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Chen YS, Kuo CY, Lu CH, Wang YW, Chou KH, Lin WC. Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease. Neurobiol Aging 2024; 140:122-129. [PMID: 38776615 DOI: 10.1016/j.neurobiolaging.2024.05.003] [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: 10/26/2023] [Revised: 04/20/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.
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Affiliation(s)
- Yueh-Sheng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Yuan Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yuan-Wei Wang
- The Science & Technology Policy Research and Information Center, National Applied Research Laboratories(NARLabs), Taipei, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
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2
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Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
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Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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3
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Beheshti I, Perron J, Ko JH. Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease. Aging Dis 2024:AD.2024.0323. [PMID: 38913040 DOI: 10.14336/ad.2024.0323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/23/2024] [Indexed: 06/25/2024] Open
Abstract
The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Jarrad Perron
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
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4
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Aghaei A, Ebrahimi Moghaddam M. Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection. Brain Inform 2024; 11:16. [PMID: 38833039 DOI: 10.1186/s40708-024-00230-1] [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: 11/03/2023] [Accepted: 05/27/2024] [Indexed: 06/06/2024] Open
Abstract
This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer's disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.
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Affiliation(s)
- Atefe Aghaei
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
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5
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Wang M, Wei M, Wang L, Song J, Rominger A, Shi K, Jiang J. Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain Sci 2024; 14:575. [PMID: 38928575 PMCID: PMC11201453 DOI: 10.3390/brainsci14060575] [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: 05/02/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
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Affiliation(s)
- Min Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Jun Song
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Computer Aided Medical Procedures, School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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Filip P, Mana J, Lasica A, Keller J, Urgošík D, May J, Mueller K, Jech R, Bezdicek O, Růžička F. Structural and microstructural predictors of cognitive decline in deep brain stimulation of subthalamic nucleus in Parkinson's disease. Neuroimage Clin 2024; 42:103617. [PMID: 38749145 PMCID: PMC11112358 DOI: 10.1016/j.nicl.2024.103617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/22/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND AND OBJECTIVES The intricate relationship between deep brain stimulation (DBS) in Parkinson's disease (PD) and cognitive impairment has lately garnered substantial attention. The presented study evaluated pre-DBS structural and microstructural cerebral patterns as possible predictors of future cognitive decline in PD DBS patients. METHODS Pre-DBS MRI data in 72 PD patients were combined with neuropsychological examinations and follow-up for an average of 2.3 years after DBS implantation procedure using a screening cognitive test validated for diagnosis of mild cognitive impairment in PD in a Czech population - Dementia Rating Scale 2. RESULTS PD patients who would exhibit post-DBS cognitive decline were found to have, already at the pre-DBS stage, significantly lower cortical thickness and lower microstructural complexity than cognitively stable PD patients. Differences in the regions directly related to cognition as bilateral parietal, insular and cingulate cortices, but also occipital and sensorimotor cortex were detected. Furthermore, hippocampi, putamina, cerebellum and upper brainstem were implicated as well, all despite the absence of pre-DBS differences in cognitive performance and in the position of DBS leads or stimulation parameters between the two groups. CONCLUSIONS Our findings indicate that the cognitive decline in the presented PD cohort was not attributable primarily to DBS of the subthalamic nucleus but was associated with a clinically silent structural and microstructural predisposition to future cognitive deterioration present already before the DBS system implantation.
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Affiliation(s)
- Pavel Filip
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic; Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic; Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Josef Mana
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Andrej Lasica
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Jiří Keller
- Department of Radiology, Na Homolce Hospital, Prague, Czech Republic; Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Dušan Urgošík
- Department of Stereotactic and Radiation Neurosurgery, Na Homolce Hospital, Prague, Czech Republic
| | - Jaromír May
- Department of Stereotactic and Radiation Neurosurgery, Na Homolce Hospital, Prague, Czech Republic
| | - Karsten Mueller
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Robert Jech
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic.
| | - Ondrej Bezdicek
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Filip Růžička
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
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7
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Guo X, Ding Y, Xu W, Wang D, Yu H, Lin Y, Chang S, Zhang Q, Zhang Y. Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers. J Neuroradiol 2024; 51:265-273. [PMID: 37722591 DOI: 10.1016/j.neurad.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/20/2023]
Abstract
The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Xiaoliang Guo
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
| | - Weizhi Xu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Dong Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunication, Beijing, China
| | - Huiying Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongkang Lin
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Shulei Chang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiqi Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongxin Zhang
- School of Mathematics and Statistics, Shandong Normal University, Jinan, China.
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8
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Yan S, Lu J, Zhu H, Tian T, Qin Y, Li Y, Zhu W. The influence of accelerated brain aging on coactivation pattern dynamics in Parkinson's disease. J Neurosci Res 2024; 102:e25357. [PMID: 38803227 DOI: 10.1002/jnr.25357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/27/2024] [Accepted: 05/05/2024] [Indexed: 05/29/2024]
Abstract
Aging is widely acknowledged as the primary risk factor for brain degeneration, with Parkinson's disease (PD) tending to follow accelerated aging trajectories. We aim to investigate the impact of structural brain aging on the temporal dynamics of a large-scale functional network in PD. We enrolled 62 PD patients and 32 healthy controls (HCs). The level of brain aging was determined by calculating global and local brain age gap estimates (G-brainAGE and L-brainAGE) from structural images. The neural network activity of the whole brain was captured by identifying coactivation patterns (CAPs) from resting-state functional images. Intergroup differences were assessed using the general linear model. Subsequently, a spatial correlation analysis between the L-brainAGE difference map and CAPs was conducted to uncover the anatomical underpinnings of functional alterations. Compared to HCs (-3.73 years), G-brainAGE was significantly higher in PD patients (+1.93 years), who also exhibited widespread elevation in L-brainAGE. G-brainAGE was correlated with disease severity and duration. PD patients spent less time in CAPs involving activated default mode and the fronto-parietal network (DMN-FPN), as well as the sensorimotor and salience network (SMN-SN), and had a reduced transition frequency from other CAPs to the DMN-FPN and SMN-SN CAPs. Furthermore, the pattern of localized brain age acceleration showed spatial similarities with the SMN-SN CAP. Accelerated structural brain aging in PD adversely affects brain function, manifesting as dysregulated brain network dynamics. These findings provide insights into the neuropathological mechanisms underlying neurodegenerative diseases and imply the possibility of interventions for modifying PD progression by slowing the brain aging process.
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Affiliation(s)
- Su Yan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Lu
- Department of CT & MRI, The First Affiliated Hospital, College of Medicine, Shihezi University, Shihezi, China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanhao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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9
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Seitz-Holland J, Haas SS, Penzel N, Reichenberg A, Pasternak O. BrainAGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev 2024; 159:105581. [PMID: 38354871 PMCID: PMC11119273 DOI: 10.1016/j.neubiorev.2024.105581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024]
Abstract
The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Chen CL, Cheng SY, Montaser-Kouhsari L, Wu WC, Hsu YC, Tai CH, Tseng WYI, Kuo MC, Wu RM. Advanced brain aging in Parkinson's disease with cognitive impairment. NPJ Parkinsons Dis 2024; 10:62. [PMID: 38493188 PMCID: PMC10944471 DOI: 10.1038/s41531-024-00673-7] [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: 08/15/2023] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.
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Affiliation(s)
- Chang-Le Chen
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shao-Ying Cheng
- Department of Neurology, National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan
| | | | - Wen-Chao Wu
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | | | - Chun-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
- Acroviz Inc, Taipei, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan.
| | - Ruey-Meei Wu
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
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11
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Kalc P, Dahnke R, Hoffstaedter F, Gaser C. BrainAGE: Revisited and reframed machine learning workflow. Hum Brain Mapp 2024; 45:e26632. [PMID: 38379519 PMCID: PMC10879910 DOI: 10.1002/hbm.26632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
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Affiliation(s)
- Polona Kalc
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
| | - Robert Dahnke
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
| | - Felix Hoffstaedter
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)JülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG)Jena‐Halle‐MagdeburgGermany
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12
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Beheshti I, Booth S, Ko JH. Differences in brain aging between sexes in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:35. [PMID: 38355735 PMCID: PMC10866976 DOI: 10.1038/s41531-024-00646-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33-85, 34% female) from the Parkinson's Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson's disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having 'older' looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Samuel Booth
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada.
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada.
- Graduate Program in Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada.
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13
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Soumya Kumari LK, Sundarrajan R. A review on brain age prediction models. Brain Res 2024; 1823:148668. [PMID: 37951563 DOI: 10.1016/j.brainres.2023.148668] [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: 06/16/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.
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Affiliation(s)
- L K Soumya Kumari
- Computer Science Engineering, Mohandas College of Engineering and Technology, Anad, India.
| | - R Sundarrajan
- Information Technology, School of Computing, Kalasalingam Academy of Research and Education, India.
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14
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Shah J, Siddiquee MMR, Su Y, Wu T, Li B. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION 2024; 2024:7867-7876. [PMID: 38606366 PMCID: PMC11008505 DOI: 10.1109/wacv57701.2024.00770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction.
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Affiliation(s)
- Jay Shah
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | | | - Yi Su
- ASU-Mayo Center for Innovative Imaging
- Banner Alzheimer's Institute
| | - Teresa Wu
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | - Baoxin Li
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
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15
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Marx GA, Kauffman J, McKenzie AT, Koenigsberg DG, McMillan CT, Morgello S, Karlovich E, Insausti R, Richardson TE, Walker JM, White CL, Babrowicz BM, Shen L, McKee AC, Stein TD, Farrell K, Crary JF. Histopathologic brain age estimation via multiple instance learning. Acta Neuropathol 2023; 146:785-802. [PMID: 37815677 PMCID: PMC10627911 DOI: 10.1007/s00401-023-02636-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023]
Abstract
Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.
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Affiliation(s)
- Gabriel A Marx
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Justin Kauffman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Andrew T McKenzie
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel G Koenigsberg
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Cory T McMillan
- Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan Morgello
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Esma Karlovich
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Timothy E Richardson
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Jamie M Walker
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bergan M Babrowicz
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Li Shen
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Ann C McKee
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Kurt Farrell
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
| | - John F Crary
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
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16
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Choi US, Park JY, Lee JJ, Choi KY, Won S, Lee KH. Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging. Cereb Cortex 2023; 33:10858-10866. [PMID: 37718166 DOI: 10.1093/cercor/bhad331] [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: 05/27/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.
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Affiliation(s)
- Uk-Su Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu 41061, Republic of Korea
| | - Jun Young Park
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Neurozen Inc., Seoul 06168, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Biomedical Sciences, Chosun University, Gwangju 61452, Republic of Korea
- Korea Brain Research Institute, Daegu 41061, Republic of Korea
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17
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Statsenko Y, Kuznetsov NV, Morozova D, Liaonchyk K, Simiyu GL, Smetanina D, Kashapov A, Meribout S, Gorkom KNV, Hamoudi R, Ismail F, Ansari SA, Emerald BS, Ljubisavljevic M. Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond. Cells 2023; 12:2451. [PMID: 37887295 PMCID: PMC10605227 DOI: 10.3390/cells12202451] [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: 08/04/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age. OBJECTIVE The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations. METHODS We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology. RESULTS (1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear. CONCLUSIONS Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.
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Affiliation(s)
- Yauhen Statsenko
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Big Data Analytic Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nik V. Kuznetsov
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Daria Morozova
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Katsiaryna Liaonchyk
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Gillian Lylian Simiyu
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Darya Smetanina
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Aidar Kashapov
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Sarah Meribout
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Klaus Neidl-Van Gorkom
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Rifat Hamoudi
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London NW3 2PS, UK
| | - Fatima Ismail
- Department of Pediatrics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
| | - Suraiya Anjum Ansari
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Biochemistry and Molecular Biology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Bright Starling Emerald
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Milos Ljubisavljevic
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Physiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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18
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Navarro-González R, García-Azorín D, Guerrero-Peral ÁL, Planchuelo-Gómez Á, Aja-Fernández S, de Luis-García R. Increased MRI-based Brain Age in chronic migraine patients. J Headache Pain 2023; 24:133. [PMID: 37798720 PMCID: PMC10557155 DOI: 10.1186/s10194-023-01670-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
INTRODUCTION Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. METHODS We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). RESULTS CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. CONCLUSION The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
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Affiliation(s)
| | - David García-Azorín
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain.
| | - Ángel L Guerrero-Peral
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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19
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Ray B, Chen J, Fu Z, Suresh P, Thapaliya B, Farahdel B, Calhoun VD, Liu J. Replication and Refinement of Brain Age Model for adolescent development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553472. [PMID: 37645839 PMCID: PMC10462059 DOI: 10.1101/2023.08.16.553472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. Our recent study has demonstrated that conventional machine learning models can achieve high accuracy on brain age prediction during development using only a small set of selected features from multimodal brain imaging data. In the current study, we tested the replicability of various brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort. We proposed a new refined model to improve the robustness of brain age prediction. The direct replication test for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age failed precisely estimating brain age variation within a narrow range. The refined model, which combined broad prediction of the pre-trained model and granular information with the narrow age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants' information processing speed and verbal comprehension ability on baseline data.
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Affiliation(s)
- Bhaskar Ray
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Pranav Suresh
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Britny Farahdel
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
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20
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Beheshti I. Cocaine Destroys Gray Matter Brain Cells and Accelerates Brain Aging. BIOLOGY 2023; 12:biology12050752. [PMID: 37237564 DOI: 10.3390/biology12050752] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Introduction: Cocaine use disorder (CUD) is a substance use disorder characterized by a strong desire to obtain, consume, and misuse cocaine. Little is known about how cocaine affects the structure of the brain. In this study, we first investigated the anatomical brain changes in individuals with CUD compared to their matched healthy controls, and then explored whether these anatomical brain abnormalities contribute to considerably accelerated brain aging among this population. Methods: At the first stage, we used anatomical magnetic resonance imaging (MRI) data, voxel-based morphometry (VBM), and deformation-based morphometry techniques to uncover the morphological and macroscopic anatomical brain changes in 74 CUD patients compared to 62 age- and sex-matched healthy controls (HCs) obtained from the SUDMEX CONN dataset, the Mexican MRI dataset of patients with CUD. Then, we computed brain-predicted age difference (i.e., brain-PAD: the brain-predicted age minus the actual age) in CUD and HC groups using a robust brain age estimation framework. Using a multiple regression analysis, we also investigated the regional gray matter (GM) and white matter (WM) changes associated with the brain-PAD. Results: Using a whole-brain VBM analysis, we observed widespread gray matter atrophy in CUD patients located in the temporal lobe, frontal lobe, insula, middle frontal gyrus, superior frontal gyrus, rectal gyrus, and limbic lobe regions compared to the HCs. In contrast, we did not observe any swelling in the GM, changes in the WM, or local brain tissue atrophy or expansion between the CUD and HC groups. Furthermore, we found a significantly higher brain-PAD in CUD patients compared to matched HCs (mean difference = 2.62 years, Cohen's d = 0.54; t-test = 3.16, p = 0.002). The regression analysis showed significant negative changes in GM volume associated with brain-PAD in the CUD group, particularly in the limbic lobe, subcallosal gyrus, cingulate gyrus, and anterior cingulate regions. Discussion: The results of our investigation reveal that chronic cocaine use is linked to significant changes in gray matter, which hasten the process of structural brain aging in individuals who use the drug. These findings offer valuable insights into the impact of cocaine on the composition of the brain.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
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21
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Shigemoto Y, Sato N, Maikusa N, Sone D, Ota M, Kimura Y, Chiba E, Okita K, Yamao T, Nakaya M, Maki H, Arizono E, Matsuda H. Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants. J Pers Med 2023; 13:jpm13030419. [PMID: 36983603 PMCID: PMC10057933 DOI: 10.3390/jpm13030419] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023] Open
Abstract
Recent developments in image analysis have enabled an individual’s brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.
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Affiliation(s)
- Yoko Shigemoto
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
| | - Miho Ota
- Department of Neuropsychiatry, University of Tsukuba, Tsukuba 305-8576, Japan
| | - Yukio Kimura
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Kyoji Okita
- Department of Drug Dependence Research, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima 960-8516, Japan
| | - Moto Nakaya
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Hiroyuki Maki
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Elly Arizono
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima 960-1295, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Fukushima 963-8052, Japan
- Correspondence: ; Tel.: +81-3-6271-8507
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22
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Pan D, Zeng A, Yang B, Lai G, Hu B, Song X, Jiang T. Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204717. [PMID: 36575159 PMCID: PMC9951348 DOI: 10.1002/advs.202204717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3-dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole-brain sMRI changes denoting AD onset and progression. A set of 2369 T1-weighted images from the multi-centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble-3DCNN-based P-score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non-invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole-brain sMRI.
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Affiliation(s)
- Dan Pan
- School of Electronics and InformationGuangdong Polytechnic Normal UniversityGuangzhou510665China
| | - An Zeng
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Baoyao Yang
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Gangyong Lai
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Bing Hu
- Department of RadiologyThe Third Affiliated Hospital of SUN Yat‐sen UniversityGuangzhou510630China
| | - Xiaowei Song
- Clinical Research CentreSurrey Memorial HospitalFraser HealthSurreyBritish ColumbiaV3V 1Z2Canada
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
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23
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Xu L, Lai L, Wen Y, Lin J, Chen B, Zhong Y, Cheng Y, Zhang X, Guan J, Mikulis DJ, Lin Y, Yan G, Wu R. Angiopep-2, an MRI Biomarker, Dynamically Monitors Amyloid Deposition in Early Alzheimer's Disease. ACS Chem Neurosci 2023; 14:226-234. [PMID: 36599050 PMCID: PMC9854622 DOI: 10.1021/acschemneuro.2c00513] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
The reliable and dynamic detection of amyloid β-protein (Aβ) deposition using imaging technology is necessary for preclinical Alzheimer's disease (AD), which may significantly improve prognosis. The present study aimed to evaluate the feasibility of applying angiopep-2 (ANG), a chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) biomarker, for monitoring Aβ deposition in vivo. ANG exerted a good chemical exchange saturation transfer (CEST) effect and displayed a moderate binding affinity to Aβ1-42 in vitro. Six-month-old mice with AD injected with ANG exhibited a significantly enhanced CEST effect than controls in vivo; this effect gradually became more apparent at 8, 10, and 12 months. Spatial learning impairment caused by abundant Aβ deposition (representing mild cognitive impairment in AD patients) develops at 12 months in APPswe/PSEN1dE9 (line 85) AD mice. To conclude, the CEST of ANG could display very earlier age-related Aβ pathological progress in mice with AD, consistent with immunohistochemistry. ANG has extraordinary potential for clinical transformation as an imaging biomarker to diagnose early AD and track its progress dynamically and nonradiationally.
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Affiliation(s)
- Liang Xu
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
- Department
of Medical Imaging, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518000, P. R. China
| | - Lingfeng Lai
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - Yaqi Wen
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - Jia Lin
- Department
of Ultrasound, First Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - Beibei Chen
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - Yazhi Zhong
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - Yan Cheng
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
| | - XiaoLei Zhang
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
- Provincial
Key Laboratory for Breast Cancer Diagnosis and Treatment, Guangdong
Province, Shantou, Guangdong 515041, P. R. China
| | - Jitian Guan
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
- Provincial
Key Laboratory for Breast Cancer Diagnosis and Treatment, Guangdong
Province, Shantou, Guangdong 515041, P. R. China
| | - David J Mikulis
- Joint
Department of Medical Imaging and the Functional Neuroimaging Laboratory
(D.J.M.), University Health Network, Toronto M2J4A6, Canada
| | - Yan Lin
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
- Provincial
Key Laboratory for Breast Cancer Diagnosis and Treatment, Guangdong
Province, Shantou, Guangdong 515041, P. R. China
| | - Gen Yan
- Department
of Radiology, The Second Affiliated Hospital
of Xiamen Medical College, Xiamen, Fujian 361023, P. R. China
| | - Renhua Wu
- Department
of Medical Imaging, Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515000, P. R. China
- Provincial
Key Laboratory for Breast Cancer Diagnosis and Treatment, Guangdong
Province, Shantou, Guangdong 515041, P. R. China
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24
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Cai H, Li A, Yu G, Yang X, Liu M. Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images. Neuroinformatics 2023; 21:5-19. [PMID: 35962180 DOI: 10.1007/s12021-022-09596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2022] [Indexed: 11/30/2022]
Abstract
It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.
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Affiliation(s)
- Hongjie Cai
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Aojie Li
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Department of Child Health Care, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China
| | - Xiujun Yang
- Department of Radiology, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China.
| | - Manhua Liu
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China. .,MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
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25
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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26
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Hermann A, Tarakdjian GN, Temp AGM, Kasper E, Machts J, Kaufmann J, Vielhaber S, Prudlo J, Cole JH, Teipel S, Dyrba M. Cognitive and behavioural but not motor impairment increases brain age in amyotrophic lateral sclerosis. Brain Commun 2022; 4:fcac239. [PMID: 36246047 PMCID: PMC9556938 DOI: 10.1093/braincomms/fcac239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/01/2022] [Accepted: 09/21/2022] [Indexed: 11/14/2022] Open
Abstract
Age is the most important single risk factor of sporadic amyotrophic lateral sclerosis. Neuroimaging together with machine-learning algorithms allows estimating individuals' brain age. Deviations from normal brain-ageing trajectories (so called predicted brain age difference) were reported for a number of neuropsychiatric disorders. While all of them showed increased predicted brain-age difference, there is surprisingly few data yet on it in motor neurodegenerative diseases. In this observational study, we made use of previously trained algorithms of 3377 healthy individuals and derived predicted brain age differences from volumetric MRI scans of 112 amyotrophic lateral sclerosis patients and 70 healthy controls. We correlated predicted brain age difference scores with voxel-based morphometry data and multiple different motoric disease characteristics as well as cognitive/behavioural changes categorized according to Strong and Rascovsky. Against our primary hypothesis, there was no higher predicted brain-age difference in the amyotrophic lateral sclerosis patients as a group. None of the motoric phenotypes/characteristics influenced predicted brain-age difference. However, cognitive/behavioural impairment led to significantly increased predicted brain-age difference, while slowly progressive as well as cognitive/behavioural normal amyotrophic lateral sclerosis patients had even younger brain ages than healthy controls. Of note, the cognitive/behavioural normal amyotrophic lateral sclerosis patients were identified to have increased cerebellar brain volume as potential resilience factor. Younger brain age was associated with longer survival. Our results raise the question whether younger brain age in amyotrophic lateral sclerosis with only motor impairment provides a cerebral reserve against cognitive and/or behavioural impairment and faster disease progression. This new conclusion needs to be tested in subsequent samples. In addition, it will be interesting to test whether a potential effect of cerebral reserve is specific for amyotrophic lateral sclerosis or can also be found in other neurodegenerative diseases with primary motor impairment.
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Affiliation(s)
- Andreas Hermann
- Translational Neurodegeneration Section “Albrecht Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
- Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)Rostock/Greifswald, 18147 Rostock, Germany
| | - Gaël Nils Tarakdjian
- Translational Neurodegeneration Section “Albrecht Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)Rostock/Greifswald, 18147 Rostock, Germany
| | - Anna Gesine Marie Temp
- Translational Neurodegeneration Section “Albrecht Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)Rostock/Greifswald, 18147 Rostock, Germany
| | - Elisabeth Kasper
- Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
| | - Judith Machts
- Institute for Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences CBBS, 39104 Magdeburg, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Magdeburg, 39120 Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Stefan Vielhaber
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Magdeburg, 39120 Magdeburg, Germany
- Department of Neurology, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Johannes Prudlo
- Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, UK
| | - Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)Rostock/Greifswald, 18147 Rostock, Germany
- Department of Psychosomatic Medicine, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
| | - Martin Dyrba
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)Rostock/Greifswald, 18147 Rostock, Germany
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27
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Ran C, Yang Y, Ye C, Lv H, Ma T. Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity. Hum Brain Mapp 2022; 43:5017-5031. [PMID: 36094058 PMCID: PMC9582375 DOI: 10.1002/hbm.26066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 11/14/2022] Open
Abstract
Neuroimaging‐driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model‐agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder‐specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.
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Affiliation(s)
- Chen Ran
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yanwu Yang
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Chenfei Ye
- International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Haiyan Lv
- MindsGo Shenzhen Life Science Co. Ltd, Shenzhen, China
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China.,International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China
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28
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Tesli N, Bell C, Hjell G, Fischer-Vieler T, I Maximov I, Richard G, Tesli M, Melle I, Andreassen OA, Agartz I, Westlye LT, Friestad C, Haukvik UK, Rokicki J. The age of violence: Mapping brain age in psychosis and psychopathy. Neuroimage Clin 2022; 36:103181. [PMID: 36088844 PMCID: PMC9474919 DOI: 10.1016/j.nicl.2022.103181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/31/2022] [Accepted: 08/30/2022] [Indexed: 12/14/2022]
Abstract
Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.
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Affiliation(s)
- Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Christina Bell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Gabriela Hjell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Østfold Hospital Trust, Graalum, Norway
| | - Thomas Fischer-Vieler
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Adult Psychiatry, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Christine Friestad
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway; University College of Norwegian Correctional Service, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway.
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Shi Y, Mao H, Gao Q, Xi G, Zeng S, Ma L, Zhang X, Li L, Wang Z, Ji W, He P, You Y, Chen K, Shao J, Mao X, Fang X, Wang F. Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients. Front Aging Neurosci 2022; 14:973054. [PMID: 36118707 PMCID: PMC9475066 DOI: 10.3389/fnagi.2022.973054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 08/16/2022] [Indexed: 12/04/2022] Open
Abstract
Background Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD. Materials and methods A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers. Results The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients. Conclusion Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- *Correspondence: Yachen Shi,
| | - Haixia Mao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Qianqian Gao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Guangjun Xi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Siyuan Zeng
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lin Ma
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiuping Zhang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lei Li
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Zhuoyi Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Wei Ji
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Ping He
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Yiping You
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Kefei Chen
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Junfei Shao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xuqiang Mao
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiangming Fang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Xiangming Fang,
| | - Feng Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Feng Wang,
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30
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Lee PL, Kuo CY, Wang PN, Chen LK, Lin CP, Chou KH, Chung CP. Regional rather than global brain age mediates cognitive function in cerebral small vessel disease. Brain Commun 2022; 4:fcac233. [PMID: 36196084 PMCID: PMC9525017 DOI: 10.1093/braincomms/fcac233] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/24/2022] [Accepted: 09/14/2022] [Indexed: 11/15/2022] Open
Abstract
The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI data sets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of cerebral small vessel disease. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in cerebral small vessel disease. Global and regional (400 cortical, 14 subcortical and 28 cerebellum regions of interest) brain-age prediction models were constructed using grey matter features from MRI of 1482 healthy individuals (age: 18–92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the cerebral small vessel disease group. The cerebral small vessel disease group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-cerebral small vessel disease group. Global brain-age gap was significantly higher in the cerebral small vessel disease (3.71 ± 7.60 years) than that in non-cerebral small vessel disease (−0.43 ± 9.47 years) group (P = 0.003, η2 = 0.012). There were 82 cerebral cortical, 3 subcortical and 4 cerebellar regions showing significantly different brain-age gap between the cerebral small vessel disease and non-cerebral small vessel disease groups. Global brain-age gap failed to mediate the relationship between cerebral small vessel disease and any of the cognitive domains. In 89 regions with increased brain-age gap in the cerebral small vessel disease group, seven regional brain-age gaps were able to show significant mediation effects in cerebral small vessel disease-related cognitive impairment (we set the statistical significance P < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age gap explained poorer global cognitive performance in cerebral small vessel disease. We demonstrated the interconnections between cerebral small vessel disease and brain age. Strategic brain aging, i.e. advanced brain aging in critical regions, may be involved in the pathophysiology of cerebral small vessel disease-related cognitive impairment. Regional rather than global brain-age gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with cerebral small vessel disease.
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Affiliation(s)
- Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Pei-Ning Wang
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital , Taipei , Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital , Taipei , Taiwan
- Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital) , Taipei , Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Chih-Ping Chung
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital , Taipei , Taiwan
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31
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Li J, Lu H. MRI-informed cortical features for brain age prediction in age-specific adulthoods. Hum Brain Mapp 2022; 44:301-303. [PMID: 35971680 PMCID: PMC9842871 DOI: 10.1002/hbm.26050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023] Open
Affiliation(s)
- Jing Li
- Department of PsychiatryThe Chinese University of Hong KongHong KongSARChina
| | - Hanna Lu
- Department of PsychiatryThe Chinese University of Hong KongHong KongSARChina,The Affiliated Brain Hospital of Guangzhou Medical UniversityGuangzhouChina
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32
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Jiang J, Sheng C, Chen G, Liu C, Jin S, Li L, Jiang X, Han Y. Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment. GeroScience 2022; 44:2319-2336. [PMID: 35581512 PMCID: PMC9616982 DOI: 10.1007/s11357-022-00588-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/07/2022] [Indexed: 12/28/2022] Open
Abstract
Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China.
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Guanqun Chen
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunhua Liu
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Shichen Jin
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Lanlan Li
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Xueyan Jiang
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- German Centre for Neurodegenerative Disease, Clinical Research Group, Venusberg Campus 1, 53121, Bonn, Germany
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
- National Clinical Research Centre for Geriatric Disorders, Beijing, 100053, China.
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33
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Advanced brain aging in multiple system atrophy compared to Parkinson's disease. Neuroimage Clin 2022; 34:102997. [PMID: 35397330 PMCID: PMC8987993 DOI: 10.1016/j.nicl.2022.102997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/07/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022]
Abstract
MSA, but not PD, exhibits advanced brain aging in gray matter and white matter. Brain age of gray matter is correlated with that of white matter in PD. Brain age measures can partly reveal associations with symptom severity. Brain features underlying brain age difference between MSA and PD are identified.
Multiple system atrophy (MSA) and Parkinson’s disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.
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34
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Hwang G, Abdulkadir A, Erus G, Habes M, Pomponio R, Shou H, Doshi J, Mamourian E, Rashid T, Bilgel M, Fan Y, Sotiras A, Srinivasan D, Morris JC, Albert MS, Bryan NR, Resnick SM, Nasrallah IM, Davatzikos C, Wolk DA. Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning. Brain Commun 2022; 4:fcac117. [PMID: 35611306 PMCID: PMC9123890 DOI: 10.1093/braincomms/fcac117] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022] Open
Abstract
Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.
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Affiliation(s)
- Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tanweer Rashid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John C. Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Diagnostic Medicine, University of Texas, Austin, TX, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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Deng B, Zhu W, Sun X, Xie Y, Dan W, Zhan Y, Xia Y, Liang X, Li J, Shi Q, Jiang L. Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output. Front Aging Neurosci 2022; 14:798132. [PMID: 35462698 PMCID: PMC9028758 DOI: 10.3389/fnagi.2022.798132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/25/2022] [Indexed: 11/30/2022] Open
Abstract
The main purpose of the study was to explore a reliable way to automatically handle emergency cases, such as intracerebral hemorrhage (ICH). Therefore, an artificial intelligence (AI) system, named, H-system, was designed to automatically recognize medical text data of ICH patients and output the treatment plan. Furthermore, the efficiency and reliability of the H-system were tested and analyzed. The H-system, which is mainly based on a pretrained language model Bidirectional Encoder Representations from Transformers (BERT) and an expert module for logical judgment of extracted entities, was designed and founded by the neurosurgeon and AI experts together. All emergency medical text data were from the neurosurgery emergency electronic medical record database (N-eEMRD) of the First Affiliated Hospital of Chongqing Medical University, Chongqing Emergency Medical Center, and Chongqing First People’s Hospital, and the treatment plans of these ICH cases were divided into two types. A total of 1,000 simulated ICH cases were randomly selected as training and validation sets. After training and validating on simulated cases, real cases from three medical centers were provided to test the efficiency of the H-system. Doctors with 1 and 5 years of working experience in neurosurgery (Doctor-1Y and Doctor-5Y) were included to compare with H-system. Furthermore, the data of the H-system, for instance, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristics curve (AUC), were calculated and compared with Doctor-1Y and Doctor-5Y. In the testing set, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. In the testing set, the accuracy of the H-system’s treatment plan was 88.55 (88.16–88.94)%, the specificity was 85.71 (84.99–86.43)%, and the sensitivity was 91.83 (91.01–92.65)%. The AUC value of the H-system in the testing set was 0.887 (0.884–0.891). Furthermore, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. The accuracy and AUC of the H-system were significantly higher than that of Doctor-1Y. In addition, the accuracy of the H-system was more closed to that of Doctor-5Y. The H-system designed in the study can automatically recognize and analyze medical text data of patients with ICH and rapidly output accurate treatment plans with high efficiency. It may provide a reliable and novel way to automatically and rapidly handle emergency cases, such as ICH.
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Affiliation(s)
- Bo Deng
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenwen Zhu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
| | - Xiaochuan Sun
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yanfeng Xie
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Dan
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yan Zhan
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yulong Xia
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xinyi Liang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Li
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
- Jie Li,
| | - Quanhong Shi
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Quanhong Shi,
| | - Li Jiang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Li Jiang,
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Hu W, Wang W, Wang Y, Chen Y, Shang X, Liao H, Huang Y, Bulloch G, Zhang S, Kiburg K, Zhang X, Tang S, Yu H, Yang X, He M, Zhu Z. Retinal age gap as a predictive biomarker of future risk of Parkinson's disease. Age Ageing 2022; 51:6555657. [PMID: 35352798 PMCID: PMC8966015 DOI: 10.1093/ageing/afac062] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Indexed: 12/21/2022] Open
Abstract
Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
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Affiliation(s)
- Wenyi Hu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yifan Chen
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Xianwen Shang
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Huan Liao
- Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Gabriella Bulloch
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Katerina Kiburg
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
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Chang Z, Xie F, Li H, Yuan F, Zeng L, Shi L, Zhu S, Lu X, Wei X, Wang Q. Retinal Nerve Fiber Layer Thickness and Associations With Cognitive Impairment in Parkinson’s Disease. Front Aging Neurosci 2022; 14:832768. [PMID: 35222000 PMCID: PMC8867012 DOI: 10.3389/fnagi.2022.832768] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/20/2022] [Indexed: 01/18/2023] Open
Abstract
ObjectiveThis study intended to investigate whether retinal nerve fiber layer (RNFL) thickness could become a potential marker in patients with Parkinson’s disease with cognitive impairment (PD-CI).MethodsFifty-seven PD patients and 45 age-matched healthy controls (HCs) were recruited in our cross-sectional study and completed optical coherence tomography (OCT) evaluations. PD with normal cognition (PD-NC) and cognitive impairment (PD-CI) patients were divided following the 2015 Movement Disorder Society criteria. RNFL thickness was quantified in subfields of the 3.0-mm circle surrounding the optic disk; while a battery of neuropsychiatric assessments was conducted to estimate the Parkinsonism severity. General linear models and one-way ANOVA were adopted to assess RNFL thickness between subgroups with different cognitive statuses; logistic regression analyses were applied to determine the relation between RNFL and PD-CI cases.ResultsCompared with HCs, more thinning of the RNFL was observed in the inferior and temporal sectors in PD patients, especially in the PD-CI group. Inferior RNFL thickness was reduced in PD-CI compared with PD-NC patients. Logistic regression analysis found that inferior RNFL thickness was independently associated with PD-CI cases (odds ratio = 0.923, p = 0.014). Receiver operating characteristic analysis showed that the RNFL-involved combined model provided a high accuracy in screening cognitive deficiency in PD cases (area under the curve = 0.85, p < 0.001).ConclusionReduced RNFL thickness especially in the inferior sector is independently associated with PD-CI patients. Our study present new perspectives into verifying possible indicators for neuropathological processes or disease severity in Parkinsonians with cognitive dysfunction.
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Affiliation(s)
- Zihan Chang
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Fen Xie
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hualing Li
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Feilan Yuan
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lina Zeng
- Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Shuzhen Zhu
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaohe Lu
- Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Xiaohe Lu,
| | - Xiaobo Wei
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Xiaobo Wei,
| | - Qing Wang
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Qing Wang, ;
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Vaughan BA, Simon JE, Grooms DR, Clark LA, Wages NP, Clark BC. Brain-Predicted Age Difference Moderates the Association Between Muscle Strength and Mobility. Front Aging Neurosci 2022; 14:808022. [PMID: 35173606 PMCID: PMC8841783 DOI: 10.3389/fnagi.2022.808022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/10/2022] [Indexed: 12/02/2022] Open
Abstract
Background Approximately 35% of individuals over age 70 report difficulty with mobility. Muscle weakness has been demonstrated to be one contributor to mobility limitations in older adults. The purpose of this study was to examine the moderating effect of brain-predicted age difference (an index of biological brain age/health derived from structural neuroimaging) on the relationship between leg strength and mobility. Methods In community dwelling older adults (N = 57, 74.7 ± 6.93 years; 68% women), we assessed the relationship between isokinetic leg extensor strength and a composite measure of mobility [mobility battery assessment (MBA)] using partial Pearson correlations and multifactorial regression modeling. Brain predicted age (BPA) was calculated from T1 MR-images using a validated machine learning Gaussian Process regression model to explore the moderating effect of BPA difference (BPAD; BPA minus chronological age). Results Leg strength was significantly correlated with BPAD (r = −0.317, p < 0.05) and MBA score (r = 0.541, p < 0.001). Chronological age, sex, leg strength, and BPAD explained 63% of the variance in MBA performance (p < 0.001). BPAD was a significant moderator of the relationship between strength and MBA, accounting for 7.0% of MBA score variance [△R2 = 0.044, F(1,51) = 6.83, p = 0.01]. Conditional moderation effects of BPAD indicate strength was a stronger predictor of mobility in those with a great BPAD. Conclusion The relationship between strength and mobility appears to be influenced by brain aging, with strength serving as a possible compensation for decline in neural integrity.
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Affiliation(s)
- Brooke A. Vaughan
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- School of Rehabilitation and Communication Sciences, Ohio University, Athens, OH, United States
- *Correspondence: Brooke A. Vaughan,
| | - Janet E. Simon
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- School of Applied Health Sciences and Wellness, Ohio University, Athens, OH, United States
| | - Dustin R. Grooms
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- School of Rehabilitation and Communication Sciences, Ohio University, Athens, OH, United States
- School of Applied Health Sciences and Wellness, Ohio University, Athens, OH, United States
| | - Leatha A. Clark
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- Department of Biomedical Sciences, Ohio University, Athens, OH, United States
- Department of Family Medicine, Ohio University, Athens, OH, United States
| | - Nathan P. Wages
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- Department of Biomedical Sciences, Ohio University, Athens, OH, United States
| | - Brian C. Clark
- Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, United States
- Department of Biomedical Sciences, Ohio University, Athens, OH, United States
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Neuroimaging-derived brain age is associated with life satisfaction in cognitively unimpaired elderly: A community-based study. Transl Psychiatry 2022; 12:25. [PMID: 35058431 PMCID: PMC8776862 DOI: 10.1038/s41398-022-01793-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/21/2021] [Accepted: 01/10/2022] [Indexed: 12/01/2022] Open
Abstract
With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman's rs= -0.102, p = 0.005) and resilience (rs= -0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a 'younger' brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.
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Beheshti I, Geddert N, Perron J, Gupta V, Albensi BC, Ko JH. Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods. J Alzheimers Dis 2022; 89:1493-1502. [PMID: 36057825 PMCID: PMC9661333 DOI: 10.3233/jad-220585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects. OBJECTIVE We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD. METHODS MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior. RESULTS The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01). CONCLUSION These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Natasha Geddert
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- St. Boniface Hospital Research, Winnipeg, MB, Canada
| | - Jarrad Perron
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Vinay Gupta
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C. Albensi
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
- Department of Pharmacology and Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- St. Boniface Hospital Research, Winnipeg, MB, Canada
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
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Duc Nguyen H, Pal Yu B, Hoang NHM, Jo WH, Young Chung H, Kim MS. Prolactin and Its Altered Action in Alzheimer's Disease and Parkinson's Disease. Neuroendocrinology 2022; 112:427-445. [PMID: 34126620 DOI: 10.1159/000517798] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 06/10/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Prolactin (PRL) is one of the most diverse pituitary hormones and is known to modulate normal neuronal function and neurodegenerative conditions. Many studies have described the influence that PRL has on the central nervous system and addressed its contribution to neurodegeneration, but little is known about the mechanisms responsible for the effects of PRL on neurodegenerative disorders, especially on Alzheimer's disease (AD) and Parkinson's disease (PD). SUMMARY We review and summarize the existing literature and current understanding of the roles of PRL on various PRL aspects of AD and PD. KEY MESSAGES In general, PRL is viewed as a promising molecule for the treatment of AD and PD. Modulation of PRL functions and targeting of immune mechanisms are needed to devise preventive or therapeutic strategies.
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Affiliation(s)
- Hai Duc Nguyen
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
| | - Byung Pal Yu
- Department of Physiology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Ngoc Hong Minh Hoang
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
| | - Won Hee Jo
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
| | - Hae Young Chung
- Department of Pharmacy, College of Pharmacy, Pusan National University, Busan, Republic of Korea
| | - Min-Sun Kim
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea
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Ray B, Duan K, Chen J, Fu Z, Suresh P, Johnson S, Calhoun VD, Liu J. Multimodal Brain Age Prediction with Feature Selection and Comparison. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3858-3864. [PMID: 34892076 DOI: 10.1109/embc46164.2021.9631007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain age, an estimated biological age from anatomical and/or functional brain imaging data, and its deviation from the chronological age (brain age gap) have shown the potential to serve as biomarkers for characterizing typical brain development, the abnormal aging process, and early indicators of clinical neuropsychiatric problems. In this study, we leverage multimodal brain imaging data for brain age prediction. We studied and compared the performance of individual data modalities (gray matter density in components and regions of interest, cortical and subcortical anatomical features, resting-state functional connectivity) and different combinations of multiple data modalities using data collected from 1417 participants with age between 8 and 22 years. The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least squares regression models. We have achieved a mean absolute error of 1.22 years on the test data with 188 features selected equally from all data sources, better than any individual source. After bias correction, the brain age gap was significantly associated with attention accuracy/speed and motor speed in addition to age. Our results conclude that traditional machine learning with proper feature selection can achieve similar if not better performance compared to complex deep learning neural network methods for the used sample size.
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Cheng J, Liu Z, Guan H, Wu Z, Zhu H, Jiang J, Wen W, Tao D, Liu T. Brain Age Estimation From MRI Using Cascade Networks With Ranking Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3400-3412. [PMID: 34086565 DOI: 10.1109/tmi.2021.3085948] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with 6586 MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.428 and Pearson's correlation coefficient (PCC) of 0.985, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was 0.904 and 0.823, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation.
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Gautherot M, Kuchcinski G, Bordier C, Sillaire AR, Delbeuck X, Leroy M, Leclerc X, Pruvo JP, Pasquier F, Lopes R. Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease. Front Aging Neurosci 2021; 13:729635. [PMID: 34803654 PMCID: PMC8596466 DOI: 10.3389/fnagi.2021.729635] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/27/2021] [Indexed: 01/28/2023] Open
Abstract
Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from "brain" age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients. Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling. Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients. Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.
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Affiliation(s)
- Morgan Gautherot
- UMS 2014–US 41–PLBS–Plateformes Lilloises en Biologie & Santé, University of Lille, Lille, France
| | - Grégory Kuchcinski
- UMS 2014–US 41–PLBS–Plateformes Lilloises en Biologie & Santé, University of Lille, Lille, France
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
- Neuroradiology Department, Lille University Medical Centre, Lille, France
| | - Cécile Bordier
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
| | - Adeline Rollin Sillaire
- Memory Center, DISTALZ, Lille, France
- Neurology Department, Lille University Medical Centre, Lille, France
| | | | - Mélanie Leroy
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
- Memory Center, DISTALZ, Lille, France
| | - Xavier Leclerc
- UMS 2014–US 41–PLBS–Plateformes Lilloises en Biologie & Santé, University of Lille, Lille, France
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
- Neuroradiology Department, Lille University Medical Centre, Lille, France
| | - Jean-Pierre Pruvo
- UMS 2014–US 41–PLBS–Plateformes Lilloises en Biologie & Santé, University of Lille, Lille, France
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
- Neuroradiology Department, Lille University Medical Centre, Lille, France
| | - Florence Pasquier
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
- Memory Center, DISTALZ, Lille, France
- Neurology Department, Lille University Medical Centre, Lille, France
| | - Renaud Lopes
- UMS 2014–US 41–PLBS–Plateformes Lilloises en Biologie & Santé, University of Lille, Lille, France
- Inserm, U1172–LilNCog–Lille Neuroscience & Cognition, University of Lille, Lille, France
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Cherbuin N, Walsh EI, Shaw M, Luders E, Anstey KJ, Sachdev PS, Abhayaratna WP, Gaser C. Optimal Blood Pressure Keeps Our Brains Younger. Front Aging Neurosci 2021; 13:694982. [PMID: 34675795 PMCID: PMC8523821 DOI: 10.3389/fnagi.2021.694982] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Elevated blood pressure (BP) is a major health risk factor and the leading global cause of premature death. Hypertension is also a risk factor for cognitive decline and dementia. However, when elevated blood pressure starts impacting cerebral health is less clear. We addressed this gap by estimating how a validated measure of brain health relates to changes in BP over a period of 12 years. Methods: Middle-age (44-46 years at baseline, n = 335, 52% female) and older-age (60-64 years, n = 351, 46% female) cognitively intact individuals underwent up to four brain scans. Brain health was assessed using a machine learning approach to produce an estimate of "observed" age (BrainAGE), which can be contrasted with chronological age. Longitudinal associations between blood pressures and BrainAGE were assessed with linear mixed-effects models. Results: A progressive increase in BP was observed over the follow up (MAP = 0.8 mmHg/year, SD = 0.92; SBP = 1.41 mmHg/year, SD = 1.49; DBP = 0.61 mmHg/year, SD = 0.78). In fully adjusted models, every additional 10 mmHg increase in blood pressure (above 90 for mean, 114 for systolic, and 74 for diastolic blood pressure) was associated with a higher BrainAGE by 65.7 days for mean, and 51.1 days for systolic/diastolic blood pressure. These effects occurred across the blood pressure range and were not exclusively driven by hypertension. Conclusion: Increasing blood pressure is associated with poorer brain health. Compared to a person becoming hypertensive, somebody with an ideal BP is predicted to have a brain that appears more than 6 months younger at midlife.
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Affiliation(s)
- Nicolas Cherbuin
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, Australia
| | - Erin I Walsh
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, Australia
| | - Marnie Shaw
- College of Engineering & Computer Science, Australian National University, Canberra, ACT, Australia
| | - Eileen Luders
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, Australia.,School of Psychology, University of Auckland, Auckland, New Zealand
| | - Kaarin J Anstey
- Neuroscience Research Australia, Sydney, NSW, Australia.,School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | | | - Christian Gaser
- Department of Neurology, Jena University Hospital, Jena, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
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46
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Deng B, Zheng Z, Zheng J, Yang W, Huang Y, Luo Y, Jin D, Shen L, Jin K, Wang Q. FTD-PSP is an Unusual Clinical Phenotype in A Frontotemporal Dementia Patient with A Novel Progranulin Mutation. Aging Dis 2021; 12:1741-1752. [PMID: 34631218 PMCID: PMC8460311 DOI: 10.14336/ad.2021.0309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/09/2021] [Indexed: 12/28/2022] Open
Abstract
Progranulin (GRN) mutations are a major cause of frontotemporal dementia (FTD); the spectrum of clinical phenotypes of FTD is much more extensive than previously reported. The frequency and locations of GRN mutations in Chinese patients with FTD remain uncertain. We performed cDNA sequencing in one sporadic male patient who initially presented FTD symptoms. Brain magnetic resonance imaging (MRI) and positron emission computed tomography/computed tomography (PET/CT) were applied to further confirm the diagnosis of FTD from this patient. Cellular apoptosis and survival test were performed to identify the function of GRN. We identified one novel missense GRN mutation (c.1498G>A, p.V500I) in this patient, who initially presented typical behavioral-variant frontotemporal dementia (bvFTD) features but then presented progressive supranuclear palsy (PSP) clinical characteristics 5 years after onset. Besides, WT GRN protein showed an adequate trophic stimulus to preserve the survival of SH-SY5Y cells in the medium free of serum, while GRN mutation (c.1498G>A, p.V500I) may impair the ability of supporting cell survival. This study owns significant implications for genetic counseling and clinical heterogeneity. We illustrate the fact that FTD presenting features of bvFTD and PSP in one patient could be considered as a specific phenotype in patients with GRN mutations. GRN p.V500I led to the neuronal degeneration in vitro; this finding provides a significant evidence that this mutation may be a new causative mutation in patients with FTD.
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Affiliation(s)
- Bin Deng
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Zhe Zheng
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Jialing Zheng
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Wanlin Yang
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Yu Huang
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Yuqi Luo
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
| | - Dana Jin
- 3College of Biological Sciences, University of California, Davis, CA 95616, USA
| | - Lu Shen
- 2Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Kunlin Jin
- 4Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Qing Wang
- 1Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China
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47
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Eickhoff CR, Hoffstaedter F, Caspers J, Reetz K, Mathys C, Dogan I, Amunts K, Schnitzler A, Eickhoff SB. Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment. Brain Commun 2021; 3:fcab191. [PMID: 34541531 PMCID: PMC8445399 DOI: 10.1093/braincomms/fcab191] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/22/2021] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson's patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson's disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.
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Affiliation(s)
- Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Institute of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kathrin Reetz
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Imis Dogan
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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48
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Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease. Neurobiol Aging 2021; 109:31-42. [PMID: 34649002 DOI: 10.1016/j.neurobiolaging.2021.08.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/20/2022]
Abstract
Recently, it was shown that patients with Parkinson's disease (PD) who exhibit an "Alzheimer's disease (AD)-like" pattern of brain atrophy are at greater risk for future cognitive decline. This study aimed to investigate whether this association is domain-specific and whether atrophy associated with brain aging also relates to cognitive impairment in PD. SPARE-AD, an MRI index capturing AD-like atrophy, and atrophy-based estimates of brain age were computed from longitudinal structural imaging data of 178 PD patients and 84 healthy subjects from the LANDSCAPE cohort. All patients underwent an extensive neuropsychological test battery. Patients diagnosed with mild cognitive impairment or dementia were found to have higher SPARE-AD scores as compared to patients with normal cognition and healthy controls. All patient groups showed increased brain age. SPARE-AD predicted impairment in memory, language and executive functions, whereas advanced brain age was associated with deficits in attention and working memory. Data suggest that SPARE-AD and brain age are differentially related to domain-specific cognitive decline in PD. The underlying pathomechanisms remain to be determined.
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49
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Liu H, Deng B, Xie F, Yang X, Xie Z, Chen Y, Yang Z, Huang X, Zhu S, Wang Q. The influence of white matter hyperintensity on cognitive impairment in Parkinson's disease. Ann Clin Transl Neurol 2021; 8:1917-1934. [PMID: 34310081 PMCID: PMC8419402 DOI: 10.1002/acn3.51429] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/05/2021] [Accepted: 07/02/2021] [Indexed: 01/11/2023] Open
Abstract
The aim of this meta‐analysis was to review systematically and to identify the relationship between the severity and location of white matter hyperintensities (WMHs) and the degree of cognitive decline in patients with Parkinson’s disease (PD). We searched the PubMed, EMBASE, Web of Science, Ovid, and Cochrane Library databases for clinical trials of the severity and location of WMHs on the degree of cognitive impairment in PD through October 2020. We conducted the survey to compare the association of WMH burden in patients with PD with mild cognitive impairment (PD‐MCI) versus those with normal cognition (PD‐NC) and in patients with PD with dementia (PDD) versus those with PD without dementia (PD‐ND). Nine studies with PD‐MCI versus PD‐NC and 10 studies with PDD versus PD‐ND comparisons were included. The WMH burden in PD‐MCI patients was significantly different compared to that in PD‐NC patients (standard mean difference, SMD = 0.39, 95% CI: 0.12 to 0.66, p = 0.005), while there was no correlation shown in the age‐matched subgroup of the comparison. In addition, PDD patients had a significantly higher burden of WMHs (SMD = 0.8, 95% CI: 0.44 to 1.71, p < 0.0001), especially deep white matter hyperintensities (SMD = 0.54, 95% CI: 0.36 to 0.73, p < 0.00001) and periventricular hyperintensities (SMD = 0.70, 95% CI: 0.36 to 1.04, p < 0.0001), than PD‐NC patients, regardless of the adjustment of age. WMHs might be imaging markers for cognitive impairment in PDD but not in PD‐MCI, regardless of age, vascular risk factors, or race. Further prospective studies are needed to validate the conclusions.
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Affiliation(s)
- Hailing Liu
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China.,Department of Neurology, Maoming People's Hospital, Maoming, Guangdong, China
| | - Bin Deng
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Fen Xie
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Xiaohua Yang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Zhenchao Xie
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Yonghua Chen
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Zhi Yang
- Department of Neurology, Maoming People's Hospital, Maoming, Guangdong, China
| | - Xiyan Huang
- Department of Rehabilitation, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Shuzhen Zhu
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
| | - Qing Wang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, 510282, P.R. China
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50
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Varzandian A, Razo MAS, Sanders MR, Atmakuru A, Di Fatta G. Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease. Front Neurosci 2021; 15:673120. [PMID: 34121998 PMCID: PMC8193935 DOI: 10.3389/fnins.2021.673120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/26/2021] [Indexed: 11/16/2022] Open
Abstract
Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the model. This work investigates an appropriate combination of data science techniques and linear models to provide, at the same time, high accuracy and good descriptiveness. The proposed method is based on a data workflow that include typical data science methods, such as outliers detection, feature selection, linear regression, and logistic regression. In particular, a novel inductive bias is introduced in the regression model, which is aimed at improving the accuracy and the specificity of the classification task. The method is compared to other machine learning approaches for AD classification based on morphological brain data with and without the use of the brain age, including Support Vector Machines and Deep Neural Networks. This study adopts brain MRI scans of 1, 901 subjects which have been acquired from three repositories (ADNI, AIBL, and IXI). A predictive model based only on the proposed apparent brain age and the chronological age has an accuracy of 88% and 92%, respectively, for male and female subjects, in a repeated cross-validation analysis, thus achieving a comparable or superior performance than state of the art machine learning methods. The advantage of the proposed method is that it maintains the morphological semantics of the input space throughout the regression and classification tasks. The accurate predictive model is also highly descriptive and can be used to generate potentially useful insights on the predictions.
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Affiliation(s)
- Ali Varzandian
- Department of Computer Science, University of Reading, Reading, United Kingdom
| | | | | | - Akhila Atmakuru
- Department of Computer Science, University of Reading, Reading, United Kingdom
| | - Giuseppe Di Fatta
- Department of Computer Science, University of Reading, Reading, United Kingdom
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