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Lu H, Li J, Chan SSM, Ma SL, Mok VCT, Shi L, Mak ADP, Lam LCW. Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial. DIALOGUES IN CLINICAL NEUROSCIENCE 2024; 26:38-52. [PMID: 38963341 PMCID: PMC11225634 DOI: 10.1080/19585969.2024.2373075] [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: 01/18/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual's brain morphometry could predict the rTMS responders and remitters. METHODS This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age. RESULTS The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3rd week) and 84% (12th week), respectively were achieved. CONCLUSION In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment. TRIAL REGISTRATION ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.
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Affiliation(s)
- Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Sandra Sau Man Chan
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Suk Ling Ma
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Arthur Dun-Ping Mak
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Linda Chiu Wa Lam
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
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Tetereva A, Pat N. Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals. eLife 2024; 12:RP87297. [PMID: 38869938 PMCID: PMC11175613 DOI: 10.7554/elife.87297] [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: 06/14/2024] Open
Abstract
One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.
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Affiliation(s)
- Alina Tetereva
- Department of Psychology, University of OtagoDunedinNew Zealand
| | - Narun Pat
- Department of Psychology, University of OtagoDunedinNew Zealand
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3
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Rinaldi M, Pezone A, Quadrini GI, Abbadessa G, Laezza MP, Passaro ML, Porcellini A, Costagliola C. Targeting shared pathways in tauopathies and age-related macular degeneration: implications for novel therapies. Front Aging Neurosci 2024; 16:1371745. [PMID: 38633983 PMCID: PMC11021713 DOI: 10.3389/fnagi.2024.1371745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
The intricate parallels in structure and function between the human retina and the central nervous system designate the retina as a prospective avenue for understanding brain-related processes. This review extensively explores the shared physiopathological mechanisms connecting age-related macular degeneration (AMD) and proteinopathies, with a specific focus on tauopathies. The pivotal involvement of oxidative stress and cellular senescence emerges as key drivers of pathogenesis in both conditions. Uncovering these shared elements not only has the potential to enhance our understanding of intricate neurodegenerative diseases but also sets the stage for pioneering therapeutic approaches in AMD.
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Affiliation(s)
- Michele Rinaldi
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Naples, Italy
| | - Antonio Pezone
- Department of Biology, University of Naples Federico II, Naples, Italy
| | - Gaia Italia Quadrini
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Naples, Italy
| | - Gianmarco Abbadessa
- Division of Neurology, Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Maria Paola Laezza
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Naples, Italy
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Maria Laura Passaro
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Naples, Italy
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | | | - Ciro Costagliola
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Naples, Italy
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Cumplido-Mayoral I, Brugulat-Serrat A, Sánchez-Benavides G, González-Escalante A, Anastasi F, Milà-Alomà M, López-Martos D, Akinci M, Falcón C, Shekari M, Cacciaglia R, Arenaza-Urquijo EM, Minguillón C, Fauria K, Molinuevo JL, Suárez-Calvet M, Grau-Rivera O, Vilaplana V, Gispert JD. The mediating role of neuroimaging-derived biological brain age in the association between risk factors for dementia and cognitive decline in middle-aged and older individuals without cognitive impairment: a cohort study. THE LANCET. HEALTHY LONGEVITY 2024; 5:e276-e286. [PMID: 38555920 DOI: 10.1016/s2666-7568(24)00025-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Neuroimaging-based brain-age delta has been shown to be a mediator linking cardiovascular risk factors to cognitive function. We aimed to assess the mediating role of brain-age delta in the association between modifiable risk factors of dementia and longitudinal cognitive decline in middle-aged and older individuals who are asymptomatic, stratified by Alzheimer's disease pathology. We also explored whether the mediation effect is specific to cognitive domain. METHODS In this cohort study, we included participants from the ALFA+ cohort aged between 45 years and 65 years who were cognitively unimpaired and who had available structural MRI, cerebrospinal fluid β-amyloid (Aβ)42 and Aβ40 measurements obtained within 1 year of each other, modifiable risk factors assessment, and cognitive evaluation over 3 years. Participants were recruited from the Barcelonaβeta Brain Research Center (Barcelona, Spain). Included individuals underwent a first assessment between Oct 25, 2016, and Jan 28, 2020, and a follow-up cognitive assessment 3·28 (SD 0·27) years later. We computed brain-age delta and composites of different cognitive function domains (preclinical Alzheimer's cognitive composite [PACC], attention, executive function, episodic memory, visual processing, and language). We used partial least squares path modelling to explore mediation effects in the associations between modifiable risk factors (including cardiovascular, mental health, mood, metabolic or endocrine history, and alcohol use) and changes in cognitive composites. To assess the role of Alzheimer's disease pathology, we computed separate models for Aβ-negative and Aβ-positive individuals. FINDINGS Of the 419 participants enrolled in ALFA+, 302 met our inclusion criteria, of which 108 participants were classified as Aβ-positive and 194 as Aβ-negative. In Aβ-positive individuals, brain-age delta partially mediated (percent mediation proportion 15·73% [95% CI 14·22-16·66]) the association between modifiable risk factors and decline in overall cognition (across cognitive domains). Brain-age delta fully mediated (mediation proportion 28·03% [26·25-29·21]) the effect of modifiable risk factors on the PACC, wherein increased values for risk factors correlated with an older brain-age delta, and, consequently, an older brain-age delta was linked to greater PACC decline. This effect appears to be primarily driven by memory decline. Mediation was not significant in Aβ-negative individuals (3·52% [0·072-4·17]) on PACC, although path coefficients were not significantly different from those in the Aβ-positive group. INTERPRETATION Our findings suggest that brain-age delta captures the association between modifiable risk factors and longitudinal cognitive decline in middle-aged and older people. In asymptomatic middle-aged and older individuals who are Aβ-positive, the pathology might be the strongest driver of cognitive decline, whereas the effect of risk factors is smaller. Our results highlight the potential of brain-age delta as an objective outcome measure for preventive lifestyle interventions targeting cognitive decline. FUNDING La Caixa Foundation, the TriBEKa Imaging Platform, and the Universities and Research Secretariat of the Catalan Government. TRANSLATION For the Spanish translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Irene Cumplido-Mayoral
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Anna Brugulat-Serrat
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Global Brain Health Institute, San Francisco, CA, USA
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - Armand González-Escalante
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Federica Anastasi
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Marta Milà-Alomà
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA
| | - David López-Martos
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Muge Akinci
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Barcelona Institute of Global Health, Barcelona, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Carolina Minguillón
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; H Lundbeck, Copenhagen, Denmark
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
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5
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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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6
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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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7
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Yang Y, Sathe A, Schilling K, Shashikumar N, Moore E, Dumitrescu L, Pechman KR, Landman BA, Gifford KA, Hohman TJ, Jefferson AL, Archer DB. A deep neural network estimation of brain age is sensitive to cognitive impairment and decline. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:148-162. [PMID: 38160276 PMCID: PMC10764074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32x10-7; T1: β=-1.331, p=6.52x10-7; FW+T1: β=-1.476, p=2.53x10-10; executive function, FW: β=-1.276, p=1.46x10-9; T1: β=-1.337, p=2.52x10-7; FW+T1: β=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62x10-11; T1: β=-0.097, p=1.40x10-8; FW+T1: β=-0.101, p=1.35x10-11; executive function, FW: β=-0.125, p=1.20x10-10; T1: β=-0.163, p=4.25x10-12; FW+T1: β=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Elizabeth Moore
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
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Yang Y, Sathe A, Schilling K, Shashikumar N, Moore E, Dumitrescu L, Pechman KR, Landman BA, Gifford KA, Hohman TJ, Jefferson AL, Archer DB. A deep neural network estimation of brain age is sensitive to cognitive impairment and decline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.10.552494. [PMID: 37645837 PMCID: PMC10461919 DOI: 10.1101/2023.08.10.552494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Elizabeth Moore
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
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Wrigglesworth J, Harding IH, Islam RM, Ward PGD, Woods RL, Bell RJ, McNeil JJ, Storey E, Egan G, Murray AM, Trevaks RE, Ward SA, Davis SR, Ryan J. The association between sex hormones and the change in brain-predicted age difference in older women. Clin Endocrinol (Oxf) 2023; 98:692-699. [PMID: 36807922 PMCID: PMC10073334 DOI: 10.1111/cen.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVE The role of circulating sex hormones on structural brain ageing is yet to be established. This study explored whether concentrations of circulating sex hormones in older women are associated with the baseline and longitudinal changes in structural brain ageing, defined by the brain-predicted age difference (brain-PAD). DESIGN Prospective cohort study using data from NEURO and Sex Hormones in Older Women; substudies of the ASPirin in Reducing Events in the Elderly clinical trial. PATIENTS Community-dwelling older women (aged 70+ years). MEASUREMENTS Oestrone, testosterone, dehydroepiandrosterone (DHEA), and sex-hormone binding globulin (SHBG) were quantified from plasma samples collected at baseline. T1-weighted magnetic resonance imaging was performed at baseline, 1 and 3 years. Brain age was derived from whole brain volume using a validated algorithm. RESULTS The sample comprised of 207 women not taking medications known to influence sex hormone concentrations. A statistically higher baseline brain-PAD (older brain age relative to chronological age) was seen for women in the highest DHEA tertile compared with the lowest in the unadjusted analysis (p = .04). This was not significant when adjusted for chronological age, and potential confounding health and behavioural factors. Oestrone, testosterone and SHBG were not associated with brain-PAD cross-sectionally, nor were any of the examined sex hormones or SHBG associated with brain-PAD longitudinally. CONCLUSION No strong evidence of an association between circulating sex hormones and brain-PAD. Given there is prior evidence to suggests sex hormones may be important for brain ageing, further studies of circulating sex hormones and brain health in postmenopausal women are warranted.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Melbourne, 3800, Victoria, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne Victoria, Australia
| | - Rakibul M. Islam
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Phillip G. D. Ward
- Monash Biomedical Imaging, Monash University, Melbourne, 3800, Victoria, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, 3800, Victoria Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Robin J. Bell
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - John J. McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Melbourne, 3800, Victoria, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
| | - Anne M. Murray
- Berman Centre for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Hennepin, Minneapolis, MN, 55404, USA
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, 55404, USA
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Stephanie A. Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, 2031, New South Wales, Australia
| | - Susan R. Davis
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
- Department of Endocrinology and Diabetes, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
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10
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Yin C, Imms P, Cheng M, Amgalan A, Chowdhury NF, Massett RJ, Chaudhari NN, Chen X, Thompson PM, Bogdan P, Irimia A. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proc Natl Acad Sci U S A 2023; 120:e2214634120. [PMID: 36595679 PMCID: PMC9926270 DOI: 10.1073/pnas.2214634120] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023] Open
Abstract
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
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Affiliation(s)
- Chenzhong Yin
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Mingxi Cheng
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nahian F. Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Roy J. Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nikhil N. Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Xinghe Chen
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Paul M. Thompson
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA90033
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
- Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
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11
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Wrigglesworth J, Ryan J, Ward PGD, Woods RL, Storey E, Egan GF, Murray A, Espinoza SE, Shah RC, Trevaks RE, Ward SA, Harding IH. Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function. Front Aging Neurosci 2023; 14:1063721. [PMID: 36688169 PMCID: PMC9846261 DOI: 10.3389/fnagi.2022.1063721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/29/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Neuroimaging-based 'brain age' can identify individuals with 'advanced' or 'resilient' brain aging. Brain-predicted age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unknown how individual health and lifestyle factors may modify the relationship between brain-PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain-PAD, and determine if membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years. Methods Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) trial. Participants were grouped as having resilient (n=159) or advanced (n=167) brain-PAD, and latent class analysis (LCA) was performed using a set of cognitive, lifestyle, and health measures. We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex and education. Results Subgroups of resilient and advanced brain aging were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 were characterized by low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient brain agers, class 1 was associated with a decrease in cognition, and class 2 with an increase over 5 years, though was a small effect that was equivalent to a 0.04 standard deviation difference per year. No significant class distinctions were evident with FI. For advanced brain agers, there was no evidence of an association between class membership and changes in cognition or FI. Conclusion These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. In particular, people with age-resilient brains had different trajectories of cognitive change depending on their cognitive and physical health status at baseline. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple lifestyle and health indices alongside brain age.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Phillip G. D. Ward
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Gary F. Egan
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Anne Murray
- Hennepin Healthcare and Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, United States
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, United States
| | - Sara E. Espinoza
- Division of Geriatrics, Gerontology & Palliative Medicine, Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, Houston, TX, United States
- Geriatric Research, Education & Clinical Center, South Texas Veterans Health Care System, San Antonio, TX, United States
| | - Raj C. Shah
- Department of Family & Preventive Medicine and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Stephanie A. Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
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12
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Weber KA, Teplin ZM, Wager TD, Law CSW, Prabhakar NK, Ashar YK, Gilam G, Banerjee S, Delp SL, Glover GH, Hastie TJ, Mackey S. Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction. Front Neurol 2022; 13:960760. [PMID: 36601297 PMCID: PMC9806266 DOI: 10.3389/fneur.2022.960760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength (R 2 = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R 2 = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.
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Affiliation(s)
- Kenneth A. Weber
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,*Correspondence: Kenneth A. Weber II
| | - Zachary M. Teplin
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Christine S. W. Law
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Nitin K. Prabhakar
- Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Yoni K. Ashar
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, United States
| | - Gadi Gilam
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Scott L. Delp
- Department of Bioengineering and Mechanical Engineering, Stanford University, Palo Alto, CA, United States
| | - Gary H. Glover
- Radiological Sciences Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Trevor J. Hastie
- Department of Statistics, Stanford University, Palo Alto, CA, United States
| | - Sean Mackey
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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13
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Han J, Kim SY, Lee J, Lee WH. Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:8077. [PMID: 36298428 PMCID: PMC9608785 DOI: 10.3390/s22208077] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22-37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18-88), and the Information eXtraction from Images (IXI, n = 567, age range 19-86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75-3.12, 7.08-10.50, and 8.04-9.86 years, as well as Pearson's correlation coefficients of 0.11-0.42, 0.64-0.85, and 0.63-0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.
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14
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Wrigglesworth J, Harding IH, Ward P, Woods RL, Storey E, Fitzgibbon B, Egan G, Murray A, Shah RC, Trevaks RE, Ward S, McNeil JJ, Ryan J. Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals. J Alzheimers Dis Rep 2022; 6:163-176. [PMID: 35591948 PMCID: PMC9108625 DOI: 10.3233/adr-220011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 12/11/2022] Open
Abstract
Background: There is considerable variability in the rate at which we age biologically, and the brain is particularly susceptible to the effects of aging. Objective: We examined the test-retest reliability of brain age at one- and three-year intervals and identified characteristics that predict the longitudinal change in brain-predicted age difference (brain-PAD, defined by deviations of brain age from chronological age). Methods: T1-weighted magnetic resonance images were acquired at three timepoints from 497 community-dwelling adults (73.8±3.5 years at baseline, 48% were female). Brain age was estimated from whole brain volume, using a publicly available algorithm trained on an independent dataset. Linear mixed models were used, adjusting for sex, age, and age2. Results: Excellent retest reliability of brain age was observed over one and three years. We identified a significant sex difference in brain-PAD, where a faster rate of brain aging (worsening in brain age relative to chronological age) was observed in men, and this finding replicated in secondary analyses. The effect size, however, was relatively weak, equivalent to 0.16 years difference per year. A higher score in physical health related quality of life and verbal fluency were associated with a faster rate of brain aging, while depression was linked to a slower rate of brain aging, but these findings were not robust. Conclusion: Our study provides consistent evidence that older men have slightly faster brain atrophy than women. Given the sparsity of longitudinal research on brain age in older populations, future prospective studies are needed to confirm our findings.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, VIC, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Bernadette Fitzgibbon
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, VIC, Australia
| | - Anne Murray
- Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, USA
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, USA
| | - Raj C. Shah
- Department of Family Medicine and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Stephanie Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, NSW, Australia
| | - John J. McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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