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Jönemo J, Eklund A. Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes. J Imaging 2023; 9:271. [PMID: 38132689 PMCID: PMC10743800 DOI: 10.3390/jimaging9120271] [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: 10/19/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.
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Affiliation(s)
- Johan Jönemo
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
| | - Anders Eklund
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden
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2
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Firoozi A, Alizadeh A, Zarifkar A, Esmaeilpour T, Namavar MR, Alavi O, Dehghani F. Comparison of the efficacy of human umbilical cord mesenchymal stem cells conditioned medium and platelet-rich plasma on the hippocampus of STZ-induced rat model of Alzheimer's disease: A behavioral and stereological study. IBRO Neurosci Rep 2023; 15:209-217. [PMID: 37780033 PMCID: PMC10539893 DOI: 10.1016/j.ibneur.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 08/13/2023] [Accepted: 09/12/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is accompanied by progressive cognitive disorders and memory loss. This study aims to determine the combined effects of conditioned medium of human umbilical cord mesenchymal stem cells (CM) and platelet-rich plasma (PRP) on AD model rats. Methods Forty-eight male Sprague Dawley rats were classified into 6 groups: Control, Sham, AD, and three treatment groups. AD was induced by streptozotocin(STZ; 3 mg/kg, intracerebroventricular (ICV)) and the treatment groups received injections of CM [(200 µl, intraperitoneally (i.p.), and/or PRP (100 µl, intravenously(i.v)] for 8 days. Behavioral tests (Morris water maze and novel objective recognition) were used to assess learning ability and memory. At the end of the behavioral tests, the rats were sacrificed and their brain was entirely removed, sectioned, and stained with cresyl violet. The hippocampus volume and number of neurons were evaluated by stereological techniques. Results In the AD group, the discrimination ratio, time spent in the target zone, volume of Cornu Ammonis1 (CA1) and Dentate Gyrus (DG), and the number of pyramidal and granular cells decreased significantly compared to the Sham group. The mentioned parameters increased in the CM and CM+PRP groups compared to the AD group (p < 0.01). PRP did not have any noticeable effect on the examined parameters. Conclusions CM may be beneficial in the treatment of AD as it led to better improvement in STZ-induced learning and memory impairments as well as the structure of the hippocampus.
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Affiliation(s)
- Amin Firoozi
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Histomorphometry & Stereology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aliakbar Alizadeh
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Asadollah Zarifkar
- Neuroscience Research Center and Department of Physiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Tahereh Esmaeilpour
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Reza Namavar
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Histomorphometry & Stereology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Omid Alavi
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farzaneh Dehghani
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Histomorphometry & Stereology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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3
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Persson K, Leonardsen EH, Edwin TH, Knapskog AB, Tangen GG, Selbæk G, Wolfers T, Westlye LT, Engedal K. Diagnostic accuracy of brain age prediction in a memory clinic population and comparison with clinically available volumetric measures. Sci Rep 2023; 13:14957. [PMID: 37696909 PMCID: PMC10495330 DOI: 10.1038/s41598-023-42354-0] [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: 05/03/2023] [Accepted: 09/08/2023] [Indexed: 09/13/2023] Open
Abstract
The aim of this study was to assess the diagnostic validity of a deep learning-based method estimating brain age based on magnetic resonance imaging (MRI) and to compare it with volumetrics obtained using NeuroQuant (NQ) in a clinical cohort. Brain age prediction was performed on minimally processed MRI data using deep convolutional neural networks and an independent training set. The brain age gap (difference between chronological and biological age) was calculated, and volumetrics were performed in 110 patients with dementia (Alzheimer's disease, frontotemporal dementia (FTD), and dementia with Lewy bodies), and 122 with non-dementia (subjective and mild cognitive impairment). Area-under-the-curve (AUC) based on receiver operating characteristics and logistic regression analyses were performed. The mean age was 67.1 (9.5) years and 48.7% (113) were females. The dementia versus non-dementia sensitivity and specificity of the volumetric measures exceeded 80% and yielded higher AUCs compared to BAG. The explained variance of the prediction of diagnostic stage increased when BAG was added to the volumetrics. Further, BAG separated patients with FTD from other dementia etiologies with > 80% sensitivity and specificity. NQ volumetrics outperformed BAG in terms of diagnostic discriminatory power but the two methods provided complementary information, and BAG discriminated FTD from other dementia etiologies.
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Affiliation(s)
- Karin Persson
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
| | - Esten H Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Gro Gujord Tangen
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Geir Selbæk
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Knut Engedal
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
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4
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Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
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Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
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Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases. Cogn Neurodyn 2022; 16:1361-1377. [DOI: 10.1007/s11571-022-09787-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/03/2022] Open
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021; 204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/11/2022]
Abstract
Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.
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Affiliation(s)
- Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
| | - Ole A Andreassen
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tomáš Paus
- ECOGENE-21, Chicoutimi, Quebec, Canada; Department of Psychology and Psychiatry, University of Toronto, Ontario, Canada; Department of Psychiatry and Centre hospitalier universitaire Sainte-Justine, University of Montreal, Canada
| | - Lars T Westlye
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
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Chávez-Fumagalli MA, Shrivastava P, Aguilar-Pineda JA, Nieto-Montesinos R, Del-Carpio GD, Peralta-Mestas A, Caracela-Zeballos C, Valdez-Lazo G, Fernandez-Macedo V, Pino-Figueroa A, Vera-Lopez KJ, Lino Cardenas CL. Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. J Alzheimers Dis Rep 2021; 5:15-30. [PMID: 33681713 PMCID: PMC7902992 DOI: 10.3233/adr-200263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries. OBJECTIVE To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc. METHODS The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics. RESULTS Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis. CONCLUSION Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.
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Affiliation(s)
- Miguel A. Chávez-Fumagalli
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Pallavi Shrivastava
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Jorge A. Aguilar-Pineda
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Rita Nieto-Montesinos
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Gonzalo Davila Del-Carpio
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Antero Peralta-Mestas
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Claudia Caracela-Zeballos
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Guillermo Valdez-Lazo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Victor Fernandez-Macedo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Alejandro Pino-Figueroa
- Department of Pharmaceutical Sciences, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Karin J. Vera-Lopez
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Christian L. Lino Cardenas
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
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9
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Cox SR, Harris MA, Ritchie SJ, Buchanan CR, Valdés Hernández MC, Corley J, Taylor AM, Madole JW, Harris SE, Whalley HC, McIntosh AM, Russ TC, Bastin ME, Wardlaw JM, Deary IJ, Tucker-Drob EM. Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life. Mol Psychiatry 2021; 26:2651-2662. [PMID: 33398085 PMCID: PMC8254824 DOI: 10.1038/s41380-020-00975-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 11/17/2020] [Accepted: 11/30/2020] [Indexed: 12/28/2022]
Abstract
Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflate information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the eighth decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.
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Affiliation(s)
- S. R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - M. A. Harris
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - S. J. Ritchie
- grid.13097.3c0000 0001 2322 6764Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
| | - C. R. Buchanan
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - M. C. Valdés Hernández
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - J. Corley
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - A. M. Taylor
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - J. W. Madole
- grid.55460.320000000121548364Department of Psychology, University of Texas, Austin, TX USA
| | - S. E. Harris
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - H. C. Whalley
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - A. M. McIntosh
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - T. C. Russ
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | - M. E. Bastin
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - J. M. Wardlaw
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK
| | - I. J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - E. M. Tucker-Drob
- grid.55460.320000000121548364Department of Psychology, University of Texas, Austin, TX USA
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10
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Dinsdale NK, Bluemke E, Smith SM, Arya Z, Vidaurre D, Jenkinson M, Namburete AIL. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 2020; 224:117401. [PMID: 32979523 DOI: 10.1016/j.neuroimage.2020.117401] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 08/17/2020] [Accepted: 09/15/2020] [Indexed: 10/23/2022] Open
Abstract
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrainAge=AgePredicted-AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.
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Affiliation(s)
- Nicola K Dinsdale
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
| | - Emma Bluemke
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Zobair Arya
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Diego Vidaurre
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Department of Psychiatry, University of Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Ana I L Namburete
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom
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11
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Maglanoc LA, Kaufmann T, Jonassen R, Hilland E, Beck D, Landrø NI, Westlye LT. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp 2020; 41:241-255. [PMID: 31571370 PMCID: PMC7267936 DOI: 10.1002/hbm.24802] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/08/2019] [Accepted: 09/09/2019] [Indexed: 01/03/2023] Open
Abstract
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.
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Affiliation(s)
- Luigi A. Maglanoc
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Rune Jonassen
- Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Eva Hilland
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- Division of PsychiatryDiakonhjemmet HospitalOsloNorway
| | - Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Nils Inge Landrø
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
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12
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13
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Marcori AJ, Okazaki VHA. Motor repertoire and gray matter plasticity: Is there a link? Med Hypotheses 2019; 130:109261. [PMID: 31383345 DOI: 10.1016/j.mehy.2019.109261] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/28/2019] [Accepted: 06/07/2019] [Indexed: 01/25/2023]
Abstract
There is a considerable amount of evidence sustaining that aerobic exercise causes positive modifications in gray matter density (GMD), especially in the hippocampus and anterior cingulate cortex. However, recent experimental researches with motor learning paradigms are consistently showing that increasing cardiorespiratory capacity is not the only mechanism able to promote positive outcomes in GMD with exercise. In the present study, we present a theoretical suggestion that expanding one's motor repertoire is another primary mechanism related to the increases in GMD. Motor repertoire can be understood as the number of movement possibilities and motor skills that can be performed by a person. Supporting our suggestion, professional athletes present higher GMD than controls, and experimental protocols repeatedly observes positive changes in GMD following motor learning. The relationship between physical inactivity, amputation, and lower GMD values also gives further support for the hypothesis. Follow-up studies monitoring GMD before and after training programs that stimulate new motor skill learning are essential to confirm this proposition. The brain regions related to sensory processing of the motor tasks and the cortical areas related to motor control (e.g., primary motor cortex, supplementary motor area) are probably the ones most affected by plastic changes. If the hypothesis turns out to be reliable, dancing, gymnastics, and other movement-rich activities are thoroughly encouraged for this purpose. Therefore, this approach might be used to attenuate GMD loss related to aging or another condition, such as Parkinson's and Alzheimer's.
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Affiliation(s)
- Alexandre Jehan Marcori
- Motor Neuroscience Research Group, Londrina State University, Rodovia Celso Garcia Cid, PR445, Km 380, Brazil.
| | - Victor Hugo Alves Okazaki
- Motor Neuroscience Research Group, Londrina State University, Rodovia Celso Garcia Cid, PR445, Km 380, Brazil
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Abstract
BACKGROUND This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. METHODS We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. RESULTS Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. CONCLUSIONS Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
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Affiliation(s)
- Adrian B R Shatte
- Federation University, School of Science, Engineering & Information Technology,Melbourne,Australia
| | - Delyse M Hutchinson
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
| | - Samantha J Teague
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
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15
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Trevisan K, Cristina-Pereira R, Silva-Amaral D, Aversi-Ferreira TA. Theories of Aging and the Prevalence of Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2019; 2019:9171424. [PMID: 31317043 PMCID: PMC6601487 DOI: 10.1155/2019/9171424] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 04/22/2019] [Accepted: 05/14/2019] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Aging and AD are associated in some way, then it is reasonable to ask whether or not it is possible to age without AD inexorably appearing at any moment, depending on the period of life. Therefore, the goal of this review is to verify, in light of some aging theories, the prevalence of AD. METHODS For the purpose of this manuscript, the indexers Alzheimer, aging, Alzheimer, and aging were considered; theories of aging were researched. The research was conducted using PubMed, Medline, Scopus, Elsevier, and Google Scholar. RESULTS The most common subjects in the papers analyzed for this manuscript were aging and Alzheimer's disease. The association between Alzheimer and theories of aging seems inconclusive. CONCLUSIONS Accordingly, the general idea is that AD is associated with aging in such a way that almost all people will present this disease; however, it is plausible to consider that the increase in life expectancy will generate a high prevalence of AD. In a general sense, it seems that the theories of aging explain the origin of AD under superlative and catastrophic considerations and use more biomolecular data than social or behavioral data as the bases of analysis, which may be the problem.
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Affiliation(s)
- Kaynara Trevisan
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Renata Cristina-Pereira
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Danyelle Silva-Amaral
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Tales Alexandre Aversi-Ferreira
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
- Department of Physiology, School of Medicine and Pharmaceutical Sciences, System Emotional Science, University of Toyama, Toyama, Japan
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16
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Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level. J Neurosci 2019; 39:6136-6149. [PMID: 31152123 DOI: 10.1523/jneurosci.2912-18.2019] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 04/15/2019] [Accepted: 04/17/2019] [Indexed: 11/21/2022] Open
Abstract
Human brain structure topography is thought to be related in part to functional specialization. However, the extent of such relationships is unclear. Here, using a data-driven, multimodal approach for studying brain structure across the lifespan (N = 484, n = 260 females), we demonstrate that numerous structural networks, covering the entire brain, follow a functionally meaningful architecture. These gray matter networks (GMNs) emerge from the covariation of gray matter volume and cortical area at the population level. We further reveal fine-grained anatomical signatures of functional connectivity. For example, within the cerebellum, a structural separation emerges between lobules that are functionally connected to distinct, mainly sensorimotor, cognitive and limbic regions of the cerebral cortex and subcortex. Structural modes of variation also replicate the fine-grained functional architecture seen in eight well defined visual areas in both task and resting-state fMRI. Furthermore, our study shows a structural distinction corresponding to the established segregation between anterior and posterior default-mode networks (DMNs). These fine-grained GMNs further cluster together to form functionally meaningful larger-scale organization. In particular, we identify a structural architecture bringing together the functional posterior DMN and its anticorrelated counterpart. In summary, our results demonstrate that the relationship between structural and functional connectivity is fine-grained, widespread across the entire brain, and driven by covariation in cortical area, i.e. likely differences in shape, depth, or number of foldings. These results suggest that neurotrophic events occur during development to dictate that the size and folding pattern of distant, functionally connected brain regions should vary together across subjects.SIGNIFICANCE STATEMENT Questions about the relationship between structure and function in the human brain have engaged neuroscientists for centuries in a debate that continues to this day. Here, by investigating intersubject variation in brain structure across a large number of individuals, we reveal modes of structural variation that map onto fine-grained functional organization across the entire brain, and specifically in the cerebellum, visual areas, and default-mode network. This functionally meaningful structural architecture emerges from the covariation of gray matter volume and cortical folding. These results suggest that the neurotrophic events at play during development, and possibly evolution, which dictate that the size and folding pattern of distant brain regions should vary together across subjects, might also play a role in functional cortical specialization.
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Finnegan ME, Visanji NP, Romero-Canelon I, House E, Rajan S, Mosselmans JFW, Hazrati LN, Dobson J, Collingwood JF. Synchrotron XRF imaging of Alzheimer's disease basal ganglia reveals linear dependence of high-field magnetic resonance microscopy on tissue iron concentration. J Neurosci Methods 2019; 319:28-39. [PMID: 30851339 DOI: 10.1016/j.jneumeth.2019.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 03/02/2019] [Accepted: 03/02/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Chemical imaging of the human brain has great potential for diagnostic and monitoring purposes. The heterogeneity of human brain iron distribution, and alterations to this distribution in Alzheimer's disease, indicate iron as a potential endogenous marker. The influence of iron on certain magnetic resonance imaging (MRI) parameters increases with magnetic field, but is under-explored in human brain tissues above 7 T. NEW METHOD Magnetic resonance microscopy at 9.4 T is used to calculate parametric images of chemically-unfixed post-mortem tissue from Alzheimer's cases (n = 3) and healthy controls (n = 2). Iron-rich regions including caudate nucleus, putamen, globus pallidus and substantia nigra are analysed prior to imaging of total iron distribution with synchrotron X-ray fluorescence mapping. Iron fluorescence calibration is achieved with adjacent tissue blocks, analysed by inductively coupled plasma mass spectrometry or graphite furnace atomic absorption spectroscopy. RESULTS Correlated MR images and fluorescence maps indicate linear dependence of R2, R2* and R2' on iron at 9.4 T, for both disease and control, as follows: [R2(s-1) = 0.072[Fe] + 20]; [R2*(s-1) = 0.34[Fe] + 37]; [R2'(s-1) = 0.26[Fe] + 16] for Fe in μg/g tissue (wet weight). COMPARISON WITH EXISTING METHODS This method permits simultaneous non-destructive imaging of most bioavailable elements. Iron is the focus of the present study as it offers strong scope for clinical evaluation; the approach may be used more widely to evaluate the impact of chemical elements on clinical imaging parameters. CONCLUSION The results at 9.4 T are in excellent quantitative agreement with predictions from experiments performed at lower magnetic fields.
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Affiliation(s)
- Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK; Department of Bioengineering, Imperial College London, London, UK
| | - Naomi P Visanji
- The Edmond J Safra Program in Parkinson's Disease and the Morton & Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, Ontario, M5T 2S8, Canada
| | - Isolda Romero-Canelon
- School of Pharmacy, Institute of Clinical Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Emily House
- Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
| | - Surya Rajan
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | | | | | - Jon Dobson
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Joanna F Collingwood
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK; Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32611, USA.
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18
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Høgestøl EA, Kaufmann T, Nygaard GO, Beyer MK, Sowa P, Nordvik JE, Kolskår K, Richard G, Andreassen OA, Harbo HF, Westlye LT. Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis. Front Neurol 2019; 10:450. [PMID: 31114541 PMCID: PMC6503038 DOI: 10.3389/fneur.2019.00450] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/12/2019] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21-49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10-6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10-15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.
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Affiliation(s)
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gro O. Nygaard
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Mona K. Beyer
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Knut Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hanne F. Harbo
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- NORMENT, 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
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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20
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Cho SH, Shin JH, Jang H, Park S, Kim HJ, Kim SE, Kim SJ, Kim Y, Lee JS, Na DL, Lockhart SN, Rabinovici GD, Seong JK, Seo SW. Amyloid involvement in subcortical regions predicts cognitive decline. Eur J Nucl Med Mol Imaging 2018; 45:2368-2376. [PMID: 29980831 DOI: 10.1007/s00259-018-4081-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Accepted: 06/25/2018] [Indexed: 01/31/2023]
Abstract
PURPOSE We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement. METHODS Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer's Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2. RESULTS Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (β [SE] = -0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (-0.0008 [0.0003], p = 0.004), and hippocampal volumes (-4.46 [0.65], p < 0.001) compared to stage 1. CONCLUSIONS We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Jeong-Hyeon Shin
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Seung Joo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Yeshin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, South Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Samuel N Lockhart
- Internal Medicine - Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, South Korea. .,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea.
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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications. SUSTAINABILITY 2017. [DOI: 10.3390/su9122309] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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