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Tweedale M, Morys F, Pastor-Bernier A, Azizi H, Tremblay C, Dagher A. Obesity and diffusion-weighted imaging of subcortical grey matter in young and older adults. Appetite 2024; 200:107527. [PMID: 38797235 DOI: 10.1016/j.appet.2024.107527] [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: 01/31/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 05/29/2024]
Abstract
Obesity and hypothalamic inflammation are causally related. It is unclear whether this neuroinflammation precedes or results from obesity. Animal studies show that an increase in food intake can lead to hypothalamic inflammation, but hypothalamic inflammation can create a feedback loop that further increases food intake. Internal and external factors mediate patterns of food intake and how it can affect the hypothalamus. Measures of water diffusivity in magnetic resonance imaging of the brain such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) are associated with grey matter inflammation. Here, we investigated how those measures are associated with obesity-related variables in groups of young and older adults. We found relationships between decreased diffusivity and obesity markers in young adults. In older adults, obesity and comorbidities were also related to significant changes in diffusivity. Here, diffusivity was strongly associated with body mass index (BMI) and blood levels of C-reactive protein (CRP) in multiple subcortical regions, rather than only the hypothalamus. Our results suggest that diffusivity measures can be used to investigate obesity-associated changes in the brain that can potentially reflect neuroinflammation. The connection seen between subcortical inflammation and obesity opens the conversation on preventative interventions needed to reduce the effects of obesity at all stages in life.
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Affiliation(s)
- Max Tweedale
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Filip Morys
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | | | - Houman Azizi
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, Canada
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Wang Q, Yu R, Dong C, Zhou C, Xie Z, Sun H, Fu C, Zhu D. Association and prediction of Life's Essential 8 score, genetic susceptibility with MCI, dementia, and MRI indices: A prospective cohort study. J Affect Disord 2024; 360:394-402. [PMID: 38844164 DOI: 10.1016/j.jad.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND To examine the associations of Life's Essential 8 (LE8) and its predictive performance with mild cognitive impairment (MCI), dementia and brain MRI indices. METHODS We used cohort data from UK Biobank. LE8 was categorized into low (<50 score), moderate (50-79 score), and high (≥80 score) levels. Cox regression models considering death as a competing risk were used to estimate the hazard ratios (HRs) and 95%CI on the association between LE8 and MCI and dementia. Multivariable linear regression models were used to analyze LE8 every 10-score increase and brain MRI indices. Area under the curve (AUC) was used to measure the predictive performances of LE8. RESULTS We included 126,785 participants with a mean (SD) age of 56.0 (8.0) years and 53.5 % were female. The median follow-up was 13.0 years. Compared to individuals with a low LE8 score, those with a high LE8 score were associated with decreased risk of MCI (0.49, 95%CI: 0.40-0.62), all-cause dementia (0.60, 0.44-0.80), vascular dementia (VD, 0.44, 0.21-0.94), and non-Alzheimer non-vascular dementia (NAVD, 0.55, 0.35-0.84). High LE8 score was associated with increased total brain volume, hippocampus volume, grey matter volume, and grey matter in hippocampus volume (p all ≤0.001). LE8 combined age and sex had good performance for predicting all-cause dementia (AUC: 84.1 %), AD (85.4 %), VD (87.6 %), NAVD (81.4 %), and MCI (75.3 %). LIMITATIONS Our findings only reflect the characteristics of UKB participants. CONCLUSIONS High LE8 score was associated with reduced risk of MCI and dementia. It was also linked to brain MRI indices. LE8 score had good predicting performance for future risk of MCI and dementia.
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Affiliation(s)
- Qi Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ruihong Yu
- Pingyin Center for Disease Control and Prevention, No. 67 Dongguan Street, Pingyin, Jinan, China
| | - Caiyun Dong
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunmiao Zhou
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ziwei Xie
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huizi Sun
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunying Fu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dongshan Zhu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; Center for Clinical Epidemiology and Evidence-Based Medicine, Shandong University, Jinan, China.
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Rivier CA, Singh S, Senff J, Tack RW, Marini S, Clocchiatti-Tuozzo S, Huo S, Renedo D, Papier K, Conroy M, Littlejohns TJ, Chemali Z, Kourkoulis C, Payabvash S, Newhouse A, Westover MB, Lazar RM, Pikula A, Ibrahim S, Howard VJ, Howard G, Brouwers HB, Van Duijn CM, Fricchione G, Tanzi RE, Yechoor N, Sheth KN, Anderson CD, Rosand J, Falcone GJ. Brain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons. Neurology 2024; 103:e209687. [PMID: 39052961 DOI: 10.1212/wnl.0000000000209687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVES To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury. METHODS This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex. RESULTS We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%-27%] first, 33% [27%-39%] repeat) and higher FA (18% [16%-20%] first, 22% [15%-28%] repeat), with a decrease in MD (9% [7%-11%] first, 10% [4%-16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%-17%] reduction in WMH progression and FA decline over time. DISCUSSION This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline.
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Affiliation(s)
- Cyprien A Rivier
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sanjula Singh
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Jasper Senff
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Reinier W Tack
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sandro Marini
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Santiago Clocchiatti-Tuozzo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Shufan Huo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Daniela Renedo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Keren Papier
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Megan Conroy
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Thomas J Littlejohns
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Zeina Chemali
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Christina Kourkoulis
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Seyedmehdi Payabvash
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Amy Newhouse
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Ronald M Lazar
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Aleksandra Pikula
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sarah Ibrahim
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Virginia J Howard
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - George Howard
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - H Bart Brouwers
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Cornelia M Van Duijn
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Gregory Fricchione
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Rudolph E Tanzi
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Nirupama Yechoor
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Kevin N Sheth
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Christopher D Anderson
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Jonathan Rosand
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Guido J Falcone
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
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Xu S, Wen S, Yang Y, He J, Yang H, Qu Y, Zeng Y, Zhu J, Fang F, Song H. Association Between Body Composition Patterns, Cardiovascular Disease, and Risk of Neurodegenerative Disease in the UK Biobank. Neurology 2024; 103:e209659. [PMID: 39047204 PMCID: PMC11314951 DOI: 10.1212/wnl.0000000000209659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/13/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Accumulating evidence connects diverse components of body composition (e.g., fat, muscle, and bone) to neurodegenerative disease risk, yet their interplay remains underexplored. This study examines the associations between patterns of body composition and the risk of neurodegenerative diseases, exploring the mediating role of cardiovascular diseases (CVDs). METHODS This retrospective analysis used data from the UK Biobank, a prospective community-based cohort study. We included participants free of neurodegenerative diseases and with requisite body composition measurements at recruitment, who were followed from 5 years after recruitment until April 1, 2023, to identify incident neurodegenerative diseases. We assessed the associations between different components and major patterns of body composition (identified by principal component analysis) with the risk of neurodegenerative diseases, using multivariable Cox models. Analyses were stratified by disease susceptibility, indexed by polygenetic risk scores for Alzheimer and Parkinson diseases, APOE genotype, and family history of neurodegenerative diseases. Furthermore, we performed mediation analysis to estimate the contribution of CVDs to these associations. In addition, in a subcohort of 40,790 participants, we examined the relationship between body composition patterns and brain aging biomarkers (i.e., brain atrophy and cerebral small vessel disease). RESULTS Among 412,691 participants (mean age 56.0 years, 55.1% female), 8,224 new cases of neurodegenerative diseases were identified over an average follow-up of 9.1 years. Patterns identified as "fat-to-lean mass," "muscle strength," "bone density," and "leg-dominant fat distribution" were associated with a lower rate of neurodegenerative diseases (hazard ratio [HR] = 0.74-0.94) while "central obesity" and "arm-dominant fat distribution" patterns were associated with a higher rate (HR = 1.13-1.18). Stratification analysis yielded comparable risk estimates across different susceptibility groups. Notably, 10.7%-35.3% of the observed associations were mediated by CVDs, particularly cerebrovascular diseases. The subcohort analysis of brain aging biomarkers corroborated the findings for "central obesity," "muscle strength," and "arm-dominant fat distribution" patterns. DISCUSSION Our analyses demonstrated robust associations of body composition patterns featured by "central obesity," "muscle strength," and "arm-dominant fat distribution" with both neurodegenerative diseases and brain aging, which were partially mediated by CVDs. These findings underscore the potential of improving body composition and early CVD management in mitigating risk of neurodegenerative diseases.
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Affiliation(s)
- Shishi Xu
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Shu Wen
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Yao Yang
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Junhui He
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Huazhen Yang
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Yuanyuan Qu
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Yu Zeng
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Jianwei Zhu
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Fang Fang
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
| | - Huan Song
- From the West China Hospital of Sichuan University (S.X., S.W., Y.Y., J.H., H.Y., Y.Q., Y.Z., J.Z., H.S.), Chengdu, China; and Karolinska Institutet (F.F.), Solna, Sweden
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5
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children With Developmental Language Disorder. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:774-794. [PMID: 39175782 PMCID: PMC11338306 DOI: 10.1162/nol_a_00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/13/2024] [Indexed: 08/24/2024]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of corticostriatal systems, little is known about the role of the cerebellum in DLD. Corticocerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of children with DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not corticocerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech and language acquisition and development.
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Affiliation(s)
- Salomi S Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, WA, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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6
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TLS, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga AW, O'Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Nick Bryan R, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Brain aging patterns in a large and diverse cohort of 49,482 individuals. Nat Med 2024:10.1038/s41591-024-03144-x. [PMID: 39147830 DOI: 10.1038/s41591-024-03144-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 06/20/2024] [Indexed: 08/17/2024]
Abstract
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- GE Healthcare, Bellevue, WA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Site Rostock/Greifswald, German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Data Science & Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sid O'Bryant
- Institute for Translational Research University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Mallar M Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Dept of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, Baltimore, MD, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Zabihi M, Kia SM, Wolfers T, de Boer S, Fraza C, Dinga R, Arenas AL, Bzdok D, Beckmann CF, Marquand A. Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps. PLoS One 2024; 19:e0308329. [PMID: 39116147 PMCID: PMC11309387 DOI: 10.1371/journal.pone.0308329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ('latent indices') and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.
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Affiliation(s)
- Mariam Zabihi
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- MRC Unit for Lifelong Health & Ageing, University College London (UCL), London, United Kingdom
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Stijn de Boer
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Charlotte Fraza
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Alberto Llera Arenas
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Mila ‐ Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Andre Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, United Kingdom
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8
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Bledsoe X, Gamazon ER. A transcriptomic atlas of the human brain reveals genetically determined aspects of neuropsychiatric health. Am J Hum Genet 2024; 111:1559-1572. [PMID: 38925120 PMCID: PMC11339608 DOI: 10.1016/j.ajhg.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Regulation of gene expression is a vital component of neurological homeostasis. Cataloging the consequences of endogenous gene expression on the physical structure and connectivity of the brain offers a means of unifying trait-associated genetic variation with trait-associated neurological features. We perform tissue-specific transcriptome-wide association studies (TWASs) on over 3,400 neuroimaging phenotypes in the UK Biobank (N = 33,224) using our joint-tissue imputation (JTI)-TWAS method. We identify highly significant associations between predicted expression for 7,192 genes and a wide variety of measures of the brain derived from magnetic resonance imaging (MRI). Our approach generates reproducible results in internal and external replication datasets. Genetically determined expression alone is sufficient for high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses toward an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we provide evidence suggesting that the genetically regulated expression of schizophrenia risk genes causally affects over 73% of neurological phenotypes that are altered in individuals with schizophrenia (as identified by neuroimaging studies). Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. By linking neuroimaging-derived phenotypes with expression levels of specific genes, this resource represents a powerful gene prioritization schema that can improve our understanding of brain function, development, and disease. The use of multiple different cortical and subcortical atlases in the resource facilitates direct integration of these data with findings from a diverse range of clinical neuroimaging studies.
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Affiliation(s)
- Xavier Bledsoe
- Medical Scientist Training Program, Vanderbilt University, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Memory & Alzheimer's Center, Nashville, TN, USA.
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9
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Mohammad S, Gentreau M, Dubol M, Rukh G, Mwinyi J, Schiöth HB. Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults. Mol Autism 2024; 15:34. [PMID: 39113134 PMCID: PMC11304666 DOI: 10.1186/s13229-024-00611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.
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Affiliation(s)
- Salahuddin Mohammad
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mélissa Gentreau
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Manon Dubol
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
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10
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Crestol A, de Lange AMG, Schindler L, Subramaniapillai S, Nerland S, Oppenheimer H, Westlye LT, Andreassen OA, Agartz I, Tamnes CK, Barth C. Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort. Horm Behav 2024; 164:105596. [PMID: 38944998 DOI: 10.1016/j.yhbeh.2024.105596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024]
Abstract
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
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Affiliation(s)
- Arielle Crestol
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Ann-Marie G de Lange
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Louise Schindler
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sivaniya Subramaniapillai
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Stener Nerland
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hannah Oppenheimer
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm Health Care Services, Karolinska Institute, Stockholm County Council, Stockholm, Sweden; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
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Volpi T, Silvestri E, Aiello M, Lee JJ, Vlassenko AG, Goyal MS, Corbetta M, Bertoldo A. The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity? J Cereb Blood Flow Metab 2024; 44:1433-1449. [PMID: 38443762 DOI: 10.1177/0271678x241237974] [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: 03/07/2024]
Abstract
Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.
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Affiliation(s)
- Tommaso Volpi
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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12
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Dove A, Dunk MM, Wang J, Guo J, Whitmer RA, Xu W. Anti-Inflammatory Diet and Dementia in Older Adults With Cardiometabolic Diseases. JAMA Netw Open 2024; 7:e2427125. [PMID: 39133488 PMCID: PMC11320167 DOI: 10.1001/jamanetworkopen.2024.27125] [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: 03/07/2024] [Accepted: 06/13/2024] [Indexed: 08/13/2024] Open
Abstract
Importance Inflammation has been proposed as a mechanism linking cardiometabolic diseases (CMDs) to increased risk of dementia. However, whether an anti-inflammatory diet can support brain and cognitive health among people with CMDs is unclear. Objective To examine CMD status and dietary inflammatory potential in association with dementia risk and brain magnetic resonance imaging (MRI) measures using joint effect analysis. Design, Setting, and Participants The UK Biobank is an ongoing community-based cohort study with baseline assessments conducted between March 13, 2006, and October 1, 2010. The present study included 84 342 dementia-free older adults (≥60 years), who were followed up until January 20, 2022 (maximum, 15 years). A subsample (n = 8917) underwent brain MRI scans between May 2, 2014, and March 13, 2020. Exposures Baseline CMDs (including type 2 diabetes, heart disease, and stroke) were ascertained from medical records. Dietary Inflammatory Index scores (anti-inflammatory [≤-1.5 points], neutral [>-1.5 to <0.5 points], or proinflammatory [≥0.5 points]) were calculated from participants' average intake of 31 nutrients, assessed up to 5 times using the Oxford WebQ, a web-based, 24-hour dietary assessment. Main Outcomes and Measures Incident dementia was identified through linkage to medical records. Regional brain volumes were collected from brain MRI scans. Results The study included 84 342 participants (mean [SD] age, 64.1 [2.9] years; 43 220 [51.2%] female). At baseline, 14 079 (16.7%) had at least 1 CMD. Over a median follow-up of 12.4 (IQR, 11.8-13.1) years, 1559 individuals (1.9%) developed dementia. With the use of joint effect analysis, the hazard ratio of dementia was 2.38 (95% CI, 1.93-2.93) for people with CMDs and a proinflammatory diet and 1.65 (95% CI, 1.36-2.00) for those with CMDs and an anti-inflammatory diet (reference: CMD-free, anti-inflammatory diet). Dementia risk was 31% lower (hazard ratio, 0.69; 95% CI, 0.55-0.88; P = .003) among people with CMDs and an anti-inflammatory diet. On brain MRI, participants with CMDs and an anti-inflammatory diet compared with a proinflammatory diet additionally had significantly larger gray matter volume (β = -0.15; 95% CI, -0.24 to -0.06 vs β = -0.27; 95% CI, -0.38 to -0.16) and smaller white matter hyperintensity volume (β = 0.05; 95% CI, -0.04 to 0.14 vs β = 0.16; 95% CI, 0.05-0.27). Conclusions and Relevance In this cohort study, people with CMDs and an anti-inflammatory compared with proinflammatory diet had a significantly lower hazard ratio of dementia, larger gray matter volume, and smaller white matter hyperintensity volume.
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Affiliation(s)
- Abigail Dove
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Michelle M. Dunk
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Jiao Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Department of Epidemiology, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jie Guo
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition and Health, China Agricultural University, Beijing, China
| | - Rachel A. Whitmer
- Department of Public Health Sciences and Neurology, University of California, Davis
| | - Weili Xu
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
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13
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Alateeq K, Walsh EI, Ambikairajah A, Cherbuin N. Association between dietary magnesium intake, inflammation, and neurodegeneration. Eur J Nutr 2024; 63:1807-1818. [PMID: 38597977 PMCID: PMC11329609 DOI: 10.1007/s00394-024-03383-1] [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: 06/08/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND Consistent evidence shows that magnesium (Mg) intake is associated with lower blood pressure (BP), and that lower BP is associated with improved cerebral health. However, recent findings indicate that the positive effect of dietary Mg intake on cerebral health is not mediated by a decrease in BP. As Mg's anti-inflammatory action is a plausible alternative mechanism, the objective of this study was to investigate the associations between Mg intake and inflammation to determine whether it mediates any neuroprotective effect. METHODS Participants from the UK Biobank (n = 5775, aged 40-73 years, 54.7% female) were assessed for dietary magnesium using an online food questionnaire, brain and white matter lesion (WML) volumes were segmented with FreeSurfer software, and inflammation markers including high-sensitivity C-reactive protein (hs-CRP), leukocyte, erythrocyte count, and Glycoprotein acetylation (GlycA) were measured using specific laboratory techniques such as immunoturbidimetry, automated cell counting, and nuclear magnetic resonance. Hierarchical linear regression models were performed to investigate the association between dietary Mg, and inflammatory markers and between dietary Mg, brain and WMLs volumes. Mediation analysis was performed to test a possible mediation role of inflammation on the association between dietary Mg and brain and WMLs volumes. RESULTS Higher dietary Mg intake was associated with lower inflammation: hs-CRP level (- 0.0497%; 95% confidence interval [CI] - 0.0497%, - 0.0199%) leukocytes count (- 0.0015%; 95%CI - 0.00151%, - 0.0011%), and GlycA (- 0.0519%; 95%CI - 0.1298%, - 0.0129%). Moreover, higher dietary Mg intake was associated with larger grey matter volume (0.010%; 95%CI 0.004%, 0.017%), white matter volume (0.012%; 95%CI 0.003, 0.022) and right hippocampal volume (0.002%; 95%CI 0.0007, -0.0025%). Lower hs-CRP levels mediated the positive association between higher dietary Mg intake and larger grey matter volume. CONCLUSIONS The anti-inflammatory effects of dietary Mg intake in the general population, appears to mediate its neuroprotective effect.
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Affiliation(s)
- Khawlah Alateeq
- National Centre for Epidemiology and Population Health, Australian National University, 54 Mills Road, Canberra, ACT, 2601, Australia.
- Radiological Science, College of Applied Medical Science, King Saud University, Riyadh, Saudi Arabia.
| | - Erin I Walsh
- National Centre for Epidemiology and Population Health, Australian National University, 54 Mills Road, Canberra, ACT, 2601, Australia
| | - Ananthan Ambikairajah
- National Centre for Epidemiology and Population Health, Australian National University, 54 Mills Road, Canberra, ACT, 2601, Australia
- Discipline of Psychology, Faculty of Health, University of Canberra, Canberra, ACT, 2617, Australia
- Centre for Ageing Research and Translation, Faculty of Health, University of Canberra, Canberra, 2617, Australia
| | - Nicolas Cherbuin
- National Centre for Epidemiology and Population Health, Australian National University, 54 Mills Road, Canberra, ACT, 2601, Australia
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14
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Ling Q, Liu A, Li Y, McKeown MJ, Chen X. fMRI-based spatio-temporal parcellations of the human brain. Curr Opin Neurol 2024; 37:369-380. [PMID: 38804205 DOI: 10.1097/wco.0000000000001280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
PURPOSE OF REVIEW Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. RECENT FINDINGS Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.
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Affiliation(s)
- Qinrui Ling
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Aiping Liu
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Yu Li
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Martin J McKeown
- Department of Medicine, University of British Columbia, Vancouver, Vancouver V6T2B5, Canada
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
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15
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Arold D, Bornstein SR, Perakakis N, Ehrlich S, Bernardoni F. Regional gray matter changes in steatotic liver disease provide a neurobiological link to depression: A cross-sectional UK Biobank cohort study. Metabolism 2024; 159:155983. [PMID: 39089490 DOI: 10.1016/j.metabol.2024.155983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 07/27/2024] [Accepted: 07/29/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Steatotic liver disease (SLD) is characterized by excessive accumulation of lipids in the liver. It is associated with elevated risk of hepatic and cardiometabolic diseases, as well as mental disorders such as depression. Previous studies revealed global gray matter reduction in SLD. To investigate a possible shared neurobiology with depression, we examined liver fat-related regional gray matter alterations in SLD and its most significant clinical subgroup metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS We analyzed regional cortical thickness and area obtained from brain MRI in 29,051 participants in UK Biobank. Liver fat amount was computed as proton density fat fraction (PDFF) from liver MRI scans. We examined the relationship between brain structure and PDFF, adjusting for sociodemographic, physical, lifestyle, and environmental factors, as well as alcohol intake and a spectrum of cardiometabolic covariates. Finally, we compared patterns of brain alterations in SLD/MASLD and major depressive disorder (MDD) using previously published results. RESULTS PDFF-related gray matter alterations were region-specific, involving both increases and decreases in cortical thickness, and increased cortical area. In several regions, PDFF effects on gray matter could also be attributed to cardiometabolic covariates. However, PDFF was consistently associated with lower cortical thickness in middle and superior temporal regions and higher cortical thickness in pericalcarine and right frontal pole regions. PDFF-related alterations for the SLD and the MASLD group correlated with those observed in MDD (Pearson r = 0.45-0.54, p < 0.01). CONCLUSION These findings suggest the presence of shared biological mechanisms linking MDD to SLD and MASLD. They might explain the well-known elevated risk of depression in these groups and support early lifestyle interventions and treatment of metabolic risk factors for the successful management of the interconnected diseases depression and SLD/MASLD.
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Affiliation(s)
- Dominic Arold
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Stefan R Bornstein
- Department of Internal Medicine III, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Paul Langerhans Institute Dresden (PLID), Helmholtz Center Munich, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Nikolaos Perakakis
- Department of Internal Medicine III, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Paul Langerhans Institute Dresden (PLID), Helmholtz Center Munich, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Fabio Bernardoni
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany.
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Kim WS, Heo DW, Maeng J, Shen J, Tsogt U, Odkhuu S, Zhang X, Cheraghi S, Kim SW, Ham BJ, Rami FZ, Sui J, Kang CY, Suk HI, Chung YC. Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders. Schizophr Bull 2024; 50:804-814. [PMID: 38085061 PMCID: PMC11283195 DOI: 10.1093/schbul/sbad167] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY RESULTS The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. CONCLUSIONS Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.
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Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Da-Woon Heo
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Junyeong Maeng
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Yanbian University, Medical School, Yanji, China
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Xuefeng Zhang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chae Yeong Kang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
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Bhatt RR, Gadewar SP, Shetty A, Ba Gari I, Haddad E, Javid S, Ramesh A, Nourollahimoghadam E, Zhu AH, de Leeuw C, Thompson PM, Medland SE, Jahanshad N. The Genetic Architecture of the Human Corpus Callosum and its Subregions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.22.603147. [PMID: 39091796 PMCID: PMC11291056 DOI: 10.1101/2024.07.22.603147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CC's role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.
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Huang H, Wang J, Dunk MM, Guo J, Dove A, Ma J, Bennett DA, Xu W. Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults. Neurology 2024; 103:e209530. [PMID: 38889383 PMCID: PMC11226327 DOI: 10.1212/wnl.0000000000209530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/05/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD). METHODS This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models. RESULTS The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]). DISCUSSION Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.
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Affiliation(s)
- Huijie Huang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jiao Wang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Michelle M Dunk
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jie Guo
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Abigail Dove
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jun Ma
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - David A Bennett
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Weili Xu
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
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19
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Wright AM, Xu T, Ingram J, Koo J, Zhao Y, Tong Y, Wen Q. Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603932. [PMID: 39091755 PMCID: PMC11290998 DOI: 10.1101/2024.07.17.603932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the method's reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.
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20
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Savage HS, Mulders PCR, van Eijndhoven PFP, van Oort J, Tendolkar I, Vrijsen JN, Beckmann CF, Marquand AF. Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task. Commun Biol 2024; 7:888. [PMID: 39033247 PMCID: PMC11271583 DOI: 10.1038/s42003-024-06573-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: 04/05/2024] [Accepted: 07/09/2024] [Indexed: 07/23/2024] Open
Abstract
Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.
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Affiliation(s)
- Hannah S Savage
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Peter C R Mulders
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Philip F P van Eijndhoven
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Jasper van Oort
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Indira Tendolkar
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Janna N Vrijsen
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
- Depression Expertise Centre, Pro Persona Mental Health Care, Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.
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21
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Dove A, Yang W, Dekhtyar S, Guo J, Wang J, Marseglia A, Vetrano DL, Whitmer RA, Xu W. High cognitive reserve attenuates the risk of dementia associated with cardiometabolic diseases. Alzheimers Res Ther 2024; 16:161. [PMID: 39030628 PMCID: PMC11264799 DOI: 10.1186/s13195-024-01528-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Cardiometabolic diseases (CMDs) including type 2 diabetes, heart disease, and stroke have been linked to a higher risk of dementia. We examined whether high levels of cognitive reserve (CR) can attenuate the increased dementia risk and brain pathologies associated with CMDs. METHODS Within the UK Biobank, 216,178 dementia-free participants aged ≥ 60 were followed for up to 15 years. Baseline CMDs and incident dementia were ascertained from medical records, medication use, and medical history. Latent class analysis was used to generate an indicator of CR (low, moderate, and high) based on education, occupational attainment, confiding in others, social contact, leisure activities, and television watching time. A subsample (n = 13,663) underwent brain MRI scans during follow-up. Volumes of total gray matter (GMV), hippocampus (HV), and white matter hyperintensities (WMHV) were ascertained, as well as mean diffusivity (MD) and fractional anisotropy (FA) in white matter tracts. RESULTS At baseline, 43,402 (20.1%) participants had at least one CMD. Over a mean follow-up of 11.7 years, 6,600 (3.1%) developed dementia. The presence of CMDs was associated with 57% increased risk of dementia (HR 1.57 [95% CI 1.48, 1.67]). In joint effect analysis, the HRs of dementia for people with CMDs and moderate-to-high CR and low CR were 1.78 [1.66, 1.91] and 2.13 [1.97, 2.30]), respectively (reference: CMD-free, moderate-to-high CR). Dementia risk was 17% lower (HR 0.83 [0.77, 0.91], p < 0.001) among people with CMDs and moderate-to-high compared to low CR. On brain MRI, CMDs were associated with smaller GMV (β -0.18 [-0.22, -0.13]) and HV (β -0.13 [-0.18, -0.08]) as well as significantly larger WMHV (β 0.06 [0.02, 0.11]) and MD (β 0.08 [0.02, 0.13]). People with CMDs and moderate-to-high compared to low CR had significantly larger GMV and HV, but no differences in WMHV, MD, or FA. CONCLUSIONS Among people with CMDs, having a higher level of CR was associated with lower dementia risk and larger gray matter and hippocampal volumes. The results highlight a mentally and socially active life as a modifiable factor that may support cognitive and brain health among people with CMDs.
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Affiliation(s)
- Abigail Dove
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Wenzhe Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Serhiy Dekhtyar
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Jie Guo
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition and Health, China Agricultural University, Beijing, China
| | - Jiao Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Department of Epidemiology, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Anna Marseglia
- Division of Clinical Geriatrics, Center for Alzheimer's Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Davide Liborio Vetrano
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Rachel A Whitmer
- Department of Public Health Sciences and Neurology, University of California Davis, Davis, CA, USA
| | - Weili Xu
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
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22
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15:6064. [PMID: 39025851 PMCID: PMC11258354 DOI: 10.1038/s41467-024-50309-w] [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: 06/23/2023] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA.
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yilin Yang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT, 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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23
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Kéri S, Kancsev A, Kelemen O. Algorithm-Based Modular Psychotherapy Alleviates Brain Inflammation in Generalized Anxiety Disorder. Life (Basel) 2024; 14:887. [PMID: 39063640 PMCID: PMC11278507 DOI: 10.3390/life14070887] [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: 06/27/2024] [Revised: 07/11/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Generalized anxiety disorder (GAD) is marked by prolonged and excessive worry, physical signs of anxiety, and associated neuroinflammation. Traditional treatments, like pharmacotherapy and cognitive-behavioral therapy (CBT), often leave residual symptoms and have high relapse rates. This study aimed to explore the efficacy of algorithm-based modular psychotherapy (MoBa), a combination of CBT and mindfulness meditation as validated by the research domain criteria (RDoC), in reducing anxiety and neuroinflammation in GAD. A longitudinal design was used, with 50 patients with GAD undergoing a 12-week MoBa treatment. The patients were investigated pre- and post-treatment using MRI to measure neuroinflammatory markers (DBSI-RF, diffusion-basis spectral imaging-based restricted fraction) in the hippocampus, amygdala, and neocortex. Clinical symptoms were assessed using the Hamilton Anxiety Rating Scale (HAM-A) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Results indicated significant reductions in both anxiety symptoms and MRI RF values in the amygdala, suggesting decreased neuroinflammation. A reduction in anxiety was associated with the amelioration of neuroinflammation in the amygdala. These results suggest that MoBa is effective in alleviating both the psychological and neuroinflammatory aspects of GAD, offering a promising personalized treatment approach. Future research should focus on long-term effects and the mechanisms through which MoBa impacts neuroinflammation and anxiety.
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Affiliation(s)
- Szabolcs Kéri
- Sztárai Institute, University of Tokaj, 3944 Sárospatak, Hungary
- Department of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, 6720 Szeged, Hungary
| | - Alexander Kancsev
- Department of Psychiatry and Psychotherapy, András Jósa Hospital, 4400 Nyíregyháza, Hungary;
| | - Oguz Kelemen
- Department of Behavioral Sciences, Albert Szent-Györgyi Medical School, University of Szeged, 6720 Szeged, Hungary;
- Department of Psychiatry and Psychotherapy, Bács-Kiskun County Hospital, 6000 Kecskemét, Hungary
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24
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Rotondi V, Allegra M, Kashyap R, Barban N, Sironi M, Reverberi C. Enduring maternal brain changes and their role in mediating motherhood's impact on well-being. Sci Rep 2024; 14:16608. [PMID: 39025996 PMCID: PMC11258333 DOI: 10.1038/s41598-024-67316-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
Parenthood, particularly motherhood, is known to impact the structure and function of the brain in the short term, but the long-term effects of parenthood and their impacts on well-being are still poorly understood. This study explores the potential longer-term associations between parenthood and the brain, parenthood and well-being, and the potential role of brain modifications in influencing mothers' well-being. Using data from the UK Biobank, which includes brain imaging information from individuals aged 45-82 at the MRI scanning, we discovered differences in brain structure between mothers and non-mothers, with mothers exhibiting widely distributed higher gray matter density, particularly strong in frontal and occipital regions. No brain changes were observed in fathers. Parents reported a higher sense of life's meaning compared to their childless counterparts. Gray matter changes did not mediate the relationship between motherhood and well-being. This suggests that the alterations in gray matter associated with motherhood do not play a deterministic role in shaping long-term changes in well-being.
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Affiliation(s)
- Valentina Rotondi
- SUPSI, Lugano, Switzerland.
- University of Oxford & Nuffield College, Oxford, UK.
- Milan Center for Neuroscience - NeuroMI, University of Milano-Bicocca, Milan, Italy.
| | | | | | | | | | - Carlo Reverberi
- Milan Center for Neuroscience - NeuroMI, University of Milano-Bicocca, Milan, Italy
- Department of Psychology, University of Milano - Bicocca, Milan, Italy
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25
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Bao Y, Chen X, Li Y, Yuan S, Han L, Deng X, Ran J. Chronic Low-Grade Inflammation and Brain Structure in the Middle-Aged and Elderly Adults. Nutrients 2024; 16:2313. [PMID: 39064755 PMCID: PMC11280392 DOI: 10.3390/nu16142313] [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: 06/20/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Low-grade inflammation (LGI) mainly acted as the mediator of the association of obesity and inflammatory diet with numerous chronic diseases, including neuropsychiatric diseases. However, the evidence about the effect of LGI on brain structure is limited but important, especially in the context of accelerating aging. This study was then designed to close the gap, and we leveraged a total of 37,699 participants from the UK Biobank and utilized inflammation score (INFLA-score) to measure LGI. We built the longitudinal relationships of INFLA-score with brain imaging phenotypes using multiple linear regression models. We further analyzed the interactive effects of specific covariates. The results showed high level inflammation reduced the volumes of the subcortex and cortex, especially the globus pallidus (β [95% confidence interval] = -0.062 [-0.083, -0.041]), thalamus (-0.053 [-0.073, -0.033]), insula (-0.052 [-0.072, -0.032]), superior temporal gyrus (-0.049 [-0.069, -0.028]), lateral orbitofrontal cortex (-0.047 [-0.068, -0.027]), and others. Most significant effects were observed among urban residents. Furthermore, males and individuals with physical frailty were susceptive to the associations. The study provided potential insights into pathological changes during disease progression and might aid in the development of preventive and control targets in an age-friendly city to promote great health and well-being for sustainable development goals.
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Affiliation(s)
- Yujia Bao
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.B.); (X.C.); (Y.L.); (S.Y.)
| | - Xixi Chen
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.B.); (X.C.); (Y.L.); (S.Y.)
| | - Yongxuan Li
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.B.); (X.C.); (Y.L.); (S.Y.)
| | - Shenghao Yuan
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.B.); (X.C.); (Y.L.); (S.Y.)
| | - Lefei Han
- School of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Xiaobei Deng
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.B.); (X.C.); (Y.L.); (S.Y.)
| | - Jinjun Ran
- School of Public Health, University of Hong Kong, Hong Kong SAR, China
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26
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Tudosiu PD, Pinaya WHL, Ferreira Da Costa P, Dafflon J, Patel A, Borges P, Fernandez V, Graham MS, Gray RJ, Nachev P, Ourselin S, Cardoso MJ. Realistic morphology-preserving generative modelling of the brain. NAT MACH INTELL 2024; 6:811-819. [PMID: 39055051 PMCID: PMC11266097 DOI: 10.1038/s42256-024-00864-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness.
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Affiliation(s)
| | | | - Pedro Ferreira Da Costa
- Institute of Psychiatry, King’s College London, London, UK
- Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Jessica Dafflon
- Data Science and Sharing Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, MD USA
- Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, MD USA
| | - Ashay Patel
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Pedro Borges
- Department of Biomedical Engineering, King’s College London, London, UK
| | | | - Mark S. Graham
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Robert J. Gray
- Queen Square Institute of Neurology, University College London, London, UK
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London, UK
| | | | - M. Jorge Cardoso
- Department of Biomedical Engineering, King’s College London, London, UK
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27
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Lu J, Yan T, Yang L, Zhang X, Li J, Li D, Xiang J, Wang B. Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability. Neuroimage 2024; 295:120651. [PMID: 38788914 DOI: 10.1016/j.neuroimage.2024.120651] [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: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024] Open
Abstract
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
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Affiliation(s)
- Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 100081, China
| | - Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xi Zhang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaxin Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
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Harnett NG, Fleming LL, Clancy KJ, Ressler KJ, Rosso IM. Affective Visual Circuit Dysfunction in Trauma and Stress-Related Disorders. Biol Psychiatry 2024:S0006-3223(24)01433-1. [PMID: 38996901 DOI: 10.1016/j.biopsych.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/12/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024]
Abstract
Posttraumatic stress disorder (PTSD) is widely recognized as involving disruption of core neurocircuitry that underlies processing, regulation, and response to threat. In particular, the prefrontal cortex-hippocampal-amygdala circuit is a major contributor to posttraumatic dysfunction. However, the functioning of core threat neurocircuitry is partially dependent on sensorial inputs, and previous research has demonstrated that dense, reciprocal connections exist between threat circuits and the ventral visual stream. Furthermore, emergent evidence suggests that trauma exposure and resultant PTSD symptoms are associated with altered structure and function of the ventral visual stream. In the current review, we discuss evidence that both threat and visual circuitry together are an integral part of PTSD pathogenesis. An overview of the relevance of visual processing to PTSD is discussed in the context of both basic and translational research, highlighting the impact of stress on affective visual circuitry. This review further synthesizes emergent literature to suggest potential timing-dependent effects of traumatic stress on threat and visual circuits that may contribute to PTSD development. We conclude with recommendations for future research to move the field toward a more complete understanding of PTSD neurobiology.
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Affiliation(s)
- Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
| | - Leland L Fleming
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Kevin J Clancy
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Kerry J Ressler
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Isabelle M Rosso
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
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Kang J, Deng YT, Wu BS, Liu WS, Li ZY, Xiang S, Yang L, You J, Gong X, Jia T, Yu JT, Cheng W, Feng J. Whole exome sequencing analysis identifies genes for alcohol consumption. Nat Commun 2024; 15:5777. [PMID: 38982111 PMCID: PMC11233704 DOI: 10.1038/s41467-024-50132-3] [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: 05/15/2023] [Accepted: 06/26/2024] [Indexed: 07/11/2024] Open
Abstract
Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.
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Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology, University of Southampton, Southampton, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China.
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
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Ward J, Cox SR, Quinn T, Lyall LM, Strawbridge RJ, Russell E, Pell JP, Stewart W, Cullen B, Whalley H, Lyall DM. Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data. Brain Commun 2024; 6:fcae220. [PMID: 39015764 PMCID: PMC11249925 DOI: 10.1093/braincomms/fcae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 05/15/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024] Open
Abstract
Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.
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Affiliation(s)
- Joey Ward
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Simon R Cox
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, EH8 9JZ, Edinburgh, UK
| | - Terry Quinn
- School of Cardiovascular and Metabolic Sciences, University of Glasgow, G12 8TA, Glasgow, UK
| | - Laura M Lyall
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Rona J Strawbridge
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, 171 64, Stockholm, Sweden
- Health Data Research (HDR)-UK, NW1 2BE, London, UK
| | - Emma Russell
- School of Psychology and Neuroscience, University of Glasgow, G12 8QB, Glasgow, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - William Stewart
- School of Psychology and Neuroscience, University of Glasgow, G12 8QB, Glasgow, UK
- Department of Neuropathology, Queen Elizabeth University Hospital, G51 4TF, Glasgow, UK
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Heather Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, EH16 4SB, Edinburgh, UK
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
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31
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Yang H, Wang G, Li Z, Li H, Zheng J, Hu Y, Cao X, Liao C, Ye H, Tian Q. Artificial intelligence for neuro MRI acquisition: a review. MAGMA (NEW YORK, N.Y.) 2024; 37:383-396. [PMID: 38922525 DOI: 10.1007/s10334-024-01182-7] [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: 01/19/2024] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024]
Abstract
OBJECT To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND METHODS A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods. RESULTS The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.
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Affiliation(s)
- Hongjia Yang
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Guanhua Wang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Haoxiang Li
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Jialan Zheng
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Congyu Liao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Huihui Ye
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Qiyuan Tian
- School of Biomedical Engineering, Tsinghua University, Beijing, China.
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.
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Ren P, Bao H, Wang S, Wang Y, Bai Y, Lai J, Yi L, Liu Q, Li W, Zhang X, Sun L, Liu Q, Cui X, Zhang X, Liang P, Liang X. Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes. Int J Cancer 2024. [PMID: 38949756 DOI: 10.1002/ijc.35068] [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: 09/30/2023] [Revised: 04/11/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.
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Affiliation(s)
- Peng Ren
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Wang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Bai
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiacheng Lai
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qing Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wenting Li
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xinyu Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Sun
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qiuyi Liu
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xuehua Cui
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiushi Zhang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China
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Valošek J, Cohen-Adad J. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox. Magn Reson Med Sci 2024; 23:307-315. [PMID: 38479843 PMCID: PMC11234946 DOI: 10.2463/mrms.rev.2023-0159] [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: 07/02/2024] Open
Abstract
The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).
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Affiliation(s)
- Jan Valošek
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
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Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. PATTERN RECOGNITION 2024; 151:110424. [PMID: 38559674 PMCID: PMC10976951 DOI: 10.1016/j.patcog.2024.110424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
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Affiliation(s)
- Hao Guan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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35
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Sun Z, Zhang B, Smith S, Atan D, Khawaja AP, Stuart KV, Luben RN, Biradar MI, McGillivray T, Patel PJ, Khaw PT, Petzold A, Foster PJ. Structural correlations between brain magnetic resonance image-derived phenotypes and retinal neuroanatomy. Eur J Neurol 2024; 31:e16288. [PMID: 38716763 PMCID: PMC11235673 DOI: 10.1111/ene.16288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND AND PURPOSE The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank. METHODS Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders. RESULTS Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10-5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers. CONCLUSIONS Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections.
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Affiliation(s)
- Zihan Sun
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Bing Zhang
- National Clinical Research Centre for Ocular Diseases, Eye HospitalWenzhou Medical UniversityWenzhouChina
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging (WIN Functional Magnetic Resonance Imaging Building)University of OxfordOxfordUK
| | - Denize Atan
- Bristol Eye HospitalUniversity Hospitals Bristol and Weston NHS Foundation TrustBristolUK
- Translational Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Anthony P. Khawaja
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Kelsey V. Stuart
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Robert N. Luben
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Mahantesh I. Biradar
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | | | - Praveen J. Patel
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Peng T. Khaw
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
| | - Axel Petzold
- Queen Square Institute of Neurology, University College London, Department of Molecular NeurosciencesMoorfields Eye Hospital and National Hospital for Neurology and NeurosurgeryLondonUK
- Departments of Neurology and Ophthalmology and Expertise Center for Neuro‐ophthalmologyAmsterdam University Medical CentreAmsterdamthe Netherlands
| | - Paul J. Foster
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital National Health Service Foundation Trust and University College London Institute of OphthalmologyLondonUK
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [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: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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Petersen M, Chevalier C, Naegele FL, Ingwersen T, Omidvarnia A, Hoffstaedter F, Patil K, Eickhoff SB, Schnabel RB, Kirchhof P, Schlemm E, Cheng B, Thomalla G, Jensen M. Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction. Alzheimers Dement 2024; 20:4512-4526. [PMID: 38837525 PMCID: PMC11247702 DOI: 10.1002/alz.13870] [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: 12/08/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 06/07/2024]
Abstract
INTRODUCTION Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Céleste Chevalier
- Department of Cardiology, University Heart and Vascular Center, Hamburg, Germany
- DZHK (German Center for Cardiovascular Research), partner site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Felix L Naegele
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thies Ingwersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Kaustubh Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Renate B Schnabel
- Department of Cardiology, University Heart and Vascular Center, Hamburg, Germany
- DZHK (German Center for Cardiovascular Research), partner site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Paulus Kirchhof
- Department of Cardiology, University Heart and Vascular Center, Hamburg, Germany
- DZHK (German Center for Cardiovascular Research), partner site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Märit Jensen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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de Lange AMG, Leonardsen EH, Barth C, Schindler LS, Crestol A, Holm MC, Subramaniapillai S, Hill D, Alnæs D, Westlye LT. Parental status and markers of brain and cellular age: A 3D convolutional network and classification study. Psychoneuroendocrinology 2024; 165:107040. [PMID: 38636355 DOI: 10.1016/j.psyneuen.2024.107040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/20/2024]
Abstract
Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.
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Affiliation(s)
- Ann-Marie G de Lange
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK.
| | | | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Louise S Schindler
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Arielle Crestol
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Sivaniya Subramaniapillai
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Dónal Hill
- Swiss Data Science Center (SDSC), EPFL-ETHZ, Switzerland
| | - Dag Alnæs
- Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Li T, Steibel JP, Willette AA. Vitamin B6, B12, and Folate's Influence on Neural Networks in the UK Biobank Cohort. Nutrients 2024; 16:2050. [PMID: 38999798 PMCID: PMC11243472 DOI: 10.3390/nu16132050] [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: 05/29/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND One-carbon metabolism coenzymes may influence brain aging in cognitively unimpaired adults. METHODS Baseline data were used from the UK Biobank cohort. Estimated intake of vitamin B6, B12, and folate was regressed onto neural network functional connectivity in five resting-state neural networks. Linear mixed models tested coenzyme main effects and interactions with Alzheimer's disease (AD) risk factors. RESULTS Increased B6 and B12 estimated intake were linked with less functional connectivity in most networks, including the posterior portion of the Default Mode Network. Conversely, higher folate was related to more connectivity in similar networks. AD family history modulated these associations: Increased estimated intake was positively associated with stronger connectivity in the Primary Visual Network and Posterior Default Mode Network in participants with an AD family history. In contrast, increased vitamin B12 estimated intake was associated with less connectivity in the Primary Visual Network and the Cerebello-Thalamo-Cortical Network in those without an AD family history. CONCLUSIONS The differential patterns of association between B vitamins and resting-state brain activity may be important in understanding AD-related changes in the brain. Notably, AD family history appears to play a key role in modulating these relationships.
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Affiliation(s)
- Tianqi Li
- Genetics and Genomics Program, Iowa State University, Ames, IA 50011, USA
| | - Juan Pedro Steibel
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Auriel A Willette
- Department of Neurology, Rutgers University, New Brunswick, NJ 07101, USA
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Omidvarnia A, Sasse L, Larabi DI, Raimondo F, Hoffstaedter F, Kasper J, Dukart J, Petersen M, Cheng B, Thomalla G, Eickhoff SB, Patil KR. Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes. Commun Biol 2024; 7:771. [PMID: 38926486 PMCID: PMC11208538 DOI: 10.1038/s42003-024-06438-5] [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: 05/23/2023] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.
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Affiliation(s)
- Amir Omidvarnia
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany.
| | - Leonard Sasse
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
- Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany
| | - Daouia I Larabi
- Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands
| | - Federico Raimondo
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Jan Kasper
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Jürgen Dukart
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Marvin Petersen
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
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Hoffmann M, Hoopes A, Greve DN, Fischl B, Dalca AV. Anatomy-aware and acquisition-agnostic joint registration with SynthMorph. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-33. [PMID: 39015335 PMCID: PMC11247402 DOI: 10.1162/imag_a_00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 07/18/2024]
Abstract
Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Adrian V. Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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Lin L, Wu Y, Liu L, Sun S, Wu S. Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data. Bioengineering (Basel) 2024; 11:647. [PMID: 39061729 PMCID: PMC11273398 DOI: 10.3390/bioengineering11070647] [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: 05/17/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
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Affiliation(s)
- Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Yutong Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Shen Sun
- 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; (Y.W.); (L.L.); (S.W.)
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43
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Jansen MG, Zwiers MP, Marques JP, Chan KS, Amelink JS, Altgassen M, Oosterman JM, Norris DG. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One 2024; 19:e0306006. [PMID: 38905233 PMCID: PMC11192316 DOI: 10.1371/journal.pone.0306006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 06/23/2024] Open
Abstract
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
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Affiliation(s)
- Michelle G. Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Marcel P. Zwiers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jose P. Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jitse S. Amelink
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, the Netherlands
| | - Mareike Altgassen
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joukje M. Oosterman
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Pu F, Chen W, Li C, Fu J, Gao W, Ma C, Cao X, Zhang L, Hao M, Zhou J, Huang R, Ma Y, Hu K, Liu Z. Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics. Nat Commun 2024; 15:4921. [PMID: 38858361 PMCID: PMC11164970 DOI: 10.1038/s41467-024-49283-0] [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: 10/24/2023] [Accepted: 05/31/2024] [Indexed: 06/12/2024] Open
Abstract
Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.
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Affiliation(s)
- Fan Pu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Weiran Chen
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Chenxi Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Jingqiao Fu
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
| | - Weijing Gao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing, 211189, Jiangsu, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Lingzhi Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Meng Hao
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Jin Zhou
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Rong Huang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Yanan Ma
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China.
| | - Kejia Hu
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
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Hristovska I, Binette AP, Kumar A, Gaiteri C, Karlsson L, Strandberg O, Janelidze S, van Westen D, Stomrud E, Palmqvist S, Ossenkoppele R, Mattsson-Carlgren N, Vogel JW, Hansson O. Identification of distinct and shared biomarker panels in different manifestations of cerebral small vessel disease through proteomic profiling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.10.24308599. [PMID: 38947084 PMCID: PMC11213103 DOI: 10.1101/2024.06.10.24308599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The pathophysiology underlying various manifestations of cerebral small vessel disease (cSVD) remains obscure. Using cerebrospinal fluid proximity extension assays and co-expression network analysis of 2,943 proteins, we found common and distinct proteomic signatures between white matter lesions (WML), microbleeds and infarcts measured in 856 living patients, and validated WML-associated proteins in three additional datasets. Proteins indicative of extracellular matrix dysregulation and vascular remodeling, including ELN, POSTN, CCN2 and MMP12 were elevated across all cSVD manifestations, with MMP12 emerging as an early cSVD indicator. cSVD-associated proteins formed a co-abundance network linked to metabolism and enriched in endothelial and arterial smooth muscle cells, showing elevated levels at early disease manifestations. Later disease stages involved changes in microglial proteins, associated with longitudinal WML progression, and changes in neuronal proteins mediating WML-associated cognitive decline. These findings provide an atlas of novel cSVD biomarkers and a promising roadmap for the next generation of cSVD therapeutics.
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Affiliation(s)
- Ines Hristovska
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Atul Kumar
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Chris Gaiteri
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Rush University Alzheimer's Disease Center, Rush University, Chicago IL, USA
| | - Linda Karlsson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University
- Imaging and Function, Skåne University Hospital, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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46
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Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.
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Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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Chaloemtoem A, Thornton V, Chang Y, Anokhin AP, Belloy ME, Bijsterbosch J, Gordon BA, Hartz SM, Bierut LJ. Hippocampal volumes in UK Biobank are associated with APOE only in older adults. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.05.24307704. [PMID: 38883747 PMCID: PMC11177915 DOI: 10.1101/2024.06.05.24307704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
INTRODUCTION The hippocampus atrophies with age and is implicated in neurodegenerative disorders including Alzheimer's disease (AD). We examined the interplay between age and APOE genotype on total hippocampal volume. METHODS Utilizing neuroimaging data from 37,463 UK Biobank participants, we applied linear regression to quantify the association of age and APOE with hippocampal volume and identified the age when volumes of ε2/ε3, ε3/ε4, and ε4/ε4 carriers significantly deviated from ε3/ε3 using generalized additive modeling. RESULTS Total hippocampal volume declined with age, with significant differences by APOE genotype emerging after age 60. ε3/ε4 and ε4/ε4 carriers displayed reduced volumes from ages 69 and 61, respectively, while ε2/ε3 showed delayed decline starting at age 76. DISCUSSION The association of APOE and hippocampal volume is age-dependent, with differences in volumes of ε4/ε4 carriers detected as early as age 61. This work underscores the importance of APOE genotype in determining when to begin screening for AD.
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48
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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49
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Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
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Affiliation(s)
- Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dezheng Ding
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yuxin Guo
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gideon Nave
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael L. Platt
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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50
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Rubien-Thomas E, Lin YC, Chan I, Conley MI, Skalaban L, Kopp H, Adake A, Richeson JA, Gee DG, Baskin-Sommers A, Casey BJ. Interactive effects of participant and stimulus race on cognitive performance in youth: Insights from the ABCD study. Dev Cogn Neurosci 2024; 67:101393. [PMID: 38838435 PMCID: PMC11214402 DOI: 10.1016/j.dcn.2024.101393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024] Open
Abstract
An extensive literature shows that race information can impact cognitive performance. Two key findings include an attentional bias to Black racial cues in U.S. samples and diminished recognition of other-race faces compared to same-race faces in predominantly White adult samples. Yet face stimuli are increasingly used in psychological research often unrelated to race (Conley et al., 2018) or without consideration for how race information may influence cognitive performance, especially among developmental participants from different racial groups. In the current study we used open-access data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study® 4.0.1 release to test for developmentally similar other- and same-race effects of Black and White face stimuli on attention, working memory, and recognition memory in 9- and 10-year-old Black and White children (n=5,659) living in the U.S. Black and White children showed better performance when attending to Black versus White faces. We also show an advantage in recognition memory of same-race compared to other-race faces in White children that did not generalize to Black children. Together the findings highlight how race information, even when irrelevant to an experiment, may indirectly lead to misinterpretation of group differences in cognitive performance in children of different racial backgrounds.
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Affiliation(s)
| | - Yen-Chu Lin
- Department of Neuroscience and Behavior, Barnard College-Columbia University, New York, USA.
| | - Ivan Chan
- Department of Psychology, Yale University, New Haven, CT, USA
| | - May I Conley
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Lena Skalaban
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Hailey Kopp
- Department of Neuroscience and Behavior, Barnard College-Columbia University, New York, USA
| | - Arya Adake
- Department of Neuroscience and Behavior, Barnard College-Columbia University, New York, USA
| | | | - Dylan G Gee
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - B J Casey
- Department of Neuroscience and Behavior, Barnard College-Columbia University, New York, USA.
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