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Poortman SR, Setiaman N, Barendse MEA, Schnack HG, Hillegers MHJ, van Haren NEM. Non-linear development of brain morphometry in child and adolescent offspring of individuals with bipolar disorder or schizophrenia. Eur Neuropsychopharmacol 2024; 87:56-66. [PMID: 39084058 DOI: 10.1016/j.euroneuro.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 08/02/2024]
Abstract
Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at increased risk of developing psychopathology. Structural brain alterations have been found in child and adolescent offspring of patients with bipolar disorder and schizophrenia, but the developmental trajectories of brain anatomy in this high-familial-risk population are still unclear. 300 T1-weighted scans were obtained of 187 offspring of at least one parent diagnosed with bipolar disorder (n=80) or schizophrenia (n=53) and offspring of parents without severe mental illness (n=54). The age range was 8 to 23 years old; 113 offspring underwent two scans. Global brain measures and regional cortical thickness and surface area were computed. A generalized additive mixed model was used to capture non-linear age trajectories. Offspring of parents with schizophrenia had smaller total brain volume than offspring of parents with bipolar disorder (d=-0.20, p=0.004) and control offspring (d=-0.22, p=0.005) and lower mean cortical thickness than control offspring (d=-0.23, p<0.001). Offspring of parents with schizophrenia showed differential age trajectories of mean cortical thickness and cerebral white matter volume compared with control offspring (both p's=0.003). Regionally, offspring of parents with schizophrenia had a significantly different trajectory of cortical thickness in the middle temporal gyrus versus control offspring (p<0.001) and bipolar disorder offspring (p=0.001), which was no longer significant after correcting for mean cortical thickness. These findings suggest that particularly familial high risk of schizophrenia is related to reductions and deviating developmental trajectories of global brain structure measures, which were not driven by specific regions.
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Affiliation(s)
- Simon R Poortman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands.
| | - Nikita Setiaman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Marjolein E A Barendse
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Hugo G Schnack
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
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Thalhammer M, Schulz J, Scheulen F, Oubaggi MEM, Kirschner M, Kaiser S, Schmidt A, Borgwardt S, Avram M, Brandl F, Sorg C. Distinct Volume Alterations of Thalamic Nuclei Across the Schizophrenia Spectrum. Schizophr Bull 2024; 50:1208-1222. [PMID: 38577901 PMCID: PMC11349018 DOI: 10.1093/schbul/sbae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND AND HYPOTHESIS Abnormal thalamic nuclei volumes and their link to cognitive impairments have been observed in schizophrenia. However, whether and how this finding extends to the schizophrenia spectrum is unknown. We hypothesized a distinct pattern of aberrant thalamic nuclei volume across the spectrum and examined its potential associations with cognitive symptoms. STUDY DESIGN We performed a FreeSurfer-based volumetry of T1-weighted brain MRIs from 137 healthy controls, 66 at-risk mental state (ARMS) subjects, 89 first-episode psychosis (FEP) individuals, and 126 patients with schizophrenia to estimate thalamic nuclei volumes of six nuclei groups (anterior, lateral, ventral, intralaminar, medial, and pulvinar). We used linear regression models, controlling for sex, age, and estimated total intracranial volume, both to compare thalamic nuclei volumes across groups and to investigate their associations with positive, negative, and cognitive symptoms. STUDY RESULTS We observed significant volume alterations in medial and lateral thalamic nuclei. Medial nuclei displayed consistently reduced volumes across the spectrum compared to controls, while lower lateral nuclei volumes were only observed in schizophrenia. Whereas positive and negative symptoms were not associated with reduced nuclei volumes across all groups, higher cognitive scores were linked to lower volumes of medial nuclei in ARMS. In FEP, cognition was not linked to nuclei volumes. In schizophrenia, lower cognitive performance was associated with lower medial volumes. CONCLUSIONS Results demonstrate distinct thalamic nuclei volume reductions across the schizophrenia spectrum, with lower medial nuclei volumes linked to cognitive deficits in ARMS and schizophrenia. Data suggest a distinctive trajectory of thalamic nuclei abnormalities along the course of schizophrenia.
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Affiliation(s)
- Melissa Thalhammer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Schulz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felicitas Scheulen
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Mohamed El Mehdi Oubaggi
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias Kirschner
- Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Felix Brandl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
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Warszawer Y, Gurevich M, Kerpel A, Dreyer Alster S, Nissan Y, Shirbint E, Hoffmann C, Achiron A. Mapping brain volume change across time in primary-progressive multiple sclerosis. Neuroradiology 2024; 66:1189-1197. [PMID: 38609687 DOI: 10.1007/s00234-024-03354-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
PURPOSE Detection and prediction of the rate of brain volume loss with age is a significant unmet need in patients with primary progressive multiple sclerosis (PPMS). In this study we construct detailed brain volume maps for PPMS patients. These maps compare age-related changes in both cortical and sub-cortical regions with those in healthy individuals. METHODS We conducted retrospective analyses of brain volume using T1-weighted Magnetic Resonance Imaging (MRI) scans of a large cohort of PPMS patients and healthy subjects. The volume of brain parenchyma (BP), cortex, white matter (WM), deep gray matter, thalamus, and cerebellum were measured using the robust SynthSeg segmentation tool. Age- and gender-related regression curves were constructed based on data from healthy subjects, with the 95% prediction interval adopted as the normality threshold for each brain region. RESULTS We analyzed 495 MRI scans from 169 PPMS patients, aged 20-79 years, alongside 563 exams from healthy subjects aged 20-86. Compared to healthy subjects, a higher proportion of PPMS patients showed lower than expected brain volumes in all regions except the cerebellum. The most affected areas were BP, WM, and thalamus. Lower brain volumes correlated with longer disease duration for BP and WM, and higher disability for BP, WM, cortex, and thalamus. CONCLUSIONS Constructing age- and gender-related brain volume maps enabled identifying PPMS patients at a higher risk of brain volume loss. Monitoring these high-risk patients may lead to better treatment decisions and improve patient outcomes.
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Affiliation(s)
- Yehuda Warszawer
- Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel.
- Arrow Program for Medical Research Education, Sheba Medical Center, Ramat-Gan, Israel.
- Adelson School of Medicine, Ariel University, Ariel, Israel.
| | - Michael Gurevich
- Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Ariel Kerpel
- Department of Radiology, Sheba Medical Center, Ramat-Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Yael Nissan
- Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel
| | - Emanuel Shirbint
- Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Department of Radiology, Sheba Medical Center, Ramat-Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Anat Achiron
- Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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Hua JPY, Fryer SL, Stuart B, Loewy RL, Vinogradov S, Mathalon DH. Adjustment of Regional Cortical Thickness Measures for Global Cortical Thickness Obscures Deficits Across the Schizophrenia Spectrum: A Cautionary Note About Normative Modeling of Brain Imaging Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00159-9. [PMID: 38908749 DOI: 10.1016/j.bpsc.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/24/2024]
Abstract
Recent neuroimaging studies and publicly disseminated analytic tools suggest that regional morphometric analyses covary for global thickness. We empirically demonstrated that this statistical approach severely underestimates regional thickness dysmorphology in psychiatric disorders. Study 1 included 90 healthy control participants, 51 participants at clinical high risk for psychosis, and 78 participants with early-illness schizophrenia. Study 2 included 56 healthy control participants, 83 participants with nonaffective psychosis, and 30 participants with affective psychosis. We examined global and regional thickness correlations, global thickness group differences, and regional thickness group differences with and without global thickness covariation. Global and regional thickness were strongly correlated across groups. Global thickness was lower in the schizophrenia spectrum groups than the other groups. Regional thickness deficits in schizophrenia spectrum groups were attenuated or eliminated with global thickness covariation. Eliminating the variation that regional thickness shares with global thickness eliminated disease-related effects. This statistical approach results in erroneous conclusions that regional thickness is normal in disorders like schizophrenia or clinical high risk syndrome.
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Affiliation(s)
- Jessica P Y Hua
- Mental Health Service, San Francisco VA Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California.
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5
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DeCarli C, Maillard P, Pase MP, Beiser AS, Kojis D, Satizabal CL, Himali JJ, Aparicio HJ, Fletcher E, Seshadri S. Trends in Intracranial and Cerebral Volumes of Framingham Heart Study Participants Born 1930 to 1970. JAMA Neurol 2024; 81:471-480. [PMID: 38526486 PMCID: PMC10964161 DOI: 10.1001/jamaneurol.2024.0469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/05/2024] [Indexed: 03/26/2024]
Abstract
Importance Human brain development and maintenance is under both genetic and environmental influences that likely affect later-life dementia risk. Objective To examine environmental influences by testing whether time-dependent secular differences occurred in cranial and brain volumes and cortical thickness over birth decades spanning 1930 to 1970. Design, Setting, and Participants This cross-sectional study used data from the community-based Framingham Heart Study cohort for participants born in the decades 1930 to 1970. Participants did not have dementia or history of stroke and had magnetic resonance imaging (MRI) obtained from March 18, 1999, to November 15, 2019. The final analysis dataset was created in October 2023. Exposure Years of birth ranging from 1925 to 1968. Main Measures Cross-sectional analysis of intracranial, cortical gray matter, white matter, and hippocampal volumes as well as cortical surface area and cortical thickness. The secular measure was the decade in which the participant was born. Covariates included age at MRI and sex. Results The main study cohort consisted of 3226 participants with a mean (SD) age of 57.7 (7.8) years at the time of their MRI. A total of 1706 participants were female (53%) and 1520 (47%) were male. The birth decades ranged from the 1930s to 1970s. Significant trends for larger intracranial, hippocampal, and white matter volumes and cortical surface area were associated with progressive birth decades. Comparing the 1930s birth decade to the 1970s accounted for a 6.6% greater volume (1234 mL; 95% CI, 1220-1248, vs 1321 mL; 95% CI, 1301-1341) for ICV, 7.7% greater volume (441.9 mL; 95% CI, 435.2-448.5, vs 476.3 mL; 95% CI, 467.0-485.7) for white matter, 5.7% greater value (6.51 mL; 95% CI, 6.42-6.60, vs 6.89 mL; 95% CI, 6.77-7.02) for hippocampal volume, and a 14.9% greater value (1933 cm2; 95% CI, 1908-1959, vs 2222 cm2; 95% CI, 2186-2259) for cortical surface area. Repeat analysis applied to a subgroup of 1145 individuals of similar age range born in the 1940s (mean [SD] age, 60.0 [2.8] years) and 1950s (mean [SD] age, 59.0 [2.8] years) resulted in similar findings. Conclusion and Relevance In this study, secular trends for larger brain volumes suggested improved brain development among individuals born between 1930 and 1970. Early life environmental influences may explain these results and contribute to the declining dementia incidence previously reported in the Framingham Heart Study cohort.
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Affiliation(s)
- Charles DeCarli
- Department of Neurology & Imaging of Dementia and Aging Laboratory, University of California Davis, Sacramento, California
| | - Pauline Maillard
- Department of Neurology & Imaging of Dementia and Aging Laboratory, University of California Davis, Sacramento, California
| | - Matthew P. Pase
- Framingham Heart Study, Framingham, Massachusetts
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Alexa S. Beiser
- Framingham Heart Study, Framingham, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Neurology, Boston University Chonbanian and Avedisian School of Medicine, Boston, Massachusetts
| | - Daniel Kojis
- Framingham Heart Study, Framingham, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Claudia L. Satizabal
- Framingham Heart Study, Framingham, Massachusetts
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, Texas
| | - Jayandra J. Himali
- Framingham Heart Study, Framingham, Massachusetts
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, Texas
| | - Hugo J. Aparicio
- Framingham Heart Study, Framingham, Massachusetts
- Department of Neurology, Boston University Chonbanian and Avedisian School of Medicine, Boston, Massachusetts
| | - Evan Fletcher
- Department of Neurology & Imaging of Dementia and Aging Laboratory, University of California Davis, Sacramento, California
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, Massachusetts
- Department of Neurology, Boston University Chonbanian and Avedisian School of Medicine, Boston, Massachusetts
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio
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6
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Alex AM, Aguate F, Botteron K, Buss C, Chong YS, Dager SR, Donald KA, Entringer S, Fair DA, Fortier MV, Gaab N, Gilmore JH, Girault JB, Graham AM, Groenewold NA, Hazlett H, Lin W, Meaney MJ, Piven J, Qiu A, Rasmussen JM, Roos A, Schultz RT, Skeide MA, Stein DJ, Styner M, Thompson PM, Turesky TK, Wadhwa PD, Zar HJ, Zöllei L, de Los Campos G, Knickmeyer RC. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood. Nat Neurosci 2024; 27:176-186. [PMID: 37996530 PMCID: PMC10774128 DOI: 10.1038/s41593-023-01501-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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Affiliation(s)
- Ann M Alex
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fernando Aguate
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Kelly Botteron
- Mallinickrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Claudia Buss
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
| | - Stephen R Dager
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Kirsten A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sonja Entringer
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nynke A Groenewold
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Heather Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Meaney
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, China
| | - Jerod M Rasmussen
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Annerine Roos
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia and the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael A Skeide
- Research Group Learning in Early Childhood, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dan J Stein
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Martin Styner
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of Southern California, Marina del Rey, CA, USA
| | - Ted K Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - Pathik D Wadhwa
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
- Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, Irvine, Irvine, CA, USA
| | - Heather J Zar
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gustavo de Los Campos
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA
| | - Rebecca C Knickmeyer
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI, USA.
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7
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Levenstein JM, Driver C, Boyes A, Parker M, Shan Z, Lagopoulos J, Hermens DF. Sex differences in brain volumes and psychological distress: The first hundred brains cohort of the longitudinal adolescent brain study. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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8
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Xu MX, Ju XD. Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis. Brain Sci 2023; 13:brainsci13050779. [PMID: 37239251 DOI: 10.3390/brainsci13050779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8-12 years old with ASD were matched with 105 children aged 8-12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.
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Affiliation(s)
- Ming-Xiang Xu
- School of Psychology, Northeast Normal University, Changchun 130024, China
| | - Xing-Da Ju
- School of Psychology, Northeast Normal University, Changchun 130024, China
- Jilin Provincial Key Laboratory of Cognitive Neuroscience and Brain Development, Changchun 130024, China
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Knoernschild K, Johnson HJ, Schroeder KE, Swier VJ, White KA, Sato TS, Rogers CS, Weimer JM, Sieren JC. Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model. Sci Rep 2023; 13:5146. [PMID: 36991106 PMCID: PMC10060411 DOI: 10.1038/s41598-023-32071-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/22/2023] [Indexed: 03/31/2023] Open
Abstract
Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (- 9.06 cm3), gray matter (- 4.37% 95 CI - 7.41; - 1.83), caudate (- 0.16%, 95 CI - 0.24; - 0.08) and putamen (- 0.11% 95 CI - 0.23; - 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (- 8.27%, 95 CI - 10.1; - 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.
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Affiliation(s)
- Kevin Knoernschild
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Hans J Johnson
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Kimberly E Schroeder
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
| | - Vicki J Swier
- Pediatrics and Rare Diseases Group, Sanford Research, Sioux Falls, SD, USA
| | - Katherine A White
- Pediatrics and Rare Diseases Group, Sanford Research, Sioux Falls, SD, USA
| | - Takashi S Sato
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
| | | | - Jill M Weimer
- Pediatrics and Rare Diseases Group, Sanford Research, Sioux Falls, SD, USA
| | - Jessica C Sieren
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA.
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA.
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10
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DeCarli C, Pase M, Beiser A, Kojis D, Satizabal C, Himali J, Aparicio H, Flether E, Maillard P, Seshadri S. Secular Trends in Head Size and Cerebral Volumes In the Framingham Heart Study for Birth Years 1902-1985. RESEARCH SQUARE 2023:rs.3.rs-2524684. [PMID: 36778357 PMCID: PMC9915799 DOI: 10.21203/rs.3.rs-2524684/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Background Recent data suggest that dementia incidence is declining. We investigated whether similar secular trends consisting of increasing size of brain structures and improving memory performance could be simultaneously occurring as a possible explanation. Method The Framingham Heart Study is a 3 generation, longitudinal study that includes cognitive assessment and medical surveillance. This study cohort consisted of 4,506 unique, non-demented, stroke free, individuals with brain MRI, cognitive assessment, and demographic information spanning dates of birth from 1902 to 1985. Outcomes consisted of height, MRI, and memory measures. Covariates included age at MRI, sex, decade of birth, and all interactions. Models with neuropsychological outcomes also included educational achievement as a covariate. Results Height and intracranial (TCV), hippocampus and cortical gray matter volumes were significantly larger, and memory performance significantly better, with advancing decades of birth after adjusting for age, sex, and interactions. Sensitivity analysis using progressively restricted age-ranges to reduce the association between age and decade of birth, confirmed the findings. Mediation analysis showed that hippocampal volume mediated approximately 5-7% of the effect of decade of birth on logical memory performance. Discussion These findings indicate improvement in brain health and memory performance with advancing decades of birth. Although brain structures are under substantial genetic influence, we conclude that improved early life environmental influences over ensuing decades likely explain these results. We hypothesize that these secular improvements are consistent with declining dementia incidence in this cohort potentially through a mechanism of increased brain reserve.
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Affiliation(s)
| | | | - Alexa Beiser
- Department of Biostatistics, Boston University School of Public Health
| | | | - Claudia Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX
| | - Jayandra Himali
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
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11
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Weber KA, Teplin ZM, Wager TD, Law CSW, Prabhakar NK, Ashar YK, Gilam G, Banerjee S, Delp SL, Glover GH, Hastie TJ, Mackey S. Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction. Front Neurol 2022; 13:960760. [PMID: 36601297 PMCID: PMC9806266 DOI: 10.3389/fneur.2022.960760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength (R 2 = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R 2 = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.
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Affiliation(s)
- Kenneth A. Weber
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,*Correspondence: Kenneth A. Weber II
| | - Zachary M. Teplin
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Christine S. W. Law
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Nitin K. Prabhakar
- Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Yoni K. Ashar
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, United States
| | - Gadi Gilam
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Scott L. Delp
- Department of Bioengineering and Mechanical Engineering, Stanford University, Palo Alto, CA, United States
| | - Gary H. Glover
- Radiological Sciences Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Trevor J. Hastie
- Department of Statistics, Stanford University, Palo Alto, CA, United States
| | - Sean Mackey
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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12
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Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size. STATS 2022. [DOI: 10.3390/stats5040080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on the outcomes. However, when potentially important covariate data is available in observational studies, ignoring these covariate effects on the outcomes can result in a flawed inference. In this article, using rank based weighted estimating equations, we propose a robust procedure for covariate effect adjusted comparison of paired outcomes in a clustered data that can also address the issue of informative cluster size. Through simulated scenarios and real-life neuroimaging data, we demonstrate the importance of considering covariate effects during paired testing and robust performances of our proposed method in covariate adjusted paired comparisons in complex clustered data settings.
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13
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Caspi Y, de Zwarte SMC, Iemenschot IJ, Lumbreras R, de Heus R, Bekker MN, Hulshoff Pol H. Automatic measurements of fetal intracranial volume from 3D ultrasound scans. FRONTIERS IN NEUROIMAGING 2022; 1:996702. [PMID: 37555155 PMCID: PMC10406279 DOI: 10.3389/fnimg.2022.996702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/15/2022] [Indexed: 08/10/2023]
Abstract
Three-dimensional fetal ultrasound is commonly used to study the volumetric development of brain structures. To date, only a limited number of automatic procedures for delineating the intracranial volume exist. Hence, intracranial volume measurements from three-dimensional ultrasound images are predominantly performed manually. Here, we present and validate an automated tool to extract the intracranial volume from three-dimensional fetal ultrasound scans. The procedure is based on the registration of a brain model to a subject brain. The intracranial volume of the subject is measured by applying the inverse of the final transformation to an intracranial mask of the brain model. The automatic measurements showed a high correlation with manual delineation of the same subjects at two gestational ages, namely, around 20 and 30 weeks (linear fitting R2(20 weeks) = 0.88, R2(30 weeks) = 0.77; Intraclass Correlation Coefficients: 20 weeks=0.94, 30 weeks = 0.84). Overall, the automatic intracranial volumes were larger than the manually delineated ones (84 ± 16 vs. 76 ± 15 cm3; and 274 ± 35 vs. 237 ± 28 cm3), probably due to differences in cerebellum delineation. Notably, the automated measurements reproduced both the non-linear pattern of fetal brain growth and the increased inter-subject variability for older fetuses. By contrast, there was some disagreement between the manual and automatic delineation concerning the size of sexual dimorphism differences. The method presented here provides a relatively efficient way to delineate volumes of fetal brain structures like the intracranial volume automatically. It can be used as a research tool to investigate these structures in large cohorts, which will ultimately aid in understanding fetal structural human brain development.
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Affiliation(s)
- Yaron Caspi
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sonja M. C. de Zwarte
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Iris J. Iemenschot
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Raquel Lumbreras
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Roel de Heus
- Department of Obstetrics and Gynaecology, St. Antonius Hospital, Utrecht, Netherlands
- Department of Obstetrics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mireille N. Bekker
- Department of Obstetrics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Hilleke Hulshoff Pol
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Psychology, Utrecht University, Utrecht, Netherlands
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14
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Nerland S, Stokkan TS, Jørgensen KN, Wortinger LA, Richard G, Beck D, van der Meer D, Westlye LT, Andreassen OA, Agartz I, Barth C. A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age. Hum Brain Mapp 2022; 43:4620-4639. [PMID: 35708198 PMCID: PMC9491281 DOI: 10.1002/hbm.25978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 11/05/2022] Open
Abstract
Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.
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Affiliation(s)
- Stener Nerland
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
| | - Therese S. Stokkan
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
| | - Kjetil N. Jørgensen
- NORMENTUniversity of OsloOsloNorway
- Department of PsychiatryTelemark HospitalSkienNorway
| | - Laura A. Wortinger
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Dani Beck
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
| | - Dennis van der Meer
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ole A. Andreassen
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Ingrid Agartz
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- Stockholm Health Care ServicesStockholm RegionStockholmSweden
| | - Claudia Barth
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- NORMENTUniversity of OsloOsloNorway
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15
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Dhamala E, Ooi LQR, Chen J, Kong R, Anderson KM, Chin R, Yeo BTT, Holmes AJ. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. Neuroimage 2022; 260:119485. [PMID: 35843514 PMCID: PMC9425854 DOI: 10.1016/j.neuroimage.2022.119485] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States.
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, United States
| | - Rowena Chin
- Department of Psychology, Yale University, New Haven, United States
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States; Department of Psychiatry, Yale University, New Haven, United States; Wu Tsai Institute, Yale University, New Haven, United States.
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16
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Gómez-Ramírez J, Fernández-Blázquez MA, González-Rosa JJ. A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain. Life (Basel) 2022; 12:1586. [PMID: 36295023 PMCID: PMC9656120 DOI: 10.3390/life12101586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 01/25/2023] Open
Abstract
We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires-at least in principle-two MRI scans, one performed at a young age and one at an old age. There is, however, a way to address this problem when having only one MRI scan obtained at an old age. We computed the total brain losses of elderly subjects as a ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 years were assessed. A causal analysis of factors affecting brain atrophy was performed using probabilistic Bayesian modelling and the mathematics of causal inference. We found that both age and sex were causally related to brain atrophy, with women reaching an elderly age with a 1% larger brain volume relative to their intracranial volume than men. How the brain ages and the rationale for sex differences in brain volume losses during the adult lifespan are questions that need to be addressed with causal inference and empirical data. The graphical causal modelling presented here can be instrumental in understanding a puzzling scientific area of study-the biological aging of the brain.
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Affiliation(s)
- Jaime Gómez-Ramírez
- Department of Psychology, University of Cadiz, 11003 Cadiz, Spain
- Institute of Biomedical Research Cadiz (INiBICA), 11009 Cadiz, Spain
| | | | - Javier J. González-Rosa
- Department of Psychology, University of Cadiz, 11003 Cadiz, Spain
- Institute of Biomedical Research Cadiz (INiBICA), 11009 Cadiz, Spain
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17
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Lemke H, Klute H, Skupski J, Thiel K, Waltemate L, Winter A, Breuer F, Meinert S, Klug M, Enneking V, Winter NR, Grotegerd D, Leehr EJ, Repple J, Dohm K, Opel N, Stein F, Meller T, Brosch K, Ringwald KG, Pfarr JK, Thomas-Odenthal F, Hahn T, Krug A, Jansen A, Heindel W, Nenadić I, Kircher T, Dannlowski U. Brain structural correlates of recurrence following the first episode in patients with major depressive disorder. Transl Psychiatry 2022; 12:349. [PMID: 36030219 PMCID: PMC9420111 DOI: 10.1038/s41398-022-02113-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/11/2022] Open
Abstract
Former prospective studies showed that the occurrence of relapse in Major Depressive Disorder (MDD) is associated with volume loss in the insula, hippocampus and dorsolateral prefrontal cortex (DLPFC). However, these studies were confounded by the patient's lifetime disease history, as the number of previous episodes predict future recurrence. In order to analyze neural correlates of recurrence irrespective of prior disease course, this study prospectively examined changes in brain structure in patients with first-episode depression (FED) over 2 years. N = 63 FED patients and n = 63 healthy controls (HC) underwent structural magnetic resonance imaging at baseline and after 2 years. According to their disease course during the follow-up interval, patients were grouped into n = 21 FED patients with recurrence (FEDrec) during follow-up and n = 42 FED patients with stable remission (FEDrem). Gray matter volume changes were analysed using group by time interaction analyses of covariance for the DLPFC, hippocampus and insula. Significant group by time interactions in the DLPFC and insula emerged. Pairwise comparisons showed that FEDrec had greater volume decline in the DLPFC and insula from baseline to follow-up compared with FEDrem and HC. No group by time interactions in the hippocampus were found. Cross-sectional analyses at baseline and follow-up revealed no differences between groups. This longitudinal study provides evidence for neural alterations in the DLPFC and insula related to a detrimental course in MDD. These effects of recurrence are already detectable at initial stages of MDD and seem to occur without any prior disease history, emphasizing the importance of early interventions preventing depressive recurrence.
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Affiliation(s)
- Hannah Lemke
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Klute
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jennifer Skupski
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabian Breuer
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany ,grid.5949.10000 0001 2172 9288Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Melissa Klug
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Verena Enneking
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R. Winter
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Frederike Stein
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tina Meller
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Kai G. Ringwald
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tim Hahn
- grid.5949.10000 0001 2172 9288Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Axel Krug
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany ,grid.10388.320000 0001 2240 3300Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Andreas Jansen
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Walter Heindel
- grid.5949.10000 0001 2172 9288University Clinic for Radiology, University of Münster, Münster, Germany
| | - Igor Nenadić
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tilo Kircher
- grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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18
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Chi CH, Yang FC, Chang YL. Age-related volumetric alterations in hippocampal subiculum region are associated with reduced retention of the “when” memory component. Brain Cogn 2022; 160:105877. [DOI: 10.1016/j.bandc.2022.105877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 04/18/2022] [Accepted: 04/22/2022] [Indexed: 11/02/2022]
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19
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de Rooij SR. Are Brain and Cognitive Reserve Shaped by Early Life Circumstances? Front Neurosci 2022; 16:825811. [PMID: 35784851 PMCID: PMC9243389 DOI: 10.3389/fnins.2022.825811] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/13/2022] [Indexed: 01/22/2023] Open
Abstract
When growing older, many people are faced with cognitive deterioration, which may even amount to a form of dementia at some point in time. Although neuropathological signs of dementia disorders can often be demonstrated in brains of patients, the degree to which clinical symptoms are present does mostly not accurately reflect the amount of neuropathology that is present. Sometimes existent pathology even goes without any obvious clinical presentation. An explanation for this phenomenon may be found in the concept of reserve capacity. Reserve capacity refers to the ability of the brain to effectively buffer changes that are associated with normal aging processes and to cope with pathological damage. A larger reserve capacity has been suggested to increase resilience against age-associated cognitive deterioration and dementia disorders. Traditionally, a division has been made between brain reserve, which is based on morphological characteristics of the brain, and cognitive reserve, which is based on functional characteristics of the brain. The present review discusses the premises that brain and cognitive reserve capacity are shaped by prenatal and early postnatal factors. Evidence is accumulating that circumstances during the first 1,000 days of life are of the utmost importance for the lifelong health of an individual. Cognitive deterioration and dementia disorders may also have their origin in early life and a potentially important pathway by which the early environment affects the risk for neurodegenerative diseases is by developmental programming of the reserve capacity of the brain. The basic idea behind developmental programming of brain and cognitive reserve is explained and an overview of studies that support this idea is presented. The review is concluded by a discussion of potential mechanisms, synthesis of the evidence and relevance and future directions in the field of developmental origins of reserve capacity.
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Affiliation(s)
- Susanne R. de Rooij
- Epidemiology and Data Science, University of Amsterdam, Amsterdam, Netherlands
- Aging and Later Life, Health Behaviors and Chronic Diseases, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
- Amsterdam Reproduction and Development, Amsterdam, Netherlands
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20
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Gómez-Ramírez J, Fernández-Blázquez MA, González-Rosa JJ. Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation. Brain Sci 2022; 12:brainsci12050579. [PMID: 35624966 PMCID: PMC9139275 DOI: 10.3390/brainsci12050579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/19/2022] [Accepted: 04/23/2022] [Indexed: 01/11/2023] Open
Abstract
Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.
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Affiliation(s)
- Jaime Gómez-Ramírez
- Institute of Biomedical Research Cadiz (INiBICA), Universidad de Cádiz, 11003 Cádiz, Spain;
- Correspondence:
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21
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de Zwarte SMC, Brouwer RM, Kahn RS, van Haren NEM. Schizophrenia and Bipolar Polygenic Risk Scores in Relation to Intracranial Volume. Genes (Basel) 2022; 13:genes13040695. [PMID: 35456501 PMCID: PMC9026378 DOI: 10.3390/genes13040695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 01/27/2023] Open
Abstract
Schizophrenia and bipolar disorder are neurodevelopmental disorders with overlapping symptoms and a shared genetic background. Deviations in intracranial volume (ICV)—a marker for neurodevelopment—differ between schizophrenia and bipolar disorder. Here, we investigated whether genetic risk for schizophrenia and bipolar disorder is related to ICV in the general population by using the UK Biobank data (n = 20,196). Polygenic risk scores for schizophrenia (SZ-PRS) and bipolar disorder (BD-PRS) were computed for 12 genome wide association study P-value thresholds (PT) for each individual and correlations with ICV were investigated. Partial correlations were performed at each PT to investigate whether disease specific genetic risk variants for schizophrenia and bipolar disorder show different relationships with ICV. ICV showed a negative correlation with SZ-PRS at PT ≥ 0.005 (r < −0.02, p < 0.005). ICV was not associated with BD-PRS; however, a positive correlation between BD-PRS and ICV at PT = 0.2 and PT = 0.4 (r = +0.02, p < 0.005) appeared when the genetic overlap between schizophrenia and bipolar disorder was accounted for. Despite small effect sizes, a higher load of schizophrenia risk genes is associated with a smaller ICV in the general population, while risk genes specific for bipolar disorder are correlated with a larger ICV. These findings suggest that schizophrenia and bipolar disorder risk genes, when accounting for the genetic overlap between both disorders, have opposite effects on early brain development.
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Affiliation(s)
- Sonja M. C. de Zwarte
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
| | - Rachel M. Brouwer
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - René S. Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre Sophia, 3000 CB Rotterdam, The Netherlands
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
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22
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Zhao W, Wang Y, Zhou F, Li G, Wang Z, Zhong H, Song Y, Gillen KM, Wang Y, Yang G, Li J. Automated Segmentation of Midbrain Structures in High-Resolution Susceptibility Maps Based on Convolutional Neural Network and Transfer Learning. Front Neurosci 2022; 16:801618. [PMID: 35221900 PMCID: PMC8866960 DOI: 10.3389/fnins.2022.801618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/17/2022] [Indexed: 11/23/2022] Open
Abstract
Background Accurate delineation of the midbrain nuclei, the red nucleus (RN), substantia nigra (SN) and subthalamic nucleus (STN), is important in neuroimaging studies of neurodegenerative and other diseases. This study aims to segment midbrain structures in high-resolution susceptibility maps using a method based on a convolutional neural network (CNN). Methods The susceptibility maps of 75 subjects were acquired with a voxel size of 0.83 × 0.83 × 0.80 mm3 on a 3T MRI system to distinguish the RN, SN, and STN. A deeply supervised attention U-net was pre-trained with a dataset of 100 subjects containing susceptibility maps with a voxel size of 0.63 × 0.63 × 2.00 mm3 to provide initial weights for the target network. Five-fold cross-validation over the training cohort was used for all the models’ training and selection. The same test cohort was used for the final evaluation of all the models. Dice coefficients were used to assess spatial overlap agreement between manual delineations (ground truth) and automated segmentation. Volume and magnetic susceptibility values in the nuclei extracted with automated CNN delineation were compared to those extracted by manual tracing. Consistencies of volume and magnetic susceptibility values by different extraction strategies were assessed by Pearson correlation coefficients and Bland-Altman analyses. Results The automated CNN segmentation method achieved mean Dice scores of 0.903, 0.864, and 0.777 for the RN, SN, and STN, respectively. There were no significant differences between the achieved Dice scores and the inter-rater Dice scores (p > 0.05 for each nucleus). The overall volume and magnetic susceptibility values of the nuclei extracted by the automatic CNN method were significantly correlated with those by manual delineation (p < 0.01). Conclusion Midbrain structures can be precisely segmented in high-resolution susceptibility maps using a CNN-based method.
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Affiliation(s)
- Weiwei Zhao
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Fangfang Zhou
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Zhichao Wang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Haodong Zhong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Kelly M. Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
- *Correspondence: Guang Yang,
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
- Jianqi Li,
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23
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Duan P, Han S, Zuo L, An Y, Liu Y, Alshareef A, Lee J, Carass A, Resnick SM, Prince JL. Cranial Meninges Reconstruction Based on Convolutional Networks and Deformable Models: Applications to Longitudinal Study of Normal Aging. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:1203215. [PMID: 36325254 PMCID: PMC9623767 DOI: 10.1117/12.2613146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.
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Affiliation(s)
- Peiyu Duan
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892
| | - Yihao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Junghoon Lee
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892
| | - Jerry L. Prince
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
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24
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Buimer EEL, Schnack HG, Caspi Y, van Haren NEM, Milchenko M, Pas P, Hulshoff Pol HE, Brouwer RM. De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages. Hum Brain Mapp 2021; 42:3643-3655. [PMID: 33973694 PMCID: PMC8249889 DOI: 10.1002/hbm.25459] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/26/2021] [Accepted: 04/05/2021] [Indexed: 11/12/2022] Open
Abstract
Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur.
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Affiliation(s)
- Elizabeth E L Buimer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hugo G Schnack
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Yaron Caspi
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Neeltje E M van Haren
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, Netherlands
| | - Mikhail Milchenko
- Department of Radiology, Washington University School of Medicine, Mallinckrodt Institute of Radiology, Saint Louis, Missouri, USA
| | - Pascal Pas
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Hilleke E Hulshoff Pol
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Rachel M Brouwer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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25
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Velemínská J, Fleischmannová N, Suchá B, Dupej J, Bejdová Š, Kotěrová A, Brůžek J. Age-related differences in cranial sexual dimorphism in contemporary Europe. Int J Legal Med 2021; 135:2033-2044. [PMID: 33649866 DOI: 10.1007/s00414-021-02547-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/17/2021] [Indexed: 11/26/2022]
Abstract
Biomechanical load and hormonal levels tended to change just like the soft and skeletal tissue of the elderly with age. Although aging in both sexes shared common traits, it was assumed that there would be a reduction of sexual dimorphism in aged individuals. The main goals of this study were (1) to evaluate age-related differences in cranial sexual dimorphism during senescence, (2) to determine age-related differences in female and male skulls separately, and (3) to compare skull senescence in Czech and French adult samples as discussed by Musilová et al. (Forensic Sci Int 269:70-77, 2016). The cranial surface was analyzed using coherent point drift-dense correspondence analysis. The study sample consisted of 245 CT scans of heads from recent Czech (83 males and 59 females) and French (52 males and 51 females) individuals. Virtual scans in the age range from 18 to 92 years were analyzed using geometric morphometrics. The cranial form was significantly greater in males in all age categories. After size normalization, sexual dimorphism of the frontal, occipital, and zygomatic regions tended to diminish in the elderly. Its development during aging was caused by morphological changes in both female and male skulls but secular changes must also be taken into account. The most notable aging changes were the widening of the neurocranium and the retrusion of the face, including the forehead, especially after the age of 60 in both sexes. Sexual dimorphism was similar between the Czech and French samples but its age-related differences were partially different because of the population specificity. Cranial senescence was found to degrade the accuracy of sex classification (92-94%) in the range of 2-3%.
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Affiliation(s)
- Jana Velemínská
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
| | - Nikola Fleischmannová
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
| | - Barbora Suchá
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
| | - Jan Dupej
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
- Department of Software and Computer Science Education, Faculty of Mathematics and Physics, Charles University, 118 00, Prague, Czech Republic
| | - Šárka Bejdová
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
| | - Anežka Kotěrová
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic.
| | - Jaroslav Brůžek
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43, Prague, Czech Republic
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