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Yu M, Risacher SL, Nho KT, Wen Q, Oblak AL, Unverzagt FW, Apostolova LG, Farlow MR, Brosch JR, Clark DG, Wang S, Deardorff R, Wu YC, Gao S, Sporns O, Saykin AJ. Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease. Cell Rep 2024; 43:113691. [PMID: 38244198 PMCID: PMC10926093 DOI: 10.1016/j.celrep.2024.113691] [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: 07/13/2023] [Revised: 11/29/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
| | - Shannon L Risacher
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik T Nho
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
| | - Qiuting Wen
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adrian L Oblak
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jared R Brosch
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David G Clark
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sophia Wang
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachael Deardorff
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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Wen Z, Bao J, Yang S, Risacher SL, Saykin AJ, Thompson PM, Davatzikos C, Huang H, Zhao Y, Shen L. Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis. Med Image Comput Comput Assist Interv MICCAI 2023 Workshops (2023) 2024; 14394:227-240. [PMID: 38584725 PMCID: PMC10993314 DOI: 10.1007/978-3-031-47425-5_21] [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: 04/09/2024]
Abstract
We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.
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Affiliation(s)
- Zixuan Wen
- University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- University of Pennsylvania, Philadelphia, PA, USA
| | - Shu Yang
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | - Heng Huang
- University of Maryland, College Park, MD, USA
| | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA, USA
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Wen Q, Wright A, Tong Y, Zhao Y, Risacher SL, Saykin AJ, Wu YC, Limaye K, Riley K. Paravascular fluid dynamics reveal arterial stiffness assessed using dynamic diffusion-weighted imaging. NMR Biomed 2024; 37:e5048. [PMID: 37798964 PMCID: PMC10810720 DOI: 10.1002/nbm.5048] [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] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023]
Abstract
Paravascular cerebrospinal fluid (pCSF) surrounding the cerebral arteries within the glymphatic system is pulsatile and moves in synchrony with the pressure waves of the vessel wall. Whether such pulsatile pCSF can infer pulse wave propagation-a property tightly related to arterial stiffness-is unknown and has never been explored. Our recently developed imaging technique, dynamic diffusion-weighted imaging (dynDWI), captures the pulsatile pCSF dynamics in vivo and can explore this question. In this work, we evaluated the time shifts between pCSF waves and finger pulse waves, where pCSF waves were measured by dynDWI and finger pulse waves were measured by the scanner's built-in finger pulse oximeter. We hypothesized that the time shifts reflect brain-finger pulse wave travel time and are sensitive to arterial stiffness. We applied the framework to 36 participants aged 18-82 years to study the age effect of travel time, as well as its associations with cognitive function within the older participants (N = 15, age > 60 years). Our results revealed a strong and consistent correlation between pCSF pulse and finger pulse (mean CorrCoeff = 0.66), supporting arterial pulsation as a major driver for pCSF dynamics. The time delay between pCSF and finger pulses (TimeDelay) was significantly lower (i.e., faster pulse propagation) with advanced age (Pearson's r = -0.44, p = 0.007). Shorter TimeDelay was further associated with worse cognitive function in the older participants. Overall, our study demonstrated pCSF as a viable pathway for measuring intracranial pulses and encouraged future studies to investigate its relevance with cerebrovascular functions.
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Affiliation(s)
- Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Adam Wright
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kaustubh Limaye
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kalen Riley
- Department of Clinical Radiology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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Manchella MK, Logan PE, Perry BL, Peng S, Risacher SL, Saykin AJ, Apostolova LG. Associations Between Social Network Characteristics and Brain Structure Among Older Adults. Alzheimers Dement 2024; 20:1406-1420. [PMID: 38015980 PMCID: PMC10916942 DOI: 10.1002/alz.13534] [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: 04/28/2023] [Revised: 09/10/2023] [Accepted: 10/11/2023] [Indexed: 11/30/2023]
Abstract
INTRODUCTION Social connectedness is associated with slower cognitive decline among older adults. Recent research suggests that distinct aspects of social networks may have differential effects on cognitive resilience, but few studies analyze brain structure. METHODS This study includes 117 cognitively impaired and 59 unimpaired older adults. The effects of social network characteristics (bridging/bonding) on brain regions of interests were analyzed using linear regressions and voxel-wise multiple linear regressions of gray matter density. RESULTS Increased social bridging was associated with greater bilateral amygdala volume and insular thickness, and left frontal lobe thickness, putamen, and thalamic volumes. Increased social bonding was associated with greater bilateral medial orbitofrontal and caudal anterior cingulate thickness, as well as right frontal lobe thickness, putamen, and amygdala volumes. DISCUSSION The associations between social connectedness and brain structure vary depending on the types of social enrichment accessible through social networks, suggesting that psychosocial interventions could mitigate neurodegeneration. HIGHLIGHTS Distinct forms of social capital are uniquely linked to gray matter density (GMD). Bridging is associated with preserved GMD in limbic system structures. Bonding is associated with preserved GMD in frontal lobe regions. Bridging is associated with increased brain reserve in sensory processing regions. Bonding is associated with increased brain reserve in regions of stress modulation.
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Affiliation(s)
- Mohit K. Manchella
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Paige E. Logan
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Brea L. Perry
- Indiana University Network Science InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Siyun Peng
- Indiana University Network Science InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Shannon L. Risacher
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Andrew J. Saykin
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana University Network Science InstituteIndiana UniversityBloomingtonIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana University Network Science InstituteIndiana UniversityBloomingtonIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge time series components of functional connectivity and cognitive function in Alzheimer's disease. Brain Imaging Behav 2024; 18:243-255. [PMID: 38008852 PMCID: PMC10844434 DOI: 10.1007/s11682-023-00822-1] [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: 11/04/2023] [Indexed: 11/28/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA.
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA.
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA.
| | - Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Shannon L Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Brenna C McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
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Hirschfeld LR, Deardorff R, Chumin EJ, Wu YC, McDonald BC, Cao S, Risacher SL, Yi D, Byun MS, Lee JY, Kim YK, Kang KM, Sohn CH, Nho K, Saykin AJ, Lee DY. White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum. Alzheimers Res Ther 2023; 15:218. [PMID: 38102714 PMCID: PMC10725037 DOI: 10.1186/s13195-023-01369-5] [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: 04/19/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations. METHODS We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥ 55 years, including 276 cognitively normal older adults (CN), 142 with mild cognitive impairment (MCI), and 87 AD patients, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University. RESULTS Compared to CN, AD and MCI subjects showed significantly higher RD, MD, and AxD values (all p-values < 0.001) and significantly lower FA values (left p ≤ 0.002, right p ≤ 0.015) after Bonferroni adjustment for multiple comparisons. Most tests of cognition and mood (p < 0.001) as well as higher medial temporal amyloid burden (p < 0.001) were associated with poorer WM integrity in the CBH after Bonferroni adjustment. CONCLUSION These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.
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Affiliation(s)
- Lauren R Hirschfeld
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Rachael Deardorff
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Evgeny J Chumin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Yu-Chien Wu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Brenna C McDonald
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sha Cao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Shannon L Risacher
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080, South Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, South Korea
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, 07061, South Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, 07061, South Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Dong Young Lee
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, South Korea
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Dempsey DA, Deardorff R, Wu YC, Yu M, Apostolova LG, Brosch J, Clark DG, Farlow MR, Gao S, Wang S, Saykin AJ, Risacher SL. BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity. medRxiv 2023:2023.12.05.23299222. [PMID: 38106123 PMCID: PMC10723496 DOI: 10.1101/2023.12.05.23299222] [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: 12/19/2023]
Abstract
The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.
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Affiliation(s)
- Desarae A. Dempsey
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Meichen Yu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Liana G. Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jared Brosch
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - David G. Clark
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Martin R. Farlow
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sophia Wang
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Bae J, Logan PE, Acri DJ, Bharthur A, Nho K, Saykin AJ, Risacher SL, Nudelman K, Polsinelli AJ, Pentchev V, Kim J, Hammers DB, Apostolova LG. A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression. Alzheimers Dement 2023; 19:5690-5699. [PMID: 37409680 PMCID: PMC10770299 DOI: 10.1002/alz.13319] [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: 01/20/2023] [Revised: 04/25/2023] [Accepted: 05/12/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. METHODS We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. RESULTS Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. DISCUSSION The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
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Affiliation(s)
- Jinhyeong Bae
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Paige E. Logan
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Dominic J. Acri
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Apoorva Bharthur
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Angelina J. Polsinelli
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Valentin Pentchev
- Department of Information Technology, Indiana University Network Science Institute, Bloomington, IN, 47408, United States
| | - Jungsu Kim
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Dustin B. Hammers
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Liana G. Apostolova
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
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Dybing KM, Vetter CJ, Dempsey DA, Chaudhuri S, Saykin AJ, Risacher SL. Traumatic brain injury and Alzheimer's Disease biomarkers: A systematic review of findings from amyloid and tau positron emission tomography (PET). medRxiv 2023:2023.11.30.23298528. [PMID: 38077068 PMCID: PMC10705648 DOI: 10.1101/2023.11.30.23298528] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Traumatic brain injury (TBI) has been discussed as a risk factor for Alzheimer's disease (AD) due to its association with dementia risk and earlier cognitive symptom onset. However, the mechanisms behind this relationship are unclear. Some studies have suggested TBI may increase pathological protein deposition in an AD-like pattern; others have failed to find such associations. This review covers literature that uses positron emission tomography (PET) of amyloid-β and/or tau to examine subjects with history of TBI who are at risk for AD due to advanced age. A comprehensive literature search was conducted on January 9, 2023, and 24 resulting citations met inclusion criteria. Common methodological concerns included small samples, limited clinical detail about subjects' TBI, recall bias due to reliance on self-reported TBI, and an inability to establish causation. For both amyloid and tau, results were widespread but inconsistent. The regions which showed the most compelling evidence for increased amyloid deposition were the cingulate gyrus, cuneus/precuneus, and parietal lobe. Evidence for increased tau was strongest in the medial temporal lobe, entorhinal cortex, precuneus, and frontal, temporal, parietal, and occipital lobes. However, conflicting findings across most regions of interest in both amyloid- and tau-PET studies indicate the critical need for future work in expanded samples and with greater clinical detail to offer a clearer picture of the relationship between TBI and protein deposition in older subjects at risk for AD.
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Affiliation(s)
- Kaitlyn M. Dybing
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Cecelia J. Vetter
- Ruth Lilly Medical Library, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Desarae A. Dempsey
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Soumilee Chaudhuri
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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10
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease. medRxiv 2023:2023.05.13.23289936. [PMID: 38014005 PMCID: PMC10680898 DOI: 10.1101/2023.05.13.23289936] [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: 11/29/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J. Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Shannon L. Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Liana G. Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Martin R. Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Brenna C. McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Andrew J. Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
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11
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Rosewood TJ, Nho K, Risacher SL, Gao S, Shen L, Foroud T, Saykin AJ. Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions. Genes (Basel) 2023; 14:2010. [PMID: 38002954 PMCID: PMC10671827 DOI: 10.3390/genes14112010] [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: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.
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Affiliation(s)
- Thea J. Rosewood
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kwangsik Nho
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, Philadelphia, PA 19104, USA;
| | - Tatiana Foroud
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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12
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Swinford CG, Risacher SL, Vosmeier A, Deardorff R, Chumin EJ, Dzemidzic M, Wu YC, Gao S, McDonald BC, Yoder KK, Unverzagt FW, Wang S, Farlow MR, Brosch JR, Clark DG, Apostolova LG, Sims J, Wang DJ, Saykin AJ. Amyloid and tau pathology are associated with cerebral blood flow in a mixed sample of nondemented older adults with and without vascular risk factors for Alzheimer's disease. Neurobiol Aging 2023; 130:103-113. [PMID: 37499587 PMCID: PMC10529454 DOI: 10.1016/j.neurobiolaging.2023.06.014] [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: 09/27/2022] [Revised: 05/30/2023] [Accepted: 06/21/2023] [Indexed: 07/29/2023]
Abstract
Identification of biomarkers for the early stages of Alzheimer's disease (AD) is an imperative step in developing effective treatments. Cerebral blood flow (CBF) is a potential early biomarker for AD; generally, older adults with AD have decreased CBF compared to normally aging peers. CBF deviates as the disease process and symptoms progress. However, further characterization of the relationships between CBF and AD risk factors and pathologies is still needed. We assessed the relationships between CBF quantified by arterial spin-labeled magnetic resonance imaging, hypertension, APOEε4, and tau and amyloid positron emission tomography in 77 older adults: cognitively normal, subjective cognitive decline, and mild cognitive impairment. Tau and amyloid aggregation were related to altered CBF, and some of these relationships were dependent on hypertension or APOEε4 status. Our findings suggest a complex relationship between risk factors, AD pathologies, and CBF that warrants future studies of CBF as a potential early biomarker for AD.
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Affiliation(s)
- Cecily G Swinford
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Aaron Vosmeier
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Evgeny J Chumin
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Mario Dzemidzic
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Karmen K Yoder
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sophia Wang
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jared R Brosch
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David G Clark
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
| | - Justin Sims
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Danny J Wang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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13
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. Alzheimers Dement (Amst) 2023; 15:e12468. [PMID: 37780863 PMCID: PMC10540270 DOI: 10.1002/dad2.12468] [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] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 10/03/2023]
Abstract
Introduction It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Lori L. Beason‐Held
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Yang An
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Andrea Shafer
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology BranchNational Institute on AgingBaltimoreMDUSA
| | - Shannon L. Risacher
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Andrew J. Saykin
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Susan M. Resnick
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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14
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Hammers DB, Kostadinova RV, Spencer RJ, Ikanga JN, Unverzagt FW, Risacher SL, Apostolova LG. Sensitivity of memory subtests and learning slopes from the ADAS-Cog to distinguish along the continuum of the NIA-AA Research Framework for Alzheimer's Disease. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2023; 30:866-884. [PMID: 36074015 PMCID: PMC9992455 DOI: 10.1080/13825585.2022.2120957] [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] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 10/14/2022]
Abstract
Despite extensive use of the Alzheimer's Disease (AD) Assessment Scale - Cognitive Subscale (ADAS-Cog) in AD research, exploration of memory subtests or process scores from the measure has been limited. The current study sought to establish validity for the ADAS-Cog Word Recall Immediate and Delayed Memory subtests and learning slope scores by showing that they are sensitive to AD biomarker status. Word Recall subtest and learning slope scores were calculated for 441 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90). All participants were categorized using the NIA-AA Research Framework - based on PET-imaging of β-amyloid (A) and tau (T) deposition - as Normal AD Biomarkers (A-T-), Alzheimer's Pathologic Change (A + T-), or Alzheimer's disease (A + T+). Memory subtest and learning slope performances were compared between biomarker status groups, and with regard to how well they discriminated samples with (A + T+) and without (A-T-) biomarkers. Lower Word Recall memory subtest scores - and scores for a particular learning slope calculation, the Learning Ratio - were observed for the AD (A + T+) group than the other biomarker groups. Memory subtest and Learning Ratio scores further displayed fair to good receiver operator characteristics when differentiating those with and without AD biomarkers. When comparing across learning slopes, the Learning Ratio metric consistently outperformed others. ADAS-Cog memory subtests and the Learning Ratio score are sensitive to AD biomarker status along the continuum of the NIA-AA Research Framework, and the results offer criterion validity for use of these subtests and process scores as unique markers of memory capacity.
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Affiliation(s)
- Dustin B. Hammers
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
| | | | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
| | - Jean N. Ikanga
- Emory University, School of Medicine, Department of Rehabilitation Medicine, GA, USA
- University of Kinshasa, Department of Psychiatry, Democratic Republic of Congo (DRC)
| | | | - Shannon L. Risacher
- Indiana University School of Medicine, Department of Radiology, Indianapolis, IN, USA
| | - Liana G. Apostolova
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
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15
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Yu M, Risacher SL, Nho KT, Wen Q, Oblak AL, Unverzagt FW, Apostolova LG, Farlow MR, Brosch JR, Clark DG, Wang S, Deardorff R, Wu YC, Gao S, Sporns O, Saykin AJ. Spatial transcriptomic patterns underlying regional vulnerability to amyloid-β and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease. medRxiv 2023:2023.08.12.23294017. [PMID: 37645867 PMCID: PMC10462206 DOI: 10.1101/2023.08.12.23294017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aβ and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aβ and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aβ and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets. One Sentence Summary We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-β and tau pathologies in AD.
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16
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Schweighauser M, Garringer HJ, Klingstedt T, Nilsson KPR, Masuda-Suzukake M, Murrell JR, Risacher SL, Vidal R, Scheres SHW, Goedert M, Ghetti B, Newell KL. Mutation ∆K281 in MAPT causes Pick's disease. Acta Neuropathol 2023; 146:211-226. [PMID: 37351604 PMCID: PMC10329087 DOI: 10.1007/s00401-023-02598-6] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/24/2023]
Abstract
Two siblings with deletion mutation ∆K281 in MAPT developed frontotemporal dementia. At autopsy, numerous inclusions of hyperphosphorylated 3R Tau were present in neurons and glial cells of neocortex and some subcortical regions, including hippocampus, caudate/putamen and globus pallidus. The inclusions were argyrophilic with Bodian silver, but not with Gallyas-Braak silver. They were not labelled by an antibody specific for tau phosphorylated at S262 and/or S356. The inclusions were stained by luminescent conjugated oligothiophene HS-84, but not by bTVBT4. Electron cryo-microscopy revealed that the core of tau filaments was made of residues K254-F378 of 3R Tau and was indistinguishable from that of Pick's disease. We conclude that MAPT mutation ∆K281 causes Pick's disease.
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Affiliation(s)
| | - Holly J Garringer
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Therése Klingstedt
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
- Department of Physics, Chemistry and Biology, Lingköping University, Lingköping, Sweden
| | - K Peter R Nilsson
- Department of Physics, Chemistry and Biology, Lingköping University, Lingköping, Sweden
| | - Masami Masuda-Suzukake
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
- Department of Brain and Neuroscience, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Jill R Murrell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Pathology and Laboratory Medicine, Children's Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ruben Vidal
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sjors H W Scheres
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK.
| | - Michel Goedert
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK.
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
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17
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Kim JP, Nho K, Wang T, Huynh K, Arnold M, Risacher SL, Bice PJ, Han X, Kristal BS, Blach C, Baillie R, Kastenmüller G, Meikle PJ, Saykin AJ, Kaddurah-Daouk R. Circulating lipid profiles are associated with cross-sectional and longitudinal changes of central biomarkers for Alzheimer's disease. medRxiv 2023:2023.06.12.23291054. [PMID: 37398438 PMCID: PMC10312871 DOI: 10.1101/2023.06.12.23291054] [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: 07/04/2023]
Abstract
Investigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD. Notably, we identified the lysoalkylphosphatidylcholine (LPC(O)) as associated with "A/N" biomarkers at baseline at lipid species, class, and module levels. Also, GM3 ganglioside showed significant association with baseline levels and longitudinal changes of the "N" biomarkers at species and class levels. Our study of circulating lipids and central AD biomarkers enabled identification of lipids that play potential roles in the cascade of AD pathogenesis. Our results suggest dysregulation of lipid metabolic pathways as precursors to AD development and progression.
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Affiliation(s)
- Jun Pyo Kim
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Neurology, Samsung Medical Center, Seoul, Korea
| | - Kwangsik Nho
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
- Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Shannon L Risacher
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paula J Bice
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xianlin Han
- University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
- Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Andrew J Saykin
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
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18
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Tong B, Risacher SL, Bao J, Feng Y, Wang X, Ritchie MD, Moore JH, Urbanowicz R, Saykin AJ, Shen L. Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline. AMIA Jt Summits Transl Sci Proc 2023; 2023:525-533. [PMID: 37350880 PMCID: PMC10283108] [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] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.
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Affiliation(s)
- Boning Tong
- University of Pennsylvania, Philadelphia, PA
| | | | | | - Yanbo Feng
- University of Pennsylvania, Philadelphia, PA
| | - Xinkai Wang
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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19
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Coleman ME, Roessler MEH, Peng S, Roth AR, Risacher SL, Saykine AJ, Apostolova LG, Perry BL. Social enrichment on the job: Complex work with people improves episodic memory, promotes brain reserve, and reduces the risk of dementia. Alzheimers Dement 2023; 19:2655-2665. [PMID: 37037592 PMCID: PMC10272079 DOI: 10.1002/alz.13035] [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: 11/14/2022] [Accepted: 12/22/2022] [Indexed: 04/12/2023]
Abstract
Individuals with more complex jobs experience better cognitive function in old age and a lower risk of dementia, yet complexity has multiple dimensions. Drawing on the Social Networks in Alzheimer Disease study, we examine the association between occupational complexity and cognition in a sample of older adults (N = 355). A standard deviation (SD) increase in complex work with people is associated with a 9% to 12% reduction in the probability of mild cognitive impairment or dementia, a 0.14-0.19 SD increase in episodic memory, and a 0.18-0.25 SD increase in brain reserve, defined as the gap (residual) between global cognitive function and magnetic resonance imaging (MRI) indicators of brain atrophy. In contrast, complexity with data or things is rarely associated with cognitive outcomes. We discuss the clinical and methodological implications of these findings, including the need to complement data-centered activities (e.g., Sudoku puzzles) with person-centered interventions that increase social complexity.
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Affiliation(s)
- Max E. Coleman
- Department of Sociology, University of Utah, Salt Lake City, Utah, USA
- Department of Sociology, Indiana University, Bloomington, Indiana, USA
| | - Meghan E. H. Roessler
- Department of Sociology, Indiana University, Bloomington, Indiana, USA
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, USA
| | - Siyun Peng
- Department of Sociology, Indiana University, Bloomington, Indiana, USA
| | - Adam R. Roth
- Department of Sociology, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Shannon L. Risacher
- Stark Neurosciences Research Institute, Indiana Alzheimer's Disease Research Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Andrew J. Saykine
- Stark Neurosciences Research Institute, Indiana Alzheimer's Disease Research Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Departments of Neurology, Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Liana G. Apostolova
- Stark Neurosciences Research Institute, Indiana Alzheimer's Disease Research Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Departments of Neurology, Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Brea L. Perry
- Department of Sociology, Indiana University, Bloomington, Indiana, USA
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20
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason-Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. bioRxiv 2023:2023.05.17.541182. [PMID: 37292885 PMCID: PMC10245725 DOI: 10.1101/2023.05.17.541182] [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: 06/10/2023]
Abstract
INTRODUCTION It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L. Beason-Held
- 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
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Shannon L. Risacher
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew J. Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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21
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He B, Xie L, Varathan P, Nho K, Risacher SL, Saykin AJ, Yan J. Fused multi-modal similarity network as prior in guiding brain imaging genetic association. Front Big Data 2023; 6:1151893. [PMID: 37215688 PMCID: PMC10196480 DOI: 10.3389/fdata.2023.1151893] [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: 01/26/2023] [Accepted: 04/04/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. Methods In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively. Results Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls). Discussion Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.
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Affiliation(s)
- Bing He
- Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, United States
| | - Linhui Xie
- School of Engineering and Technology, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, United States
| | - Pradeep Varathan
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, United States
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, United States
| | - Shannon L. Risacher
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, United States
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, United States
| | - Jingwen Yan
- Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, United States
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22
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Rabin LA, Sikkes SA, Tommet D, Jones RN, Crane PK, Elbulok-Charcape MM, Dubbelman MA, Koscik R, Amariglio RE, Buckley RF, Boada M, Chételat G, Dubois B, Ellis KA, Gifford KA, Jefferson AL, Jessen F, Johnson S, Katz MJ, Lipton RB, Luck T, Margioti E, Maruff P, Molinuevo JL, Perrotin A, Petersen RC, Rami L, Reisberg B, Rentz DM, Riedel-Heller SG, Risacher SL, Rodriguez-Gomez O, Sachdev PS, Saykin AJ, Scarmeas N, Smart C, Snitz BE, Sperling RA, Taler V, van der Flier WM, van Harten AC, Wagner M, Wolfsgruber S. Linking self-perceived cognitive functioning questionnaires using item response theory: The subjective cognitive decline initiative. Neuropsychology 2023; 37:463-499. [PMID: 37276136 PMCID: PMC10564559 DOI: 10.1037/neu0000888] [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: 06/07/2023] Open
Abstract
OBJECTIVE Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies. METHOD We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies. RESULTS We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change. CONCLUSIONS This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Laura A. Rabin
- Department of Psychology, Brooklyn College, Brooklyn, NY, USA and The Graduate Center of CUNY, NY, NY, USA
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sietske A.M. Sikkes
- Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Douglas Tommet
- Department of Psychiatry and Human Behavior and Neurology, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Richard N. Jones
- Department of Psychiatry and Human Behavior and Neurology, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Paul K. Crane
- Division of General Internal Medicine, University of Washington, Seattle, WA, USA
| | | | - Mark A. Dubbelman
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Rebecca Koscik
- Wisconsin Alzheimer's Institute University of Wisconsin School of Medicine and Public Health Madison WI, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health Madison WI, USA
| | - Rebecca E. Amariglio
- Department of Neurology, Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F. Buckley
- Department of Neurology, Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry and Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Mercè Boada
- Alzheimer Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Gaël Chételat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", NeuroPresage Team, Cyceron, 14000 Caen, France
| | - Bruno Dubois
- Université Pierre et Marie Curie-Paris 6, AP-HP, Hôpital de la Salpêtrière, Paris, France
- Centre des Maladies Cognitives et Comportementales, Institut du Cerveau et de la Moelle épinière (ICM), UMRS975, Paris, France
| | - Kathryn A. Ellis
- Department of Psychiatry and Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Katherine A. Gifford
- Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Sterling Johnson
- Geriatric Research Education and Clinical Center William S. Middleton Memorial Veterans Hospital Madison WI, USA
- University of Wisconsin School of Medicine and Public Health, Madison WI, USA
| | - Mindy J. Katz
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Richard B. Lipton
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Psychiatry and Behavioral Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tobias Luck
- Faculty of Applied Social Sciences, University of Applied Sciences Erfurt, Erfurt, Germany
| | - Eleni Margioti
- Laboratory of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Aviv Clinics, Jumeirah Lake Towers, Dubai, United Arab Emirates
| | | | - Jose Luis Molinuevo
- Alzheimer’s Disease and Other Cognitive Disorders Unit, IDIBAPS, Hospital Clinic, Barcelona, Spain
| | - Audrey Perrotin
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", NeuroPresage Team, Cyceron, 14000 Caen, France
| | - Ronald C. Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Lorena Rami
- Alzheimer’s Disease and Other Cognitive Disorders Unit, IDIBAPS, Hospital Clinic, Barcelona, Spain
| | - Barry Reisberg
- Department of Psychiatry, New York University Langone Medical Center, New York, NY, USA
- Silberstein Aging and Dementia Research Center, New York University School of Medicine, New York, NY, USA
| | - Dorene M. Rentz
- Department of Neurology, Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Steffi G. Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health (ISAP), University of Leipzig, Leipzig, Germany
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Octavio Rodriguez-Gomez
- Alzheimer Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Greece
- Department of Neurology, Columbia University, New York, USA
| | - Colette Smart
- Department of Psychology, University of Victoria, Victoria, BC, Canada
- Centre on Aging, University of Victoria, Victoria, BC, Canada
| | - Beth E. Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Reisa A. Sperling
- Department of Neurology, Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Vanessa Taler
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
- Bruyère Research Institute, Ottawa, ON, Canada
| | - Wiesje M. van der Flier
- Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Argonde C. van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry and Psychotherapy University of Bonn, Bonn, Germany
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Hirschfeld LR, Deardorff R, Chumin EJ, Wu YC, McDonald BC, Cao S, Risacher SL, Yi D, Byun MS, Lee JY, Kim YK, Kang KM, Sohn CH, Nho K, Saykin AJ, Lee DY. White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum. medRxiv 2023:2023.04.05.23288147. [PMID: 37066317 PMCID: PMC10104207 DOI: 10.1101/2023.04.05.23288147] [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: 04/18/2023]
Abstract
BACKGROUND White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations. METHODS We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥55 years, including 276 cognitively normal older adults (CN), 142 mild cognitive impairment (MCI), and 87 AD, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University. RESULTS Compared to CN, AD and MCI subjects showed decreased WM integrity in the bilateral CBH. Cognition, mood, and higher amyloid burden were also associated with poorer WM integrity in the CBH. CONCLUSION These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.
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Affiliation(s)
- Lauren Rose Hirschfeld
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Rachael Deardorff
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Evgeny J Chumin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA, 47405
| | - Yu-Chien Wu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Brenna C McDonald
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Sha Cao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Shannon L Risacher
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Korea, 03080
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea, 03080
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea, 03080
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea, 03080
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, Korea, 07061
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Korea, 07061
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea, 03080
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Korea, 03080
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Indiana University School of Informatics and Computing, Indianapolis, IN USA, 46202
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA, 46202
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA, 46202
| | - Dong Young Lee
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Korea, 03080
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea, 03080
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea, 03080
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24
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Eliacin J, Polsinelli AJ, Epperson F, Gao S, Van Heiden S, Westmoreland G, Richards R, Richards M, Campbell C, Hendrie H, Risacher SL, Saykin AJ, Wang S. Barriers and facilitators to participating in Alzheimer's disease biomarker research in black and white older adults. Alzheimers Dement (N Y) 2023; 9:e12399. [PMID: 37287470 PMCID: PMC10242196 DOI: 10.1002/trc2.12399] [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] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/13/2023] [Accepted: 05/01/2023] [Indexed: 06/09/2023]
Abstract
Introduction The study examined Black and White prospective participants' views of barriers to and facilitators of participation in Alzheimer's disease (AD) biomarker research. Methods In a mixed-methods study, 399 community-dwelling Black and White older adults (age ≥55) who had never participated in AD research completed a survey about their perceptions of AD biomarker research. Individuals from lower socioeconomic and education backgrounds and Black men were over-sampled to address perspectives of traditionally under-represented groups. A subset of participants (n = 29) completed qualitative interviews. Results Most participants expressed interest in biomarker research (overall 69%). However, Black participants were comparatively more hesitant than White participants (28.9% vs 15.1%), were more concerned about study risks (28.9% vs 15.1%), and perceived multiple barriers to participating in brain scans. These results persisted even after adjusting for trust and perceived knowledge of AD. Information was a primary barrier (when absent) and incentive (when provided) for AD biomarker research participation. Black older adults desired more information about AD (eg, risk, prevention), general research processes, and specific biomarker procedures. They also desired return of results to make informed decisions about their health, research-sponsored community awareness events, and for researchers to mitigate the burden placed on participants in research (eg, transportation, basic needs). Conclusion Our findings increase representativeness in the literature by focusing on individuals with no history of AD research experience and those from traditionally underrepresented groups in research. Results suggest that the research community needs to improve information sharing and raising awareness, increase their presence in the communities of underrepresented groups, reduce incidental costs, and provide valuable personal health information to participants to increase interest. Specific recommendations for improving recruitment are addressed. Future studies will assess the implementation of evidence-based, socioculturally sensitive recruitment strategies to increase enrollment of Black older adults into AD biomarker studies.HIGHLIGHTS: Individuals from under-represented groups are interested in Alzheimer's disease (AD) biomarker research.After adjusting for trust and AD knowledge, Black participants were still more hesitant.Information is a barrier (when absent) to and incentive (when given) for biomarker studies.Reducing burden (e.g., transportation) is essential for recruiting Black older adults.
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Affiliation(s)
- Johanne Eliacin
- Department of Internal General MedicineIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- VA HSR&D Center for Health Information and CommunicationRoudebush VA Medical CenterIndianapolisIndianaUSA
- Regenstrief Institute, Inc.IndianapolisIndianaUSA
- Women's Health Sciences DivisionNational Center for PTSDVA Boston Healthcare SystemBostonMassachusettsUSA
| | - Angelina J. Polsinelli
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Sujuan Gao
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of Biostatistics and Health Data ScienceIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sarah Van Heiden
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Glenda Westmoreland
- Department of MedicineIndiana University School of MedicineIndianapolisIndianaUSA
| | - Ralph Richards
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Mollie Richards
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | | | - Hugh Hendrie
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Shannon L. Risacher
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana University Network Science InstituteBloomingtonIndianaUSA
| | - Sophia Wang
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
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25
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Swinford CG, Risacher SL, Wu YC, Apostolova LG, Gao S, Bice PJ, Saykin AJ. Altered cerebral blood flow in older adults with Alzheimer's disease: a systematic review. Brain Imaging Behav 2023; 17:223-256. [PMID: 36484922 PMCID: PMC10117447 DOI: 10.1007/s11682-022-00750-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [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: 03/23/2022] [Revised: 09/26/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022]
Abstract
The prevalence of Alzheimer's disease is projected to reach 13 million in the U.S. by 2050. Although major efforts have been made to avoid this outcome, so far there are no treatments that can stop or reverse the progressive cognitive decline that defines Alzheimer's disease. The utilization of preventative treatment before significant cognitive decline has occurred may ultimately be the solution, necessitating a reliable biomarker of preclinical/prodromal disease stages to determine which older adults are most at risk. Quantitative cerebral blood flow is a promising potential early biomarker for Alzheimer's disease, but the spatiotemporal patterns of altered cerebral blood flow in Alzheimer's disease are not fully understood. The current systematic review compiles the findings of 81 original studies that compared resting gray matter cerebral blood flow in older adults with mild cognitive impairment or Alzheimer's disease and that of cognitively normal older adults and/or assessed the relationship between cerebral blood flow and objective cognitive function. Individuals with Alzheimer's disease had relatively decreased cerebral blood flow in all brain regions investigated, especially the temporoparietal and posterior cingulate, while individuals with mild cognitive impairment had consistent results of decreased cerebral blood flow in the posterior cingulate but more mixed results in other regions, especially the frontal lobe. Most papers reported a positive correlation between regional cerebral blood flow and cognitive function. This review highlights the need for more studies assessing cerebral blood flow changes both spatially and temporally over the course of Alzheimer's disease, as well as the importance of including potential confounding factors in these analyses.
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Affiliation(s)
- Cecily G Swinford
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paula J Bice
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
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26
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Yang Y, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer A, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ, Archer DB. White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease. Alzheimers Dement (Amst) 2023; 15:e12425. [PMID: 37213219 PMCID: PMC10192723 DOI: 10.1002/dad2.12425] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/06/2023] [Accepted: 03/12/2023] [Indexed: 05/23/2023]
Abstract
Introduction White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Lori L. Beason‐Held
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Yang An
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Andrea Shafer
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Shannon L. Risacher
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Andrew J. Saykin
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Susan M. Resnick
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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27
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Nho K, Risacher SL, Apostolova L, Bice PJ, Brosch J, Deardorff R, Faber K, Farlow MR, Foroud T, Gao S, Rosewood T, Kim JP, Nudelman K, Yu M, Aisen P, Sperling R, Hooli B, Shcherbinin S, Svaldi D, Jack CR, Jagust WJ, Landau S, Vasanthakumar A, Waring JF, Doré V, Laws SM, Masters CL, Porter T, Rowe CC, Villemagne VL, Dumitrescu L, Hohman TJ, Libby JB, Mormino E, Buckley RF, Johnson K, Yang HS, Petersen RC, Ramanan VK, Vemuri P, Cohen AD, Fan KH, Kamboh MI, Lopez OL, Bennett DA, Ali M, Benzinger T, Cruchaga C, Hobbs D, De Jager PL, Fujita M, Jadhav V, Lamb BT, Tsai AP, Castanho I, Mill J, Weiner MW, Saykin AJ. Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET. medRxiv 2023:2023.02.27.23286048. [PMID: 36993271 PMCID: PMC10055458 DOI: 10.1101/2023.02.27.23286048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aβ positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aβ. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.
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28
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Risacher SL, Apostolova LG. Neuroimaging in Dementia. Continuum (Minneap Minn) 2023; 29:219-254. [PMID: 36795879 DOI: 10.1212/con.0000000000001248] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE Neurodegenerative diseases are significant health concerns with regard to morbidity and social and economic hardship around the world. This review describes the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases. It briefly discusses findings in these diseases in studies using MRI and metabolic and molecular-based imaging (eg, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]). LATEST DEVELOPMENTS Neuroimaging studies with MRI and PET have demonstrated differential patterns of brain atrophy and hypometabolism in different neurodegenerative disorders, which can be useful in differential diagnoses. Advanced MRI sequences, such as diffusion-based imaging, and functional MRI (fMRI) provide important information about underlying biological changes in dementia and new directions for development of novel measures for future clinical use. Finally, advancements in molecular imaging allow clinicians and researchers to visualize dementia-related proteinopathies and neurotransmitter levels. ESSENTIAL POINTS Diagnosis of neurodegenerative diseases is primarily based on symptomatology, although the development of in vivo neuroimaging and fluid biomarkers is changing the scope of clinical diagnosis, as well as the research into these devastating diseases. This article will help inform the reader about the current state of neuroimaging in neurodegenerative diseases, as well as how these tools might be used for differential diagnoses.
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Affiliation(s)
- Shannon L Risacher
- Address correspondence to Dr Shannon L. Risacher, 355 W 16th St, Indianapolis, IN 46202,
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29
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Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu CC, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T, Dickson DW, Bu G, Allen M, Ertekin-Taner N. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins. Mol Neurodegener 2023; 18:2. [PMID: 36609403 PMCID: PMC9825010 DOI: 10.1186/s13024-022-00592-2] [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] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. METHODS Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. RESULTS We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. CONCLUSIONS Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.
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Affiliation(s)
- Stephanie R. Oatman
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Joseph S. Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | | | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yu Yamazaki
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Thuy T. Nguyen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kimberly Malphrus
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Michael Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Kristi Biswas
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
| | - Matthew Baker
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yuka A. Martens
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Na Zhao
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Takahisa Kanekiyo
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
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Mathew S, WuDunn D, Mackay DD, Vosmeier A, Tallman EF, Deardorff R, Harris A, Farlow MR, Brosch JR, Gao S, Apostolova LG, Saykin AJ, Risacher SL. Association of Brain Volume and Retinal Thickness in the Early Stages of Alzheimer's Disease. J Alzheimers Dis 2023; 91:743-752. [PMID: 36502316 PMCID: PMC9990456 DOI: 10.3233/jad-210533] [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] [Indexed: 12/13/2022]
Abstract
BACKGROUND The eye has been considered a 'window to the brain,' and several neurological diseases including neurodegenerative conditions like Alzheimer's disease (AD) also show changes in the retina. OBJECTIVE To investigate retinal nerve fiber layer (RNFL) thickness and its association with brain volume via magnetic resonance imaging (MRI) in older adults with subjective or objective cognitive decline. METHODS 75 participants underwent ophthalmological and neurological evaluation including optical coherence tomography and MRI (28 cognitively normal subjects, 26 with subjective cognitive decline, 17 patients diagnosed with mild cognitive impairment, and 4 with AD). Differences in demographics, thickness of RNFL, and brain volume were assessed using ANCOVA, while partial Pearson correlations, covaried for age and sex, were used to compare thickness of the peripapillary RNFL with brain volumes, with p < 0.05 considered statistically significant. RESULTS Mean RNFL thickness was significantly correlated with brain volumes, including global volume (right eye r = 0.235 p = 0.046, left eye r = 0.244, p = 0.037), temporal lobe (right eye r = 0.242 p = 0.039, left eye r = 0.290, p = 0.013), hippocampal (right eye r = 0.320 p = 0.005, left eye r = 0.306, p = 0.008), amygdala (left eye r = 0.332, p = 0.004), and occipital lobe (right eye r = 0.264 p = 0.024) volumes. CONCLUSION RNFL thickness in both eyes was positively associated with brain volumes in subjects with subjective and objective cognitive decline. The RNFL, however, did not correlate with the disease, but the small sample number makes it important to conduct larger studies. RNFL thickness may be a useful non-invasive and inexpensive tool for detection of brain neurodegeneration and may assist with diagnosis and monitoring of progression and treatment in AD.
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Affiliation(s)
- Sunu Mathew
- Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Devin D. Mackay
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Aaron Vosmeier
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Eileen F. Tallman
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Rachael Deardorff
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | | | - Martin R. Farlow
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Jared R. Brosch
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Liana G. Apostolova
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Shannon L. Risacher
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
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31
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Hammers DB, Lin JH, Polsinelli AJ, Logan PE, Risacher SL, Schwarz AJ, Apostolova LG. Criterion Validation of Tau PET Staging Schemes in Relation to Cognitive Outcomes. J Alzheimers Dis 2023; 96:197-214. [PMID: 37742649 PMCID: PMC10825758 DOI: 10.3233/jad-230512] [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: 08/14/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Utilization of NIA-AA Research Framework requires dichotomization of tau pathology. However, due to the novelty of tau-PET imaging, there is no consensus on methods to categorize scans into "positive" or "negative" (T+ or T-). In response, some tau topographical pathologic staging schemes have been developed. OBJECTIVE The aim of the current study is to establish criterion validity to support these recently-developed staging schemes. METHODS Tau-PET data from 465 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90) were classified as T+ or T- using decision rules for the Temporal-Occipital Classification (TOC), Simplified TOC (STOC), and Lobar Classification (LC) tau pathologic schemes of Schwarz, and Chen staging scheme. Subsequent dichotomization was analyzed in comparison to memory and learning slope performances, and diagnostic accuracy using actuarial diagnostic methods. RESULTS Tau positivity was associated with worse cognitive performance across all staging schemes. Cognitive measures were nearly all categorized as having "fair" sensitivity at classifying tau status using TOC, STOC, and LC schemes. Results were comparable between Schwarz schemes, though ease of use and better data fit preferred the STOC and LC schemes. While some evidence was supportive for Chen's scheme, validity lagged behind others-likely due to elevated false positive rates. CONCLUSIONS Tau-PET staging schemes appear to be valuable for Alzheimer's disease diagnosis, tracking, and screening for clinical trials. Their validation provides support as options for tau pathologic dichotomization, as necessary for use of NIA-AA Research Framework. Future research should consider other staging schemes and validation with other outcome benchmarks.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Joshua H. Lin
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam J. Schwarz
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Takeda Pharmaceuticals Ltd., Cambridge, MA, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Manchella MK, Logan PE, Perry BL, Peng S, Risacher SL, Saykin AJ, Apostolova LG. Increased Bridging Characteristics in Social Networks are Associated with Increasing Amygdala Volume and Grey Matter Density. Alzheimers Dement 2022. [DOI: 10.1002/alz.068348] [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)
- Mohit K. Manchella
- University of Southern Indiana Evansville IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Paige E. Logan
- Indiana University School of Medicine Indianapolis IN USA
| | - Brea L Perry
- Indiana University Network Science Institute Bloomington IN USA
| | - Siyun Peng
- Indiana University Network Science Institute Bloomington IN USA
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33
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Archer DB, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer AT, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Sex differences in white matter microstructure in aging and Alzheimer’s disease: A multi‐site free‐water imaging study. Alzheimers Dement 2022. [DOI: 10.1002/alz.066752] [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)
- Derek B Archer
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
| | - Niranjana Shashikumar
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Varuna Jasodanand
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Elizabeth E. Moore
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Kimberly R. Pechman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN 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
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | | | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Timothy J. Hohman
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center Nashville TN USA
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34
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Risacher SL, Deardorff R, Saykin AJ. Subjective vs. Objective Memory Predictors of AD Biomarkers and Future Cognitive, Clinical, and Functional Decline. Alzheimers Dement 2022. [DOI: 10.1002/alz.068041] [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)
- Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Rachael Deardorff
- Indiana Alzheimer’s Disease Research Center Indianapolis IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis IN USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
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35
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Wen Q, Yu M, Shahid SS, Zhao Y, Risacher SL, Saykin AJ, Wu Y. Genetic and Structural Network Contributions to The Regional Vulnerability of Tauopathy. Alzheimers Dement 2022. [DOI: 10.1002/alz.067455] [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)
- Qiuting Wen
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer Disease Center Indianapolis IN USA
| | - Meichen Yu
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine indianapolis IN USA
| | | | - Yi Zhao
- INDIANA UNIVERSITY Indianapolis IN USA
| | - Shannon L. Risacher
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Andrew J. Saykin
- Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Yu‐Chien Wu
- Indiana University School of Medicine Indianapolis IN USA
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36
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Archer DB, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer AT, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal, multi‐site diffusion MRI data in conjunction with free‐water analysis to characterize patterns of white matter neurodegeneration in aging. Alzheimers Dement 2022. [DOI: 10.1002/alz.068097] [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)
- Derek B Archer
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
| | - Niranjana Shashikumar
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Varuna Jasodanand
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Elizabeth E. Moore
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Kimberly R. Pechman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN 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
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | | | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Timothy J. Hohman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
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37
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Mathew S, WuDunn D, Mackay D, Dage JL, Russ KA, Blennow K, Zetterberg H, Vosmeier A, Tallman EF, Deardorff R, Harris A, Farlow MR, Brosch JR, Gao S, Apostolova LG, Saykin AJ, Risacher SL. Association of Retinal Nerve Fiber Layer Thickness with Plasma pTau181 and Aβ42/Aβ40 ratio. Alzheimers Dement 2022. [DOI: 10.1002/alz.067861] [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)
- Sunu Mathew
- Indiana University School of Medicine Indianapolis IN USA
| | | | - Devin Mackay
- Indiana University School of Medicine Indianapolis IN USA
- Department of Neurology, Indiana University School of Medicine Indianapolis IN USA
| | - Jeffrey L. Dage
- Indiana University School of Medicine Indianapolis IN USA
- Indiana University Indianapolis IN USA
| | - Kristen A Russ
- Indiana University School of Medicine Indianapolis IN USA
| | | | | | - Aaron Vosmeier
- Indiana University School of Medicine Indianapolis IN USA
| | | | | | | | | | | | - Sujuan Gao
- Indiana University School of Medicine Indianapolis IN USA
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38
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Manchella MK, Logan PE, Perry BL, Peng S, Risacher SL, Saykin AJ, Apostolova LG. Increased Bridging Characteristics in Social Networks are Associated with Increasing Amygdala Volume and Grey Matter Density. Alzheimers Dement 2022. [DOI: 10.1002/alz.067762] [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)
- Mohit K. Manchella
- University of Southern Indiana Evansville IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Paige E. Logan
- Indiana University School of Medicine Indianapolis IN USA
| | - Brea L Perry
- Indiana University Network Science Institute Bloomington IN USA
| | - Siyun Peng
- Indiana University Network Science Institute Bloomington IN USA
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39
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Lahti J, Tuominen S, Yang Q, Pergola G, Ahmad S, Amin N, Armstrong NJ, Beiser A, Bey K, Bis JC, Boerwinkle E, Bressler J, Campbell A, Campbell H, Chen Q, Corley J, Cox SR, Davies G, De Jager PL, Derks EM, Faul JD, Fitzpatrick AL, Fohner AE, Ford I, Fornage M, Gerring Z, Grabe HJ, Grodstein F, Gudnason V, Simonsick E, Holliday EG, Joshi PK, Kajantie E, Kaprio J, Karell P, Kleineidam L, Knol MJ, Kochan NA, Kwok JB, Leber M, Lam M, Lee T, Li S, Loukola A, Luck T, Marioni RE, Mather KA, Medland S, Mirza SS, Nalls MA, Nho K, O'Donnell A, Oldmeadow C, Painter J, Pattie A, Reppermund S, Risacher SL, Rose RJ, Sadashivaiah V, Scholz M, Satizabal CL, Schofield PW, Schraut KE, Scott RJ, Simino J, Smith AV, Smith JA, Stott DJ, Surakka I, Teumer A, Thalamuthu A, Trompet S, Turner ST, van der Lee SJ, Villringer A, Völker U, Wilson RS, Wittfeld K, Vuoksimaa E, Xia R, Yaffe K, Yu L, Zare H, Zhao W, Ames D, Attia J, Bennett DA, Brodaty H, Chasman DI, Goldman AL, Hayward C, Ikram MA, Jukema JW, Kardia SLR, Lencz T, Loeffler M, Mattay VS, Palotie A, Psaty BM, Ramirez A, Ridker PM, Riedel-Heller SG, Sachdev PS, Saykin AJ, Scherer M, Schofield PR, Sidney S, Starr JM, Trollor J, Ulrich W, Wagner M, Weir DR, Wilson JF, Wright MJ, Weinberger DR, Debette S, Eriksson JG, Mosley TH, Launer LJ, van Duijn CM, Deary IJ, Seshadri S, Räikkönen K. Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning. Mol Psychiatry 2022; 27:4419-4431. [PMID: 35974141 PMCID: PMC9734053 DOI: 10.1038/s41380-022-01710-8] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022]
Abstract
Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.
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Affiliation(s)
- Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
- Turku Institute of Advanced Studies, University of Turku, Turku, Finland.
| | - Samuli Tuominen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Qiong Yang
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Murdoch University, Murdoch, WA, Australia
| | - Alexa Beiser
- Department of Biostatistics, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Katharina Bey
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Janie Corley
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
| | - Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Annette L Fitzpatrick
- Department of Family Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Alison E Fohner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Institute of Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
| | - Myriam Fornage
- McGovern Medical School, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Francine Grodstein
- Channing Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Harvard School of Public Health, Boston, MA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Assocation, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Eleanor Simonsick
- Translational Gerontology Branch, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Elizabeth G Holliday
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland
| | - Eero Kajantie
- National Institute for Health and Welfare, Helsinki and Oulu, Oulu, Finland
- Hospital for Children and Adolescents, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Pauliina Karell
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - John B Kwok
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Markus Leber
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Max Lam
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Teresa Lee
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Shuo Li
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Anu Loukola
- Helsinki Biobank, University of Helsinki Central Hospital, Helsinki, Finland
| | - Tobias Luck
- Department of Economic and Social Sciences & Institute of Social Medicine, Rehabilitation Sciences and Healthcare Research, University of Applied Sciences Nordhausen, Nordhausen, Germany
- University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, Leipzig, Germany
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Sunnybrook Health Sciences Centre, University of Toronto, Randwick, NSW, Australia
| | - Sarah Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Saira S Mirza
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adrienne O'Donnell
- Department of Biostatistics, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Christopher Oldmeadow
- Clinical Research Design, IT and Statistical Support Unit, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Jodie Painter
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Alison Pattie
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Simone Reppermund
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard J Rose
- Department of Psychological & Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Vijay Sadashivaiah
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Claudia L Satizabal
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Peter W Schofield
- Neuropsychiatry Service, Hunter New England Local Health District, Charlestown, NSW, Australia
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, Royal Infirmary of Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Jeannette Simino
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Albert V Smith
- Icelandic Heart Assocation, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Institute of Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Stella Trompet
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Sven J van der Lee
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, Department Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Robert S Wilson
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Rui Xia
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kristine Yaffe
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Habil Zare
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas, San Antonio, TX, USA
- University of Texas Health Sciences Center, Houston, NA, US
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - David Ames
- National Ageing Research Institute, Parkville, Melbourne, VIC, Australia
- University of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Melbourne, VIC, Australia
| | - John Attia
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
- Clinical Research Design, IT and Statistical Support Unit, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Aaron L Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Todd Lencz
- Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Venkata S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Food and Drug Administration, Washington, DC, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology and Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Heath Research Institute, Seattle, WA, USA
| | - Alfredo Ramirez
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin Scherer
- Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, NSW, Australia
| | - Stephen Sidney
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - John M Starr
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Julian Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - William Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Michael Wagner
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephanie Debette
- Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
- Bordeaux University Hospital (CHU Bordeaux), Department of Neurology, Bordeaux, France
| | - Johan G Eriksson
- Folkhälsan Research Center, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Helsinki, Singapore
| | - Thomas H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Public Health, Oxford University, Oxford, UK
| | - Ian J Deary
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
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Hirschfeld LR, Risacher SL, Nho K, Saykin AJ. Myelin repair in Alzheimer's disease: a review of biological pathways and potential therapeutics. Transl Neurodegener 2022; 11:47. [PMID: 36284351 PMCID: PMC9598036 DOI: 10.1186/s40035-022-00321-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [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: 08/17/2022] [Accepted: 10/15/2022] [Indexed: 11/29/2022] Open
Abstract
This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer's disease (AD). We discuss previously investigated therapeutic targets of amyloid, tau, and ApoE, as well as other potential therapeutic targets that have been empirically shown to contribute to both remyelination and progression of AD. Current evidence shows that there are multiple AD-relevant pathways which overlap significantly with remyelination and myelin repair through the encouragement of oligodendrocyte proliferation, maturation, and myelin production. There is a present need for a single, cohesive model of myelin homeostasis in AD. While determining a causative pathway is beyond the scope of this review, it may be possible to investigate the pathological overlap of myelin repair and AD through therapeutic approaches.
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Affiliation(s)
- Lauren Rose Hirschfeld
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Shannon L Risacher
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Andrew J Saykin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
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Kim B, Vasanthakumar A, Li QS, Nudelman KN, Risacher SL, Davis JW, Idler K, Lee J, Seo SW, Waring JF, Saykin AJ, Nho K. Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease. Alzheimers Dement (Amst) 2022; 14:e12354. [PMID: 36187194 PMCID: PMC9489162 DOI: 10.1002/dad2.12354] [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] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022]
Abstract
Introduction The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD. Methods We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers. Results WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers. Conclusion Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD. Highlights Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.
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Affiliation(s)
- Bo‐Hyun Kim
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA,Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea,Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Qingqin S. Li
- Neuroscience Therapeutic AreaJanssen Research & Development, LLCTitusvilleNew JerseyUSA
| | - Kelly N.H. Nudelman
- National Centralized Repository for Alzheimer's Disease and Related DementiasIndiana University School of MedicineIndianapolisIndianaUSA,Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Shannon L. Risacher
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA,Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Kenneth Idler
- Genomics Research CenterAbbVieNorth ChicagoIllinoisUSA
| | - Jong‐Min Lee
- Department of Biomedical EngineeringHanyang UniversitySeoulRepublic of Korea
| | - Sang Won Seo
- Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea,Department of NeurologySamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea,Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Andrew J. Saykin
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA,Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA,Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA,Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
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Shahid SS, Wen Q, Risacher SL, Farlow MR, Unverzagt FW, Apostolova LG, Foroud TM, Zetterberg H, Blennow K, Saykin AJ, Wu YC. Hippocampal-subfield microstructures and their relation to plasma biomarkers in Alzheimer's disease. Brain 2022; 145:2149-2160. [PMID: 35411392 PMCID: PMC9630875 DOI: 10.1093/brain/awac138] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [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: 07/01/2021] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 11/14/2022] Open
Abstract
Hippocampal subfields exhibit differential vulnerabilities to Alzheimer's disease-associated pathology including abnormal accumulation of amyloid-β deposition and neurofibrillary tangles. These pathological processes extensively impact on the structural and functional interconnectivities of the subfields and may explain the association between hippocampal dysfunction and cognitive deficits. In this study, we investigated the degree of alterations in the microstructure of hippocampal subfields across the clinical continuum of Alzheimer's disease. We applied a grey matter-specific multi-compartment diffusion model (Cortical-Neurite orientation dispersion and density imaging) to understand the differential effects of Alzheimer's disease pathology on the hippocampal subfield microstructure. A total of 119 participants were included in this cross-sectional study. Participants were stratified into three categories, cognitively normal (n = 47), mild cognitive impairment (n = 52), and Alzheimer's disease (n = 19). Diffusion MRI, plasma biomarkers and neuropsychological test scores were used to determine the association between the microstructural integrity and Alzheimer's disease-associated molecular indicators and cognition. For Alzheimer's disease-related plasma biomarkers, we studied amyloid-β, total tau and neurofilament light; for Alzheimer's disease-related neuropsychological tests, we included the Trail Making Test, Rey Auditory Verbal Learning Test, Digit Span and Montreal Cognitive Assessment. Comparisons between cognitively normal subjects and those with mild cognitive impairment showed significant microstructural alterations in the hippocampal cornu ammonis (CA) 4 and dentate gyrus region, whereas CA 1-3 was the most sensitive region for the later stages in the Alzheimer's disease clinical continuum. Among imaging metrics for microstructures, the volume fraction of isotropic diffusion for interstitial free water demonstrated the largest effect size in between-group comparisons. Regarding the plasma biomarkers, neurofilament light appeared to be the most sensitive biomarker for associations with microstructural imaging findings in CA4-dentate gyrus. CA 1-3 was the subfield which had stronger correlations between cognitive performance and microstructural metrics. Particularly, poor performance on the Rey Auditory Verbal Learning Test and Montreal Cognitive Assessment was associated with decreased intracellular volume fraction. Overall, our findings support the value of tissue-specific microstructural imaging for providing pathologically relevant information manifesting in the plasma biomarkers and neuropsychological outcomes across various stages of Alzheimer's disease.
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Affiliation(s)
- Syed Salman Shahid
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Qiuting Wen
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana M Foroud
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu Chien Wu
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
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Varathan P, Gorijala P, Jacobson T, Chasioti D, Nho K, Risacher SL, Saykin AJ, Yan J. Integrative analysis of eQTL and GWAS summary statistics reveals transcriptomic alteration in Alzheimer brains. BMC Med Genomics 2022; 15:93. [PMID: 35461270 PMCID: PMC9035239 DOI: 10.1186/s12920-022-01245-5] [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: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Large-scale genome-wide association studies have successfully identified many genetic variants significantly associated with Alzheimer's disease (AD), such as rs429358, rs11038106, rs723804, rs13591776, and more. The next key step is to understand the function of these SNPs and the downstream biology through which they exert the effect on the development of AD. However, this remains a challenging task due to the tissue-specific nature of transcriptomic and proteomic data and the limited availability of brain tissue.In this paper, instead of using coupled transcriptomic data, we performed an integrative analysis of existing GWAS findings and expression quantitative trait loci (eQTL) results from AD-related brain regions to estimate the transcriptomic alterations in AD brain. RESULTS We used summary-based mendelian randomization method along with heterogeneity in dependent instruments method and were able to identify 32 genes with potential altered levels in temporal cortex region. Among these, 10 of them were further validated using real gene expression data collected from temporal cortex region, and 19 SNPs from NECTIN and TOMM40 genes were found associated with multiple temporal cortex imaging phenotype. CONCLUSION Significant pathways from enriched gene networks included neutrophil degranulation, Cell surface interactions at the vascular wall, and Regulation of TP53 activity which are still relatively under explored in Alzheimer's Disease while also encouraging a necessity to bind further trans-eQTL effects into this integrative analysis.
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Affiliation(s)
- Pradeep Varathan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Priyanka Gorijala
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Tanner Jacobson
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Danai Chasioti
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
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Duran T, Woo E, Otero D, Risacher SL, Stage E, Sanjay AB, Nho K, West JD, Phillips ML, Goukasian N, Hwang KS, Apostolova LG. Associations between Cortical Thickness and Metamemory in Alzheimer’s Disease. Brain Imaging Behav 2022; 16:1495-1503. [PMID: 35064438 PMCID: PMC9450553 DOI: 10.1007/s11682-021-00627-0] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 12/27/2022]
Abstract
Metacognitive deficits affect Alzheimer's disease (AD) patient safety and increase caregiver burden. The brain areas that support metacognition are not well understood. 112 participants from the Imaging and Genetic Biomarkers for AD (ImaGene) study underwent comprehensive cognitive testing and brain magnetic resonance imaging. A performance-prediction paradigm was used to evaluate metacognitive abilities for California Verbal Learning Test-II learning (CVLT-II 1-5) and delayed recall (CVLT-II DR); Visual Reproduction-I immediate recall (VR-I Copy) and Visual Reproduction-II delayed recall (VR-II DR); Rey-Osterrieth Complex Figure Copy (Rey-O Copy) and delayed recall (Rey-O DR). Vertex-wise multivariable regression of cortical thickness was performed using metacognitive scores as predictors while controlling for age, sex, education, and intracranial volume. Subjects who overestimated CVLT-II DR in prediction showed cortical atrophy, most pronounced in the bilateral temporal and left greater than right (L > R) frontal cortices. Overestimation of CVLT-II 1-5 prediction and DR performance in postdiction showed L > R associations with medial, inferior and lateral temporal and left posterior cingulate cortical atrophy. Overconfident prediction of VR-I Copy performance was associated with right greater than left medial, inferior and lateral temporal, lateral parietal, anterior and posterior cingulate and lateral frontal cortical atrophy. Underestimation of Rey-O Copy performance in prediction was associated with atrophy localizing to the temporal and cingulate areas, and in postdiction, with diffuse cortical atrophy. Impaired metacognition was associated to cortical atrophy. Our results indicate that poor insight into one's cognitive abilities is a pervasive neurodegenerative feature associated with AD across the cognitive spectrum.
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Affiliation(s)
- Tugce Duran
- Department of Internal Medicine-Section of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, 525 Vine Street, Suite 150, Winston-Salem, NC, 27101, USA.
| | - Ellen Woo
- Department of Psychology, California State University, Fresno, Fresno, CA, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Diana Otero
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eddie Stage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Apoorva B Sanjay
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John D West
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Meredith L Phillips
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Naira Goukasian
- Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kristy S Hwang
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
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Stage E, Risacher SL, Lane KA, Gao S, Nho K, Saykin AJ, Apostolova LG. Association of the top 20 Alzheimer's disease risk genes with [ 18F]flortaucipir PET. Alzheimers Dement (Amst) 2022; 14:e12308. [PMID: 35592828 PMCID: PMC9092485 DOI: 10.1002/dad2.12308] [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] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/03/2022] [Accepted: 03/07/2022] [Indexed: 04/12/2023]
Abstract
Introduction We previously reported genetic associations of the top Alzheimer's disease (AD) risk alleles with amyloid deposition and neurodegeneration. Here, we report the association of these variants with [18F]flortaucipir standardized uptake value ratio (SUVR). Methods We analyzed the [18F]flortaucipir scans of 352 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 54 dementia (DEM) participants from Alzheimer's Disease Neuroimaging Initiative (ADNI)2 and 3. We ran step-wise regression with log-transformed [18F]flortaucipir meta-region of interest SUVR as the outcome measure and genetic variants, age, sex, and apolipoprotein E (APOE) ε4 as predictors. The results were visualized using parametric mapping at familywise error cluster-level-corrected P < .05. Results APOE ε4 showed significant (P < .05) associations with tau deposition across all disease stages. Other significantly associated genes include variants in ABCA7 in CN, CR1 in MCI, BIN1 and CASS4 in MCI and dementia participants. Discussion We found significant associations to tau deposition for ABCA7, BIN1, CASS4, and CR1, in addition to APOE ε4. These four variants have been previously associated with tau metabolism through model systems.
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Affiliation(s)
- Eddie Stage
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Shannon L. Risacher
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kathleen A. Lane
- Department of BiostatisticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sujuan Gao
- Department of BiostatisticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Andrew J. Saykin
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
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Wells LF, Risacher SL, McDonald BC, Farlow MR, Brosch J, Gao S, Apostolova LG, Saykin AJ. Measuring Subjective Cognitive Decline in Older Adults: Harmonization Between the Cognitive Change Index and the Measurement of Everyday Cognition Instruments. J Alzheimers Dis 2022; 87:761-769. [PMID: 35367962 PMCID: PMC9169561 DOI: 10.3233/jad-215388] [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: 11/15/2022]
Abstract
BACKGROUND Self and informant (proxy or study partner) reports of everyday cognitive functioning have been shown to be associated with incipient neurodegenerative disease. The 20-item Cognitive Change Index (CCI) and the 39-item Measurement of Everyday Cognition (ECog) were each developed to characterize early subjective changes in cognitive function. OBJECTIVE We examined the relationship between CCI and ECog self and informant-based evaluations to determine content overlap and provide a co-calibration for converting between these widely used instruments. METHODS 950 participants (57.1% female, mean age = 71.2 years) from ADNI and the Indiana ADRC with self-based evaluations and 279 participants (60.9% female, mean age = 71.8 years) with informant-based evaluations (Indiana ADRC) were included. Analyzed variables for the CCI and ECog included domain mean scores, memory domain total scores, and total scores for all items. Spearman correlations, regression analyses, and frequency distributions were used to assess the relationship between CCI and ECog. Sex, age, years of education, race/ethnicity, APOE ɛ4 carrier status, and baseline diagnosis were also analyzed as potentially relevant covariates. RESULTS CCI and ECog total scores were highly correlated for the self (r = 0.795, p < 0.001) and informant-based (r = 0.840, p < 0.001) versions, as expected. Frequency distributions of self and informant total scores were generated and plotted separately. Quadratic regressions for self (r2 = 0.626) and informant (r2 = 0.741) scores were used to create a translation table between the CCI and ECog total scores. CONCLUSION Self and informant total scores can be harmonized and translated between the CCI and ECog to facilitate cross-study and longitudinal assessment of perceived cognitive change, an important patient-reported outcome.
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Affiliation(s)
| | - Shannon L. Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C. McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R. Farlow
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jared Brosch
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G. Apostolova
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Stage E, Svaldi D, Sokolow S, Risacher SL, Marosi K, Rotter JI, Saykin AJ, Apostolova LG. Prescribing cholinesterase inhibitors in mild cognitive impairment-Observations from the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement (N Y) 2021; 7:e12168. [PMID: 35005201 PMCID: PMC8719350 DOI: 10.1002/trc2.12168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 09/22/2020] [Revised: 03/05/2021] [Accepted: 03/14/2021] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Analyses of off-label use of acetylcholinesterase inhibitors (AChEIs) in mild cognitive impairment (MCI) has produced mixed results. Post hoc analyses of observational cohorts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have reported deleterious effects in AChEI-treated subjects (AChEI+). Here, we used neuroimaging biomarkers to determine whether AChEI+ subjects had a greater rate of neurodegeneration than untreated (AChEI-) subjects while accounting for baseline differences. METHODS We selected 121 ADNI MCI AChEI+ subjects and 151 AChEI- subjects with a magnetic resonance imaging (MRI) scan; 82 AChEI+ and 110 AChEI- also had a fluorodeoxyglucose (FDG) scan. A subset (83 AChEI+ and 98 AChEI-) had cerebrospinal fluid (CSF) or amyloid positron emission tomography (PET) assessment for amyloid positivity. Linear regression models were used to compare the effect of treatment on changes in Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes scores. We used standard regression in SPM (for baseline) and the SPM toolbox sandwich estimator, SwE (for longitudinal) for comparisons of AChEI+ and AChEI- FDG PET and MRI data. RESULTS At baseline, the AChEI+ group had significantly reduced cortical gray matter density (GMD) and more hypometabolism than AChEI- subjects. The greater rate of atrophy and hypometabolic changes over time in AChEI+ compared to AChEI- subjects did not survive correction for baseline differences. AChEI+ participants were more likely to be amyloid-positive and have lower GMD and FDG standardized uptake value ratio than AChEI- at baseline. AChEI+ subjects showed greater atrophy over time, which remained significant after controlling for amyloid status. DISCUSSION Our data suggest that the observed differences in rates of cognitive decline, atrophy, and hypometabolism are likely the result of significant baseline differences between the groups. Furthermore, the data indicate no treatment effect of AChEI (positive of negative), rather that physicians prescribe AChEI to subjects who present with more severe clinical impairment. This alone may account for the negative effect seen previously in the ADNI population of AChEI use among MCI subjects.
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Affiliation(s)
- Eddie Stage
- Indiana Alzheimer Disease CenterIndianapolisINUSA
- Department of NeurologyIU School of MedicineIndianapolisINUSA
| | - Diana Svaldi
- Department of NeurologyIU School of MedicineIndianapolisINUSA
| | - Sophie Sokolow
- UCLA School of NursingLos AngelesCAUSA
- UCLA Brain Research InsituteLos AngelesCAUSA
- UCLA Clinical and Translational Science InstituteLos AngelesCAUSA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease CenterIndianapolisINUSA
- Department of Radiology and Imaging SciencesIU School of MedicineIndianapolisINUSA
| | | | - Jerome I. Rotter
- The Institute for Translational Genomics and Population SciencesHarbor‐UCLA Medical CenterTorranceCAUSA
- Department of PediatricsHarbor‐UCLA Medical CenterTorranceCAUSA
- The Lundquist Institute for Biomedical InnovationHarbor‐UCLA Medical CenterTorranceCAUSA
| | - Andrew J. Saykin
- Indiana Alzheimer Disease CenterIndianapolisINUSA
- Department of Radiology and Imaging SciencesIU School of MedicineIndianapolisINUSA
| | - Liana G. Apostolova
- Indiana Alzheimer Disease CenterIndianapolisINUSA
- Department of NeurologyIU School of MedicineIndianapolisINUSA
- Department of Radiology and Imaging SciencesIU School of MedicineIndianapolisINUSA
- Department of Medical and Molecular GeneticsIU School of MedicineIndianapolisINUSA
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Mathew S, WuDunn D, Mackay D, Vosmeier A, Tallman EF, Garrett MN, West JD, Deardorff R, Harris A, Farlow MR, Brosch JR, Gao S, Apostolova LG, Saykin AJ, Risacher SL. Association of retinal nerve fiber layer thickness with temporal lobe atrophy. Alzheimers Dement 2021. [DOI: 10.1002/alz.053991] [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: 11/10/2022]
Affiliation(s)
- Sunu Mathew
- Indiana University School of Medicine Indianapolis IN USA
| | | | - Devin Mackay
- Indiana University School of Medicine Indianapolis IN USA
| | - Aaron Vosmeier
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Eileen F. Tallman
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Makaylah N. Garrett
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - John D. West
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Rachael Deardorff
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | | | - Martin R. Farlow
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Jared R. Brosch
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Sujuan Gao
- Indiana University School of Medicine Indianapolis IN USA
- Department of Biostatistics, Indiana University School of Medicine Indianapolis IN USA
| | - Liana G. Apostolova
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Andrew J. Saykin
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Shannon L. Risacher
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
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Price LR, Deardorff R, Wu Y, McDonald BC, West JD, Risacher SL, Saykin AJ. Comparison of diffusion weighted, imaging‐derived, fractional anisotropy with a novel MRI contrast‐weighted ratio method for measuring myelin in older adults at risk for Alzheimer’s disease. Alzheimers Dement 2021. [DOI: 10.1002/alz.055560] [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: 11/10/2022]
Affiliation(s)
| | - Rachael Deardorff
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Yu‐Chien Wu
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer Disease Center Indianapolis IN USA
| | - Brenna C. McDonald
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - John D. West
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Shannon L. Risacher
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
| | - Andrew J. Saykin
- Indiana University School of Medicine Indianapolis IN USA
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA
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Risacher SL, Deardorff R, West JD, Vosmeier A, Tallman EF, McDonald BC, Farlow MR, Brosch JR, Gao S, Apostolova LG, Saykin AJ. Brain activation during episodic scene encoding is associated with amyloid and tau levels in amyloid‐positive older adults. Alzheimers Dement 2021. [DOI: 10.1002/alz.056463] [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: 11/05/2022]
Affiliation(s)
- Shannon L. Risacher
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Rachael Deardorff
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - John D. West
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Aaron Vosmeier
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Eileen F. Tallman
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Brenna C. McDonald
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Martin R. Farlow
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Jared R. Brosch
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Liana G. Apostolova
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research Center Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
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