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Zhou J, Chen Y, Jin X, Mao W, Xiao Z, Zhang S, Zhang T, Liu T, Kendrick K, Jiang X. Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction. Neural Netw 2024; 179:106592. [PMID: 39168070 DOI: 10.1016/j.neunet.2024.106592] [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: 08/07/2023] [Revised: 06/03/2024] [Accepted: 07/31/2024] [Indexed: 08/23/2024]
Abstract
Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.
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Affiliation(s)
- Jingchao Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuzhong Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuewei Jin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Mao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhenxiang Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, USA
| | - Keith Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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2
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Busby N, Newman-Norlund S, Sayers S, Rorden C, Newman-Norlund R, Wilmskoetter J, Roth R, Wilson S, Schwen-Blackett D, Kristinsson S, Teghipco A, Fridriksson J, Bonilha L. Regional brain aging: premature aging of the domain general system predicts aphasia severity. Commun Biol 2024; 7:718. [PMID: 38862747 PMCID: PMC11167062 DOI: 10.1038/s42003-024-06211-8] [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: 10/17/2023] [Accepted: 04/17/2024] [Indexed: 06/13/2024] Open
Abstract
Premature brain aging is associated with poorer cognitive reserve and lower resilience to injury. When there are focal brain lesions, brain regions may age at different rates within the same individual. Therefore, we hypothesize that reduced gray matter volume within specific brain systems commonly associated with language recovery may be important for long-term aphasia severity. Here we show that individuals with stroke aphasia have a premature brain aging in intact regions of the lesioned hemisphere. In left domain-general regions, premature brain aging, gray matter volume, lesion volume and age were all significant predictors of aphasia severity. Increased brain age following a stroke is driven by the lesioned hemisphere. The relationship between brain age in left domain-general regions and aphasia severity suggests that degradation is possible to specific brain regions and isolated aging matters for behavior.
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Affiliation(s)
- Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA.
| | - Sarah Newman-Norlund
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Sara Sayers
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | | | - Janina Wilmskoetter
- Department of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Sarah Wilson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Deena Schwen-Blackett
- Department of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Sigfus Kristinsson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Alex Teghipco
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- School of Medicine, University of South Carolina, Columbia, SC, USA
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3
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Carta E, Riccardi A, Marinetto S, Mattivi S, Selini E, Pucci V, Mondini S. Over ninety years old: Does high cognitive reserve still help brain efficiency? PSYCHOLOGICAL RESEARCH 2024; 88:678-683. [PMID: 37801087 PMCID: PMC10858058 DOI: 10.1007/s00426-023-01881-1] [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: 03/13/2023] [Accepted: 09/17/2023] [Indexed: 10/07/2023]
Abstract
Nonagenarians and centenarians, also called oldest-old, are a very heterogeneous population that counts a limited number of individuals as it is a real challenge to reach this goal. Even if it is well known that cognitive reserve can be considered a factor in maintaining good cognitive functioning in ageing, only very few studies have been carried out on the role of cognitive reserve (CR) in the oldest-old people. The aim of this study is to investigate the relationship between cognitive reserve and cognitive functioning in a population living in a specific region of Italy, the Blue Zone in Sardinia. This population is characterised by extreme longevity and distinctive historical, geographic, social, linguistic and nutritional features. The cognitive Reserve Index questionnaire (CRIq) and the short cognitive Esame Neuropsicologico Breve-2 (ENB-2, Brief Neuropsychological Examination) were administered to 67 participants, all aged between 90 and 105 years old. The CRIq was a predictor of neuropsychological performance for the global score of the battery of tests, ENB-2 (ENB-tot) and also for 7 out of 16 of its sub-tests. All except one (Token) tapped executive functions (Interference memory at 10 and 30 s, TMT-B, Overlapping figures, Abstraction, Fluency). Results highlight that also in the oldest-old population CR has a positive effect on cognition, especially on executive functioning.
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Affiliation(s)
- Elisa Carta
- Department of Biomedical Sciences, Multiple Sclerosis Centre Binaghi Hospital, University of Cagliari, Cagliari, Italy
| | - Alice Riccardi
- Multiple Sclerosis Centre, Department of Neurosciences-DNS, University-Hospital of Padua, Padua, Italy
- Independent Researcher, Padua, Italy
| | | | | | - Enrico Selini
- Dipartimento di Filosofia Sociologia Pedagogia e Psicologia Applicata (FISPPA), University of Padua, Padua, Italy
| | - Veronica Pucci
- Dipartimento di Filosofia Sociologia Pedagogia e Psicologia Applicata (FISPPA), University of Padua, Padua, Italy
- Human Inspired Technology Research-Centre, University of Padua, Padua, Italy
| | - Sara Mondini
- Dipartimento di Filosofia Sociologia Pedagogia e Psicologia Applicata (FISPPA), University of Padua, Padua, Italy.
- Human Inspired Technology Research-Centre, University of Padua, Padua, Italy.
- Servizi Clinici Universitari Psicologici (SCUP), University of Padua, Padua, Italy.
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4
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Gavett BE, Widaman KF, McKenzie C, De Leon FS, Fletcher E, Tomaszewski Farias S, Mungas D. Self-reported mid- to late-life physical and recreational activities: Associations with late-life cognition. J Int Neuropsychol Soc 2024; 30:209-219. [PMID: 37721128 PMCID: PMC10922209 DOI: 10.1017/s1355617723000553] [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: 09/19/2023]
Abstract
OBJECTIVE Physical and recreational activities are behaviors that may modify risk of late-life cognitive decline. We sought to examine the role of retrospectively self-reported midlife (age 40) physical and recreational activity engagement - and self-reported change in these activities from age 40 to initial study visit - in predicting late-life cognition. METHOD Data were obtained from 898 participants in a longitudinal study of cognitive aging in demographically and cognitively diverse older adults (Age: range = 49-93 years, M = 75, SD = 7.19). Self-reported physical and recreational activity participation at age 40 and at the initial study visit were quantified using the Life Experiences Assessment Form. Change in activities was modeled using latent change scores. Cognitive outcomes were obtained annually (range = 2-17 years) using the Spanish and English Neuropsychological Assessment Scales, which measure verbal episodic memory, semantic memory, visuospatial processing, and executive functioning. RESULTS Physical activity engagement at age 40 was strongly associated with cognitive performance in all four domains at the initial visit and with global cognitive slope. However, change in physical activities after age 40 was not associated with cognitive outcomes. In contrast, recreational activity engagement - both at age 40 and change after 40 - was predictive of cognitive intercepts and slope. CONCLUSIONS Retrospectively self-reported midlife physical and recreational activity engagement were strongly associated with late-life cognition - both level of performance and rate of future decline. However, the data suggest that maintenance of recreational activity engagement (e.g., writing, taking classes, reading) after age 40 is more strongly associated with late-life cognition than continued maintenance of physical activity levels.
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Affiliation(s)
- Brandon E Gavett
- Department of Neurology, University of California Davis, Sacramento, CA, USA
- School of Psychological Science, University of Western Australia, Crawley, WA, Australia
| | - Keith F Widaman
- Graduate School of Education, University of California, Riverside, CA, USA
| | - Cathryn McKenzie
- School of Psychological Science, University of Western Australia, Crawley, WA, Australia
| | - Fransia S De Leon
- School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Evan Fletcher
- Department of Neurology, University of California Davis, Sacramento, CA, USA
| | | | - Dan Mungas
- Department of Neurology, University of California Davis, Sacramento, CA, USA
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5
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Zhang Y, Xie R, Beheshti I, Liu X, Zheng G, Wang Y, Zhang Z, Zheng W, Yao Z, Hu B. Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN. Comput Biol Med 2024; 169:107873. [PMID: 38181606 DOI: 10.1016/j.compbiomed.2023.107873] [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: 03/31/2023] [Revised: 11/17/2023] [Accepted: 12/17/2023] [Indexed: 01/07/2024]
Abstract
Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.
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Affiliation(s)
- Yu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Rui Xie
- Department of Psychiatric, Tianshui Third People's Hospital, Tianshui, 741000, China
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Canada
| | - Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China
| | - Guowei Zheng
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Zhenwen Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; School of Medical Technology, Beijing Institute of Technology, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China.
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6
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Liu L, Lin L, Sun S, Wu S. Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset. Bioengineering (Basel) 2024; 11:124. [PMID: 38391610 PMCID: PMC10886122 DOI: 10.3390/bioengineering11020124] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.
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Affiliation(s)
- Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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7
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Millar PR, Gordon BA, Wisch JK, Schultz SA, Benzinger TL, Cruchaga C, Hassenstab JJ, Ibanez L, Karch C, Llibre-Guerra JJ, Morris JC, Perrin RJ, Supnet-Bell C, Xiong C, Allegri RF, Berman SB, Chhatwal JP, Chrem Mendez PA, Day GS, Hofmann A, Ikeuchi T, Jucker M, Lee JH, Levin J, Lopera F, Niimi Y, Sánchez-González VJ, Schofield PR, Sosa-Ortiz AL, Vöglein J, Bateman RJ, Ances BM, McDade EM. Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease. Mol Neurodegener 2023; 18:98. [PMID: 38111006 PMCID: PMC10729487 DOI: 10.1186/s13024-023-00688-3] [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: 07/03/2023] [Accepted: 11/28/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND "Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. METHODS We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE ε4 carrier status, sex, and education. RESULTS Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. CONCLUSIONS We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Stephanie A Schultz
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Tammie Ls Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Laura Ibanez
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- NeuroGenomics & Informatics Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | | | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology & Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Chengjie Xiong
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Anna Hofmann
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Bunkyo-Ku, Tokyo, Japan
| | - Victor J Sánchez-González
- Departamento de Clínicas, CUALTOS, Universidad de Guadalajara, Tepatitlán de Morelos, Jalisco, México
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Ana Luisa Sosa-Ortiz
- Instituto Nacional de Neurologia y Neurocirugía MVS, CDMX, Ciudad de México, Mexico
| | - Jonathan Vöglein
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric M McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
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8
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Dempsey DA, Deardorff R, Wu YC, Yu M, Apostolova LG, Brosch J, Clark DG, Farlow MR, Gao S, Wang S, Saykin AJ, Risacher SL. BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299222. [PMID: 38106123 PMCID: PMC10723496 DOI: 10.1101/2023.12.05.23299222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.
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Affiliation(s)
- Desarae A. Dempsey
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Meichen Yu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Liana G. Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jared Brosch
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - David G. Clark
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Martin R. Farlow
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sophia Wang
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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9
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He S, Guan Y, Cheng CH, Moore TL, Luebke JI, Killiany RJ, Rosene DL, Koo BB, Ou Y. Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Front Aging Neurosci 2023; 15:1249415. [PMID: 38020785 PMCID: PMC10646581 DOI: 10.3389/fnagi.2023.1249415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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Affiliation(s)
- Sheng He
- Harvard Medical School, Boston Children's Hospital, Boston, MA, United States
| | - Yi Guan
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Tara L. Moore
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jennifer I. Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Douglas L. Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Yangming Ou
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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10
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Hong GS, Jang M, Kyung S, Cho K, Jeong J, Lee GY, Shin K, Kim KD, Ryu SM, Seo JB, Lee SM, Kim N. Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning. Korean J Radiol 2023; 24:1061-1080. [PMID: 37724586 PMCID: PMC10613849 DOI: 10.3348/kjr.2023.0393] [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: 04/27/2023] [Revised: 07/01/2023] [Accepted: 07/30/2023] [Indexed: 09/21/2023] Open
Abstract
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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Affiliation(s)
- Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Miso Jang
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sunggu Kyung
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyungjin Cho
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jiheon Jeong
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Grace Yoojin Lee
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Keewon Shin
- Laboratory for Biosignal Analysis and Perioperative Outcome Research, Biomedical Engineering Center, Asan Institute of Lifesciences, Asan Medical Center, Seoul, Republic of Korea
| | - Ki Duk Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Min Ryu
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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11
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Navarro-González R, García-Azorín D, Guerrero-Peral ÁL, Planchuelo-Gómez Á, Aja-Fernández S, de Luis-García R. Increased MRI-based Brain Age in chronic migraine patients. J Headache Pain 2023; 24:133. [PMID: 37798720 PMCID: PMC10557155 DOI: 10.1186/s10194-023-01670-6] [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: 06/05/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
INTRODUCTION Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. METHODS We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). RESULTS CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. CONCLUSION The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
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Affiliation(s)
| | - David García-Azorín
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain.
| | - Ángel L Guerrero-Peral
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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12
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Giaquinto F, Tosi G, Abbatantuono C, Pepe I, Iaia M, Macchitella L, Rizzi E, De Caro MF, Romano D, Taurisano P, Angelelli P. The indirect effect of cognitive reserve on the relationship between age and cognition in pathological ageing: A cross-sectional retrospective study in an unselected and consecutively enrolled sample. J Neuropsychol 2023; 17:477-490. [PMID: 37184066 DOI: 10.1111/jnp.12323] [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: 11/14/2022] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 05/16/2023]
Abstract
Cognitive reserve (CR) allows individuals to maintain cognitive functionality even in the presence of pathologies. The compensation hypothesis suggests that CR plays an indirect role between age and cognitive decline, contrasting the negative effect of ageing on cognition. We test this hypothesis in an unselected and consecutively enrolled sample of memory clinic attendees (n = 134) who completed the CR Index questionnaire and three neuropsychological tests assessing global cognition (MMSE, FAB, CDT). Participants were divided into two groups based on standard diagnostic criteria (DSM-5): those who were cognitively impaired (n = 92) and those who were preserved (n = 42). A principal component analysis was used to extract a composite measure of global cognitive functioning from the three neuropsychological tests, and mediation analysis was used to examine the relationship between CR, age and global cognitive functioning in the two groups. Results revealed that: (i) age had a significant direct negative effect on the global cognitive score in both groups; (ii) the three socio-behavioural proxies of CR together suppress the direct negative relationship between age and global cognitive score in cognitively impaired patients but not in cognitively preserved participants. This study confirms the association between CR, age and cognition and allows us to validate its role in a population with cognitive impairment and extend findings to a low-to-middle educated population. These results hold important implications for public health and wellness promotion, emphasising the beneficial role of maintaining healthy and active physical, cognitive and social lifestyles.
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Affiliation(s)
- Francesco Giaquinto
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
| | - Giorgia Tosi
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | - Chiara Abbatantuono
- Department of Translational Biomedicine and Neuroscience "DiBraiN", University of Bari "Aldo Moro", Bari, Italy
| | - Ilaria Pepe
- Department of Translational Biomedicine and Neuroscience "DiBraiN", University of Bari "Aldo Moro", Bari, Italy
| | - Marika Iaia
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
| | - Luigi Macchitella
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
- Scientific Institute I.R.C.C.S. "E. Medea"- Unit for Severe Disabilities in Developmental Age and Young Adults (Developmental Neurology and Neurorehabilitation), Brindisi, Italy
| | - Ezia Rizzi
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | - Maria Fara De Caro
- Department of Translational Biomedicine and Neuroscience "DiBraiN", University of Bari "Aldo Moro", Bari, Italy
| | - Daniele Romano
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | - Paolo Taurisano
- Department of Translational Biomedicine and Neuroscience "DiBraiN", University of Bari "Aldo Moro", Bari, Italy
| | - Paola Angelelli
- Laboratory of Applied Psychology and Intervention, Department of Human and Social Sciences, University of Salento, Lecce, Italy
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13
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Funk-White M, Wing D, Eyler LT, Moore AA, Reas ET, McEvoy L. Neuroimaging-Derived Predicted Brain Age and Alcohol Use Among Community-Dwelling Older Adults. Am J Geriatr Psychiatry 2023; 31:669-678. [PMID: 36925380 DOI: 10.1016/j.jagp.2023.02.043] [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] [Received: 12/08/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVES Observational studies have suggested that moderate alcohol use is associated with reduced risk of dementia. However, the nature of this association is not understood. We investigated whether light to moderate alcohol use may be associated with slower brain aging, among a cohort of older community-dwelling adults using a biomarker of brain age based on structural neuroimaging measures. DESIGN Cross-sectional observational study. PARTICIPANTS Well-characterized members of a longitudinal cohort study who underwent neuroimaging. We categorized the 163 participants (mean age 76.7 ± 7.7, 60% women) into current nondrinkers, light drinkers (1-7 drinks/week) moderate drinkers (>7-14 drinks/week), or heavier drinkers (>14 drinks/week). MEASUREMENTS We calculated brain-predicted age using structural MRIs processed with the BrainAgeR program, and calculated the difference between brain-predicted age and chronological age (brain-predicted age difference, or brain-PAD). We used analysis of variance to determine if brain-PAD differed across alcohol groups, controlling for potential confounders. RESULTS Brain-PAD differed across alcohol groups (F[3, 150] = 4.02; p = 0.009) with heavier drinkers showing older brain-PAD than light drinkers (by about 6 years). Brain-PAD did not differ across light, moderate, and nondrinkers. Similar results were obtained after adjusting for potentially mediating health-related measures, and after excluding individuals with a history of heavier drinking. DISCUSSION Among this sample of healthy older adults, consumption of more than 14 drinks/week was associated with a biomarker of advanced brain aging. Light and moderate drinking was not associated with slower brain aging relative to non-drinking.
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Affiliation(s)
- Makaya Funk-White
- Interdisciplinary Research on Substance Use (MFW), University of California San Diego, La Jolla, CA
| | - David Wing
- Herbert Wertheim School of Public Health and Human Longevity Science (DW, LKM), University of California San Diego, La Jolla, CA
| | - Lisa T Eyler
- Department of Psychiatry (LTE), University of California San Diego, La Jolla, CA; Desert-Pacific Mental Illness Research (LTE), Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA
| | - Alison A Moore
- Division of Geriatrics, Gerontology, and Palliative Care, Department of Medicine (AAM), University of California San Diego, La Jolla, CA
| | - Emilie T Reas
- Department of Neurosciences (ETR), University of California San Diego, La Jolla, CA
| | - Linda McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science (DW, LKM), University of California San Diego, La Jolla, CA; Department of Radiology (LKM), University of California San Diego, La Jolla, CA
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14
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Leonardsen EH, Vidal-Piñeiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, de Lange AMG, Kaufmann T, Taschler B, Smith SM, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry 2023; 28:3111-3120. [PMID: 37165155 PMCID: PMC10615751 DOI: 10.1038/s41380-023-02087-y] [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] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
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Affiliation(s)
- Esten H Leonardsen
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1015, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, OX1 2JD, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway.
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Brier MR, Li Z, Ly M, Karim HT, Liang L, Du W, McCarthy JE, Cross AH, Benzinger TLS, Naismith RT, Chahin S. "Brain age" predicts disability accumulation in multiple sclerosis. Ann Clin Transl Neurol 2023; 10:990-1001. [PMID: 37119507 PMCID: PMC10270248 DOI: 10.1002/acn3.51782] [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: 01/09/2023] [Revised: 03/17/2023] [Accepted: 04/10/2023] [Indexed: 05/01/2023] Open
Abstract
OBJECTIVE Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset. METHODS Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.
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Affiliation(s)
- Matthew R. Brier
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Zhuocheng Li
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Maria Ly
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Helmet T. Karim
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Leda Liang
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Weixin Du
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - John E. McCarthy
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Anne H. Cross
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
| | - Robert T. Naismith
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Salim Chahin
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
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Mendes SL, Pinaya WHL, Pan PM, Jackowski AP, Bressan RA, Sato JR. Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI. Sci Rep 2023; 13:6886. [PMID: 37106035 PMCID: PMC10140022 DOI: 10.1038/s41598-023-33920-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI). These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score. The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). Models' performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age (p value < 0.001) within and between datasets. In addition, most models showed a moderate to high correlation in age estimation. The results, including the models' brain regions of interest (ROI), were analyzed and discussed in light of the youth neurodevelopmental structural changes. Among other interesting discoveries, we found that less confounded training datasets produce models with higher generalization capacity.
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Affiliation(s)
- Sergio Leonardo Mendes
- Center of Mathematics, Computing, and Cognition, Universidade Federal Do ABC, Rua Arcturus N. 03, São Bernardo Do Campo, SP, 09606-070, Brazil
| | | | - Pedro Mario Pan
- Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil
| | - Andrea Parolin Jackowski
- Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil
- Department of Education, ICT and Learning, Østfold University College, Halden, Norway
| | - Rodrigo Affonseca Bressan
- Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil
| | - João Ricardo Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal Do ABC, Rua Arcturus N. 03, São Bernardo Do Campo, SP, 09606-070, Brazil
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More S, Antonopoulos G, Hoffstaedter F, Caspers J, Eickhoff SB, Patil KR. Brain-age prediction: A systematic comparison of machine learning workflows. Neuroimage 2023; 270:119947. [PMID: 36801372 DOI: 10.1016/j.neuroimage.2023.119947] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-dataset accuracy, (2) cross-dataset generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-dataset mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-dataset MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-dataset and cross-dataset predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients compared to healthy controls. However, in the presence of age bias, the delta estimates in the patients varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.
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Affiliation(s)
- Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Georgios Antonopoulos
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Fingelkurts AA, Fingelkurts AA. Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications. Brain Sci 2023; 13:brainsci13030520. [PMID: 36979330 PMCID: PMC10046544 DOI: 10.3390/brainsci13030520] [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: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. METHODS AND OBJECTIVE Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). RESULTS We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. CONCLUSIONS Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.
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Busby N, Wilmskoetter J, Gleichgerrcht E, Rorden C, Roth R, Newman-Norlund R, Hillis AE, Keller SS, de Bezenac C, Kristinsson S, Fridriksson J, Bonilha L. Advanced Brain Age and Chronic Poststroke Aphasia Severity. Neurology 2023; 100:e1166-e1176. [PMID: 36526425 PMCID: PMC10074460 DOI: 10.1212/wnl.0000000000201693] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/31/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Chronic poststroke language impairment is typically worse in older individuals or those with large stroke lesions. However, there is unexplained variance that likely depends on intact tissue beyond the lesion. Brain age is an emerging concept, which is partially independent from chronologic age. Advanced brain age is associated with cognitive decline in healthy older adults; therefore, we aimed to investigate the relationship with stroke aphasia. We hypothesized that advanced brain age is a significant factor associated with chronic poststroke language impairments, above and beyond chronologic age, and lesion characteristics. METHODS This cohort study retrospectively evaluated participants from the Predicting Outcomes of Language Rehabilitation in Aphasia clinical trial (NCT03416738), recruited through local advertisement in South Carolina (US). Primary inclusion criteria were left hemisphere stroke and chronic aphasia (≥12 months after stroke). Participants completed baseline behavioral testing including the Western Aphasia Battery-Revised (WAB-R), Philadelphia Naming Test (PNT), Pyramids and Palm Trees Test (PPTT), and Wechsler Adult Intelligence Scale Matrices subtest, before completing 6 weeks of language therapy. The PNT was repeated 1 month after therapy. We leveraged modern neuroimaging techniques to estimate brain age and computed a proportional difference between chronologic age and estimated brain age. Multiple linear regression models were used to evaluate the relationship between proportional brain age difference (PBAD) and behavior. RESULTS Participants (N = 93, 58 males and 35 females, average age = 61 years) had estimated brain ages ranging from 14 years younger to 23 years older than chronologic age. Advanced brain age predicted performance on semantic tasks (PPTT) and language tasks (WAB-R). For participants with advanced brain aging (n = 47), treatment gains (improvement on the PNT) were independently predicted by PBAD (T = -2.0474, p = 0.0468, 9% of variance explained). DISCUSSION Through the application of modern neuroimaging techniques, advanced brain aging was associated with aphasia severity and performance on semantic tasks. Notably, therapy outcome scores were also associated with PBAD, albeit only among participants with advanced brain aging. These findings corroborate the importance of brain age as a determinant of poststroke recovery and underscore the importance of personalized health factors in determining recovery trajectories, which should be considered during the planning or implementation of therapeutic interventions.
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Affiliation(s)
- Natalie Busby
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom.
| | - Janina Wilmskoetter
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Ezequiel Gleichgerrcht
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Chris Rorden
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Rebecca Roth
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Roger Newman-Norlund
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Argye Elizabeth Hillis
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Simon S Keller
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Christophe de Bezenac
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Sigfus Kristinsson
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Julius Fridriksson
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
| | - Leonardo Bonilha
- From the Departments of Communication Sciences and Disorders (N.B., J.F.), and Psychology (C.R., R.N.N.), University of South Carolina, Columbia; Department of Health and Rehabilitation Sciences (J.W., E.G., S.K., J.F.), Medical University of South Carolina, Charleston; Department of Neurology (R.R., L.B.), Emory University, Atlanta, GA; Department of Neurology (A.E.H.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pharmacology and Therapeutics (S.S.K., C.d.B.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust (S.S.K., C.d.B.), Liverpool, United Kingdom
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Zhou J, Cattaneo G, Yu W, Lo OY, Gouskova NA, Delgado-Gallén S, Redondo-Camós M, España-Irla G, Solana-Sánchez J, Tormos JM, Lipsitz LA, Bartrés-Faz D, Pascual-Leone A, Manor B. The age-related contribution of cognitive function to dual-task gait in middle-aged adults in Spain: observations from a population-based study. THE LANCET. HEALTHY LONGEVITY 2023; 4:e98-e106. [PMID: 36870341 PMCID: PMC9992865 DOI: 10.1016/s2666-7568(23)00009-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 03/05/2023] Open
Abstract
BACKGROUND Poor dual-task gait performance is associated with a risk of falls and cognitive decline in adults aged 65 years or older. When and why dual-task gait performance begins to deteriorate is unknown. This study aimed to characterise the relationships between age, dual-task gait, and cognitive function in middle age (ie, aged 40-64 years). METHODS We conducted a secondary analysis of data from community-dwelling adults aged 40-64 years that took part in the Barcelona Brain Health Initiative (BBHI) study, an ongoing longitudinal cohort study in Barcelona, Spain. Participants were eligible for inclusion if they were able to walk independently without assistance and had completed assessments of both gait and cognition at the time of analysis and ineligble if they could not understand the study protocol, had any clinically diagnosed neurological or psychiatric diseases, were cognitively impaired, or had lower-extremity pain, osteoarthritis, or rheumatoid arthritis that could cause abnormal gait. Stride time and stride time variability were measured under single-task (ie, walking only) and dual-task (ie, walking while performing serial subtractions) conditions. Dual-task cost (DTC; the percentage increase in the gait outcomes from single-task to dual-task conditions) to each gait outcome was calculated and used as the primary measure in analyses. Global cognitive function and composite scores of five cognitive domains were derived from neuropsychological testing. We used locally estimated scatterplot smoothing to characterise the relationship between age and dual-task gait, and structural equation modelling to establish whether cognitive function mediated the association between observed biological age and dual tasks. FINDINGS 996 people were recruited to the BBHI study between May 5, 2018, and July 7, 2020, of which 640 participants completed gait and cognitive assessments during this time (mean 24 days [SD 34] between first and second visit) and were included in our analysis (342 men and 298 women). Non-linear associations were observed between age and dual-task performance. Starting at 54 years, the DTC to stride time (β=0·27 [95% CI 0·11 to 0·36]; p<0·0001) and stride time variability (0·24 [0·08 to 0·32]; p=0·0006) increased with advancing age. In individuals aged 54 years or older, decreased global cognitive function correlated with increased DTC to stride time (β=-0·27 [-0·38 to -0·11]; p=0·0006) and increased DTC to stride time variability (β=-0·19 [-0·28 to -0·08]; p=0·0002). INTERPRETATION Dual-task gait performance begins to deteriorate in the sixth decade of life and, after this point, interindividual variance in cognition explains a substantial portion of dual-task performance. FUNDING La Caixa Foundation, Institut Guttmann, and Fundació Abertis.
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Affiliation(s)
- Junhong Zhou
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Wanting Yu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA
| | - On-Yee Lo
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Natalia A Gouskova
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Selma Delgado-Gallén
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria Redondo-Camós
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Goretti España-Irla
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Javier Solana-Sánchez
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Josep M Tormos
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Lewis A Lipsitz
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - David Bartrés-Faz
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain; Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences and August Pi i Sunyer Biomedical Research Institute, University of Barcelona, Barcelona, Spain
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Roslindale, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA; Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Bretzner M, Bonkhoff AK, Schirmer MD, Hong S, Dalca A, Donahue K, Giese AK, Etherton MR, Rist PM, Nardin M, Regenhardt RW, Leclerc X, Lopes R, Gautherot M, Wang C, Benavente OR, Cole JW, Donatti A, Griessenauer C, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McArdle PF, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Wu O, Zand R, Worrall BB, Maguire J, Lindgren AG, Jern C, Golland P, Kuchcinski G, Rost NS. Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke. Neurology 2023; 100:e822-e833. [PMID: 36443016 PMCID: PMC9984219 DOI: 10.1212/wnl.0000000000201596] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 10/06/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND OBJECTIVES While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes. METHODS We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input. RESULTS We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
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Affiliation(s)
- Martin Bretzner
- From the J. Philip Kistler Stroke Research Center (M.B., A.K.B., M.D.S., S.H., A. Dalca, K.D., A.-K.G., M.R.E., P.M.R., M.N., R.W.R., C.W., N.S.R.), A.A. Martinos Center for Biomedical Imaging (A. Dalca, O.W.), and Henry and Allison McCance Center for Brain Health (J. Rosand), Massachusetts General Hospital, Harvard Medical School, Boston; Lille Neuroscience & Cognition (M.B., X.L., R. Lopes, G.K.), Inserm, CHU Lille, U1172 and Institut Pasteur de Lille (M.G.), CNRS, Inserm, CHU Lille, US 41 - UMS 2014 - PLBS, Lille University, France; Computer Science and Artificial Intelligence Lab (A. Dalca, C.W., P.G.), Massachusetts Institute of Technology, Cambridge; Division of Preventive Medicine (P.M.R.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Medicine (O.R.B.), Division of Neurology, University of British Columbia, Vancouver, Canada; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD; School of Medical Sciences (A. Donatti, A. Sousa), University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo; Departments of Neurosurgery (C.G.) and Neurology (R.Z.), Geisinger, Danville, PA; Department of Neurosurgery (C.G.), Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria; Division of Emergency Medicine (Laura Heitsch), Washington University School of Medicine, St. Louis; Department of Neurology (Laura Heitsch, C.-L.P.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; Department of Clinical Neuroscience (L. Holmegaard, K.J., T.M.S., T.T.), Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Neurology (J.J.-C.), Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions M`ediques), Universitat Autonoma de Barcelona, Spain; Department of Neurosciences (R. Lemmens), Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Belgium; Department of Neurology (R. Lemmens), Laboratory of Neurobiology, VIB Vesalius Research Center, University Hospitals Leuven, Belgium; School of Medicine and Public Health (C.R.L.), University of Newcastle, New South Wales; Department of Neurology, John Hunter Hospital, Newcastle, New South Wales, Australia; Division of Endocrinology (P.F.M.), Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore; Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics (C.W.M.), University of Florida, Gainesville; Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL; Klinik und Poliklinik für Neurologie (A.R.), Universitätsmedizin Rostock, Germany; Department of Neurology (S.R., R.S.), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Center for Genomic Medicine (J. Rosand), Massachusetts General Hospital, Boston; Broad Institute (J. Rosand), Cambridge, MA; Department of Neurology and Evelyn F. McKnight Brain Institute (J. Roquer, T.R., R.L.S./M.S.), Miller School of Medicine, University of Miami, FL; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London (ICR2UL), UK St Peter's and Ashford Hospitals, Egham, United Kingdom; Department of Neurology (A. Slowik), Jagiellonian University Medical College, Krakow, Poland; Division of Neurocritical Care & Emergency Neurology (D.S.), Department of Neurology, Helsinki University Central Hospital, Finland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Australia; Departments of Radiology (A.V.) and Neurology and Rehabilitation Medicine (D.W.), University of Cincinnati College of Medicine, OH; Department of Clinical Sciences Lund, Radiology (J.W.) and Neurology (A.G.L.), Lund University, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville, VA; University of Technology Sydney (J.M.), Australia; Section of Neurology (A.G.L.), Skåne University Hospital, Lund, Sweden; Department of Laboratory Medicine (C.J.), Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Sweden; and Department of Clinical Genetics and Genomics (C.J.), Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.
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22
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Sex-specific relationships between obesity, physical activity, and gray and white matter volume in cognitively unimpaired older adults. GeroScience 2023:10.1007/s11357-023-00734-4. [PMID: 36781598 PMCID: PMC10400512 DOI: 10.1007/s11357-023-00734-4] [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: 08/09/2022] [Accepted: 01/17/2023] [Indexed: 02/15/2023] Open
Abstract
Independently, obesity and physical activity (PA) influence cerebral structure in aging, yet their interaction has not been investigated. We examined sex differences in the relationships among PA, obesity, and cerebral structure in aging with 340 participants who completed magnetic resonance imaging (MRI) acquisition to quantify grey matter volume (GMV) and white matter volume (WMV). Height and weight were measured to calculate body mass index (BMI). A PA questionnaire was used to estimate weekly Metabolic Equivalents. The relationships between BMI, PA, and their interaction on GMV Regions of Interest (ROIs) and WMV ROIs were examined. Increased BMI was associated with higher GMV in females, an inverse U relationship was found between PA and GMV in females, and the interaction indicated that regardless of BMI greater PA was associated with enhanced GMV. Males demonstrated an inverse U shape between BMI and GMV, and in males with high PA and had normal weight demonstrated greater GMV than normal weight low PA revealed by the interaction. WMV ROIs had a linear relationship with moderate PA in females, whereas in males, increased BMI was associated with lower WMV as well as a positive relationship with moderate PA and WMV. Males and females have unique relationships among GMV, PA and BMI, suggesting sex-aggregated analyses may lead to biased or non-significant results. These results suggest higher BMI, and PA are associated with increased GMV in females, uniquely different from males, highlighting the importance of sex-disaggregated models. Future work should include other imaging parameters, such as perfusion, to identify if these differences co-occur in the same regions as GMV.
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23
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Soehner AM, Hayes RA, Franzen PL, Goldstein TR, Hasler BP, Buysse DJ, Siegle GJ, Dahl RE, Forbes EE, Ladouceur CD, McMakin DL, Ryan ND, Silk JS, Jalbrzikowski M. Naturalistic Sleep Patterns are Linked to Global Structural Brain Aging in Adolescence. J Adolesc Health 2023; 72:96-104. [PMID: 36270890 PMCID: PMC9881228 DOI: 10.1016/j.jadohealth.2022.08.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE We examined whether interindividual differences in naturalistic sleep patterns correlate with any deviations from typical brain aging. METHODS Our sample consisted of 251 participants without current psychiatric diagnoses (9-25 years; mean [standard deviation] = 17.4 ± 4.52 yr; 58% female) drawn from the Neuroimaging and Pediatric Sleep Databank. Participants completed a T1-weighted structural magnetic resonance imaging scan and 5-7 days of wrist actigraphy to assess naturalistic sleep patterns (duration, timing, continuity, and regularity). We estimated brain age from extracted structural magnetic resonance imaging indices and calculated brain age gap (estimated brain age-chronological age). Robust regressions tested cross-sectional associations between brain age gap and sleep patterns. Exploratory models investigated moderating effects of age and biological gender and, in a subset of the sample, links between sleep, brain age gap, and depression severity (Patient-Reported Outcomes Measurement Information System Depression). RESULTS Later sleep timing (midsleep) was associated with more advanced brain aging (larger brain age gap), β = 0.1575, puncorr = .0042, pfdr = .0167. Exploratory models suggested that this effect may be driven by males, although the interaction of gender and brain age gap did not survive multiple comparison correction (β = 0.2459, puncorr = .0336, pfdr = .1061). Sleep duration, continuity, and regularity were not significantly associated with brain age gap. Age did not moderate any brain age gap-sleep relationships. In this psychiatrically healthy sample, depression severity was also not associated with brain age gap or sleep. DISCUSSION Later midsleep may be one behavioral cause or correlate of more advanced brain aging, particularly among males. Future studies should examine whether advanced brain aging and individual differences in sleep precede the onset of suboptimal cognitive-emotional outcomes in adolescents.
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Affiliation(s)
- Adriane M Soehner
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rebecca A Hayes
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts
| | - Peter L Franzen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Tina R Goldstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Brant P Hasler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Daniel J Buysse
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Greg J Siegle
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ronald E Dahl
- School of Public Health, University of California, Berkeley, Berkeley, California
| | - Erika E Forbes
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Cecile D Ladouceur
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Dana L McMakin
- Department of Psychology, Florida International University, Miami, Florida
| | - Neal D Ryan
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jennifer S Silk
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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24
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McEvoy LK, Bergstrom J, Hagler DJ, Wing D, Reas ET. Elevated Pure Tone Thresholds Are Associated with Altered Microstructure in Cortical Areas Related to Auditory Processing and Attentional Allocation. J Alzheimers Dis 2023; 96:1163-1172. [PMID: 37955091 PMCID: PMC10793660 DOI: 10.3233/jad-230767] [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: 11/14/2023]
Abstract
BACKGROUND Hearing loss is associated with cognitive decline and increased risk for Alzheimer's disease, but the basis of this association is not understood. OBJECTIVE To determine whether hearing impairment is associated with advanced brain aging or altered microstructure in areas involved with auditory and cognitive processing. METHODS 130 participants, (mean 76.4±7.3 years; 65% women) of the Rancho Bernardo Study of Healthy Aging had a screening audiogram in 2003-2005 and brain magnetic resonance imaging in 2014-2016. Hearing ability was defined as the average pure tone threshold (PTA) at 500, 1000, 2000, and 4000 Hz in the better-hearing ear. Brain-predicted age difference (Brain-pad) was calculated as the difference between brain-predicted age based on a validated structural imaging biomarker of brain age, and chronological age. Regional diffusion metrics in temporal and frontal cortex regions were obtained from diffusion-weighted MRIs. Linear regression analyses adjusted for age, gender, education, and health-related measures. RESULTS PTAs were not associated with brain-PAD (β= 0.09; 95% CI: -0.084 to 0.243; p = 0.34). PTAs were associated with reduced restricted diffusion and increased free water diffusion primarily in right hemisphere temporal and frontal areas (restricted diffusion: βs = -0.21 to -0.30; 95% CIs from -0.48 to -0.02; ps < 0.03; free water: βs = 0.18 to 0.26; 95% CIs 0.01 to 0.438; ps < 0.04). CONCLUSIONS Hearing impairment is not associated with advanced brain aging but is associated with differences in brain regions involved with auditory processing and attentional control. It is thus possible that increased dementia risk associated with hearing impairment arises, in part, from compensatory brain changes that may decrease resilience.
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Affiliation(s)
- Linda K McEvoy
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Jaclyn Bergstrom
- Division of Geriatrics, Gerontology, and Palliative Care, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - David Wing
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Emilie T Reas
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
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25
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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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26
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Wing D, Eyler LT, Lenze EJ, Wetherell JL, Nichols JF, Meeusen R, Godino JG, Shimony JS, Snyder AZ, Nishino T, Nicol GE, Nagels G, Roelands B. Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body composition. NEUROIMAGE. REPORTS 2022; 2:100146. [PMID: 36743444 PMCID: PMC9894084 DOI: 10.1016/j.ynirp.2022.100146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Introduction Changes in brain structure and function occur with aging. However, there is substantial heterogeneity both in terms of when these changes begin, and the rate at which they progress. Understanding the mechanisms and/or behaviors underlying this heterogeneity may allow us to act to target and slow negative changes associated with aging. Methods Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. We also assessed maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry. Associations were explored both linearly and logistically using categorical groupings. Results Visceral Adipose Tissue (VAT), Total Sleep Time (TST) and maximal aerobic capacity all showed significant associations with BrainPAD. Greater VAT was associated with higher (i.e,. older than chronological) BrainPAD (r = 0.149 p = 0.001)Greater TST was associated with higher BrainPAD (r = 0.087 p = 0.042) and greater aerobic capacity was associated with lower BrainPAD (r = - 0.088 p = 0.040). With linear regression, both VAT and TST remained significant (p = 0.036 and 0.008 respectively). Each kg of VAT predicted a 0.741 year increase in BrainPAD, and each hour of increased TST predicted a 0.735 year increase in BrainPAD. Maximal aerobic capacity did not retain statistical significance in fully adjusted linear models. Discussion Accumulation of visceral adipose tissue and greater total sleep time, but not aerobic capacity, total daily physical activity, or sleep quantity and/or quality are associated with brains that are physiologically older than would be expected based upon chronological age alone (BrainPAD).
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Affiliation(s)
- David Wing
- Herbert Wertheim School of Public Health and Human Longevity, University of California, San Diego, United States
- Exercise and Physical Activity Resource Center (EPARC), University of California, San Diego, United States
| | - Lisa T. Eyler
- Department of Psychiatry, University of California, San Diego, United States
- San Diego Veterans Administration Health Care System, San Diego, United States
| | - Eric J. Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Julie Loebach Wetherell
- Mental Health Service, VA San Diego Healthcare System, United States
- Department of Psychiatry, University of California, San Diego, United States
| | - Jeanne F. Nichols
- Herbert Wertheim School of Public Health and Human Longevity, University of California, San Diego, United States
- Exercise and Physical Activity Resource Center (EPARC), University of California, San Diego, United States
| | - Romain Meeusen
- Human Physiology & Sports Physiotherapy Research Group, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
| | - Job G. Godino
- Herbert Wertheim School of Public Health and Human Longevity, University of California, San Diego, United States
- Exercise and Physical Activity Resource Center (EPARC), University of California, San Diego, United States
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Ginger E. Nicol
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Guy Nagels
- Department of Neurology, UZ Brussel, Brussels, Belgium
- Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Bart Roelands
- Human Physiology & Sports Physiotherapy Research Group, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
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Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance. eNeuro 2022; 9:9/6/ENEURO.0212-22.2022. [PMID: 36376083 PMCID: PMC9665883 DOI: 10.1523/eneuro.0212-22.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Human cognitive abilities decline with increasing chronological age, with decreased explicit memory performance being most strongly affected. However, some older adults show "successful aging," that is, relatively preserved cognitive ability in old age. One explanation for this could be higher brain-structural integrity in these individuals. Alternatively, the brain might recruit existing resources more efficiently or employ compensatory cognitive strategies. Here, we approached this question by testing multiple candidate variables from structural and functional neuroimaging for their ability to predict chronological age and memory performance, respectively. Prediction was performed using support vector machine (SVM) classification and regression across and within two samples of young (N = 106) and older (N = 153) adults. The candidate variables were (1) behavioral response frequencies in an episodic memory test; (2) recently described functional magnetic resonance imaging (fMRI) scores reflecting preservation of functional memory networks; (3) whole-brain fMRI contrasts for novelty processing and subsequent memory; (4) resting-state fMRI maps quantifying voxel-wise signal fluctuation; and (5) gray matter volume estimated from structural MRIs. While age group could be reliably decoded from all variables, chronological age within young and older subjects was best predicted from gray matter volume. In contrast, memory performance was best predicted from task-based fMRI contrasts and particularly single-value fMRI scores, whereas gray matter volume has no predictive power with respect to memory performance in healthy adults. Our results suggest that superior memory performance in healthy older adults is better explained by efficient recruitment of memory networks rather than by preserved brain structure.
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28
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Sanford N, Ge R, Antoniades M, Modabbernia A, Haas SS, Whalley HC, Galea L, Popescu SG, Cole JH, Frangou S. Sex differences in predictors and regional patterns of brain age gap estimates. Hum Brain Mapp 2022; 43:4689-4698. [PMID: 35790053 PMCID: PMC9491279 DOI: 10.1002/hbm.25983] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/11/2022] Open
Abstract
The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.
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Affiliation(s)
- Nicole Sanford
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ruiyang Ge
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Liisa Galea
- Department of Psychology, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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29
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Basodi S, Raja R, Ray B, Gazula H, Sarwate AD, Plis S, Liu J, Verner E, Calhoun VD. Decentralized Brain Age Estimation Using MRI Data. Neuroinformatics 2022; 20:981-990. [PMID: 35380365 DOI: 10.1007/s12021-022-09570-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age regression models can achieve similar performance compared to the models trained with all the data in one location.
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Affiliation(s)
- Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Rajikha Raja
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Bhaskar Ray
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | | | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA.,Department of Psychology, Georgia State University, Atlanta, GA, USA
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Zhang Z, Jiang R, Zhang C, Williams B, Jiang Z, Li CT, Chazot P, Pavese N, Bouridane A, Beghdadi A. Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2146-2156. [PMID: 35830403 DOI: 10.1109/tnsre.2022.3190467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.
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Johnson AJ, Cole J, Fillingim RB, Cruz-Almeida Y. Persistent Non-pharmacological Pain Management and Brain-Predicted Age Differences in Middle-Aged and Older Adults With Chronic Knee Pain. FRONTIERS IN PAIN RESEARCH (LAUSANNE, SWITZERLAND) 2022; 3:868546. [PMID: 35903307 PMCID: PMC9314648 DOI: 10.3389/fpain.2022.868546] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/22/2022] [Indexed: 02/01/2023]
Abstract
Chronic pain has been associated with changes in pain-related brain structure and function, including advanced brain aging. Non-pharmacological pain management is central to effective pain management. However, it is currently unknown how use of non-pharmacological pain management is associated with pain-related brain changes. The objective of the current study was to examine the association between brain-predicted age difference and use of non-pharmacological pain management (NPM) in a sample of middle-aged and older adults with and without chronic knee pain across two time points. One-hundred and 12 adults (mean age = 57.9 ± 8.2 years) completed sociodemographic measures, clinical pain measures, structural T1-weighted brain magnetic resonance imaging, and self-reported non-pharmacological pain management. Using a validated approach, we estimated a brain-predicted age difference (brain-PAD) biomarker, calculated as brain-predicted age minus chronological age, and the change in brain-PAD across 2 years. Repeated measures analysis of covariance was conducted to determine associations of non-pharmacological pain management and brain-PAD, adjusting for age, sex, study site, and clinical pain. There was a significant time*pain/NPM interaction effect in brain-PAD (p < 0.05). Tests of simple main effects indicated that those persistently using NPM had a "younger" brain-PAD over time, suggesting a potential protective factor in persistent NPM use. Future studies are warranted to determine the influence of NPM in brain aging and pain-related neurological changes.
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Affiliation(s)
- Alisa J. Johnson
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States,Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - James Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom,Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Roger B. Fillingim
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States,Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - Yenisel Cruz-Almeida
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States,Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, United States,*Correspondence: Yenisel Cruz-Almeida
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32
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Wrigglesworth J, Harding IH, Ward P, Woods RL, Storey E, Fitzgibbon B, Egan G, Murray A, Shah RC, Trevaks RE, Ward S, McNeil JJ, Ryan J. Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals. J Alzheimers Dis Rep 2022; 6:163-176. [PMID: 35591948 PMCID: PMC9108625 DOI: 10.3233/adr-220011] [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: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 12/11/2022] Open
Abstract
Background: There is considerable variability in the rate at which we age biologically, and the brain is particularly susceptible to the effects of aging. Objective: We examined the test-retest reliability of brain age at one- and three-year intervals and identified characteristics that predict the longitudinal change in brain-predicted age difference (brain-PAD, defined by deviations of brain age from chronological age). Methods: T1-weighted magnetic resonance images were acquired at three timepoints from 497 community-dwelling adults (73.8±3.5 years at baseline, 48% were female). Brain age was estimated from whole brain volume, using a publicly available algorithm trained on an independent dataset. Linear mixed models were used, adjusting for sex, age, and age2. Results: Excellent retest reliability of brain age was observed over one and three years. We identified a significant sex difference in brain-PAD, where a faster rate of brain aging (worsening in brain age relative to chronological age) was observed in men, and this finding replicated in secondary analyses. The effect size, however, was relatively weak, equivalent to 0.16 years difference per year. A higher score in physical health related quality of life and verbal fluency were associated with a faster rate of brain aging, while depression was linked to a slower rate of brain aging, but these findings were not robust. Conclusion: Our study provides consistent evidence that older men have slightly faster brain atrophy than women. Given the sparsity of longitudinal research on brain age in older populations, future prospective studies are needed to confirm our findings.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, VIC, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Bernadette Fitzgibbon
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, VIC, Australia
| | - Anne Murray
- Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, USA
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, USA
| | - Raj C. Shah
- Department of Family Medicine and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Stephanie Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, NSW, Australia
| | - John J. McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Tseng WYI, Hsu YC, Kao TW. Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years. J Alzheimers Dis 2022; 86:613-627. [PMID: 35094993 DOI: 10.3233/jad-215380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk. OBJECTIVE Predicted age difference (PAD) was proposed to predict CDR change. METHODS All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction. RESULTS PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up. CONCLUSION Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.
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Affiliation(s)
- Wen-Yih Isaac Tseng
- AcroViz Inc. Taipei, Taiwan (R.O.C.).,Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan (R.O.C.).,Molecular Imaging Center, National Taiwan University, Taipei, Taiwan (R.O.C.)
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Linli Z, Feng J, Zhao W, Guo S. Associations between smoking and accelerated brain ageing. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110471. [PMID: 34740709 DOI: 10.1016/j.pnpbp.2021.110471] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 12/31/2022]
Abstract
Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age - true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10-20) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = -0.015, p = 2.14 × 10-05). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
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35
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Validation of Neuroimaging-based Brain Age Gap as a Mediator between Modifiable Risk Factors and Cognition. Neurobiol Aging 2022; 114:61-72. [DOI: 10.1016/j.neurobiolaging.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 03/08/2022] [Accepted: 03/09/2022] [Indexed: 11/17/2022]
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36
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Angebrandt A, Abulseoud OA, Kisner M, Diazgranados N, Momenan R, Yang Y, Stein EA, Ross TJ. Dose-dependent relationship between social drinking and brain aging. Neurobiol Aging 2022; 111:71-81. [PMID: 34973470 PMCID: PMC8929531 DOI: 10.1016/j.neurobiolaging.2021.11.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/18/2021] [Accepted: 11/26/2021] [Indexed: 12/25/2022]
Abstract
Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n = 240, mean age 35.1 (±10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age ("brain age gap"). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap (β = 0.014, p = 0.023). We replicated these results in an independent cohort [n = 231 adults, mean age 34.3 (±11.1) years, 55% male, 28% African American: β = 0.014, p = 0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.
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Affiliation(s)
- Alexanndra Angebrandt
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Osama A. Abulseoud
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA,Department of Psychiatry and Psychology, Mayo Clinic, Phoenix, AZ, USA,Corresponding author at: Department of Psychiatry and Psychology, Mayo Clinic, 5777 E Mayo Blvd., Phoenix, AZ 85054, USA. Phone: 480-301-8297, Fax: 480-301-6258. (O.A. Abulseoud)
| | - Mallory Kisner
- Clinical NeuroImaging Research Core, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Reza Momenan
- Clinical NeuroImaging Research Core, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA,Corresponding author at: Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd, Baltimore, MD 21244, USA. Phone 443-740-2645, Fax 443-740-2734. (T.J. Ross)
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37
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Ramduny J, Bastiani M, Huedepohl R, Sotiropoulos SN, Chechlacz M. The Association Between Inadequate Sleep and Accelerated Brain Ageing. Neurobiol Aging 2022; 114:1-14. [PMID: 35344818 PMCID: PMC9084918 DOI: 10.1016/j.neurobiolaging.2022.02.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/23/2021] [Accepted: 02/14/2022] [Indexed: 01/18/2023]
Affiliation(s)
- Jivesh Ramduny
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; School of Psychology, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; National Institute for Health Research (NIHR), Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, UK
| | - Robin Huedepohl
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; National Institute for Health Research (NIHR), Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, UK.
| | - Magdalena Chechlacz
- School of Psychology, University of Birmingham, Birmingham, UK; Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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38
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Beck D, de Lange AG, Pedersen ML, Alnæs D, Maximov II, Voldsbekk I, Richard G, Sanders A, Ulrichsen KM, Dørum ES, Kolskår KK, Høgestøl EA, Steen NE, Djurovic S, Andreassen OA, Nordvik JE, Kaufmann T, Westlye LT. Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Hum Brain Mapp 2022; 43:700-720. [PMID: 34626047 PMCID: PMC8720200 DOI: 10.1002/hbm.25680] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/02/2021] [Accepted: 09/25/2021] [Indexed: 11/17/2022] Open
Abstract
The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Ann‐Marie G. de Lange
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- LREN, Centre for Research in Neurosciences‐Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Mads L. Pedersen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Bjørknes CollegeOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Anne‐Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Kristine M. Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Erlend S. Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Einar A. Høgestøl
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Nils Eiel Steen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Srdjan Djurovic
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | | | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of Psychiatry and PsychotherapyUniversity of TübingenTubingenGermany
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
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Kourtidou-Papadeli C, Frantzidis CA, Bakirtzis C, Petridou A, Gilou S, Karkala A, Machairas I, Kantouris N, Nday CM, Dermitzakis EV, Bakas E, Mougios V, Bamidis PD, Vernikos J. Therapeutic Benefits of Short-Arm Human Centrifugation in Multiple Sclerosis-A New Approach. Front Neurol 2022; 12:746832. [PMID: 35058870 PMCID: PMC8764123 DOI: 10.3389/fneur.2021.746832] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 12/03/2021] [Indexed: 12/16/2022] Open
Abstract
Short-arm human centrifugation (SAHC) is proposed as a robust countermeasure to treat deconditioning and prevent progressive disability in a case of secondary progressive multiple sclerosis. Based on long-term physiological knowledge derived from space medicine and missions, artificial gravity training seems to be a promising physical rehabilitation approach toward the prevention of musculoskeletal decrement due to confinement and inactivity. So, the present study proposes a novel infrastructure based on SAHC to investigate the hypothesis that artificial gravity ameliorates the degree of disability. The patient was submitted to a 4-week training programme including three weekly sessions of 30 min of intermittent centrifugation at 1.5–2 g. During sessions, cardiovascular, muscle oxygen saturation (SmO2) and electroencephalographic (EEG) responses were monitored, whereas neurological and physical performance tests were carried out before and after the intervention. Cardiovascular parameters improved in a way reminiscent of adaptations to aerobic exercise. SmO2 decreased during sessions concomitant with increased g load, and, as training progressed, SmO2 of the suffering limb dropped, both effects suggesting increased oxygen use, similar to that seen during hard exercise. EEG showed increased slow and decreased fast brain waves, with brain reorganization/plasticity evidenced through functional connectivity alterations. Multiple-sclerosis-related disability and balance capacity also improved. Overall, this study provides novel evidence supporting SAHC as a promising therapeutic strategy in multiple sclerosis, based on mechanical loading, thereby setting the basis for future randomized controlled trials.
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Affiliation(s)
- Chrysoula Kourtidou-Papadeli
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece.,Laboratory of Aerospace and Rehabilitation Applications "Joan Vernikos", AROGI Rehabilitation Centre, Thessaloniki, Greece.,Aeromedical Center of Thessaloniki (AeMC), Thessaloniki, Greece
| | - Christos A Frantzidis
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Christos Bakirtzis
- Department of Neurology, Multiple Sclerosis Center, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anatoli Petridou
- Laboratory of Evaluation of Human Biological Performance, School of Physical Education and Sport Science at Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sotiria Gilou
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aliki Karkala
- Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Ilias Machairas
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Kantouris
- Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Christiane M Nday
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Eleftherios Bakas
- Laboratory of Aerospace and Rehabilitation Applications "Joan Vernikos", AROGI Rehabilitation Centre, Thessaloniki, Greece
| | - Vassilis Mougios
- Laboratory of Evaluation of Human Biological Performance, School of Physical Education and Sport Science at Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Biomedical Engineering and Aerospace Neuroscience (BEAN), Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Joan Vernikos
- Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece.,Thirdage LLC, Culpeper, VA, United States
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40
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Guo Y, Yang X, Yuan Z, Qiu J, Lu W. A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches. J Neural Eng 2022; 19. [PMID: 35038689 DOI: 10.1088/1741-2552/ac4bfe] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/17/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain. APPROACH Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively. MAIN RESULTS Age predictions using FA features were more accurate than predictions using QA features for all the 6 machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with MAE ranging from 7.74 to 10.54, MSE ranging from 87.79 to 150.86, and nMSE ranging from 0.05 to 0.14 Significance: FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.
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Affiliation(s)
- Yingying Guo
- Department of Radiology, Shandong First Medical University, No.619 Changcheng Road, Jinan, Shandong, 250000, CHINA
| | - Xi Yang
- Pennsylvania State University, Department of Mathematics, The Pennsylvania State University, University Park, PA, 16801, USA, State College, Pennsylvania, 16801, UNITED STATES
| | - Zilong Yuan
- Hubei Cancer Hospital, No. 116 South Zhuodaoquan Road, Wuhan, Hubei, 430079, CHINA
| | - Jianfeng Qiu
- Shandong Medical University, No. 6699 Qingdao Road, Jinan, 250100, CHINA
| | - Weizhao Lu
- Department of Radiology, Taishan Medical University, No.619 Changcheng Road, Taian, Shandong, 271016, CHINA
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41
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Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, Dalca AV, Eickhoff S, Fox M, Franke L, Golland P, Haehn D, Iglesias JE, O’Donnell LJ, Ou Y, Rathi Y, Siddiqi SH, Sun H, Westover MB, Whitfield-Gabrieli S, Gollub RL. How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics 2022; 20:943-964. [PMID: 35347570 PMCID: PMC9515245 DOI: 10.1007/s12021-022-09572-9] [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] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
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Affiliation(s)
- Nalini M. Singh
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordan B. Harrod
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Sandya Subramanian
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Mitchell Robinson
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Ken Chang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | | | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital and Harvard Medical School, 02115 Boston, USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, London, UK ,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114 USA ,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, MA 02115 Boston, USA
| | - Yangming Ou
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Shan H. Siddiqi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Haoqi Sun
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | - M. Brandon Westover
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | | | - Randy L. Gollub
- Department of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114 USA
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42
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Brusini I, MacNicol E, Kim E, Smedby Ö, Wang C, Westman E, Veronese M, Turkheimer F, Cash D. MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention. Neurobiol Aging 2022; 109:204-215. [PMID: 34775211 DOI: 10.1016/j.neurobiolaging.2021.10.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022]
Abstract
The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.
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Affiliation(s)
- Irene Brusini
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden; Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Örjan Smedby
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chunliang Wang
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Information Engineering, University of Padua, Padua, Italy
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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43
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Jawinski P, Markett S, Drewelies J, Düzel S, Demuth I, Steinhagen-Thiessen E, Wagner GG, Gerstorf D, Lindenberger U, Gaser C, Kühn S. Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II. Front Aging Neurosci 2022; 14:791222. [PMID: 35936763 PMCID: PMC9355695 DOI: 10.3389/fnagi.2022.791222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.
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Affiliation(s)
- Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Johanna Drewelies
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Ilja Demuth
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BCRT-Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
| | - Elisabeth Steinhagen-Thiessen
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gert G Wagner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany.,Federal Institute for Population Research (BiB), Berlin, Germany
| | - Denis Gerstorf
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Psychiatry and Neurology, Jena University Hospital, Jena, Germany
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg Eppendorf, Hamburg, Germany
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44
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Kokošová V, Filip P, Kec D, Baláž M. Bidirectional Association Between Sleep and Brain Atrophy in Aging. Front Aging Neurosci 2021; 13:726662. [PMID: 34955805 PMCID: PMC8693777 DOI: 10.3389/fnagi.2021.726662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 10/29/2021] [Indexed: 11/23/2022] Open
Abstract
Human brain aging is characterized by the gradual deterioration of its function and structure, affected by the interplay of a multitude of causal factors. The sleep, a periodically repeating state of reversible unconsciousness characterized by distinct electrical brain activity, is crucial for maintaining brain homeostasis. Indeed, insufficient sleep was associated with accelerated brain atrophy and impaired brain functional connectivity. Concurrently, alteration of sleep-related transient electrical events in senescence was correlated with structural and functional deterioration of brain regions responsible for their generation, implying the interconnectedness of sleep and brain structure. This review discusses currently available data on the link between human brain aging and sleep derived from various neuroimaging and neurophysiological methods. We advocate the notion of a mutual relationship between the sleep structure and age-related alterations of functional and structural brain integrity, pointing out the position of high-quality sleep as a potent preventive factor of early brain aging and neurodegeneration. However, further studies are needed to reveal the causality of the relationship between sleep and brain aging.
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Affiliation(s)
- Viktória Kokošová
- Department of Neurology, Faculty of Medicine, University Hospital Brno and Masaryk University, Brno, Czechia
| | - Pavel Filip
- Department of Neurology, First Faculty of Medicine, General University Hospital Prague and Charles University, Prague, Czechia.,Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States
| | - David Kec
- Department of Neurology, Faculty of Medicine, University Hospital Brno and Masaryk University, Brno, Czechia
| | - Marek Baláž
- First Department of Neurology, Faculty of Medicine, University Hospital of St. Anne and Masaryk University, Brno, Czechia
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45
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Ledesma S, Ibarra-Manzano MA, Almanza-Ojeda DL, Fallavollita P, Steffener J. Artificial Intelligence to Analyze the Cortical Thickness Through Age. Front Artif Intell 2021; 4:549255. [PMID: 34723171 PMCID: PMC8548778 DOI: 10.3389/frai.2021.549255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/30/2021] [Indexed: 11/30/2022] Open
Abstract
In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.
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Affiliation(s)
- Sergio Ledesma
- Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.,School of Engineering, University of Guanajuato, Guanajuato, Mexico
| | | | | | | | - Jason Steffener
- Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
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46
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Basodi S, Raja R, Ray B, Gazula H, Liu J, Verner E, Calhoun VD. Federation of Brain Age Estimation in Structural Neuroimaging Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3854-3857. [PMID: 34892075 DOI: 10.1109/embc46164.2021.9629865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain age estimation is a widely used approach to evaluate the impact of various neurological or psychiatric brain disorders on the brain developmental or aging process. Current studies show that neuroimaging data can be used to predict brain age, as it captures structural and functional changes that the brain undergoes during development and the aging process. A robust brain age prediction model not only has the potential in assisting early diagnosis of brain disorders but also helps in monitoring and evaluating effects of a treatment. Although access to large amounts of data helps build better models and validate their effectiveness, researchers often have limited access to brain data because of its challenging and expensive acquisition process. This data is not always sharable due to privacy restrictions. Decentralized models provide a way which does not require data exchange between the multiple involved groups. In this work, we propose a decentralized approach for brain age prediction and evaluate our models using features extracted from structural MRI data. Results demonstrate that our decentralized brain age model achieves similar performance compared to the models trained with all the data in one location.
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47
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Ballester PL, Suh JS, Nogovitsyn N, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Frey BN. Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report. NEUROIMAGE-CLINICAL 2021; 32:102864. [PMID: 34710675 PMCID: PMC8556529 DOI: 10.1016/j.nicl.2021.102864] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response. METHODS Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data. RESULTS Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD. CONCLUSION We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Nikita Nogovitsyn
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, ON, Canada
| | | | - Luciano Minuzzi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Roberto B Sassi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, and Providence Care, Kingston, ON, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Valerie H Taylor
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Benicio N Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
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48
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Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 2021; 72:103600. [PMID: 34614461 PMCID: PMC8498228 DOI: 10.1016/j.ebiom.2021.103600] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
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Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Department of General Psychology, University of Padua, Italy
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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49
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Yang CY, Pan YJ, Chou Y, Yang CJ, Kao CC, Huang KC, Chang JS, Chen HC, Kuo KH. Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs. J Clin Med 2021; 10:jcm10194431. [PMID: 34640449 PMCID: PMC8509558 DOI: 10.3390/jcm10194431] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 11/18/2022] Open
Abstract
Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs.
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Affiliation(s)
- Chung-Yi Yang
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan;
- Department of Medical Imaging, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Yi-Ju Pan
- Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City 22060, Taiwan;
- Institute of Public Health, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei 11267, Taiwan
| | - Yen Chou
- Division of Medical Image, Far Eastern Memorial Hospital, New Taipei City 22060, Taiwan;
| | - Chia-Jung Yang
- Department of Radiology, Taitung MacKay Memorial Hospital, Taitung 95054, Taiwan;
| | - Ching-Chung Kao
- AI Lab, Quanta Computer Inc., Taoyuan City 33377, Taiwan; (C.-C.K.); (K.-C.H.); (J.-S.C.)
| | - Kuan-Chieh Huang
- AI Lab, Quanta Computer Inc., Taoyuan City 33377, Taiwan; (C.-C.K.); (K.-C.H.); (J.-S.C.)
| | - Jing-Shan Chang
- AI Lab, Quanta Computer Inc., Taoyuan City 33377, Taiwan; (C.-C.K.); (K.-C.H.); (J.-S.C.)
| | - Hung-Chieh Chen
- School of Medicine, National Yang-Ming Chiao-Tung University, Taipei 11267, Taiwan
- Department of Radiology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Correspondence: (H.-C.C.); (K.-H.K.); Tel.: +886-4-23592525 (H.-C.C.); +886-(02)-7728-1264 (K.-H.K.); Fax: +886-4-2359-0296 (H.-C.C.); +886-(02)-8966-5567 (K.-H.K.)
| | - Kuei-Hong Kuo
- Division of Medical Image, Far Eastern Memorial Hospital, New Taipei City 22060, Taiwan;
- School of Medicine, National Yang-Ming Chiao-Tung University, Taipei 11267, Taiwan
- Correspondence: (H.-C.C.); (K.-H.K.); Tel.: +886-4-23592525 (H.-C.C.); +886-(02)-7728-1264 (K.-H.K.); Fax: +886-4-2359-0296 (H.-C.C.); +886-(02)-8966-5567 (K.-H.K.)
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Steffener J, Al Aïn S, Frasnelli J. Quantifying neural efficiency and neural capacity across the brain during a working memory task using a quadratic model of neural-cognitive relationships. Eur J Neurosci 2021; 54:7176-7192. [PMID: 34555233 DOI: 10.1111/ejn.15473] [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: 03/03/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 11/30/2022]
Abstract
Task-based functional magnetic resonance imaging (MRI) neuroimaging for the study of cognitive neuroscience provides insight into how the brain responds to increasing cognitive demands. Theoretical models of neural-cognitive relationships define neural efficiency and neural capacity as two parameters to describe the rate of gain of brain activity and the upper limits of the increases, respectively. Although qualitatively well described, there are limited discussions and demonstrations of estimating these parameters from data. The current work presents a method using a quadratic equation model of cognitive demands. The methods were applied to 21 healthy young adults while engaging in four levels of the verbal n-back working memory task (0, 1, 2 and 3-back). Measures of the area under the curve captured both linear and curvilinear responses to quantify the locations and amounts of task-related increases in brain activity. In addition, we calculated voxel-wise maps of neural efficiency and neural capacity. Results showed increases in brain activation in regions typically involved in working memory. The relationships between increasing cognitive demands and brain activity also differed across the brain within and between individuals, as indicated by differential neural efficiency and neural capacity values. This approach provides tools for evaluating current theories about the relationships between brain activity and cognition. Neural efficiency and neural capacity provide physiological measures of brain activity, which will help evaluate how age, disease and intervention processes affect brain activity. Finally, all presented methods are readily applicable using standard current software packages for neuroimaging.
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Affiliation(s)
- Jason Steffener
- Interdisciplinary School of Health Science, University of Ottawa, Ottawa, Ontario, Canada
| | - Syrina Al Aïn
- Department of Anatomy, University of Quebec in Trois-Rivières, Trois-Rivières, Quebec, Canada
| | - Johannes Frasnelli
- Department of Anatomy, University of Quebec in Trois-Rivières, Trois-Rivières, Quebec, Canada
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