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Liang Q, Xu Z, Chen S, Lin S, Lin X, Li Y, Zhang Y, Peng B, Hou G, Qiu Y. Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder. J Affect Disord 2024; 365:134-143. [PMID: 39154985 DOI: 10.1016/j.jad.2024.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/03/2024] [Accepted: 08/10/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. METHODS We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. RESULTS SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). LIMITATIONS Evaluation of brain dynamics was constrained by MRI temporal resolution. CONCLUSIONS Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.
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Affiliation(s)
- Qunjun Liang
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, People's Republic of China
| | - Ziyun Xu
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China
| | - Shengli Chen
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Shiwei Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Xiaoshan Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Ying Li
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Yingli Zhang
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Bo Peng
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Gangqiang Hou
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China.
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2
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La Rosa F, Dos Santos Silva J, Dereskewicz E, Invernizzi A, Cahan N, Galasso J, Garcia N, Graney R, Levy S, Verma G, Balchandani P, Reich DS, Horton M, Greenspan H, Sumowski J, Cuadra MB, Beck ES. BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.10.24311686. [PMID: 39148818 PMCID: PMC11326330 DOI: 10.1101/2024.08.10.24311686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.
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Affiliation(s)
- Francesco La Rosa
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Emma Dereskewicz
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Azzurra Invernizzi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Noa Cahan
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Julia Galasso
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nadia Garcia
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robin Graney
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah Levy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gaurav Verma
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Priti Balchandani
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Megan Horton
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hayit Greenspan
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Sumowski
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Merixtell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Radiology Department, University of Lausanne and Lausanne University Hospital, Switzerland
| | - Erin S Beck
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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3
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Dennis EL, Vervoordt S, Adamson MM, Houshang A, Bigler ED, Caeyenberghs K, Cole JH, Dams-O'Connor K, Deutscher EM, Dobryakova E, Genova HM, Grafman JH, Håberg AK, Hellstrøm T, Irimia A, Koliatsos VE, Lindsey HM, Livny A, Menon DK, Merkley TL, Mohamed AZ, Mondello S, Monti MM, Newcombe VF, Newsome MR, Ponsford J, Rabinowitz A, Smevik H, Spitz G, Venkatesan UM, Westlye LT, Zafonte R, Thompson PM, Wilde EA, Olsen A, Hillary FG. Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis. Ann Neurol 2024; 96:365-377. [PMID: 38845484 DOI: 10.1002/ana.26952] [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: 10/21/2023] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 07/11/2024]
Abstract
OBJECTIVE The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression. METHODS Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI. RESULTS Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance. INTERPRETATION This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.
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Affiliation(s)
- Emily L Dennis
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- George E. Whalen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | | | - Maheen M Adamson
- Women's Operational Military Exposure Network (WOMEN) & Rehabilitation, VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Amiri Houshang
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Erin D Bigler
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - James H Cole
- Centre for Medical Image Computing, Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| | - Kristen Dams-O'Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evelyn M Deutscher
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA
- Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Helen M Genova
- Rutgers New Jersey Medical School, Newark, NJ, USA
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, East Hanover, NJ, USA
| | | | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Torgeir Hellstrøm
- Department of Physical Medicine and Rehabilitation, Oslo University Hospital, Oslo, Norway
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Vassilis E Koliatsos
- Departments of Pathology (Neuropathology), Neurology, and Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neuropsychiatry Program, Sheppard and Enoch Pratt Hospital, Baltimore, MD, USA
| | - Hannah M Lindsey
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Sagol Neuroscience School, Tel-Aviv University, Tel-Aviv, Israel
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Tricia L Merkley
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Abdalla Z Mohamed
- Thompson Institute, University of the Sunshine Coast, Birtinya, Australia
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Martin M Monti
- Department of Psychology, UCLA, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, UCLA, Los Angeles, CA, USA
| | | | - Mary R Newsome
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- George E. Whalen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Jennie Ponsford
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Amanda Rabinowitz
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
- Department of Rehabilitation Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Hanne Smevik
- Department of Psychology, NTNU, Trondheim, Norway
- NorHEAD - Norwegian Centre for Headache Research, NTNU, Trondheim, Norway
| | - Gershon Spitz
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Umesh M Venkatesan
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
- Department of Rehabilitation Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ross Zafonte
- Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital/Brigham & Women's Hospital, Boston, MA, USA
- Spaulding Rehabilitation Hospital, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- George E. Whalen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Alexander Olsen
- Department of Psychology, NTNU, Trondheim, Norway
- NorHEAD - Norwegian Centre for Headache Research, NTNU, Trondheim, Norway
- Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Frank G Hillary
- Department of Psychology, Penn State University, State College, PA, USA
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4
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Panikratova YR, Tomyshev AS, Abdullina EG, Rodionov GI, Arkhipov AY, Tikhonov DV, Bozhko OV, Kaleda VG, Strelets VB, Lebedeva IS. Resting-state functional connectivity correlates of brain structural aging in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01837-5. [PMID: 38914851 DOI: 10.1007/s00406-024-01837-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/27/2024] [Indexed: 06/26/2024]
Abstract
A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.
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Affiliation(s)
| | | | | | - Georgiy I Rodionov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
| | - Andrey Yu Arkhipov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
| | | | | | | | - Valeria B Strelets
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
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5
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Constantinides C, Baltramonaityte V, Caramaschi D, Han LKM, Lancaster TM, Zammit S, Freeman TP, Walton E. Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study. Cortex 2024; 172:1-13. [PMID: 38154374 DOI: 10.1016/j.cortex.2023.11.015] [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: 04/28/2023] [Revised: 09/22/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.
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Affiliation(s)
| | | | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia
| | | | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
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6
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Cohen JW, Ramphal B, DeSerisy M, Zhao Y, Pagliaccio D, Colcombe S, Milham MP, Margolis AE. Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth. Dev Psychol 2024; 60:199-209. [PMID: 37747510 PMCID: PMC10993304 DOI: 10.1037/dev0001593] [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] [Indexed: 09/26/2023]
Abstract
Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = .01), lower public assistance enrollment (p = .03), and more parent psychiatric diagnoses (p = .01), but not parent education or INR. Lower parent occupational prestige (p = .02) and lower RBA (p = .04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Jacob W. Cohen
- New York State Psychiatric Institute and Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University
| | - Bruce Ramphal
- New York State Psychiatric Institute and Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University
- T.H. Chan School of Public Health, Harvard Medical School
| | - Mariah DeSerisy
- Department of Epidemiology, Mailman School of Public Health, Columbia University
| | - Yihong Zhao
- Columbia University School of Nursing
- Center for Biological Imaging and Neuromodulation, Nathan S. Kline Institute, Orangeburg, New York, United States
| | - David Pagliaccio
- New York State Psychiatric Institute and Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University
| | - Stan Colcombe
- Center for Biological Imaging and Neuromodulation, Nathan S. Kline Institute, Orangeburg, New York, United States
| | - Michael P. Milham
- Child Mind Institute, New York, New York, United States
- Nathan S. Kline Institute, Orangeburg, New York, United States
| | - Amy E. Margolis
- New York State Psychiatric Institute and Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University
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7
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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [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: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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8
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Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, Sotiras A. A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. PATTERNS (NEW YORK, N.Y.) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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Affiliation(s)
- Robert J. Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M. Gatavins
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Undergraduate Neuroscience Program, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Denis A. Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Corresponding author
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9
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Monsour M, Borlongan CV. No one left behind: Inclusion of individuals experiencing homelessness in TBI stem cell therapy. Med Hypotheses 2023. [DOI: 10.1016/j.mehy.2022.111002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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10
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Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies. J Psychiatr Res 2023; 157:180-191. [PMID: 36473289 DOI: 10.1016/j.jpsychires.2022.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/14/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022]
Abstract
Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.
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11
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Abram SV, Roach BJ, Hua JPY, Han LKM, Mathalon DH, Ford JM, Fryer SL. Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia. Neuroimage Clin 2022; 37:103301. [PMID: 36586360 PMCID: PMC9830317 DOI: 10.1016/j.nicl.2022.103301] [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: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Individual variation in brain aging trajectories is linked with several physical and mental health outcomes. Greater stress levels, worry, and rumination correspond with advanced brain age, while other individual characteristics, like mindfulness, may be protective of brain health. Multiple lines of evidence point to advanced brain aging in schizophrenia (i.e., neural age estimate > chronological age). Whether psychological dimensions such as mindfulness, rumination, and perceived stress contribute to brain aging in schizophrenia is unknown. METHODS We estimated brain age from high-resolution anatomical scans in 54 healthy controls (HC) and 52 individuals with schizophrenia (SZ) and computed the brain predicted age difference (BrainAGE-diff), i.e., the delta between estimated brain age and chronological age. Emotional well-being summary scores were empirically derived to reflect individual differences in trait mindfulness, rumination, and perceived stress. Core analyses evaluated relationships between BrainAGE-diff and emotional well-being, testing for slopes differences across groups. RESULTS HC showed higher emotional well-being (greater mindfulness and less rumination/stress), relative to SZ. We observed a significant group difference in the relationship between BrainAge-diff and emotional well-being, explained by BrainAGE-diff negatively correlating with emotional well-being scores in SZ, and not in HC. That is, SZ with younger appearing brains (predicted age < chronological age) had emotional summary scores that were more like HC, a relationship that endured after accounting for several demographic and clinical variables. CONCLUSIONS These data reveal clinically relevant aspects of brain age heterogeneity among SZ and point to case-control differences in the relationship between advanced brain aging and emotional well-being.
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Affiliation(s)
- Samantha V Abram
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco Veterans Affairs Medical Center, and the University of California, San Francisco, CA, United States; Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Brian J Roach
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco Veterans Affairs Medical Center, and the University of California, San Francisco, CA, United States; Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Laura K M Han
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Daniel H Mathalon
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Judith M Ford
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Susanna L Fryer
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States.
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12
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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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13
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Han LK, Dinga R, Leenings R, Hahn T, Cole JH, Aftanas LI, Amod AR, Besteher B, Colle R, Corruble E, Couvy-Duchesne B, Danilenko KV, Fuentes-Claramonte P, Gonul AS, Gotlib IH, Goya-Maldonado R, Groenewold NA, Hamilton P, Ichikawa N, Ipser JC, Itai E, Koopowitz SM, Li M, Okada G, Okamoto Y, Churikova OS, Osipov EA, Penninx BW, Pomarol-Clotet E, Rodríguez-Cano E, Sacchet MD, Shinzato H, Sim K, Stein DJ, Uyar-Demir A, Veltman DJ, Schmaal L. A large-scale ENIGMA multisite replication study of brain age in depression. NEUROIMAGE: REPORTS 2022. [DOI: 10.1016/j.ynirp.2022.100149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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14
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Busby N, Newman-Norlund S, Sayers S, Newman-Norlund R, Wilson S, Nemati S, Rorden C, Wilmskoetter J, Riccardi N, Roth R, Fridriksson J, Bonilha L. White matter hyperintensity load is associated with premature brain aging. Aging (Albany NY) 2022; 14:9458-9465. [PMID: 36455869 PMCID: PMC9792198 DOI: 10.18632/aging.204397] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND Brain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort. METHODS Brain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age). RESULTS Brain and chronological age were strongly correlated (r(163)=0.932, p<0.001). There was significant negative correlation between BrainGAP scores and chronological age (r(163)=-0.244, p<0.001) indicating that younger participants had higher BrainGAP (premature brain aging). Chronological age also showed a positive correlation with WMH load (r(163)=0.506, p<0.001) indicating older participants had increased WMH load. Controlling for chronological age, there was a statistically significant relationship between premature brain aging and WMHs load (r(163)=0.216, p=0.003). Each additional year in brain age beyond chronological age corresponded to an additional 1.1mm3 in WMH load. CONCLUSIONS WMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy.
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Affiliation(s)
- Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | - Sarah Newman-Norlund
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | - Sara Sayers
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | | | - Sarah Wilson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | - Samaneh Nemati
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC 29201, USA
| | - Janina Wilmskoetter
- Department of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Nicholas Riccardi
- Department of Psychology, University of South Carolina, Columbia, SC 29201, USA
| | - Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
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15
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Wagen AZ, Coath W, Keshavan A, James SN, Parker TD, Lane CA, Buchanan SM, Keuss SE, Storey M, Lu K, Macdougall A, Murray-Smith H, Freiberger T, Cash DM, Malone IB, Barnes J, Sudre CH, Wong A, Pavisic IM, Street R, Crutch SJ, Escott-Price V, Leonenko G, Zetterberg H, Wellington H, Heslegrave A, Barkhof F, Richards M, Fox NC, Cole JH, Schott JM. Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study. THE LANCET. HEALTHY LONGEVITY 2022; 3:e607-e616. [PMID: 36102775 PMCID: PMC10499760 DOI: 10.1016/s2666-7568(22)00167-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. METHODS Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. FINDINGS Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. INTERPRETATION Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility. FUNDING Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.
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Affiliation(s)
- Aaron Z Wagen
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK; Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
| | - William Coath
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah-Naomi James
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Thomas D Parker
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Brain Sciences, Imperial College London, London, UK; UK Dementia Research Institute Centre for Care Research and Technology, Imperial College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Mathew Storey
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Kirsty Lu
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Amy Macdougall
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - David M Cash
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK
| | - Ian B Malone
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Josephine Barnes
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Carole H Sudre
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Wong
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Ivanna M Pavisic
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Rebecca Street
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | | | - Ganna Leonenko
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Henrik Zetterberg
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrietta Wellington
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Amanda Heslegrave
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Frederik Barkhof
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, Netherlands
| | - Marcus Richards
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK
| | - James H Cole
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK.
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16
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Sun D, Rakesh G, Clarke-Rubright EK, Haswell CC, Logue MW, O'Leary EN, Cotton AS, Xie H, Dennis EL, Jahanshad N, Salminen LE, Thomopoulos SI, Rashid FM, Ching CRK, Koch SBJ, Frijling JL, Nawijn L, van Zuiden M, Zhu X, Suarez-Jimenez B, Sierk A, Walter H, Manthey A, Stevens JS, Fani N, van Rooij SJH, Stein MB, Bomyea J, Koerte I, Choi K, van der Werff SJA, Vermeiren RRJM, Herzog JI, Lebois LAM, Baker JT, Ressler KJ, Olson EA, Straube T, Korgaonkar MS, Andrew E, Zhu Y, Li G, Ipser J, Hudson AR, Peverill M, Sambrook K, Gordon E, Baugh LA, Forster G, Simons RM, Simons JS, Magnotta VA, Maron-Katz A, du Plessis S, Disner SG, Davenport ND, Grupe D, Nitschke JB, deRoon-Cassini TA, Fitzgerald J, Krystal JH, Levy I, Olff M, Veltman DJ, Wang L, Neria Y, De Bellis MD, Jovanovic T, Daniels JK, Shenton ME, van de Wee NJA, Schmahl C, Kaufman ML, Rosso IM, Sponheim SR, Hofmann DB, Bryant RA, Fercho KA, Stein DJ, Mueller SC, Phan KL, McLaughlin KA, Davidson RJ, Larson C, May G, Nelson SM, Abdallah CG, Gomaa H, Etkin A, Seedat S, Harpaz-Rotem I, Liberzon I, Wang X, Thompson PM, Morey RA. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:935-948. [PMID: 35307575 PMCID: PMC9835553 DOI: 10.1016/j.bpsc.2022.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 02/10/2022] [Accepted: 02/18/2022] [Indexed: 01/16/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2-85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis-Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2-148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. RESULTS Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.
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Affiliation(s)
- Delin Sun
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina; Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, North Carolina
| | - Gopalkumar Rakesh
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina; Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, North Carolina
| | - Emily K Clarke-Rubright
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina; Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, North Carolina
| | - Courtney C Haswell
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina; Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, North Carolina
| | - Mark W Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts; Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts; Biomedical Genetics, Boston University School of Medicine, Boston, Massachusetts; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Erin N O'Leary
- Department of Psychiatry, University of Toledo, Toledo, Ohio
| | - Andrew S Cotton
- Department of Psychiatry, University of Toledo, Toledo, Ohio
| | - Hong Xie
- Department of Psychiatry, University of Toledo, Toledo, Ohio
| | - Emily L Dennis
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, Massachusetts; Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California; Stanford Neurodevelopment, Affect, and Psychopathology Laboratory, Stanford, California; Department of Neurology, University of Utah, Salt Lake City, Utah
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Lauren E Salminen
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Faisal M Rashid
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Christopher R K Ching
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Saskia B J Koch
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jessie L Frijling
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Laura Nawijn
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mirjam van Zuiden
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Xi Zhu
- Department of Psychiatry, Columbia University Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - Benjamin Suarez-Jimenez
- Department of Psychiatry, Columbia University Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York; University of Rochester Medical Center, Rochester, New York
| | - Anika Sierk
- University Medical Centre Charité, Berlin, Germany
| | | | | | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Jessica Bomyea
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Inga Koerte
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, Massachusetts; Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Kyle Choi
- Health Services Research Center, University of California San Diego, San Diego, California
| | - Steven J A van der Werff
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | | | - Julia I Herzog
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Depression and Anxiety Disorders, McLean Hospital, Harvard University, Belmont, Massachusetts
| | - Justin T Baker
- Institute for Technology in Psychiatry, McLean Hospital, Harvard University, Belmont, Massachusetts
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Depression and Anxiety Disorders, McLean Hospital, Harvard University, Belmont, Massachusetts; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Elizabeth A Olson
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard University, Belmont, Massachusetts
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute of Medical Research, Westmead, New South Wales, Australia
| | - Elpiniki Andrew
- Department of Psychology, University of Sydney, Westmead, New South Wales, Australia
| | - Ye Zhu
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Gen Li
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jonathan Ipser
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Anna R Hudson
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Matthew Peverill
- Department of Psychology, University of Washington, Seattle, Washington
| | - Kelly Sambrook
- Department of Radiology, University of Washington, Seattle, Washington
| | - Evan Gordon
- Veterans Integrated Service Network-17 Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Washington University School of Medicine, St. Louis, Missouri
| | - Lee A Baugh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, Vermillion, South Dakota; Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota; Sioux Falls VA Health Care System, Sioux Falls, South Dakota
| | - Gina Forster
- Division of Basic Biomedical Sciences, Sanford School of Medicine, Vermillion, South Dakota; Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota; Brain Health Research Centre, Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Raluca M Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota; Department of Psychology, University of South Dakota, Vermillion, South Dakota
| | - Jeffrey S Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota; Department of Psychology, University of South Dakota, Vermillion, South Dakota
| | - Vincent A Magnotta
- Department of Radiology, Psychiatry, and Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Stefan du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Seth G Disner
- Minneapolis VA Health Care System, University of Minnesota, Minneapolis, Minnesota; Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Nicholas D Davenport
- Minneapolis VA Health Care System, University of Minnesota, Minneapolis, Minnesota; Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Dan Grupe
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jack B Nitschke
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Terri A deRoon-Cassini
- Division of Trauma and Acute Care Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - John H Krystal
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Ifat Levy
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Miranda Olff
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; ARQ National Psychotrauma Centre, Diemen, the Netherlands
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam University Medical Centers, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Li Wang
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yuval Neria
- Department of Psychiatry, Columbia University Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - Michael D De Bellis
- Healthy Childhood Brain Development Developmental Traumatology Research Program, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia; Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan
| | - Judith K Daniels
- Department of Clinical Psychology, University of Groningen, Groningen, the Netherlands
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, Massachusetts; VA Boston Healthcare System, Brockton Division, Brockton, Massachusetts
| | - Nic J A van de Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Milissa L Kaufman
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Women's Mental Health, McLean Hospital, Harvard University, Belmont, Massachusetts
| | - Isabelle M Rosso
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard University, Belmont, Massachusetts
| | - Scott R Sponheim
- Minneapolis VA Health Care System, University of Minnesota, Minneapolis, Minnesota; Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - David Bernd Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany
| | - Richard A Bryant
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Kelene A Fercho
- Division of Basic Biomedical Sciences, Sanford School of Medicine, Vermillion, South Dakota; Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota; Sioux Falls VA Health Care System, Sioux Falls, South Dakota; Civil Aerospace Medical Institute, US Federal Aviation Administration, Oklahoma City, Oklahoma
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium; Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | - K Luan Phan
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Mental Health Service Line, Jesse Brown VA Chicago Health Care System, Chicago, Illinois
| | | | - Richard J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisconsin; Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin; Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Christine Larson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
| | - Geoffrey May
- Veterans Integrated Service Network-17 Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, Bryan, Texas
| | - Steven M Nelson
- Veterans Integrated Service Network-17 Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, Bryan, Texas
| | - Chadi G Abdallah
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Hassaan Gomaa
- Department of Psychiatry, Pennsylvania State University, State College, Pennsylvania
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California; VA Palo Alto Health Care System, Palo Alto, California
| | - Soraya Seedat
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Ilan Harpaz-Rotem
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Israel Liberzon
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Xin Wang
- Department of Psychiatry, University of Toledo, Toledo, Ohio
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California
| | - Rajendra A Morey
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina; Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, North Carolina.
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17
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Biondo F, Jewell A, Pritchard M, Aarsland D, Steves CJ, Mueller C, Cole JH. Brain-age is associated with progression to dementia in memory clinic patients. Neuroimage Clin 2022; 36:103175. [PMID: 36087560 PMCID: PMC9467894 DOI: 10.1016/j.nicl.2022.103175] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/30/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. METHODS We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. RESULTS Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). CONCLUSIONS Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.
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Affiliation(s)
- Francesca Biondo
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK.
| | | | | | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; Centre for Age-Related Research, Stavanger University Hospital, Stavanger, Norway
| | - Claire J Steves
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, SE1 7EH, UK; Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EH, UK
| | - Christoph Mueller
- South London and Maudsley NHS Foundation Trust, UK; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK; Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR, UK.
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18
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Demro C, Shen C, Hendrickson TJ, Arend JL, Disner SG, Sponheim SR. Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins. Front Aging Neurosci 2022; 14:872867. [PMID: 35527740 PMCID: PMC9074783 DOI: 10.3389/fnagi.2022.872867] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.
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Affiliation(s)
- Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Chen Shen
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | | | - Jessica L. Arend
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Seth G. Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
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