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Vakli P, Weiss B, Rozmann D, Erőss G, Nárai Á, Hermann P, Vidnyánszky Z. The effect of head motion on brain age prediction using deep convolutional neural networks. Neuroimage 2024; 294:120646. [PMID: 38750907 DOI: 10.1016/j.neuroimage.2024.120646] [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: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/23/2024] Open
Abstract
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest 1034, Hungary.
| | - Dorina Rozmann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - György Erőss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs 7624, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
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2
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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [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: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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3
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Feng L, Ye Z, Du Z, Pan Y, Canida T, Ke H, Liu S, Chen S, Hong LE, Kochunov P, Chen J, Lei DK, Shenassa E, Ma T. Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.26.24301793. [PMID: 38343822 PMCID: PMC10854327 DOI: 10.1101/2024.01.26.24301793] [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: 02/19/2024]
Abstract
White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.
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Affiliation(s)
- Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zewen Du
- Department of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Travis Canida
- Department of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - L. Elliot Hong
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Peter Kochunov
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Jie Chen
- Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - David K.Y. Lei
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
| | - Edmond Shenassa
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Maternal & Child Health Program, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology, School of Public Health, Brown University, Rhode Island, United States of America
- Department of Epidemiology & Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
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Chattopadhyay T, Joshy NA, Ozarkar SS, Buwa KS, Feng Y, Laltoo E, Thomopoulos SI, Villalon-Reina JE, Joshi H, Venkatasubramanian G, John JP, Thompson PM. Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578829. [PMID: 38370641 PMCID: PMC10871286 DOI: 10.1101/2024.02.04.578829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.
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5
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Valdes-Hernandez PA, Johnson AJ, Montesino-Goicolea S, Laffitte Nodarse C, Bashyam V, Davatzikos C, Fillingim RB, Cruz-Almeida Y. Accelerated Brain Aging Mediates the Association Between Psychological Profiles and Clinical Pain in Knee Osteoarthritis. THE JOURNAL OF PAIN 2024; 25:104423. [PMID: 37952863 DOI: 10.1016/j.jpain.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/12/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Chronic pain is driven by factors across the biopsychosocial spectrum. Previously, we demonstrated that magnetic resonance images (MRI)-based brain-predicted age differences (brain-PAD: brain-predicted age minus chronological age) were significantly associated with pain severity in individuals with chronic knee pain. We also previously identified four distinct, replicable, multidimensional psychological profiles significantly associated with clinical pain. The brain aging-psychological characteristics interface in persons with chronic pain promises elucidating factors contributing to their poor health outcomes, yet this relationship is barely understood. That is why we examined the interplay between the psychological profiles in participants having chronic knee pain impacting function, brain-PAD, and clinical pain severity. Controlling for demographics and MRI scanner, we compared the brain-PAD among psychological profiles at baseline (n = 164) and over two years (n = 90). We also explored whether profile-related differences in pain severity were mediated by brain-PAD. Brain-PAD differed significantly between profiles (ANOVA's omnibus test, P = .039). Specifically, participants in the profile 3 group (high negative/low positive emotions) had an average brain-PAD ∼4 years higher than those in profile- (low somatic reactivity), with P = .047, Bonferroni-corrected, and than those in profile 2 (high coping), with P = .027, uncorrected. Repeated measures ANOVA revealed no significant change in profile-related brain-PAD differences over time, but there was a significant decrease in brain-PAD for profile 4 (high optimism/high positive affect), with P = .045. Moreover, profile-related differences in pain severity at baseline were partly explained by brain-PAD differences between profile 3 and 1, or 2; but brain-PAD did not significantly mediate the influence of variations in profiles on changes in pain severity over time. PERSPECTIVE: Accelerated brain aging could underlie the psychological-pain relationship, and psychological characteristics may predispose individuals with chronic knee pain to worse health outcomes via neuropsychological processes.
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Affiliation(s)
- Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida
| | - Alisa J Johnson
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida
| | - Soamy Montesino-Goicolea
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida
| | - Vishnu Bashyam
- Center for Biomedical Image Computing & Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Artificial Intelligence in Biomedical Imaging Lab (AIBIL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Center for Biomedical Image Computing & Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Roger B Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, Florida
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6
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Pisanu C, Congiu D, Meloni A, Paribello P, Patrinos GP, Severino G, Ardau R, Chillotti C, Manchia M, Squassina A. Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets. Neuropsychopharmacology 2024; 49:1033-1041. [PMID: 38402365 DOI: 10.1038/s41386-024-01822-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/17/2024] [Accepted: 02/04/2024] [Indexed: 02/26/2024]
Abstract
Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate method to detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.
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Affiliation(s)
- Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.
| | - Donatella Congiu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Anna Meloni
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Pasquale Paribello
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, School of Health Sciences, Department of Pharmacy, University of Patras, Patras, Greece
- College of Medicine and Health Sciences, Department of Genetics and Genomics, United Arab Emirates University, Al‑Ain, Abu Dhabi, UAE
- Zayed Center for Health Sciences, United Arab Emirates University, Al‑Ain, Abu Dhabi, UAE
| | - Giovanni Severino
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.
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7
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Ho NCW, Bethlehem RA, Seidlitz J, Nogovitsyn N, Metzak P, Ballester PL, Hassel S, Rotzinger S, Poppenk J, Lam RW, Taylor VH, Milev R, Bullmore ET, Alexander-Bloch AF, Frey BN, Harkness KL, Addington J, Kennedy SH, Dunlop K. Atypical brain aging and its association with working memory performance in major depressive disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00110-1. [PMID: 38679324 DOI: 10.1016/j.bpsc.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. METHODS 304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. RESULTS Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.
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Affiliation(s)
- Natalie C W Ho
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Canada; Faculty of Arts and Sciences, University of Toronto, Toronto, Canada
| | | | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, USA; Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, USA; Institute of Translational Medicine & Therapeutics, University of Pennsylvania, Philadelphia, USA
| | - Nikita Nogovitsyn
- Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Paul Metzak
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Pedro L Ballester
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada
| | - Stefanie Hassel
- Department of Psychiatry, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Susan Rotzinger
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Mood Disorders Treatment and Research Centre, St. Joseph's Healthcare, Hamilton, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Jordan Poppenk
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada; Department of Psychology, Queen's University, Kingston, Canada; School of Computing, Queen's University, Kingston, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Valerie H Taylor
- Department of Psychiatry, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Roumen Milev
- Department of Psychology, Queen's University, Kingston, Canada; Department of Psychiatry, Queen's University, Kingston, Canada; Providence Care Hospital, Kingston, Canada
| | | | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, USA; Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, USA; Institute of Translational Medicine & Therapeutics, University of Pennsylvania, Philadelphia, USA
| | - Benicio N Frey
- Mood Disorders Treatment and Research Centre, St. Joseph's Healthcare, Hamilton, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Kate L Harkness
- Department of Psychology, Queen's University, Kingston, Canada; Department of Psychiatry, Queen's University, Kingston, Canada
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Sidney H Kennedy
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Katharine Dunlop
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
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8
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Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590811. [PMID: 38712238 PMCID: PMC11071351 DOI: 10.1101/2024.04.23.590811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.
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9
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Sorooshyari SK. Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI. Neuroimage 2024; 290:120570. [PMID: 38467344 DOI: 10.1016/j.neuroimage.2024.120570] [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: 11/20/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024] Open
Abstract
The brain is a complex, dynamic organ that shows differences in the same subject at various periods. Understanding how brain activity changes across age as a function of the brain networks has been greatly abetted by fMRI. Canonical analysis consists of determining how alterations in connectivity patterns (CPs) of certain regions are affected. An alternative approach is taken here by not considering connectivity but rather features computed from recordings at the regions of interest (ROIs). Using machine learning (ML) we assess how neural signals are altered by and prospectively predictive of age and sex via a methodology that is novel in drawing upon pairwise classification across six decades of subjects' chronological ages. ML is used to answer the equally important questions of what properties of the computed features are most predictive as well as which brain networks are most affected by aging. It was found that there is decreased differentiation among the neural signals of older subjects that are separated in age by the same number of years as younger subjects. Furthermore, the burstiness of the signals change at different rates between males and females. The findings provide insight into brain aging via an ROI-based analysis, the consideration of several feature groups, and a novel classification-based ML pipeline. There is also a contribution to understanding the effects of data aggregated from different recording centers on the conclusions of fMRI studies.
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10
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Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [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: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
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11
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Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
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Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
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12
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Wing D, Eyler LT, Lenze EJ, Wetherell JL, Nichols JF, Meeusen R, Godino J, Shimony JS, Snyder AZ, Nishino T, Nicol GE, Nagels G, Roelands B. Fatness but Not Fitness Linked to BrainAge: Longitudinal Changes in Brain Aging during an Exercise Intervention. Med Sci Sports Exerc 2024; 56:655-662. [PMID: 38079309 PMCID: PMC10947938 DOI: 10.1249/mss.0000000000003337] [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: 03/16/2024]
Abstract
PURPOSE Fitness, physical activity, body composition, and sleep have all been proposed to explain differences in brain health. We hypothesized that an exercise intervention would result in improved fitness and body composition and would be associated with improved structural brain health. METHODS In a randomized controlled trial, we studied 485 older adults who engaged in an exercise intervention ( n = 225) or a nonexercise comparison condition ( n = 260). Using magnetic resonance imaging, we estimated the physiological age of the brain (BrainAge) and derived a predicted age difference compared with chronological age (brain-predicted age difference (BrainPAD)). Aerobic capacity, physical activity, sleep, and body composition were assessed and their impact on BrainPAD explored. RESULTS There were no significant differences between experimental groups for any variable at any time point. The intervention group gained fitness, improved body composition, and increased total sleep time but did not have significant changes in BrainPAD. Analyses of changes in BrainPAD independent of group assignment indicated significant associations with changes in body fat percentage ( r (479) = 0.154, P = 0.001), and visceral adipose tissue (VAT) ( r (478) = 0.141, P = 0.002), but not fitness ( r (406) = -0.075, P = 0.129), sleep ( r (467) range, -0.017 to 0.063; P range, 0.171 to 0.710), or physical activity ( r (471) = -0.035, P = 0.444). With linear regression, changes in body fat percentage and VAT significantly predicted changes in BrainPAD ( β = 0.948, P = 0.003) with 1-kg change in VAT predicting 0.948 yr of change in BrainPAD. CONCLUSIONS In cognitively normal older adults, exercise did not appear to impact BrainPAD, although it was effective in improving fitness and body composition. Changes in body composition, but not fitness, physical activity, or sleep impacted BrainPAD. These findings suggest that focus on weight control, particularly reduction of central obesity, could be an interventional target to promote healthier brains.
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Affiliation(s)
- David Wing
- Herbert Wertheim School of Public Health; University of California, San Diego, CA
- Exercise and Physical Activity Resource Center (EPARC); University of California, San Diego, CA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California, San Diego, CA
- Desert-Pacific Mental Illness Research, Education, and Clinical Center, San Diego Veterans Administration Healthcare System, San Diego, CA
| | - Eric J. Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Julie Loebach Wetherell
- Mental Health Service, VA San Diego Healthcare System, San Diego, CA
- Department of Psychiatry, University of California, San Diego, CA
| | - Jeanne F. Nichols
- Herbert Wertheim School of Public Health; University of California, San Diego, CA
- Exercise and Physical Activity Resource Center (EPARC); University of California, San Diego, CA
| | - Romain Meeusen
- Human Physiology & Sports Physiotherapy Research Group, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, BELGIUM
- Brubotics, Vrije Universiteit Brussel, Brussels, BELGIUM
| | - Job Godino
- Herbert Wertheim School of Public Health; University of California, San Diego, CA
- Exercise and Physical Activity Resource Center (EPARC); University of California, San Diego, CA
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Ginger E. Nicol
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Guy Nagels
- Department of Neurology, UZ Brussel, Brussel, 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
- Brubotics, Vrije Universiteit Brussel, Brussels, BELGIUM
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13
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Chen R, Zhang S, Peng G, Meng W, Borchert G, Wang W, Yu Z, Liao H, Ge Z, He M, Zhu Z. Deep neural network-estimated age using optical coherence tomography predicts mortality. GeroScience 2024; 46:1703-1711. [PMID: 37733221 PMCID: PMC10828229 DOI: 10.1007/s11357-023-00920-4] [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: 04/04/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.
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Affiliation(s)
- Ruiye Chen
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Guankai Peng
- Guangzhou Vision Tech Medical Technology Co., Ltd, GuangZhou, China
| | - Wei Meng
- Guangzhou Vision Tech Medical Technology Co., Ltd, GuangZhou, China
| | - Grace Borchert
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Zhen Yu
- Central Clinical School, Monash University, Melbourne, Australia
| | - Huan Liao
- Epigenetics and Neural Plasticity Laboratory, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Zongyuan Ge
- Faculty of IT, Monash University, Melbourne, Australia
- Monash Medical AI, Monash University, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
| | - Zhuoting Zhu
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
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14
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Chang JR, Yao ZF, Hsieh S, Nordling TEM. Age Prediction Using Resting-State Functional MRI. Neuroinformatics 2024; 22:119-134. [PMID: 38341830 DOI: 10.1007/s12021-024-09653-x] [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] [Accepted: 12/21/2023] [Indexed: 02/13/2024]
Abstract
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
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Affiliation(s)
- Jose Ramon Chang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Zai-Fu Yao
- College of Education, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Department of Kinesiology, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Basic Psychology Group, Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Department of Public Health, College of Medicine, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Torbjörn E M Nordling
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
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15
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Iyer KK, Roberts JA, Waak M, Vogrin SJ, Kevat A, Chawla J, Haataja LM, Lauronen L, Vanhatalo S, Stevenson NJ. A growth chart of brain function from infancy to adolescence based on EEG. EBioMedicine 2024; 102:105061. [PMID: 38537603 PMCID: PMC11026939 DOI: 10.1016/j.ebiom.2024.105061] [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: 07/28/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.
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Affiliation(s)
- Kartik K Iyer
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Brisbane, Australia.
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Michaela Waak
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | | | - Ajay Kevat
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Jasneek Chawla
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Leena M Haataja
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Leena Lauronen
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
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16
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Seitz-Holland J, Haas SS, Penzel N, Reichenberg A, Pasternak O. BrainAGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev 2024; 159:105581. [PMID: 38354871 PMCID: PMC11119273 DOI: 10.1016/j.neubiorev.2024.105581] [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: 11/09/2023] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024]
Abstract
The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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17
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van der Markt A, Klumpers U, Dols A, Korten N, Boks MP, Ophoff RA, Beekman A, Kupka R, van Haren NEM, Schnack H. Accelerated brain aging as a biomarker for staging in bipolar disorder: an exploratory study. Psychol Med 2024; 54:1016-1025. [PMID: 37749940 DOI: 10.1017/s0033291723002829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
BACKGROUND Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging. METHODS In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored. RESULTS A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages. CONCLUSIONS These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.
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Affiliation(s)
- Afra van der Markt
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ursula Klumpers
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
| | - Annemiek Dols
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Nicole Korten
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
| | - Marco P Boks
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Roel A Ophoff
- Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aartjan Beekman
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ralph Kupka
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
- Erasmus Medical Center - Sophia, Child and Adolescent Psychiatry and Psychology, Rotterdam, The Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
- Department of Languages, Literature and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
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18
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Phyo AZZ, Fransquet PD, Wrigglesworth J, Woods RL, Espinoza SE, Ryan J. Sex differences in biological aging and the association with clinical measures in older adults. GeroScience 2024; 46:1775-1788. [PMID: 37747619 PMCID: PMC10828143 DOI: 10.1007/s11357-023-00941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023] Open
Abstract
Females live longer than males, and there are sex disparities in physical health and disease incidence. However, sex differences in biological aging have not been consistently reported and may differ depending on the measure used. This study aimed to determine the correlations between epigenetic age acceleration (AA), and other markers of biological aging, separately in males and females. We additionally explored the extent to which these AA measures differed according to socioeconomic characteristics, clinical markers, and diseases. Epigenetic clocks (HorvathAge, HannumAge, PhenoAge, GrimAge, GrimAge2, and DunedinPACE) were estimated in blood from 560 relatively healthy Australians aged ≥ 70 years (females, 50.7%) enrolled in the ASPREE study. A system-wide deficit accumulation frailty index (FI) composed of 67 health-related measures was generated. Brain age and subsequently brain-predicted age difference (brain-PAD) were estimated from neuroimaging. Females had significantly reduced AA than males, but higher FI, and there was no difference in brain-PAD. FI had the strongest correlation with DunedinPACE (range r: 0.21 to 0.24 in both sexes). Brain-PAD was not correlated with any biological aging measures. Significant correlations between AA and sociodemographic characteristics and health markers were more commonly found in females (e.g., for DunedinPACE and systolic blood pressure r = 0.2, p < 0.001) than in males. GrimAA and Grim2AA were significantly associated with obesity and depression in females, while in males, hypertension, diabetes, and chronic kidney disease were associated with these clocks, as well as DunedinPACE. Our findings highlight the importance of considering sex differences when investigating the link between biological age and clinical measures.
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Affiliation(s)
- Aung Zaw Zaw Phyo
- Biological Neuropsychiatry & Dementia Unit, School of Public Health and Preventive Medicine, Monash University, 553, St. Kilda Road, Melbourne, VIC, 3004, Australia.
| | - Peter D Fransquet
- Biological Neuropsychiatry & Dementia Unit, School of Public Health and Preventive Medicine, Monash University, 553, St. Kilda Road, Melbourne, VIC, 3004, Australia
- School of Psychology, Deakin University, Burwood, Melbourne, VIC, 3125, Australia
| | - Jo Wrigglesworth
- Biological Neuropsychiatry & Dementia Unit, School of Public Health and Preventive Medicine, Monash University, 553, St. Kilda Road, Melbourne, VIC, 3004, Australia
| | - Robyn L Woods
- ASPREE Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Sara E Espinoza
- Center for Translational Geroscience, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joanne Ryan
- Biological Neuropsychiatry & Dementia Unit, School of Public Health and Preventive Medicine, Monash University, 553, St. Kilda Road, Melbourne, VIC, 3004, Australia
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19
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Montagnese M, Rittman T. Bridging modifiable risk factors and cognitive decline: the mediating role of brain age. THE LANCET. HEALTHY LONGEVITY 2024; 5:e243-e244. [PMID: 38555918 DOI: 10.1016/s2666-7568(24)00042-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2024] Open
Affiliation(s)
- Marcella Montagnese
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK.
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK
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20
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Lv H, Zeng N, Li M, Sun J, Wu N, Xu M, Chen Q, Zhao X, Chen S, Liu W, Li X, Zhao P, Wintermark M, Hui Y, Li J, Wu S, Wang Z. Association between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study. HEALTH DATA SCIENCE 2024; 4:0087. [PMID: 38500551 PMCID: PMC10944701 DOI: 10.34133/hds.0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/26/2024] [Indexed: 03/20/2024]
Abstract
Background: The cumulative effect of body mass index (BMI) on brain health remains ill-defined. The effects of overweight on brain health across different age groups need clarification. We analyzed the effect of cumulative BMI on neuroimaging features of brain health in adults of different ages. Methods: This study was based on a multicenter, community-based cohort study. We modeled the trajectories of BMI over 16 years to evaluate cumulative exposure. Multimodality neuroimaging data were collected once for volumetric measurements of the brain macrostructure, white matter hyperintensity (WMH), and brain microstructure. We used a generalized linear model to evaluate the association between cumulative BMI and neuroimaging features. Two-sample Mendelian randomization analysis was performed using summary level of BMI genetic data from 681,275 individuals and neuroimaging genetic data from 33,224 individuals to analyze the causal relationships. Results: Clinical and neuroimaging data were obtained from 1,074 adults (25 to 83 years). For adults aged under 45 years, brain volume differences in participants with a cumulative BMI of >26.2 kg/m2 corresponded to 12.0 years [95% confidence interval (CI), 3.0 to 20.0] of brain aging. Differences in WMH were statistically substantial for participants aged over 60 years, with a 6.0-ml (95% CI, 1.5 to 10.5) larger volume. Genetic analysis indicated causal relationships between high BMI and smaller gray matter and higher fractional anisotropy in projection fibers. Conclusion: High cumulative BMI is associated with smaller brain volume, larger volume of white matter lesions, and abnormal microstructural integrity. Adults younger than 45 years are suggested to maintain their BMI below 26.2 kg/m2 for better brain health. Trial Registration: This study was registered on clinicaltrials.gov (Clinical Indicators and Brain Image Data: A Cohort Study Based on Kailuan Cohort; No. NCT05453877; https://clinicaltrials.gov/ct2/show/NCT05453877).
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Affiliation(s)
- Han Lv
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Na Zeng
- Peking University School of Public Health, Beijing 100191, China
| | - Mengyi Li
- Department of General Surgery, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Ning Wu
- Department of Medical Imaging Technology,
Capital Medical University Yanjing College, Beijing 101300, China
| | - Mingze Xu
- Center for MRI Research,
Peking University Academy for Advanced Interdisciplinary Studies, Beijing 100871, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Xinyu Zhao
- Clinical Epidemiology and Evidence-based Medicine Unit, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, Hebei, Tangshan 063000, China
| | - Wenjuan Liu
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Xiaoshuai Li
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
| | - Max Wintermark
- Department of Neuroradiology,
The University of Texas MD Anderson Cancer Center, Houston, TX 78701, USA
| | - Ying Hui
- Department of Radiology, Kailuan General Hospital, Hebei, Tangshan 063000, China
| | - Jing Li
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine,
Tsinghua University, Beijing, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Hebei, Tangshan 063000, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital,
Capital Medical University, Beijing 100050, China
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21
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Lim H, Joo Y, Ha E, Song Y, Yoon S, Shin T. Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network. Bioengineering (Basel) 2024; 11:265. [PMID: 38534539 DOI: 10.3390/bioengineering11030265] [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: 01/29/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.
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Affiliation(s)
- Heejoo Lim
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Eunji Ha
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yumi Song
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Taehoon Shin
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
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22
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Casanova R, Walker KA, Justice JN, Anderson A, Duggan MR, Cordon J, Barnard RT, Lu L, Hsu FC, Sedaghat S, Prizment A, Kritchevsky SB, Wagenknecht LE, Hughes TM. Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort. GeroScience 2024:10.1007/s11357-024-01112-4. [PMID: 38438772 DOI: 10.1007/s11357-024-01112-4] [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: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA.
| | | | - Jamie N Justice
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Andrea Anderson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | | | | | - Ryan T Barnard
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Lingyi Lu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Sanaz Sedaghat
- School of Public Health, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, USA
| | - Anna Prizment
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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23
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Kamarajan C, Ardekani BA, Pandey AK, Meyers JL, Chorlian DB, Kinreich S, Pandey G, Richard C, de Viteri SS, Kuang W, Porjesz B. Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582844. [PMID: 38496639 PMCID: PMC10942318 DOI: 10.1101/2024.03.01.582844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Babak A. Ardekani
- Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Christian Richard
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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24
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Constantinides C, Baltramonaityte V, Caramaschi D, Han LKM, Lancaster TM, Zammit S, Freeman TP, Walton E. Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study. Cortex 2024; 172:1-13. [PMID: 38154374 DOI: 10.1016/j.cortex.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/22/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.
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Affiliation(s)
| | | | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia
| | | | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
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25
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Troeung L, Mann G, Martini A. Patterns and predictors of ten-year mortality after discharge from community-based post-acute care for acquired brain injury: A retrospective cohort study (ABI-RESTaRT), Western Australia, 1991-2017. Disabil Health J 2024:101591. [PMID: 38429203 DOI: 10.1016/j.dhjo.2024.101591] [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: 03/29/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Survivors of acquired brain injury (ABI) are left with long-term disability and an increased risk of mortality years post-injury. OBJECTIVE To examine 10-year mortality in adults with ABI after discharge from post-acute care and identify modifiable risk factors to reduce long-term mortality risk. METHODS Retrospective cohort study of 586 adults with traumatic (TBI) or non-traumatic brain injury (NTBI), or neurologic condition, consecutively discharged from a post-acute rehabilitation service in Western Australia from 1-Mar-1991 to 31-Dec-2017. Data sources included rehabilitation records, and linked mortality, hospital, and emergency department data. Survival status at 10 years post-discharge was determined. All-cause and cause-specific age- and sex-adjusted standardised mortality ratios (SMR) by ABI diagnosis were calculated using Australian population reference data. Risk factors were examined using multilevel cox proportional hazards regression. RESULTS Compared with the Australian population, 10-year all-cause mortality was significantly elevated for all diagnosis cohorts, with the first 12 months the highest risk period. Accidents or intentional self-harm deaths were elevated in TBI (13.2, 95%CI 5.4; 12.1). Neurodegenerative disease deaths were elevated in Neurologic (21.9, 95%CI 13.0; 30.9) and Stroke (19.8; 95%CI 2.4; 27.2) cohorts. Stroke (20.8; 95%CI 7.9; 33.8) and circulatory disease deaths (6.2; 95%CI 2.3; 9.9) were also elevated in Stroke. Psychiatric comorbidity was the strongest risk factor followed by older age, geographical remoteness, and cardiac, vascular, genitourinary and renal comorbidity. Clinically significant improvement in functional independence and psychosocial functioning significantly reduced mortality risk. CONCLUSIONS Individuals with ABI have an elevated risk of mortality years post-injury. Comorbidity management, continuity of care, and rehabilitation are important to reduce long-term mortality risk.
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Affiliation(s)
- Lakkhina Troeung
- Brightwater Research Centre, Brightwater Care Group, Inglewood, Western Australia, Australia.
| | - Georgina Mann
- Brightwater Research Centre, Brightwater Care Group, Inglewood, Western Australia, Australia; School of Psychological Science, The University of Western Australia, Crawley, Western Australia, Australia
| | - Angelita Martini
- Brightwater Research Centre, Brightwater Care Group, Inglewood, Western Australia, Australia
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26
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Kalc P, Dahnke R, Hoffstaedter F, Gaser C. BrainAGE: Revisited and reframed machine learning workflow. Hum Brain Mapp 2024; 45:e26632. [PMID: 38379519 PMCID: PMC10879910 DOI: 10.1002/hbm.26632] [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: 08/18/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
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Affiliation(s)
- Polona Kalc
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
| | - Robert Dahnke
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
| | - Felix Hoffstaedter
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)JülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG)Jena‐Halle‐MagdeburgGermany
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27
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Frenzel S, Bülow R, Dörr M, Felix SB, Friedrich N, Völzke H, Wittfeld K, Grabe HJ, Bahls M. Left ventricular hypertrophy as a risk factor for accelerated brain aging: Results from the Study of Health in Pomerania. Hum Brain Mapp 2024; 45:e26567. [PMID: 38391110 PMCID: PMC10885183 DOI: 10.1002/hbm.26567] [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: 08/28/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 02/24/2024] Open
Abstract
Previous studies provided evidence for the importance of cardiac structure abnormalities, in particular greater left ventricular (LV) mass, for brain aging, but longitudinal studies are lacking to date. We included 926 individuals (median age 48 years; 53% women) from the TREND cohort of the Study of Health in Pomerania (SHIP) without reduced ejection fraction or a history of myocardial infarction. LV mass index (LVMI) was determined by echocardiography at baseline. Brain morphometric measurements were derived from magnetic resonance images at baseline and 7-year follow-up. Direct effects of baseline LVMI on brain morphometry at follow-up were estimated using linear regression models with adjustment for baseline brain morphometry. At baseline, median LVMI was 40 g/m2.7 and 241 individuals (26%) met the criterion of LV hypertrophy. After correction for multiple testing, baseline LVMI was directly associated with reduced global cortical thickness and increased cortical brain age at follow-up independent from hypertension and blood pressure. Exposure-outcome relations were nonlinear and significantly stronger in the upper half of the exposure distribution. Specifically, an increase in baseline LVMI from the 50% quantile to the 95% quantile was associated additional 2.7 years (95% confidence interval = [1.5 years, 3.8 years]) of cortical brain age at follow-up. Additional regional analyses yielded bilateral effects on multiple frontal cortical regions. Our findings highlight the role of cardiac structure in brain aging. LVMI constitutes an easily measurable marker that might help to identify persons at risk for cognitive impairment and dementia.
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Affiliation(s)
- Stefan Frenzel
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Robin Bülow
- Institute of Diagnostic Radiology and NeuroradiologyUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Marcus Dörr
- Department of Internal Medicine BUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- German Centre for Cardiovascular Research (DZHK), Partner Site GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Stephan B. Felix
- Department of Internal Medicine BUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- German Centre for Cardiovascular Research (DZHK), Partner Site GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Nele Friedrich
- German Centre for Cardiovascular Research (DZHK), Partner Site GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- Institute of Clinical Chemistry and Laboratory MedicineUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Henry Völzke
- German Centre for Cardiovascular Research (DZHK), Partner Site GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- Institute for Community MedicineUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- German Center for Neurodegenerative Disease (DZNE), Partner Site Rostock/GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Hans J. Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- German Center for Neurodegenerative Disease (DZNE), Partner Site Rostock/GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
| | - Martin Bahls
- Department of Internal Medicine BUniversity Medicine GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
- German Centre for Cardiovascular Research (DZHK), Partner Site GreifswaldGreifswaldMecklenburg‐Western PomeraniaGermany
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Finkelstein O, Levakov G, Kaplan A, Zelicha H, Meir AY, Rinott E, Tsaban G, Witte AV, Blüher M, Stumvoll M, Shelef I, Shai I, Riklin Raviv T, Avidan G. Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention. Hum Brain Mapp 2024; 45:e26595. [PMID: 38375968 PMCID: PMC10878010 DOI: 10.1002/hbm.26595] [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: 05/01/2023] [Revised: 11/16/2023] [Accepted: 01/03/2024] [Indexed: 02/21/2024] Open
Abstract
Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.
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Affiliation(s)
- Ofek Finkelstein
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Gidon Levakov
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Alon Kaplan
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- The Chaim Sheba Medical Center, Tel HashomerRamat‐GanIsrael
| | - Hila Zelicha
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Anat Yaskolka Meir
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Ehud Rinott
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Gal Tsaban
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Soroka University Medical CenterBeer ShevaIsrael
| | - Anja Veronica Witte
- Department of Neurology, Max Planck‐Institute for Human Cognitive and Brain Sciences, and Cognitive NeurologyUniversity of Leipzig Medical CenterLeipzigGermany
| | | | | | - Ilan Shelef
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Soroka University Medical CenterBeer ShevaIsrael
| | - Iris Shai
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Department of Nutrition, Harvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Tammy Riklin Raviv
- The School of Electrical and Computer EngineeringBen Gurion University of the NegevBeer ShevaIsrael
| | - Galia Avidan
- Department of PsychologyBen‐Gurion University of the NegevBeer ShevaIsrael
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29
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Beheshti I, Booth S, Ko JH. Differences in brain aging between sexes in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:35. [PMID: 38355735 PMCID: PMC10866976 DOI: 10.1038/s41531-024-00646-w] [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: 07/06/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33-85, 34% female) from the Parkinson's Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson's disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having 'older' looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Samuel Booth
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada.
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada.
- Graduate Program in Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada.
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30
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Stolicyn A, Lyall LM, Lyall DM, Høier NK, Adams MJ, Shen X, Cole JH, McIntosh AM, Whalley HC, Smith DJ. Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort. Sleep 2024; 47:zsad274. [PMID: 37889226 PMCID: PMC10851840 DOI: 10.1093/sleep/zsad274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/22/2023] [Indexed: 10/28/2023] Open
Abstract
STUDY OBJECTIVES To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen. CONCLUSIONS Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Laura M Lyall
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Donald M Lyall
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Nikolaj Kjær Høier
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Copenhagen Research Center for Mental Health CORE, Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mark J Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - James H Cole
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Daniel J Smith
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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31
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [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: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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32
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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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33
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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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He Y, Li Z, Niu Y, Duan Y, Wang Q, Liu X, Dong Z, Zheng Y, Chen Y, Wang Y, Zhao D, Sun X, Cai G, Feng Z, Zhang W, Chen X. Progress in the study of aging marker criteria in human populations. Front Public Health 2024; 12:1305303. [PMID: 38327568 PMCID: PMC10847233 DOI: 10.3389/fpubh.2024.1305303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.
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Affiliation(s)
- Yan He
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Li
- The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yuting Duan
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Qian Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiaomin Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yizhi Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- Department of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Hainan Province Academician Team Innovation Center, Sanya, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Delong Zhao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Feng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiangmei Chen
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
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Gao C, Kim ME, Lee HH, Yang Q, Khairi NM, Kanakaraj P, Newlin NR, Archer DB, Jefferson AL, Taylor WD, Boyd BD, Beason-Held LL, Resnick SM, Huo Y, Van Schaik KD, Schilling KG, Moyer D, Išgum I, Landman BA. Predicting Age from White Matter Diffusivity with Residual Learning. ARXIV 2024:arXiv:2311.03500v2. [PMID: 37986731 PMCID: PMC10659451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
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Affiliation(s)
- Chenyu Gao
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Michael E Kim
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ho Hin Lee
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Qi Yang
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Nazirah Mohd Khairi
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | | | - Nancy R Newlin
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Warren D Taylor
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Brian D Boyd
- Vanderbilt Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Yuankai Huo
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Katherine D Van Schaik
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Kurt G Schilling
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Daniel Moyer
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ivana Išgum
- Dept. of Biomedical Engineering and Physics, Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bennett A Landman
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
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Wang J, Gao Y, Wang F, Zeng S, Li J, Miao H, Wang T, Zeng J, Baptista-Hon D, Monteiro O, Guan T, Cheng L, Lu Y, Luo Z, Li M, Zhu JK, Nie S, Zhang K, Zhou Y. Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system. Proc Natl Acad Sci U S A 2024; 121:e2308812120. [PMID: 38190540 PMCID: PMC10801873 DOI: 10.1073/pnas.2308812120] [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: 06/09/2023] [Accepted: 10/12/2023] [Indexed: 01/10/2024] Open
Abstract
Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.
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Affiliation(s)
- Jinzhuo Wang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Yuanxu Gao
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Fangfei Wang
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Simiao Zeng
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jiahui Li
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Hanpei Miao
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Taorui Wang
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jin Zeng
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Daniel Baptista-Hon
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Olivia Monteiro
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Taihua Guan
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Linling Cheng
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Yuxing Lu
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Zhengchao Luo
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Ming Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou325027, China
| | - Jian-kang Zhu
- Institute of Advanced Biotechnology and School of Life Sciences, Southern University of Science and Technology, Shenzhen518055, China
| | - Sheng Nie
- National Clinical Research Center for Kidney Diseases, State Key Laboratory for Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou510515, China
| | - Kang Zhang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Yong Zhou
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai201620, China
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Chen H, Wang H, Yu M, Duan B. Structure-decoupled functional connectome-based brain age prediction provides higher association to cognition. Neuroreport 2024; 35:42-48. [PMID: 37994631 PMCID: PMC10756698 DOI: 10.1097/wnr.0000000000001976] [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: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/24/2023]
Abstract
Brain age prediction as well as the prediction difference has been well examined to be a potential biomarker for brain disease or abnormal aging process. However, less knowledge was reported for the cognitive association within normal population. In this study, we proposed a novel approach to brain age prediction by structure-decoupled functional connectome. The original functional connectome was decomposed and decoupled into a structure-decoupled functional connectome using structural connectome harmonics. Our method was applied to a large dataset of normal aging individuals and achieved a high correlation between predicted and chronological age (r = 0.77). Both the original FC and structure-decoupled FC could be well-trained in a brain age prediction model. Significant remarkable relationships between the brain age prediction difference (predicted age minus chronological age) and cognitive scores were discovered. However, the brain age-predicted difference driven by structure-decoupled FC showed a stronger correction to the two cognitive scores (MMSE: r = -0.27, P -value = 0.002; MoCA: r = -0.32, P -value = 0.0003). Our findings suggest that our structure-decoupled functional connectivity approach could provide a more individual-specific functional network, leading to improved brain age prediction performance and a better understanding of cognitive decline in aging.
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Affiliation(s)
- Huan Chen
- Department of Internal Medicine, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Haiyan Wang
- Department of Internal Medicine, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Mingxia Yu
- Department of Internal Medicine, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Bin Duan
- Department of Internal Medicine, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
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Benali F, Singh N, Fladt J, Jaroenngarmsamer T, Bala F, Ospel JM, Buck BH, Dowlatshahi D, Field TS, Hanel RA, Peeling L, Tymianski M, Hill MD, Goyal M, Ganesh A. Mediation of Age and Thrombectomy Outcome by Neuroimaging Markers of Frailty in Patients With Stroke. JAMA Netw Open 2024; 7:e2349628. [PMID: 38165676 PMCID: PMC10762575 DOI: 10.1001/jamanetworkopen.2023.49628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/08/2023] [Indexed: 01/04/2024] Open
Abstract
Importance Age is a leading predictor of poor outcomes after brain injuries like stroke. The extent to which age is associated with preexisting burdens of brain changes, visible on neuroimaging but rarely considered in acute decision-making or trials, is unknown. Objectives To explore the mediation of age on functional outcome by neuroimaging markers of frailty (hereinafter neuroimaging frailty) in patients with acute ischemic stroke receiving endovascular thrombectomy (EVT). Design, Setting, and Participants This cohort study was a post hoc analysis of the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) randomized clinical trial, which investigated intravenous (IV) nerinetide in patients who underwent EVT within a 12-hour treatment window. Patients from 48 acute care hospitals in 8 countries (Canada, US, Germany, Korea, Australia, Ireland, UK, and Sweden) were enrolled between March 1, 2017, and August 12, 2019. Markers of brain frailty (brain atrophy [subcortical or cortical], white matter disease [periventricular or deep], and the number of lacunes and chronic infarctions) were retrospectively assessed while reviewers were blinded to other imaging (eg, computed tomography angiography, computed tomography perfusion) or outcome variables. All analyses were done between December 1, 2022, and January 31, 2023. Exposures All patients received EVT and were randomized to IV nerinetide (2.6 mg/kg of body weight) and alteplase (if indicated) treatment vs best medical management. Main Outcome and Measures The primary outcome was the proportion of the total effect of age on 90-day outcome, mediated by neuroimaging frailty. A combined mediation was also examined by clinical features associated with frailty and neuroimaging markers (total frailty). Structural equation modeling was used to create latent variables as potential mediators, adjusting for baseline, early ischemic changes; stroke severity; onset-to-puncture time; nerinetide treatment; and alteplase treatment. Results Among a total of 1105 patients enrolled in the study, 1102 (median age, 71 years [IQR, 61-80 years]; 554 [50.3%] male) had interpretable imaging at baseline. Of these participants, 549 (49.8%) were treated with IV nerinetide. The indirect effect of age on 90-day outcome, mediated by neuroimaging frailty, was associated with 85.1% of the total effect (β coefficient, 0.04 per year [95% CI, 0.02-0.06 per year]; P < .001). When including both frailty constructs, the indirect pathway was associated with essentially 100% of the total effect (β coefficient, 0.07 per year [95% CI, 0.03-0.10 per year]; P = .001). Conclusions and Relevance In this cohort study, a secondary analysis of the ESCAPE-NA1 trial, most of the association between age and 90-day outcome was mediated by neuroimaging frailty, underscoring the importance of features like brain atrophy and small vessel disease, as opposed to chronological age alone, in predicting poststroke outcomes. Future trials could include such frailty features to stratify randomization or improve adjustment in outcome analyses.
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Affiliation(s)
- Faysal Benali
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology and Nuclear Medicine, Maastricht UMC+, Maastricht, the Netherlands
| | - Nishita Singh
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Neurology Division, Department of Internal Medicine, University of Manitoba, Max Rady College of Medicine, Winnipeg, Manitoba, Canada
| | - Joachim Fladt
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Tanaporn Jaroenngarmsamer
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
| | - Fouzi Bala
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Diagnostic and Interventional Neuroradiology Department, University Hospital of Tours, Tours, France
| | - Johanna M. Ospel
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Brian H. Buck
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Dar Dowlatshahi
- Department of Medicine (Neurology), Neuroradiology Section, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Thalia S. Field
- Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ricardo A. Hanel
- Lyerly Neurosurgery, Baptist Neurological Institute, Baptist Health, Jacksonville, Florida
| | - Lissa Peeling
- Saskatoon Stroke Program, Royal University Hospital, University of Saskatchewan, Saskatoon, Canada
| | | | - Michael D. Hill
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Community Health Sciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- O’Brien Institute for Public Health, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
| | - Mayank Goyal
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
| | - Aravind Ganesh
- Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- Department of Community Health Sciences, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
- O’Brien Institute for Public Health, University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
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Wang L, Zhang X, Wang L, Guo M, Yang Q, Chen X, Sha H. Association of Age with Dual-Task Objective Cognitive Indicators and Gait Parameters in Older Adults. J Alzheimers Dis 2024; 99:993-1004. [PMID: 38728188 DOI: 10.3233/jad-240066] [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] [Indexed: 05/12/2024]
Abstract
Background Early recognition of dementia like Alzheimer's disease is crucial for disease diagnosis and treatment, and existing objective tools for early screening of cognitive impairment are limited. Objective To investigate age-related behavioral indicators of dual-task cognitive performance and gait parameters and to explore potential objective markers of early cognitive decline. Methods The community-based cognitive screening data was analyzed. Hierarchical cluster analysis and Pearson correlation analysis were performed on the 9-item subjective cognitive decline (SCD-9) scores, walking-cognitive dual-task performance, walking speed, and gait parameters of 152 participants. The significant differences of indicators that may related to cognitive decline were statistically analyzed across six age groups. A mathematical model with age as the independent variable and motor cognition composite score as the dependent variable was established to observe the trend of motor cognition dual-task performance with age. Results Strong correlation was found between motor cognitive scores and SCD and age. Gait parameters like the mean value of ankle angle, the left-right difference rate of ankle angle and knee angle and the coefficient of variation of gait cycle showed an excellent correlation with age. Motor cognition scores showed a decreasing trend with age. The slope of motor cognition scores with age after 50 years (k = -1.06) was six times higher than that before 50 years (k = -0.18). Conclusions Cognitive performance and gait parameters in the walking-cognitive dual-task state are promising objective markers that could characterize age-related cognitive decline.
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Affiliation(s)
- Linlin Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xuezhen Zhang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Institut National des Sciences Appliquées de Lyon, Lyon, France
| | - Lei Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Miaomiao Guo
- School of Health Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin, China
| | | | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Hong Sha
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
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Shah J, Siddiquee MMR, Su Y, Wu T, Li B. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION 2024; 2024:7867-7876. [PMID: 38606366 PMCID: PMC11008505 DOI: 10.1109/wacv57701.2024.00770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction.
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Affiliation(s)
- Jay Shah
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | | | - Yi Su
- ASU-Mayo Center for Innovative Imaging
- Banner Alzheimer's Institute
| | - Teresa Wu
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | - Baoxin Li
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
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Nguyen H, Clément M, Mansencal B, Coupé P. Brain structure ages-A new biomarker for multi-disease classification. Hum Brain Mapp 2024; 45:e26558. [PMID: 38224546 PMCID: PMC10785199 DOI: 10.1002/hbm.26558] [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: 05/11/2023] [Revised: 11/20/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024] Open
Abstract
Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.
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Affiliation(s)
- Huy‐Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
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Kim S, Wang SM, Kang DW, Um YH, Yang H, Lee H, Kim REY, Kim D, Lee CU, Lim HK. Development of Efficient Brain Age Estimation Method Based on Regional Brain Volume From Structural Magnetic Resonance Imaging. Psychiatry Investig 2024; 21:37-43. [PMID: 38281737 PMCID: PMC10822742 DOI: 10.30773/pi.2023.0183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/17/2023] [Accepted: 09/20/2023] [Indexed: 01/30/2024] Open
Abstract
OBJECTIVE We aimed to create an efficient and valid predicting model which can estimate individuals' brain age by quantifying their regional brain volumes. METHODS A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. RESULTS The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. CONCLUSION The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual's cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.
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Affiliation(s)
- Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyeonsik Yang
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Regina EY Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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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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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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Qiu W, Chen H, Kaeberlein M, Lee SI. ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age. THE LANCET. HEALTHY LONGEVITY 2023; 4:e711-e723. [PMID: 37944549 DOI: 10.1016/s2666-7568(23)00189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/10/2023] [Accepted: 08/30/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations. METHODS To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses. FINDINGS Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms. INTERPRETATION ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes. FUNDING National Science Foundation and National Institutes of Health.
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Affiliation(s)
- Wei Qiu
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Hugh Chen
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington, Washington, DC, USA
| | - Su-In Lee
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA.
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Valdes-Hernandez PA, Nodarse CL, Johnson AJ, Montesino-Goicolea S, Bashyam V, Davatzikos C, Peraza JA, Cole JH, Huo Z, Fillingim RB, Cruz-Almeida Y. Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function-a multi-institutional and multiscanner study. Pain 2023; 164:2822-2838. [PMID: 37490099 PMCID: PMC10805955 DOI: 10.1097/j.pain.0000000000002984] [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: 09/28/2022] [Accepted: 05/31/2023] [Indexed: 07/26/2023]
Abstract
ABSTRACT Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types. However, some remaining methodological issues regarding training sample size and sex-specific effects should be tackled before settling this controversy. Here, we explored differences in brain-PAD between musculoskeletal pain types and controls using a novel convolutional neural network for predicting brain-PADs, ie, DeepBrainNet. Based on a very large, multi-institutional, and heterogeneous training sample and requiring less magnetic resonance imaging preprocessing than other methods for brain age prediction, DeepBrainNet offers robust and reproducible brain-PADs, possibly highly sensitive to neuropathology. Controlling for scanner-related variability, we used a large sample (n = 660) with different scanners, ages (19-83 years), and musculoskeletal pain types (chronic low back [CBP] and osteoarthritis [OA] pain). Irrespective of sex, brain-PAD of OA pain participants was ∼3 to 4.7 years higher than that of CBP and controls, whereas brain-PAD did not significantly differ among controls and CBP. Moreover, brain-PAD was significantly related to multiple variables underlying the multidimensional pain experience. This comprehensive work adds evidence of pain type-specific effects of chronic pain on brain age. This could help in the clarification of the debate around possible relationships between brain aging mechanisms and pain.
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Affiliation(s)
- Pedro A. Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Alisa J. Johnson
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
| | - Soamy Montesino-Goicolea
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Vishnu Bashyam
- AI2D Center for AI and Data Science for Integrated Diagnostics; and Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, USA
- Artificial Intelligence in Biomedical Imaging Lab (AIBIL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- AI2D Center for AI and Data Science for Integrated Diagnostics; and Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Julio A. Peraza
- Department of Physics, Florida International University, USA
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Zhiguang Huo
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, USA
| | - Roger B. Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
- Department of Neuroscience, College of Medicine, University of Florida, USA
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47
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Oh HSH, Rutledge J, Nachun D, Pálovics R, Abiose O, Moran-Losada P, Channappa D, Urey DY, Kim K, Sung YJ, Wang L, Timsina J, Western D, Liu M, Kohlfeld P, Budde J, Wilson EN, Guen Y, Maurer TM, Haney M, Yang AC, He Z, Greicius MD, Andreasson KI, Sathyan S, Weiss EF, Milman S, Barzilai N, Cruchaga C, Wagner AD, Mormino E, Lehallier B, Henderson VW, Longo FM, Montgomery SB, Wyss-Coray T. Organ aging signatures in the plasma proteome track health and disease. Nature 2023; 624:164-172. [PMID: 38057571 PMCID: PMC10700136 DOI: 10.1038/s41586-023-06802-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/31/2023] [Indexed: 12/08/2023]
Abstract
Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.
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Affiliation(s)
- Hamilton Se-Hwee Oh
- Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Jarod Rutledge
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Graduate Program in Genetics, Stanford University, Stanford, CA, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Róbert Pálovics
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Olamide Abiose
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Patricia Moran-Losada
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Divya Channappa
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Deniz Yagmur Urey
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Kate Kim
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Dan Western
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Menghan Liu
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Pat Kohlfeld
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Edward N Wilson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yann Guen
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Taylor M Maurer
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Haney
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Andrew C Yang
- Departments of Neurology and Anatomy, University of California San Francisco, San Francisco, CA, USA
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Bakar Aging Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Katrin I Andreasson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Sanish Sathyan
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Erica F Weiss
- Department of Neurology, Montefiore Medical Center, New York, NY, USA
| | - Sofiya Milman
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Anthony D Wagner
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Benoit Lehallier
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Victor W Henderson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Frank M Longo
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Tony Wyss-Coray
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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48
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Mayer AR, Meier TB, Ling JM, Dodd AB, Brett BL, Robertson-Benta CR, Huber DL, Van der Horn HJ, Broglio SP, McCrea MA, McAllister T. Increased brain age and relationships with blood-based biomarkers following concussion in younger populations. J Neurol 2023; 270:5835-5848. [PMID: 37594499 PMCID: PMC10632216 DOI: 10.1007/s00415-023-11931-8] [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: 06/09/2023] [Revised: 07/19/2023] [Accepted: 08/03/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVE Brain age is increasingly being applied to the spectrum of brain injury to define neuropathological changes in conjunction with blood-based biomarkers. However, data from the acute/sub-acute stages of concussion are lacking, especially among younger cohorts. METHODS Predicted brain age differences were independently calculated in large, prospectively recruited cohorts of pediatric concussion and matched healthy controls (total N = 446), as well as collegiate athletes with sport-related concussion and matched non-contact sport controls (total N = 184). Effects of repetitive head injury (i.e., exposure) were examined in a separate cohort of contact sport athletes (N = 82), as well as by quantifying concussion history through semi-structured interviews and years of contact sport participation. RESULTS Findings of increased brain age during acute and sub-acute concussion were independently replicated across both cohorts, with stronger evidence of recovery for pediatric (4 months) relative to concussed athletes (6 months). Mixed evidence existed for effects of repetitive head injury, as brain age was increased in contact sport athletes, but was not associated with concussion history or years of contact sport exposure. There was no difference in brain age between concussed and contact sport athletes. Total tau decreased immediately (~ 1.5 days) post-concussion relative to the non-contact group, whereas pro-inflammatory markers were increased in both concussed and contact sport athletes. Anti-inflammatory markers were inversely related to brain age, whereas markers of axonal injury (neurofilament light) exhibited a trend positive association. CONCLUSION Current and previous findings collectively suggest that the chronicity of brain age differences may be mediated by age at injury (adults > children), with preliminary findings suggesting that exposure to contact sports may also increase brain age.
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Affiliation(s)
- Andrew R Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA.
- Neurology and Psychiatry Departments, University of New Mexico School of Medicine, Albuquerque, NM, USA.
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
| | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Josef M Ling
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA
| | - Andrew B Dodd
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA
| | - Benjamin L Brett
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cidney R Robertson-Benta
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA
| | - Daniel L Huber
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Harm J Van der Horn
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA
| | - Steven P Broglio
- Michigan Concussion Center, University of Michigan, Ann Arbor, MI, USA
| | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Thomas McAllister
- Department of Psychiatry, Indiana University School of Medicine, Bloomington, IN, USA
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49
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Filippi M, Cividini C, Basaia S, Spinelli EG, Castelnovo V, Leocadi M, Canu E, Agosta F. Age-related vulnerability of the human brain connectome. Mol Psychiatry 2023; 28:5350-5358. [PMID: 37414925 DOI: 10.1038/s41380-023-02157-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 06/05/2023] [Accepted: 06/22/2023] [Indexed: 07/08/2023]
Abstract
Multifactorial models integrating brain variables at multiple scales are warranted to investigate aging and its relationship with neurodegeneration. Our aim was to evaluate how aging affects functional connectivity of pivotal regions of the human brain connectome (i.e., hubs), which represent potential vulnerability 'stations' to aging, and whether such effects influence the functional and structural changes of the whole brain. We combined the information of the functional connectome vulnerability, studied through an innovative graph-analysis approach (stepwise functional connectivity), with brain cortical thinning in aging. Using data from 128 cognitively normal participants (aged 20-85 years), we firstly investigated the topological functional network organization in the optimal healthy condition (i.e., young adults) and observed that fronto-temporo-parietal hubs showed a highly direct functional connectivity with themselves and among each other, while occipital hubs showed a direct functional connectivity within occipital regions and sensorimotor areas. Subsequently, we modeled cortical thickness changes over lifespan, revealing that fronto-temporo-parietal hubs were among the brain regions that changed the most, whereas occipital hubs showed a quite spared cortical thickness across ages. Finally, we found that cortical regions highly functionally linked to the fronto-temporo-parietal hubs in healthy adults were characterized by the greatest cortical thinning along the lifespan, demonstrating that the topology and geometry of hub functional connectome govern the region-specific structural alterations of the brain regions.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo G Spinelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Veronica Castelnovo
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Michela Leocadi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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50
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Valdes-Hernandez PA, Laffitte Nodarse C, Cole JH, Cruz-Almeida Y. Feasibility of brain age predictions from clinical T1-weighted MRIs. Brain Res Bull 2023; 205:110811. [PMID: 37952679 DOI: 10.1016/j.brainresbull.2023.110811] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2-3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R2 (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.
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Affiliation(s)
- Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA; Department of Neuroscience, College of Medicine, University of Florida, USA.
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