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Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, Mathalon DH. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophr Bull 2024; 50:1159-1170. [PMID: 38815987 PMCID: PMC11349027 DOI: 10.1093/schbul/sbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND HYPOTHESIS Brain development/aging is not uniform across individuals, spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or aging map onto specific symptom facets. STUDY DESIGN Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. STUDY RESULTS ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. CONCLUSIONS Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
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Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Samantha V Abram
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
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2
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Hu X, Cheng B, Tang Y, Long T, Huang Y, Li P, Song Y, Song X, Li K, Yin Y, Chen X. Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder. Brain Res 2024; 1837:148986. [PMID: 38714227 DOI: 10.1016/j.brainres.2024.148986] [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: 10/13/2023] [Revised: 04/06/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.
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Affiliation(s)
- Xiao Hu
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610041, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yuying Tang
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Tong Long
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yan Huang
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Pei Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yu Song
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Xiyang Song
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Kun Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yijie Yin
- School of Sociality and Psychology, Southwest Minzu University, Chengdu 610041, China
| | - Xijian Chen
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
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Wang X, Fu S, Yoo K, Wang X, Gan L, Zou T, Gao Q, Han H, Yang Z, Hu X, Chen H, Liu D, Li R. Individualized Structural Perturbations on Normative Brain Connectome Restrict Deep Brain Stimulation Outcomes in Parkinson's Disease. Mov Disord 2024; 39:1352-1363. [PMID: 38894532 DOI: 10.1002/mds.29874] [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: 02/02/2024] [Accepted: 05/13/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) respond to deep brain stimulation (DBS) variably. However, how brain substrates restrict DBS outcomes remains unclear. OBJECTIVE In this article, we aim to identify prognostic brain signatures for explaining the response variability. METHODS We retrospectively investigated a cohort of patients with PD (n = 141) between 2017 and 2022, and defined DBS outcomes as the improvement ratio of clinical motor scores. We used a deviation index to quantify individual perturbations on a reference structural covariance network acquired with preoperative T1-weighted magnetic resonance imaging. The neurobiological perturbations of patients were represented as z scored indices based on the chronological perturbations measured on a group of normal aging adults. RESULTS After applying stringent statistical tests (z > 2.5) and correcting for false discoveries (P < 0.01), we found that accelerated deviations mainly affected the prefrontal cortex, motor strip, limbic system, and cerebellum in PD. Particularly, a negative network within the accelerated deviations, expressed as "more preoperative deviations, less postoperative improvements," could predict DBS outcomes (mean absolute error = 0.09, R2 = 0.15). Moreover, a fusion of personal brain predictors and medical responses significantly improved traditional evaluations of DBS outcomes. Notably, the most important brain predictor, a pathway connecting the cognitive unit (prefrontal cortex) and motor control unit (cerebellum and motor strip), partially mediates DBS outcomes with the age at surgery. CONCLUSIONS Our findings suggest that individual structural perturbations on the cognitive motor control circuit are critical for modulating DBS outcomes. Interventions toward the circuit have the potential for additional clinical improvements. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Xuyang Wang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Shiyu Fu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Kwangsun Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Xiaoyue Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Lin Gan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Ting Zou
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Honghao Han
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Zhenzhe Yang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, People's Republic of China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Rong Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
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Ng C, Huang P, Cho Y, Lee P, Liu Y, Chang T. Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth. Hum Brain Mapp 2024; 45:e26777. [PMID: 39046114 PMCID: PMC11267564 DOI: 10.1002/hbm.26777] [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: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.
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Affiliation(s)
- Chan‐Tat Ng
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Po‐Hsien Huang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Cheng Cho
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Pei‐Hong Lee
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Chang Liu
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Ting‐Ting Chang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
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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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Kim WS, Heo DW, Maeng J, Shen J, Tsogt U, Odkhuu S, Zhang X, Cheraghi S, Kim SW, Ham BJ, Rami FZ, Sui J, Kang CY, Suk HI, Chung YC. Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders. Schizophr Bull 2024; 50:804-814. [PMID: 38085061 PMCID: PMC11283195 DOI: 10.1093/schbul/sbad167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY RESULTS The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. CONCLUSIONS Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.
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Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Da-Woon Heo
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Junyeong Maeng
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Yanbian University, Medical School, Yanji, China
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Xuefeng Zhang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chae Yeong Kang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
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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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Sun H, Sun Q, Li Y, Zhang J, Xing H, Wang J. Mapping individual structural covariance network in development brain with dynamic time warping. Cereb Cortex 2024; 34:bhae039. [PMID: 38342688 DOI: 10.1093/cercor/bhae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/04/2024] [Accepted: 01/21/2024] [Indexed: 02/13/2024] Open
Abstract
A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.
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Affiliation(s)
- Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Qinyao Sun
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Yuanyuan Li
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Haoyang Xing
- Magnetic Resonance Research Center, West China Hospital, Sichuan University, Chengdu 610065, China
- School of Physics, Sichuan University, Chengdu 610065, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
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9
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Liu H, Ma Z, Wei L, Chen Z, Peng Y, Jiao Z, Bai H, Jing B. A radiomics-based brain network in T1 images: construction, attributes, and applications. Cereb Cortex 2024; 34:bhae016. [PMID: 38300184 PMCID: PMC10839838 DOI: 10.1093/cercor/bhae016] [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: 11/28/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/02/2024] Open
Abstract
T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.
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Affiliation(s)
- Han Liu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishilu Road, Xicheng District, Beijing 100045, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Zhe Ma
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, 127 Dongming Road, Jinshui District, Zhengzhou, Henan 450008, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Lijiang Wei
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Zhenpeng Chen
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishilu Road, Xicheng District, Beijing 100045, China
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Brown University, 593 Eddy Street, Providence, Rhode Island 02903, United States
| | - Harrison Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University, 1800 Orleans Street, Baltimore, Maryland 21205, United States
| | - Bin Jing
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
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10
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Niu J, Jiao Q, Cui D, Dou R, Guo Y, Yu G, Zhang X, Sun F, Qiu J, Dong L, Cao W. Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures. Cereb Cortex 2024; 34:bhad454. [PMID: 38037843 DOI: 10.1093/cercor/bhad454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.
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Affiliation(s)
- Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yongxin Guo
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fengzhu Sun
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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11
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Paitel ER, Nielson KA. Cerebellar EEG source localization reveals age-related compensatory activity moderated by genetic risk for Alzheimer's disease. Psychophysiology 2023; 60:e14395. [PMID: 37493042 DOI: 10.1111/psyp.14395] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023]
Abstract
The apolipoprotein-E (APOE) ε4 allele is the greatest genetic risk factor for late-onset Alzheimer's disease (AD), but alone it is not sufficiently predictive. Because neuropathological changes associated with AD begin decades before cognitive symptoms, neuroimaging of healthy, cognitively intact ε4 carriers (ε4+) may enable early characterization of patterns associated with risk for future decline. Research in the cerebral cortex highlights a period of compensatory recruitment in elders and ε4+, which serves to maintain cognitive functioning. Yet, AD-related changes may occur even earlier in the cerebellum. Advances in electroencephalography (EEG) source localization now allow effective modeling of cerebellar activity. Importantly, healthy aging and AD are associated with declines in both cerebellar functions and executive functioning (EF). However, it is not known whether cerebellar activity can detect pre-symptomatic AD risk. Thus, the current study analyzed cerebellar EEG source localization during an EF-dependent stop-signal task (i.e., inhibitory control) in healthy, intact older adults (Mage = 80 years; 20 ε4+, 25 ε4-). Task performance was comparable between groups. Older age predicted greater activity in left crus II and lobule VIIb during the P300 window (i.e., performance evaluation), consistent with age-related compensation. Age*ε4 moderations specifically showed that compensatory patterns were evident only in ε4-, suggesting that cerebellar compensatory resources may already be depleted in healthy ε4+ elders. Thus, the posterolateral cerebellum is sensitive to AD-related neural deficits in healthy elders. Characterization of these patterns may be essential for the earliest possible detection of AD risk, which would enable critical early intervention prior to symptom onset.
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Affiliation(s)
- Elizabeth R Paitel
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
| | - Kristy A Nielson
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
- Department of Neurology, Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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12
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Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study. Transl Psychiatry 2023; 13:82. [PMID: 36882419 PMCID: PMC9992684 DOI: 10.1038/s41398-023-02379-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.
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13
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Zhou Y, Müller HG, Zhu C, Chen Y, Wang JL, O'Muircheartaigh J, Bruchhage M, Deoni S, Bruchhage M, Carnell S, Deoni S, D’Sa V, Huentelman M, Klepac-Ceraj V, LeBourgeois M, Müller HG, O’Muircheartaigh J, Wang JL. Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education. Sci Rep 2023; 13:2984. [PMID: 36804963 PMCID: PMC9941570 DOI: 10.1038/s41598-023-29797-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.
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Affiliation(s)
- Yidong Zhou
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Changbo Zhu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Yaqing Chen
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Muriel Bruchhage
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, USA.,Department of Diagnostic Imaging, Rhode Island Hospital, Providence, USA.,Institute of Social Sciences, Stavanger University, Stavanger, 4021, Norway
| | - Sean Deoni
- Maternal, Newborn, and Child Health Discovery and Tools, Bill and Melinda Gates Foundation, Seattle, WA, USA
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14
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Wang Y, Zhang Y, Zheng W, Liu X, Zhao Z, Li S, Chen N, Yang L, Fang L, Yao Z, Hu B. Age-Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals. J Magn Reson Imaging 2023; 57:434-443. [PMID: 35924281 DOI: 10.1002/jmri.28318] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Healthy aging is usually accompanied by alterations in brain network architecture, influencing information processing and cognitive performance. However, age-associated coordination patterns of morphological networks and cognitive variation are not well understood. PURPOSE To investigate the age-related differences of cortical topology in morphological brain networks from multiple perspectives. STUDY TYPE Prospective, observational multisite study. POPULATION A total of 1427 healthy participants (59.1% female, 51.75 ± 19.82 years old) from public datasets. FIELD STRENGTH/SEQUENCE 1.5 T/3 T, T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence. ASSESSMENT The multimodal parcellation atlas was used to define regions of interest (ROIs). The Jensen-Shannon divergence-based individual morphological networks were constructed by estimating the interregional similarity of cortical thickness distribution. Graph-theory based global network properties were then calculated, followed by ROI analysis (including global/nodal topological analysis and hub analysis) with statistical tests. STATISTICAL TESTS Chi-square test, Jensen-Shannon divergence-based similarity measurement, general linear model with false discovery rate correction. Significance was set at P < 0.05. RESULTS The clustering coefficient (q = 0.016), global efficiency (q = 0.007), and small-worldness (q = 0.006) were significantly negatively quadratic correlated with age. The group-level hubs of seven age groups were found mainly distributed in default mode network, visual network, salient network, and somatosensory motor network (the sum of these hubs' distribution in each group exceeds 55%). Further ROI-wise analysis showed significant nodal trajectories of intramodular connectivities. DATA CONCLUSION These results demonstrated the age-associated reconfiguration of morphological networks. Specifically, network segregation/integration had an inverted U-shaped relationship with age, which indicated age-related differences in transmission efficiency. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,Guangyuan Mental Health Center, Guangyuan, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xia Liu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lei Fang
- PET/CT Center, The 940th Hospital of Joint Logistic Support Force of PLA, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.,Engineering Research Center of Open Source Software and Real-Time System, Lanzhou University, Ministry of Education, Lanzhou, China
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15
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Ren J, Xu D, Mei H, Zhong X, Yu M, Ma J, Fan C, Lv J, Xiao Y, Gao L, Xu H. Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks. Front Aging Neurosci 2023; 14:1091829. [PMID: 36711201 PMCID: PMC9878604 DOI: 10.3389/fnagi.2022.1091829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/26/2022] [Indexed: 01/15/2023] Open
Abstract
Background and purpose Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS). Methods Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms. Results In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests. Conclusion We conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.
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Affiliation(s)
- Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hao Mei
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jiaojiao Ma
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chenhong Fan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jinfeng Lv
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China,*Correspondence: Lei Gao, ✉
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China,Haibo Xu, ✉
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16
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Zhu JD, Tsai SJ, Lin CP, Lee YJ, Yang AC. Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:1. [PMID: 36596800 PMCID: PMC9810255 DOI: 10.1038/s41537-022-00325-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 12/20/2022] [Indexed: 01/05/2023]
Abstract
Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.
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Affiliation(s)
- Jun-Ding Zhu
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.278247.c0000 0004 0604 5314Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ching-Po Lin
- grid.260539.b0000 0001 2059 7017Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ju Lee
- grid.28665.3f0000 0001 2287 1366Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Albert C. Yang
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.278247.c0000 0004 0604 5314Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
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17
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Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies. J Psychiatr Res 2023; 157:180-191. [PMID: 36473289 DOI: 10.1016/j.jpsychires.2022.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/14/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022]
Abstract
Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.
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18
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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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Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022; 12:397. [PMID: 36130921 PMCID: PMC9492670 DOI: 10.1038/s41398-022-02162-y] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
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20
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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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21
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Tang H, Guo L, Fu X, Qu B, Ajilore O, Wang Y, Thompson PM, Huang H, Leow AD, Zhan L. A Hierarchical Graph Learning Model for Brain Network Regression Analysis. Front Neurosci 2022; 16:963082. [PMID: 35903810 PMCID: PMC9315240 DOI: 10.3389/fnins.2022.963082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.
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Affiliation(s)
- Haoteng Tang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Lei Guo
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiyao Fu
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Benjamin Qu
- Mission San Jose High School, Fremont, CA, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Yalin Wang
- Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, CA, United States
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
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22
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Ballester PL, Romano MT, de Azevedo Cardoso T, Hassel S, Strother SC, Kennedy SH, Frey BN. Brain age in mood and psychotic disorders: a systematic review and meta-analysis. Acta Psychiatr Scand 2022; 145:42-55. [PMID: 34510423 DOI: 10.1111/acps.13371] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Maria T Romano
- Integrated Science Undergraduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Taiane de Azevedo Cardoso
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre, and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
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23
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Li S, Bai R, Yang Y, Zhao R, Upreti B, Wang X, Liu S, Cheng Y, Xu J. Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations. Arthritis Res Ther 2022; 24:259. [PMID: 36443835 PMCID: PMC9703716 DOI: 10.1186/s13075-022-02954-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/11/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE. METHODS We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE. RESULTS By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution. CONCLUSION Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.
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Affiliation(s)
- Shu Li
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ru Bai
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yifan Yang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruotong Zhao
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bibhuti Upreti
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiangyu Wang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuang Liu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Jian Xu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
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24
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Prasad K, Rubin J, Mitra A, Lewis M, Theis N, Muldoon B, Iyengar S, Cape J. Structural covariance networks in schizophrenia: A systematic review Part II. Schizophr Res 2022; 239:176-191. [PMID: 34902650 PMCID: PMC8785680 DOI: 10.1016/j.schres.2021.11.036] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/02/2021] [Accepted: 11/23/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. METHODS A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. RESULTS A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. CONCLUSIONS Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
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Affiliation(s)
- Konasale Prasad
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America; University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America; VA Pittsburgh Healthcare System, University Dr C, Pittsburgh, PA 15240, United States of America.
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, 917 Cathedral of Learning, Pittsburgh, PA 15260, United States of America
| | - Anirban Mitra
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
| | - Madison Lewis
- University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Nicholas Theis
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Brendan Muldoon
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
| | - Joshua Cape
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
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25
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Hartley P, Monaghan A, Donoghue OA, Kenny RA, Romero-Ortuno R. Exploring bi-directional temporal associations between timed-up-and-go and cognitive domains in the Irish longitudinal study on ageing (TILDA). Arch Gerontol Geriatr 2021; 99:104611. [PMID: 34998129 DOI: 10.1016/j.archger.2021.104611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The bi-directional longitudinal associations between mobility and cognition in older adults are poorly understood. Our objective was to study the temporal associations between timed-up-and-go (TUG) and five cognitive function domains: global cognition, processing speed, verbal fluency, executive function, and sustained attention. METHODS We designed two longitudinal samples: A (for cognition as predictor of mobility), and B (for mobility as predictor of cognition). To examine the associations between the five cognitive domains at wave 1 and change in TUG times up to wave 5 (eight years), five linear mixed-effect models were fitted. To examine the associations between TUG times at wave 1 and change in the five cognitive domains between waves 1 and 3 (four years), five linear-regression models were fitted. RESULTS After removing participants with missing data, sample A numbered 4913 participants (mean age 62), and sample B 3675 (mean age 61). Baseline cognitive domains were all significant predictors of future change in TUG times. Baseline TUG time was also a significant predictor of future change in all five cognitive domains. In both cases, poorer performance at baseline predicted greater future loss of function. CONCLUSION There was evidence of bi-directional temporal relationships between cognition and mobility. In both directions, the effect of the explanatory variable was small, though cognition as predictor of future mobility may have greater clinical relevance than vice versa. Our findings underscore the importance for clinicians of considering the bi-directional associations between cognition and mobility when observing subtle changes in either, especially as impairments emerge.
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Affiliation(s)
- Peter Hartley
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland; Department of Public Health and Primary Care, University of Cambridge, United Kingdom.
| | - Ann Monaghan
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland
| | - Orna A Donoghue
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland; Mercer's Institute for Successful Ageing, St James's Hospital, Dublin, Ireland
| | - Roman Romero-Ortuno
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland; Mercer's Institute for Successful Ageing, St James's Hospital, Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Ireland
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26
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Petrican R, Miles S, Rudd L, Wasiewska W, Graham KS, Lawrence AD. Pubertal timing and functional neurodevelopmental alterations independently mediate the effect of family conflict on adolescent psychopathology. Dev Cogn Neurosci 2021; 52:101032. [PMID: 34781251 PMCID: PMC10436252 DOI: 10.1016/j.dcn.2021.101032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 12/28/2022] Open
Abstract
This study tested the hypothesis that early life adversity (ELA) heightens psychopathology risk by concurrently altering pubertal and neurodevelopmental timing, and associated gene transcription signatures. Analyses focused on threat- (family conflict/neighbourhood crime) and deprivation-related ELAs (parental inattentiveness/unmet material needs), using longitudinal data from 1514 biologically unrelated youths in the Adolescent Brain and Cognitive Development (ABCD) study. Typical developmental changes in white matter microstructure corresponded to widespread BOLD signal variability (BOLDsv) increases (linked to cell communication and biosynthesis genes) and region-specific task-related BOLDsv increases/decreases (linked to signal transduction, immune and external environmental response genes). Increasing resting-state (RS), but decreasing task-related BOLDsv predicted normative functional network segregation. Family conflict was the strongest concurrent and prospective contributor to psychopathology, while material deprivation constituted an additive risk factor. ELA-linked psychopathology was predicted by higher Time 1 threat-evoked BOLDSV (associated with axonal development, myelination, cell differentiation and signal transduction genes), reduced Time 2 RS BOLDsv (associated with cell metabolism and attention genes) and greater Time 1 to Time 2 control/attention network segregation. Earlier pubertal timing and neurodevelopmental alterations independently mediated ELA effects on psychopathology. Our results underscore the differential roles of the immediate and wider external environment(s) in concurrent and longer-term ELA consequences.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom.
| | - Sian Miles
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Lily Rudd
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Wiktoria Wasiewska
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Kim S Graham
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
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27
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Chou KH, Lee PL, Peng LN, Lee WJ, Wang PN, Chen LK, Lin CP, Chung CP. Classification differentiates clinical and neuroanatomic features of cerebral small vessel disease. Brain Commun 2021; 3:fcab107. [PMID: 34131645 PMCID: PMC8196251 DOI: 10.1093/braincomms/fcab107] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 11/13/2022] Open
Abstract
Age-related cerebral small vessel disease involves heterogeneous pathogenesis, such as arteriosclerosis/lipohyalinosis and cerebral amyloid angiopathy. MRI can visualize the brain lesions attributable to small vessel disease pathologies, including white-matter hyperintensities, lacune and cerebral microbleeds. However, these MRI markers usually coexist in small vessel disease of different aetiologies. Currently, there is no available classification integrating these neuroimaging markers for differentiating clinical and neuroanatomic features of small vessel disease yet. In this study, we tested whether our proposed stratification scheme could characterize specific clinical, neuroanatomic and potentially pathogenesis/aetiologies in classified small vessel disease subtypes. Cross-sectional analyses from a community-based non-demented non-stroke cohort consisting of ≥50 years old individuals were conducted. All participants were scanned 3T brain MRI for small vessel disease detection and neuroanatomic measurements and underwent physical and cognitive assessments. Study population were classified into robust and four small vessel disease groups based on imaging markers indicating (i) bleeding or non-bleeding; (ii) specific location of cerebral microbleeds; and (iii) the severity and combination of white-matter hyperintensities and lacune. We used whole-brain voxel-based morphometry analyses and tract-based spatial statistics to evaluate the regional grey-matter volume and white-matter microstructure integrity for comparisons among groups. Among the 735 participants with eligible brain MRI images, quality screening qualified 670 for grey-matter volume analyses and 617 for white-matter microstructural analyses. Common and distinct patterns of the clinical and neuroimaging manifestations were found in the stratified four small vessel disease subgroups. Hierarchical clustering analysis revealed that small vessel disease type 4 had features distinct from the small vessel disease types 1, 2 and 3. Abnormal white-matter microstructures and cognitive function but preserved physical function and grey-matter volume were found in small vessel disease type 4. Among small vessel disease types 1, 2 and 3, there were similar characteristics but different severity; the clinical features showed both physical frail and cognitive impairment and the neuroanatomic features revealed frontal–subcortical white-matter microstructures and remote, diffuse cortical abnormalities. This novel stratification scheme highlights the distinct clinical and neuroanatomic features of small vessel disease and the possible underlying pathogenesis. It could have potential application in research and clinical settings.
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Affiliation(s)
- Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan
| | - Li-Ning Peng
- Department of Neurology in School of Medicine, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Center for Geriatric and Gerontology, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Wei-Ju Lee
- Aging and Health Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Center for Geriatric and Gerontology, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Family Medicine, Taipei Veterans General Hospital Yuanshan Branch, Yi-Lan 264, Taiwan
| | - Pei-Ning Wang
- Brain Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Department of Neurology in School of Medicine, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Center for Geriatric and Gerontology, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan
| | - Chih-Ping Chung
- Department of Neurology in School of Medicine, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan
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28
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Chen LK. Machine Learning Improves Analysis of Multi-Omics Data in Aging Research and Geroscience. Arch Gerontol Geriatr 2021; 93:104360. [PMID: 33546901 DOI: 10.1016/j.archger.2021.104360] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan; Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan.
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29
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Kuo CY, Tai TM, Lee PL, Tseng CW, Chen CY, Chen LK, Lee CK, Chou KH, See S, Lin CP. Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework. Front Psychiatry 2021; 12:626677. [PMID: 33833699 PMCID: PMC8021919 DOI: 10.3389/fpsyt.2021.626677] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/22/2021] [Indexed: 01/02/2023] Open
Abstract
Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R 2 = 0.88; support vector regression, MAE = 4.42 years, R 2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R 2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.
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Affiliation(s)
- Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | | | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, 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
| | - Simon See
- NVIDIA AI Technology Center, NVIDIA, Taipei, Taiwan
| | - Ching-Po Lin
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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