cells using the senolytic compounds dasatinib (D) and quercetin (Q) reduced neuropathological burden and improved clinically relevant outcomes in the mice. Herein, we conducted a vanguard open-label clinical trial of senolytic therapy for AD with the primary aim of evaluating central nervous system (CNS) penetrance, as well as exploratory data collection relevant to safety, feasibility, and efficacy. Participants with early-stage symptomatic AD were enrolled in an open-label, 12-week pilot study of intermittent orally-delivered D+Q. CNS penetrance was assessed by evaluating drug levels in cerebrospinal fluid (CSF) using high performance liquid chromatography with tandem mass spectrometry. Safety was continuously monitored with adverse event reporting, vitals, and laboratory work. Cognition, neuroimaging, and plasma and CSF biomarkers were assessed at baseline and post-treatment. Five participants (mean age: 76±5 years; 40% female) completed the trial. The treatment increased D and Q levels in the blood of all participants ranging from 12.7 to 73.5 ng/ml for D and 3.29-26.30 ng/ml for Q. D levels were detected in the CSF of four participants ranging from 0.281 to 0.536 ng/ml (t(4)=3.123, p=0.035); Q was not detected. Treatment was well-tolerated with no early discontinuation and six mild to moderate adverse events occurring across the study. Cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment. CNS levels of IL-6 and GFAP increased from baseline to post-treatment (t(4)=3.913, p=008 and t(4)=3.354, p=0.028, respectively) concomitant with decreased levels of several cytokines and chemokines associated with senescence, and a trend toward higher levels of Aβ42 (t(4)=-2.338, p=0.079). Collectively the data indicate the CNS penetrance of D and provide preliminary support for the safety, tolerability, and feasibility of the intervention and suggest that astrocytes and Aβ may be particularly responsive to the treatment. While early results are promising, fully powered, placebo-controlled studies are needed to evaluate the potential of AD modification with the novel approach of targeting cellular senescence.
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Affiliation(s)
- Mitzi M Gonzales
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Valentina R Garbarino
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tiffany Kautz
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Juan Pablo Palavicini
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Marisa Lopez-Cruzan
- Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Shiva Kazempour Dehkordi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Julia Mathews
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Crystal Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Suzanne Craft
- Department of Internal Medicine Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Tamara Tchkonia
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - James Kirkland
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Arash Salardini
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Nicolas Musi
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Geriatric Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Miranda E Orr
- Department of Internal Medicine Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Klingenberg M, Stark D, Eitel F, Budding C, Habes M, Ritter K. Higher performance for women than men in MRI-based Alzheimer's disease detection. Alzheimers Res Ther 2023; 15:84. [PMID: 37081528 PMCID: PMC10116672 DOI: 10.1186/s13195-023-01225-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 04/03/2023] [Indexed: 04/22/2023]
Abstract
INTRODUCTION Although machine learning classifiers have been frequently used to detect Alzheimer's disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a state-of-the-art AD classifier for potential sex and age bias even in the case of balanced training data. METHODS Based on an age- and sex-balanced cohort of 432 subjects (306 healthy controls, 126 subjects with AD) extracted from the ADNI data base, we trained a convolutional neural network to detect AD in MRI brain scans and performed ten different random training-validation-test splits to increase robustness of the results. Classifier decisions for single subjects were explained using layer-wise relevance propagation. RESULTS The classifier performed significantly better for women (balanced accuracy [Formula: see text]) than for men ([Formula: see text]). No significant differences were found in clinical AD scores, ruling out a disparity in disease severity as a cause for the performance difference. Analysis of the explanations revealed a larger variance in regional brain areas for male subjects compared to female subjects. DISCUSSION The identified sex differences cannot be attributed to an imbalanced training dataset and therefore point to the importance of examining and reporting classifier performance across population subgroups to increase transparency and algorithmic fairness. Collecting more data especially among underrepresented subgroups and balancing the dataset are important but do not always guarantee a fair outcome.
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Affiliation(s)
- Malte Klingenberg
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Didem Stark
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Fabian Eitel
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Céline Budding
- Eindhoven University of Technology, Eindhoven, Netherlands
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
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Charisis S, Rashid T, Liu H, Ware JB, Jensen PN, Austin TR, Li K, Fadaee E, Hilal S, Chen C, Hughes TM, Romero JR, Toledo JB, Longstreth WT, Hohman TJ, Nasrallah I, Bryan RN, Launer LJ, Davatzikos C, Seshadri S, Heckbert SR, Habes M. Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis. JAMA Netw Open 2023; 6:e239196. [PMID: 37093602 PMCID: PMC10126873 DOI: 10.1001/jamanetworkopen.2023.9196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/07/2023] [Indexed: 04/25/2023] Open
Abstract
Importance Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain. Objective Toperform a whole-brain investigation of ePVSs in a large community-based cohort. Design, Setting, and Participants This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included. Main Outcomes and Measures Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models. Results In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (β = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (β = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (β = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (β = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (β = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (β = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (β = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (β = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (β = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (β = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (β = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (β = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (β = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2). Conclusions and Relevance In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.
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Affiliation(s)
- Sokratis Charisis
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
- Department of Neurology, University of Texas Health Science Center at San Antonio
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
| | - Hangfan Liu
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
- AI2D Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jeffrey B. Ware
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Paul N. Jensen
- Department of Medicine, University of Washington, Seattle
| | | | - Karl Li
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
| | - Elyas Fadaee
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore
| | - Christopher Chen
- Memory Aging and Cognition Centre, National University Health System, Singapore
| | - Timothy M. Hughes
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jose Rafael Romero
- Department of Neurology, School of Medicine, Boston University, Boston, Massachusetts
| | - Jon B. Toledo
- Nantz National Alzheimer Center, Stanley Appel Department of Neurology, Houston Methodist Hospital, Houston, Texas
| | - Will T. Longstreth
- Department of Epidemiology, University of Washington, Seattle
- Department of Neurology, University of Washington, Seattle
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ilya Nasrallah
- AI2D Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - R. Nick Bryan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Lenore J. Launer
- Intramural Research Program, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
| | - Christos Davatzikos
- AI2D Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Sudha Seshadri
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
- Department of Neurology, University of Texas Health Science Center at San Antonio
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
- AI2D Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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Liu H, Grothe MJ, Rashid T, Labrador-Espinosa MA, Toledo JB, Habes M. ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data. IEEE Trans Emerg Top Comput Intell 2023; 7:308-318. [PMID: 36969108 PMCID: PMC10038331 DOI: 10.1109/tetci.2021.3136587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson's disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.
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Affiliation(s)
- Hangfan Liu
- Neuroimage Analytics Laboratory (NAL) and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Miguel A Labrador-Espinosa
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Jon B Toledo
- Department of Neurology, University of Florida College of Medicine, Gainesville, and also with Fixel Institute for Neurologic Diseases, University of Florida, Gainesville
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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Wang D, Honnorat N, Fox PT, Ritter K, Eickhoff SB, Seshadri S, Habes M. Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies. Neuroimage 2023; 269:119929. [PMID: 36740029 DOI: 10.1016/j.neuroimage.2023.119929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/06/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.
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Affiliation(s)
- Di Wang
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Nicolas Honnorat
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Peter T Fox
- Biomedical Image Analytics Division, Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Kerstin Ritter
- Department of Psychiatry and Neurosciences, Charite - University of Medicine Berlin and Humboldt-University Berlin, Berlin, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich-Heine University Düsseldorf, Germany
| | - Sudha Seshadri
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Biomedical Image Analytics Division, Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
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Rashid T, Li K, Toledo JB, Nasrallah I, Pajewski NM, Dolui S, Detre J, Wolk DA, Liu H, Heckbert SR, Bryan RN, Williamson J, Davatzikos C, Seshadri S, Launer LJ, Habes M. Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers: Post Hoc Secondary Analysis of the SPRINT MIND Randomized Clinical Trial. JAMA Netw Open 2023; 6:e231055. [PMID: 36857053 PMCID: PMC9978954 DOI: 10.1001/jamanetworkopen.2023.1055] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
IMPORTANCE Little is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers. OBJECTIVE To investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF). DESIGN, SETTING, AND PARTICIPANTS The Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target <120 mm Hg) vs standard control (SBP target <140 mm Hg) among participants aged 50 years or older with hypertension and without diabetes or a history of stroke. The study began randomization on November 8, 2010, and stopped July 1, 2016, with a follow-up duration of approximately 4 years. A total of 670 and 458 participants completed brain magnetic resonance imaging at baseline and follow-up, respectively, and comprise the cohort for this post hoc analysis. Statistical analyses for this post hoc analysis were performed between August 2020 and October 2022. INTERVENTIONS At baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment. MAIN OUTCOMES AND MEASURES The main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest). RESULTS At baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]). CONCLUSIONS AND RELEVANCE This study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01206062.
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Affiliation(s)
- Tanweer Rashid
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Karl Li
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Jon B. Toledo
- Department of Neurology, University of Florida, Gainesville
- Department of Neurology, Houston Methodist Hospital, Houston, Texas
| | - Ilya Nasrallah
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Nicholas M. Pajewski
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sudipto Dolui
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
| | - John Detre
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Hangfan Liu
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - R. Nick Bryan
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
| | - Jeff Williamson
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Christos Davatzikos
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Sudha Seshadri
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Lenore J. Launer
- Intramural Research Program, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
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23
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Brenowitz WD, Fornage M, Launer LJ, Habes M, Davatzikos C, Yaffe K. Alzheimer's Disease Genetic Risk, Cognition, and Brain Aging in Midlife. Ann Neurol 2023; 93:629-634. [PMID: 36511390 PMCID: PMC9974745 DOI: 10.1002/ana.26569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/10/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
We examined associations of an Alzheimer's disease (AD) Genetic Risk Score (AD-GRS) and midlife cognitive and neuroimaging outcomes in 1,252 middle-aged participants (311 with brain MRI). A higher AD-GRS based on 25 previously identified loci (excluding apolipoprotein E [APOE]) was associated with worse Montreal Cognitive Assessment (-0.14 standard deviation [SD] [95% confidence interval {CI}: -0.26, -0.02]), older machine learning predicted brain age (2.35 years[95%CI: 0.01, 4.69]), and white matter hyperintensity volume (0.35 SD [95% CI: 0.00, 0.71]), but not with a composite cognitive outcome, total brain, or hippocampal volume. APOE ε4 allele was not associated with any outcomes. AD risk genes beyond APOE may contribute to subclinical differences in cognition and brain health in midlife. ANN NEUROL 2023;93:629-634.
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Affiliation(s)
- Willa D Brenowitz
- Departments of Psychiatry and Behavioral Sciences, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Myriam Fornage
- The University of Texas, Health Science Center at Houston, Houston, Texas, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kristine Yaffe
- Departments of Psychiatry and Behavioral Sciences, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- San Francisco VA Medical Center, San Francisco, California, USA
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24
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Rashid T, Liu H, Ware JB, Li K, Romero JR, Fadaee E, Nasrallah IM, Hilal S, Bryan RN, Hughes TM, Davatzikos C, Launer L, Seshadri S, Heckbert SR, Habes M. Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI. Neuroimage Rep 2023; 3:100162. [PMID: 37035520 PMCID: PMC10078801 DOI: 10.1016/j.ynirp.2023.100162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.
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Affiliation(s)
- Tanweer Rashid
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Hangfan Liu
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B. Ware
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Karl Li
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jose Rafael Romero
- Department of Neurology, School of Medicine, Boston University, Boston, MA, USA
| | - Elyas Fadaee
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - R. Nick Bryan
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Timothy M. Hughes
- Department of Internal Medicine and Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Christos Davatzikos
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sudha Seshadri
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Susan R. Heckbert
- Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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25
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. ArXiv 2023:arXiv:2301.10772v1. [PMID: 36748000 PMCID: PMC9900969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.
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26
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Evans TE, Knol MJ, Schwingenschuh P, Wittfeld K, Hilal S, Ikram MA, Dubost F, van Wijnen KMH, Katschnig P, Yilmaz P, de Bruijne M, Habes M, Chen C, Langer S, Völzke H, Ikram MK, Grabe HJ, Schmidt R, Adams HHH, Vernooij MW. Determinants of Perivascular Spaces in the General Population: A Pooled Cohort Analysis of Individual Participant Data. Neurology 2023; 100:e107-e122. [PMID: 36253103 PMCID: PMC9841448 DOI: 10.1212/wnl.0000000000201349] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/19/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Perivascular spaces (PVS) are emerging markers of cerebral small vessel disease (CSVD), but research on their determinants has been hampered by conflicting results from small single studies using heterogeneous rating methods. In this study, we therefore aimed to identify determinants of PVS burden in a pooled analysis of multiple cohort studies using 1 harmonized PVS rating method. METHODS Individuals from 10 population-based cohort studies with adult participants from the Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium and the UK Biobank were included. On MRI scans, we counted PVS in 4 brain regions (mesencephalon, hippocampus, basal ganglia, and centrum semiovale) according to a uniform and validated rating protocol, both manually and automated using a deep learning algorithm. As potential determinants, we considered demographics, cardiovascular risk factors, APOE genotypes, and other imaging markers of CSVD. Negative binomial regression models were used to examine the association between these determinants and PVS counts. RESULTS In total, 39,976 individuals were included (age range 20-96 years). The average count of PVS in the 4 regions increased from the age 20 years (0-1 PVS) to 90 years (2-7 PVS). Men had more mesencephalic PVS (OR [95% CI] = 1.13 [1.08-1.18] compared with women), but less hippocampal PVS (0.82 [0.81-0.83]). Higher blood pressure, particularly diastolic pressure, was associated with more PVS in all regions (ORs between 1.04-1.05). Hippocampal PVS showed higher counts with higher high-density lipoprotein cholesterol levels (1.02 [1.01-1.02]), glucose levels (1.02 [1.01-1.03]), and APOE ε4-alleles (1.02 [1.01-1.04]). Furthermore, white matter hyperintensity volume and presence of lacunes were associated with PVS in multiple regions, but most strongly with the basal ganglia (1.13 [1.12-1.14] and 1.10 [1.09-1.12], respectively). DISCUSSION Various factors are associated with the burden of PVS, in part regionally specific, which points toward a multifactorial origin beyond what can be expected from PVS-related risk factor profiles. This study highlights the power of collaborative efforts in population neuroimaging research.
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Affiliation(s)
- Tavia E Evans
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Maria J Knol
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Petra Schwingenschuh
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Katharina Wittfeld
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Saima Hilal
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - M Arfan Ikram
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Florian Dubost
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Kimberlin M H van Wijnen
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Petra Katschnig
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pinar Yilmaz
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Marleen de Bruijne
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Mohamad Habes
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Christopher Chen
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Sönke Langer
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Henry Völzke
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - M Kamran Ikram
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Hans J Grabe
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Reinhold Schmidt
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Hieab H H Adams
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Meike W Vernooij
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
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Dintica CS, Habes M, Erus G, Simone T, Schreiner P, Yaffe K. Long-term depressive symptoms and midlife brain age. J Affect Disord 2023; 320:436-441. [PMID: 36202300 PMCID: PMC10115134 DOI: 10.1016/j.jad.2022.09.164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI. METHODS From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition. RESULTS Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97). LIMITATIONS Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging. CONCLUSIONS Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.
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Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA, USA; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA.
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA, USA.
| | - Tamar Simone
- Northern California Institute for Research and Education, San Francisco, CA, USA.
| | - Pamela Schreiner
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA.
| | - Kristine Yaffe
- University of California, San Francisco, California, CA, USA; VA Medical Center, San Francisco, CA, USA.
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Li K, Rashid T, Li J, Honnorat N, Nirmala AB, Fadaee E, Wang D, Charisis S, Liu H, Franklin C, Maybrier M, Katragadda H, Abazid L, Ganapathy V, Valaparla VL, Badugu P, Vasquez E, Solano L, Clarke G, Maestre G, Richardson T, Walker J, Fox PT, Bieniek K, Seshadri S, Habes M. Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository. J Alzheimers Dis 2023; 96:1267-1283. [PMID: 37955086 PMCID: PMC10693476 DOI: 10.3233/jad-230389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. OBJECTIVE The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. METHODS Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. RESULTS A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. CONCLUSION This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.
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Affiliation(s)
- Karl Li
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tanweer Rashid
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jinqi Li
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Nicolas Honnorat
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Anoop Benet Nirmala
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Elyas Fadaee
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Di Wang
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sokratis Charisis
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hangfan Liu
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Crystal Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mallory Maybrier
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Haritha Katragadda
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Leen Abazid
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Vinutha Ganapathy
- Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA
| | | | - Pradeepthi Badugu
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Eliana Vasquez
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Leigh Solano
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Geoffrey Clarke
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Gladys Maestre
- Department of Neuroscience, School of Medicine, University of Texas Rio Grande Valley, Harlingen, TX, USA
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Tim Richardson
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jamie Walker
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Kevin Bieniek
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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Bahrani AA, Abner EL, DeCarli CS, Barber JM, Sutton AC, Maillard P, Sandoval F, Arfanakis K, Yang YC, Evia AM, Schneider JA, Habes M, Franklin CG, Seshadri S, Satizabal CL, Caprihan A, Thompson JF, Rosenberg GA, Wang DJ, Jann K, Zhao C, Lu H, Rosenberg PB, Albert MS, Ali DG, Singh H, Schwab K, Greenberg SM, Helmer KG, Powel DK, Gold BT, Goldstein LB, Wilcock DM, Jicha GA. Multi-Site Cross-Site Inter-Rater and Test-Retest Reliability and Construct Validity of the MarkVCID White Matter Hyperintensity Growth and Regression Protocol. J Alzheimers Dis 2023; 96:683-693. [PMID: 37840499 PMCID: PMC11009792 DOI: 10.3233/jad-230629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
BACKGROUND White matter hyperintensities (WMH) that occur in the setting of vascular cognitive impairment and dementia (VCID) may be dynamic increasing or decreasing volumes or stable over time. Quantifying such changes may prove useful as a biomarker for clinical trials designed to address vascular cognitive-impairment and dementia and Alzheimer's Disease. OBJECTIVE Conducting multi-site cross-site inter-rater and test-retest reliability of the MarkVCID white matter hyperintensity growth and regression protocol. METHODS The NINDS-supported MarkVCID Consortium evaluated a neuroimaging biomarker developed to track WMH change. Test-retest and cross-site inter-rater reliability of the protocol were assessed. Cognitive test scores were analyzed in relation to WMH changes to explore its construct validity. RESULTS ICC values for test-retest reliability of WMH growth and regression were 0.969 and 0.937 respectively, while for cross-site inter-rater ICC values for WMH growth and regression were 0.995 and 0.990 respectively. Word list long-delay free-recall was negatively associated with WMH growth (p < 0.028) but was not associated with WMH regression. CONCLUSIONS The present data demonstrate robust ICC validity of a WMH growth/regression protocol over a one-year period as measured by cross-site inter-rater and test-retest reliability. These data suggest that this approach may serve an important role in clinical trials of disease-modifying agents for VCID that may preferentially affect WMH growth, stability, or regression.
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Affiliation(s)
- Ahmed A. Bahrani
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Erin L. Abner
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Epidemiology & Environmental Health, University of Kentucky, College of Public Health, Lexington, KY, USA
| | | | - Justin M. Barber
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Abigail C. Sutton
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, CA, USA
| | | | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Yung-Chuan Yang
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Arnold M. Evia
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Mohamad Habes
- Research Imaging Institute, University of Texas Health San Antonio, San Antonio, TX, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Crystal G. Franklin
- Research Imaging Institute, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | | | | | - Gary A. Rosenberg
- Center for Memory and Aging, University of New Mexico, Health Sciences Center, Albuquerque, NM, USA
| | - Danny J.J. Wang
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kay Jann
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chenyang Zhao
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanzhang Lu
- Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Paul B. Rosenberg
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Doaa G. Ali
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Karl G. Helmer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David K. Powel
- Department of Neuroscience, University of Kentucky, College of Medicine, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
| | - Brian T. Gold
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Neuroscience, University of Kentucky, College of Medicine, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
| | - Larry B. Goldstein
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Donna M. Wilcock
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Physiology, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Gregory A. Jicha
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
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Maillard P, Hillmer LJ, Lu H, Arfanakis K, Gold BT, Bauer CE, Kramer JH, Staffaroni AM, Stables L, Wang DJ, Seshadri S, Satizabal CL, Beiser A, Habes M, Fornage M, Mosley TH, Rosenberg GA, Singh B, Singh H, Schwab K, Helmer KG, Greenberg SM, DeCarli C, Caprihan A. MRI free water as a biomarker for cognitive performance: Validation in the MarkVCID consortium. Alzheimers Dement (Amst) 2022; 14:e12362. [PMID: 36523847 PMCID: PMC9745638 DOI: 10.1002/dad2.12362] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/11/2022] [Accepted: 08/29/2022] [Indexed: 12/15/2022]
Abstract
Introduction To evaluate the clinical validity of free water (FW), a diffusion tensor imaging-based biomarker kit proposed by the MarkVCID consortium, by investigating the association between mean FW (mFW) and executive function. Methods Baseline mFW was related to a baseline composite measure of executive function (EFC), adjusting for relevant covariates, in three MarkVCID sub-cohorts, and replicated in five, large, independent legacy cohorts. In addition, we tested whether baseline mFW predicted accelerated EFC score decline (mean follow-up time: 1.29 years). Results Higher mFW was found to be associated with lower EFC scores in MarkVCID legacy and sub-cohorts (p-values < 0.05). In addition, higher baseline mFW was associated significantly with accelerated decline in EFC scores (p = 0.0026). Discussion mFW is a sensitive biomarker of cognitive decline, providing a strong clinical rational for its use as a marker of white matter (WM) injury in multi-site observational studies and clinical trials of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Rush Alzheimer's Disease CenterDepartment of Diagnostic Radiology and Nuclear MedicineRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | - Joel H. Kramer
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Adam M. Staffaroni
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Lara Stables
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Alexa Beiser
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular MedicineMcGovern Medical SchoolSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Human Genetics CenterSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Thomas H. Mosley
- MIND CenterUniversity of Mississippi Medical CenterJacksonMississippiUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | | | - Charles DeCarli
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Arvind Caprihan
- The Mind Research NetworkAlbuquerqueNew MexicoAlbuquerqueNew MexicoUSA
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Dintica CS, Habes M, Simone T, Schreiner P, Erus G, Yaffe K. Depression in young adulthood to midlife and brain aging in midlife: A 30‐year follow‐up of The CARDIA Study. Alzheimers Dement 2022. [DOI: 10.1002/alz.064749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio TX USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Tamar Simone
- NCIRE‐The Veterans Health Research Institute San Francisco CA USA
- University of California, San Francsico San Francisco CA USA
| | | | - Guray Erus
- University of Pennsylvania Philadelphia PA USA
| | - Kristine Yaffe
- University of California, San Francisco San Francisco CA USA
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Govindarajan ST, Mamourian E, Erus G, Abdulkadir A, Melhem R, Doshi J, Pomponio R, Tosun D, Bilgel M, An Y, Sotiras A, Marcus DS, LaMontagne PJ, Espeland MA, Masters CL, Maruff P, Launer LJ, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Habes M, Shou H, Wolk DA, Nasrallah IM, Davatzikos C. Machine‐learning based MRI neuro‐anatomical signatures associated with cardiovascular and metabolic risk factors. Alzheimers Dement 2022. [DOI: 10.1002/alz.061530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Randa Melhem
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Duygu Tosun
- University of California, San Francisco San Francisco CA USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Daniel S. Marcus
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | | | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health Parkville VIC Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health Melbourne VIC Australia
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Baltimore MD USA
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian E‐Health Research Centre Brisbane QLD Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health Madison WI USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Marilyn S. Albert
- Department of Neurology, Division of Cognitive Neuroscience, John’s Hopkins University School of Medicine Baltimore MD USA
| | | | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio TX USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
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Dintica CS, Habes M, Davatzikos C, Sidney S, Yaffe K. The association between cognitive reserve, cognition and brain age in Midlife: The CARDIA Study. Alzheimers Dement 2022. [DOI: 10.1002/alz.064738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio TX USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | | | | | - Kristine Yaffe
- University of California, San Francisco San Francisco CA USA
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Toledo JB, Rashid T, Liu H, Launer L, Shaw LM, Heckbert SR, Weiner M, Seshadri S, Habes M. SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline. PLoS One 2022; 17:e0276392. [PMID: 36327215 PMCID: PMC9632811 DOI: 10.1371/journal.pone.0276392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI). METHODS 587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aβ biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aβ positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group. RESULTS Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aβ+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction. DISCUSSION Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.
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Affiliation(s)
- Jon B. Toledo
- Department of Neurology, University of Florida College of Medicine, Gainesville, Florida, United States of America
- Department of Neurology Houston Methodist Hospital, Houston, Texas, United States of America
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, Texas, United States of America
| | - Hangfan Liu
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, Texas, United States of America
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Susan R. Heckbert
- Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
| | - Michael Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, United States of America
- Department of Radiology, University of California, San Francisco, California, United States of America
- Department of Medicine, University of California, San Francisco, California, United States of America
- Department of Psychiatry, University of California, San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco, California, United States of America
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, Texas, United States of America
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, Texas, United States of America
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, Texas, United States of America
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Wortha SM, Frenzel S, Bahls M, Habes M, Wittfeld K, Van der Auwera S, Bülow R, Zylla S, Friedrich N, Nauck M, Völzke H, Grabe HJ, Schwarz C, Flöel A. Association of spermidine plasma levels with brain aging in a population‐based study. Alzheimers Dement 2022; 19:1832-1840. [PMID: 36321615 DOI: 10.1002/alz.12815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/16/2022] [Accepted: 09/02/2022] [Indexed: 03/31/2023]
Abstract
INTRODUCTION Supplementation with spermidine may support healthy aging, but elevated spermidine tissue levels were shown to be an indicator of Alzheimer's disease (AD). METHODS Data from 659 participants (age range: 21-81 years) of the population-based Study of Health in Pomerania TREND were included. We investigated the association between spermidine plasma levels and markers of brain aging (hippocampal volume, AD score, global cortical thickness [CT], and white matter hyperintensities [WMH]). RESULTS Higher spermidine levels were significantly associated with lower hippocampal volume (ß = -0.076; 95% confidence interval [CI]: -0.13 to -0.02; q = 0.026), higher AD score (ß = 0.118; 95% CI: 0.05 to 0.19; q = 0.006), lower global CT (ß = -0.104; 95% CI: -0.17 to -0.04; q = 0.014), but not WMH volume. Sensitivity analysis revealed no substantial changes after excluding participants with cancer, depression, or hemolysis. DISCUSSION Elevated spermidine plasma levels are associated with advanced brain aging and might serve as potential early biomarker for AD and vascular brain pathology.
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Austin TR, Jensen PN, Nasrallah IM, Habes M, Rashid T, Ware JB, Chen LY, Greenland P, Hughes TM, Post WS, Shea SJ, Watson KE, Sitlani CM, Floyd JS, Kronmal RA, Longstreth WT, Bertoni AG, Shah SJ, Bryan RN, Heckbert SR. Left Atrial Function and Arrhythmias in Relation to Small Vessel Disease on Brain MRI: The Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc 2022; 11:e026460. [PMID: 36250665 PMCID: PMC9673671 DOI: 10.1161/jaha.122.026460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background Atrial fibrillation (AF) is associated with increased stroke risk and accelerated cognitive decline, but the association of early manifestations of left atrial (LA) impairment with subclinical changes in brain structure is unclear. We investigated whether abnormal LA structure and function, greater supraventricular ectopy, and intermittent AF are associated with small vessel disease on magnetic resonance imaging of the brain. Methods and Results In the Multi‐Ethnic Study of Atherosclerosis, 967 participants completed 14‐day ambulatory electrocardiographic monitoring, speckle tracking echocardiography and, a median 17 months later, magnetic resonance imaging of the brain. We assessed associations of LA volume index and reservoir strain, supraventricular ectopy, and prevalent AF with brain magnetic resonance imaging measures of small vessel disease and atrophy. The mean age of participants was 72 years; 53% were women. In multivariable models, LA enlargement was associated with lower white matter fractional anisotropy and greater prevalence of microbleeds; reduced LA strain, indicating worse LA function, was associated with more microbleeds. More premature atrial contractions were associated with lower total gray matter volume. Compared with no AF, intermittent AF (prevalent AF with <100% AF during electrocardiographic monitoring) was associated with lower white matter fractional anisotropy (−0.25 SDs [95% CI, −0.44 to −0.07]) and greater prevalence of microbleeds (prevalence ratio: 1.42 [95% CI, 1.12–1.79]). Conclusions In individuals without a history of stroke or transient ischemic attack, alterations of LA structure and function, including enlargement, reduced strain, frequent premature atrial contractions, and intermittent AF, were associated with increased markers of small vessel disease. Detailed assessment of LA structure and function and extended ECG monitoring may enable early identification of individuals at greater risk of small vessel disease.
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Affiliation(s)
- Thomas R Austin
- Department of Epidemiology University of Washington Seattle WA
| | - Paul N Jensen
- Department of Medicine University of Washington Seattle WA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, Department of Radiology University of Pennsylvania Philadelphia PA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio TX
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio TX
| | - Jeffrey B Ware
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Lin Yee Chen
- Cardiovascular Division, Department of Medicine University of Minnesota Medical School Minneapolis MN
| | - Philip Greenland
- Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago IL.,Division of Cardiology, Department of Medicine Northwestern University Feinberg School of Medicine Chicago IL
| | - Timothy M Hughes
- Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC
| | - Wendy S Post
- Division of Cardiology, Department of Medicine Johns Hopkins University Baltimore MD
| | - Steven J Shea
- Departments of Medicine and Epidemiology Columbia University New York NY
| | - Karol E Watson
- Department of Medicine, David Geffen School of Medicine University of California Los Angeles CA
| | | | - James S Floyd
- Department of Epidemiology University of Washington Seattle WA.,Department of Medicine University of Washington Seattle WA
| | | | - W T Longstreth
- Department of Epidemiology University of Washington Seattle WA.,Department of Neurology University of Washington Seattle WA
| | - Alain G Bertoni
- Department of Epidemiology and Prevention Wake Forest School of Medicine Winston-Salem NC
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine Northwestern University Feinberg School of Medicine Chicago IL
| | - R Nick Bryan
- Department of Radiology University of Pennsylvania Philadelphia PA
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Honnorat N, Habes M. Covariance shrinkage can assess and improve functional connectomes. Neuroimage 2022; 256:119229. [PMID: 35460918 PMCID: PMC9189899 DOI: 10.1016/j.neuroimage.2022.119229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/24/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.
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Affiliation(s)
- Nicolas Honnorat
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
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Jacobson AM, Braffett BH, Erus G, Ryan CM, Biessels GJ, Luchsinger JA, Bebu I, Gubitosi-Klug RA, Desiderio L, Lorenzi GM, Trapani VR, Lachin JM, Bryan RN, Habes M, Nasrallah IM. Brain Structure Among Middle-aged and Older Adults With Long-standing Type 1 Diabetes in the DCCT/EDIC Study. Diabetes Care 2022; 45:1779-1787. [PMID: 35699949 PMCID: PMC9346989 DOI: 10.2337/dc21-2438] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/17/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Individuals with type 1 diabetes mellitus (T1DM) are living to ages when neuropathological changes are increasingly evident. We hypothesized that middle-aged and older adults with long-standing T1DM will show abnormal brain structure in comparison with control subjects without diabetes. RESEARCH DESIGN AND METHODS MRI was used to compare brain structure among 416 T1DM participants in the Epidemiology of Diabetes Interventions and Complications (EDIC) study with that of 99 demographically similar control subjects without diabetes at 26 U.S. and Canadian sites. Assessments included total brain (TBV) (primary outcome), gray matter (GMV), white matter (WMV), ventricle, and white matter hyperintensity (WMH) volumes and total white matter mean fractional anisotropy (FA). Biomedical assessments included HbA1c and lipid levels, blood pressure, and cognitive assessments of memory and psychomotor and mental efficiency (PME). Among EDIC participants, HbA1c, severe hypoglycemia history, and vascular complications were measured longitudinally. RESULTS Mean age of EDIC participants and control subjects was 60 years. T1DM participants showed significantly smaller TBV (least squares mean ± SE 1,206 ± 1.7 vs. 1,229 ± 3.5 cm3, P < 0.0001), GMV, and WMV and greater ventricle and WMH volumes but no differences in total white matter mean FA versus control subjects. Structural MRI measures in T1DM were equivalent to those of control subjects who were 4-9 years older. Lower PME scores were associated with altered brain structure on all MRI measures in T1DM participants. CONCLUSIONS Middle-aged and older adults with T1DM showed brain volume loss and increased vascular injury in comparison with control subjects without diabetes, equivalent to 4-9 years of brain aging.
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Affiliation(s)
- Alan M Jacobson
- NYU Long Island School of Medicine, NYU Langone Hospital-Long Island, Mineola
| | - Barbara H Braffett
- The Biostatistics Center, The George Washington University, Rockville, MD
| | - Guray Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Geert J Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Netherlands
| | | | - Ionut Bebu
- The Biostatistics Center, The George Washington University, Rockville, MD
| | - Rose A Gubitosi-Klug
- Case Western Reserve University School of Medicine, Rainbow Babies & Children's Hospital, Cleveland, OH
| | - Lisa Desiderio
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | | | - John M Lachin
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
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Dintica CS, Habes M, Erus G, Vittinghoff E, Davatzikos C, Nasrallah IM, Launer LJ, Sidney S, Yaffe K. Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study. Alzheimers Dement 2022; 19:10.1002/alz.12725. [PMID: 35779250 PMCID: PMC9806185 DOI: 10.1002/alz.12725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. METHODS We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. RESULTS Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). CONCLUSION Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.
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Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA
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Hwang G, Abdulkadir A, Erus G, Habes M, Pomponio R, Shou H, Doshi J, Mamourian E, Rashid T, Bilgel M, Fan Y, Sotiras A, Srinivasan D, Morris JC, Albert MS, Bryan NR, Resnick SM, Nasrallah IM, Davatzikos C, Wolk DA. Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning. Brain Commun 2022; 4:fcac117. [PMID: 35611306 PMCID: PMC9123890 DOI: 10.1093/braincomms/fcac117] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022] Open
Abstract
Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.
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Affiliation(s)
- Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Tanweer Rashid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, USA
| | - Nick R. Bryan
- Department of Diagnostic Medicine, University of Texas, Austin; Austin, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, USA
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Lu QY, Towne JM, Lock M, Jiang C, Cheng ZX, Habes M, Zuo XN, Zang YF. Toward coordinate-based cognition dictionaries: A brainmap and neurosynth demo. Neuroscience 2022; 493:109-118. [DOI: 10.1016/j.neuroscience.2022.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/09/2022] [Accepted: 02/14/2022] [Indexed: 10/18/2022]
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Wen J, Fu CHY, Tosun D, Veturi Y, Yang Z, Abdulkadir A, Mamourian E, Srinivasan D, Skampardoni I, Singh A, Nawani H, Bao J, Erus G, Shou H, Habes M, Doshi J, Varol E, Mackin RS, Sotiras A, Fan Y, Saykin AJ, Sheline YI, Shen L, Ritchie MD, Wolk DA, Albert M, Resnick SM, Davatzikos C. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry 2022; 79:464-474. [PMID: 35262657 PMCID: PMC8908227 DOI: 10.1001/jamapsychiatry.2022.0020] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/19/2021] [Indexed: 12/14/2022]
Abstract
Importance Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
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Affiliation(s)
- Junhao Wen
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Cynthia H. Y. Fu
- University of East London, School of Psychology, London, United Kingdom
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Yogasudha Veturi
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ioanna Skampardoni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Hema Nawani
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Erdem Varol
- Department of Statistics, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, New York
| | - R. Scott Mackin
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, St Louis, Missouri
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Andrew J. Saykin
- Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis
| | - Yvette I. Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Austin TR, Nasrallah IM, Erus G, Desiderio LM, Chen LY, Greenland P, Harding BN, Hughes TM, Jensen PN, Longstreth WT, Post WS, Shea SJ, Sitlani CM, Davatzikos C, Habes M, Nick Bryan R, Heckbert SR. Association of Brain Volumes and White Matter Injury With Race, Ethnicity, and Cardiovascular Risk Factors: The Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc 2022; 11:e023159. [PMID: 35352569 PMCID: PMC9075451 DOI: 10.1161/jaha.121.023159] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross-sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese-American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.
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Affiliation(s)
- Thomas R Austin
- Department of Epidemiology University of Washington Seattle WA
| | - Ilya M Nasrallah
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Guray Erus
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Lisa M Desiderio
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Lin Y Chen
- Cardiovascular Division University of Minnesota Minneapolis MN
| | - Philip Greenland
- Department of Preventative Medicine and Department of MedicineFeinberg School of Medicine Chicago IL
| | | | - Timothy M Hughes
- Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC
| | - Paul N Jensen
- Department of Medicine University of Washington Seattle WA
| | - W T Longstreth
- Department of Epidemiology University of Washington Seattle WA.,Department of Neurology University of Washington Seattle WA
| | - Wendy S Post
- Division of Cardiology Department of Medicine Johns Hopkins University Baltimore Maryland
| | - Steven J Shea
- Departments of Medicine and Epidemiology Columbia University New York NY
| | | | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases University of Texas Health Science Center San Antonio TX
| | - R Nick Bryan
- Department of Diagnostic Medicine University of Texas at Austin Austin TX
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Maillard P, Lu H, Arfanakis K, Gold BT, Bauer CE, Zachariou V, Stables L, Wang DJ, Jann K, Seshadri S, Duering M, Hillmer LJ, Rosenberg GA, Snoussi H, Sepehrband F, Habes M, Singh B, Kramer JH, Corriveau RA, Singh H, Schwab K, Helmer KG, Greenberg SM, Caprihan A, DeCarli C, Satizabal CL. Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits. Alzheimers Dement (Amst) 2022; 14:e12261. [PMID: 35382232 PMCID: PMC8959640 DOI: 10.1002/dad2.12261] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Introduction To describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging. Methods The instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC). Results The three biomarkers demonstrated excellent inter-rater reliability (ICC >0.94, P-values < .001), very high agreement between test and retest sessions (ICC >0.98, P-values < .001), and were extremely consistent across the three scanners (ICC >0.98, P-values < .001). Discussion The three biomarker kits demonstrated very high inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility, offering robust biomarkers suitable for future multi-site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | | | - Lara Stables
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Marco Duering
- Department of Biomedical EngineeringMedical Image Analysis Center (MIAC AG)University of BaselBaselSwitzerland
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Haykel Snoussi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Farshid Sepehrband
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Joel H. Kramer
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
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Bashyam VM, Doshi J, Erus G, Srinivasan D, Abdulkadir A, Habes M, Fan Y, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf DH, Gur RE, Gur RC, Morris JC, Albert MS, Grabe HJ, Resnick SM, Bryan RN, Wittfeld K, Bülow R, Wolk DA, Shou H, Nasrallah IM, Davatzikos C, Davatzikos C. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors. J Magn Reson Imaging 2022; 55:908-916. [PMID: 34564904 PMCID: PMC8844038 DOI: 10.1002/jmri.27908] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE Retrospective. POPULATION Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Vishnu M. Bashyam
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Corresponding authors: Vishnu Bashyam and Christos Davatzikos, ; , 3700 Hamilton Walk, 7th Floor, Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA 19104; https://www.med.upenn.edu/cbica/
| | - Jimit Doshi
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China,Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Germany,German Centre for Cardiovascular Research, Partner Site Greifswald, Germany
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO
| | | | - Theodore D. Satterthwaite
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Department of Psychiatry, University of Pennsylvania
| | | | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania,Department of Radiology, University of Pennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania,Department of Radiology, University of Pennsylvania
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany,German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging
| | - R. Nick Bryan
- Department of Diagnostic Medicine, University of Texas at Austin
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany,German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Germany
| | | | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | | | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Corresponding authors: Vishnu Bashyam and Christos Davatzikos, ; , 3700 Hamilton Walk, 7th Floor, Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA 19104; https://www.med.upenn.edu/cbica/
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Zheng Y, Habes M, Gonzales M, Pomponio R, Nasrallah I, Khan S, Vaughan DE, Davatzikos C, Seshadri S, Launer L, Sorond F, Sedaghat S, Wainwright D, Baccarelli A, Sidney S, Bryan N, Greenland P, Lloyd-Jones D, Yaffe K, Hou L. Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study. Aging (Albany NY) 2022; 14:1691-1712. [PMID: 35220276 PMCID: PMC8908939 DOI: 10.18632/aging.203918] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 02/08/2022] [Indexed: 11/25/2022]
Abstract
The proportion of aging populations affected by dementia is increasing. There is an urgent need to identify biological aging markers in mid-life before symptoms of age-related dementia present for early intervention to delay the cognitive decline and the onset of dementia. In this cohort study involving 1,676 healthy participants (mean age 40) with up to 15 years of follow up, we evaluated the associations between cognitive function and two classes of novel biological aging markers: blood-based epigenetic aging and neuroimaging-based brain aging. Both accelerated epigenetic aging and brain aging were prospectively associated with worse cognitive outcomes. Specifically, every year faster epigenetic or brain aging was on average associated with 0.19-0.28 higher (worse) Stroop score, 0.04-0.05 lower (worse) RAVLT score, and 0.23-0.45 lower (worse) DSST (all false-discovery-rate-adjusted p <0.05). While epigenetic aging is a more stable biomarker with strong long-term predictive performance for cognitive function, brain aging biomarker may change more dynamically in temporal association with cognitive decline. The combined model using epigenetic and brain aging markers achieved the highest accuracy (AUC: 0.68, p<0.001) in predicting global cognitive function status. Accelerated epigenetic age and brain age at midlife may aid timely identification of individuals at risk for accelerated cognitive decline and promote the development of interventions to preserve optimal functioning across the lifespan.
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Affiliation(s)
- Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mohamad Habes
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mitzi Gonzales
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Raymond Pomponio
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sadiya Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Douglas E. Vaughan
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sudha Seshadri
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Lenore Launer
- Laboratory of Epidemiology and Population Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Farzaneh Sorond
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Derek Wainwright
- Departments of Neurological Surgery, Medicine-Hematology and Oncology, Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA 94612, USA
| | - Nick Bryan
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Kristine Yaffe
- Departments of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
- Department of Neurology University of California, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, CA 94143, USA
- San Francisco VA Medical Center, San Francisco, CA 94143, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Toledo JB, Liu H, Grothe MJ, Rashid T, Launer L, Shaw LM, Snoussi H, Heckbert S, Weiner M, Trojanwoski JQ, Seshadri S, Habes M. Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum. A&D Transl Res & Clin Interv 2022; 8:e12305. [PMID: 35619830 PMCID: PMC9127251 DOI: 10.1002/trc2.12305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 12/01/2022]
Abstract
Introduction Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum. Methods We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD‐like (SPARE‐AD index) and brain age (SPARE‐BA) atrophy. Results We identified four tau (I–IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated. Conclusions Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.
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Affiliation(s)
- Jon B. Toledo
- Department of Neurology University of Florida College of Medicine Gainesville Florida USA
| | - Hangfan Liu
- Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia Pennsylvania USA
| | - Michel J. Grothe
- Unidad de Trastornos del Movimiento, Instituto de Bioedicina de Sevilla (IBiS) Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla Seville Spain
| | - Tanweer Rashid
- Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia Pennsylvania USA
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases University of Texas Health Science Center San Antonio (UTHSCSA) San Antonio Texas USA
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program National Institute on Aging Bethesda Maryland USA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
| | - Haykel Snoussi
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases University of Texas Health Science Center San Antonio (UTHSCSA) San Antonio Texas USA
| | - Susan Heckbert
- Department of Epidemiology and Cardiovascular Health Research Unit University of Washington Seattle Washington USA
- Department of Radiology University of California San Francisco California USA
- Department of Medicine University of California San Francisco California USA
- Department of Psychiatry University of California San Francisco California USA
| | - Michael Weiner
- Department of Veterans Affairs Medical Center Center for Imaging of Neurodegenerative Diseases San Francisco California USA
- Department of Neurology University of California San Francisco California USA
- Alzheimer's Disease Core Center, Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
| | - John Q. Trojanwoski
- Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
- Institute on Aging Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
| | - Sudha Seshadri
- Udall Parkinson's Research Center Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Sciences Center San Antonio Texas USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia Pennsylvania USA
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases University of Texas Health Science Center San Antonio (UTHSCSA) San Antonio Texas USA
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Zuroff L, Wisse LEM, Glenn T, Xie SX, Nasrallah IM, Habes M, Dubroff J, de Flores R, Xie L, Yushkevich P, Doshi J, Davatsikos C, Shaw LM, Tropea TF, Chen-Plotkin AS, Wolk DA, Das S, Mechanic-Hamilton D. Self- and Partner-Reported Subjective Memory Complaints: Association with Objective Cognitive Impairment and Risk of Decline. J Alzheimers Dis Rep 2022; 6:411-430. [PMID: 36072364 PMCID: PMC9397901 DOI: 10.3233/adr-220013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2022] [Indexed: 11/15/2022] Open
Abstract
Background Episodic memory decline is a hallmark of Alzheimer's disease (AD). Subjective memory complaints (SMCs) may represent one of the earliest signs of impending cognitive decline. The degree to which self- or partner-reported SMCs predict cognitive change remains unclear. Objective We aimed to evaluate the relationship between self- and partner-reported SMCs, objective cognitive performance, AD biomarkers, and risk of future decline in a well-characterized longitudinal memory center cohort. We also evaluated whether study partner characteristics influence reports of SMCs. Methods 758 participants and 690 study partners were recruited from the Penn Alzheimer's Disease Research Center Clinical Core. Participants included those with Normal Cognition, Mild Cognitive Impairment, and AD. SMCs were measured using the Prospective and Retrospective Memory Questionnaire (PRMQ), and were evaluated for their association with cognition, genetic, plasma, and neuroimaging biomarkers of AD, cognitive and functional decline, and diagnostic progression over an average of four years. Results We found that partner-reported SMCs were more consistent with cognitive test performance and increasing symptom severity than self-reported SMCs. Partner-reported SMCs showed stronger correlations with AD-associated brain atrophy, plasma biomarkers of neurodegeneration, and longitudinal cognitive and functional decline. A 10-point increase on baseline PRMQ increased the annual risk of diagnostic progression by approximately 70%. Study partner demographics and relationship to participants influenced reports of SMCs in AD participants only. Conclusion Partner-reported SMCs, using the PRMQ, have a stronger relationship with the neuroanatomic and cognitive changes associated with AD than patient-reported SMCs. Further work is needed to evaluate whether SMCs could be used to screen for future decline.
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Affiliation(s)
- Leah Zuroff
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Jacob Dubroff
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Université de Caen Normandie, INSERM UMRS U1237, Caen, France
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatsikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas F. Tropea
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alice S. Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Correspondence to: Dawn Mechanic-Hamilton, PCAM-2 South, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA. Tel.: +1 215 662 4516; E-mail:
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Wittfeld K, Raman MR, Conner SC, Aslam A, Teumer A, Nauck M, Hosten N, Habes M, DeCarli C, Vasan RS, Beiser AS, Himali JJ, Seshadri S, Grabe HJ, Satizabal CL. Insulin-Like Growth Factor, Inflammation, and MRI Markers of Alzheimer's Disease in Predominantly Middle-Aged Adults. J Alzheimers Dis 2022; 88:311-322. [PMID: 35599493 PMCID: PMC9472289 DOI: 10.3233/jad-220356] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Insulin-like growth factor 1 (IGF-1) signaling has been implicated in Alzheimer's disease pathogenesis, and further evidence suggests inflammation can be a moderator of this association. However, most research to date has been conducted on older adults. OBJECTIVE To investigate the association of serum IGF-1 and IGF binding protein 3 (IGFBP-3) concentrations with MRI markers of Alzheimer's disease in predominantly middle-aged adults, and further assess moderation by chronic inflammation. METHODS We included participants from the Framingham Heart Study (n = 1,852, mean age 46±8, 46% men) and the Study of Health in Pomerania (n = 674, mean age 50±13, 42% men) with available serum IGF-1, IFGBP-3, as well as brain MRI. IGF-1 and IFGBP-3 were related to MRI outcomes (i.e., total brain, cortical gray matter, white matter, white matter hyperintensities (WMH), and hippocampal volumes) using multivariable regression models adjusting for potential confounders. Subgroup analyses by C-reactive protein (CRP) concentrations were also performed. Cohort-specific summary statistics were meta-analyzed using random-effects models and corrected for multiple comparisons. RESULTS Meta-analysis results revealed that higher IGF-1 concentrations were associated with lower WMH (estimate [β] [95% CI], -0.05 [-0.09, -0.02], p = 0.006) and larger hippocampal volumes (0.07 [0.02, 0.12], p = 0.01), independent of vascular risk factors. These associations occurred predominantly in individuals with CRP concentrations < 75th percentile. We did not observe associations between IGFBP-3 and MRI outcomes. CONCLUSION Our findings suggest that IGF-1-related signaling may be implicated in brain health as early as midlife.
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Affiliation(s)
- Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Mekala R Raman
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Sarah C Conner
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Asra Aslam
- Long School of Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Norbert Hosten
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis School of Medicine, Sacramento, CA, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston MA, USA
| | - Alexa S Beiser
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jayandra J Himali
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Hans J Grabe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Claudia L Satizabal
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
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Wisse LEM, Xie L, Das SR, De Flores R, Hansson O, Habes M, Doshi J, Davatzikos C, Yushkevich PA, Wolk DA. Tau pathology mediates age effects on medial temporal lobe structure. Neurobiol Aging 2022; 109:135-144. [PMID: 34740075 PMCID: PMC8800343 DOI: 10.1016/j.neurobiolaging.2021.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023]
Abstract
Hippocampal atrophy is endemic in 'normal aging' but it is unclear what factors drive age-related changes in medial temporal lobe (MTL) structural measures. We investigated cross-sectional (n = 191) and longitudinal (n = 164) MTL atrophy patterns in cognitively normal older adults from ADNI-GO/2 with no to low cerebral β-amyloid and assessed whether white matter hyperintensities (WMHs) and cerebrospinal fluid (CSF) phospho tau (p-tau) levels can explain age-related changes in the MTL. Age was significantly associated with hippocampal volumes and Brodmann Area (BA) 35 thickness, regions affected early by neurofibrillary tangle pathology, in the cross-sectional analysis and with anterior and/or posterior hippocampus, entorhinal cortex and BA35 in the longitudinal analysis. CSF p-tau was significantly associated with hippocampal volumes and atrophy rates. Mediation analyses showed that CSF p-tau levels partially mediated age effects on hippocampal atrophy rates. No significant associations were observed for WMHs. These findings point toward a role of tau pathology, potentially reflecting Primary Age-Related Tauopathy, in age-related MTL structural changes and suggests a potential role for tau-targeted interventions in age-associated neurodegeneration and memory decline.
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Affiliation(s)
- LEM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - SR Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - R De Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - M Habes
- Biggs Alzheimer’s Institute, UT Health, San Antonio, USA
| | - J Doshi
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - C Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - PA Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - DA Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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