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Shi Y, Wang Z, Chen P, Cheng P, Zhao K, Zhang H, Shu H, Gu L, Gao L, Wang Q, Zhang H, Xie C, Liu Y, Zhang Z. Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:171-180. [PMID: 33712376 DOI: 10.1016/j.bpsc.2020.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/16/2020] [Accepted: 12/16/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD. METHODS Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques. RESULTS The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups. CONCLUSIONS Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Piaoyue Cheng
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Kun Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lihua Gu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lijuan Gao
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Haisan Zhang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China; School of Life Science and Technology, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
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Berente DB, Zsuffa J, Werber T, Kiss M, Drotos A, Kamondi A, Csukly G, Horvath AA. Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study. Front Aging Neurosci 2022; 14:854368. [PMID: 35754966 PMCID: PMC9226394 DOI: 10.3389/fnagi.2022.854368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/27/2022] [Indexed: 11/30/2022] Open
Abstract
Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.
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Affiliation(s)
| | - Janos Zsuffa
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Family Medicine, Semmelweis University, Budapest, Hungary
| | - Tom Werber
- Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Anita Drotos
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Gabor Csukly
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Research Group of Clinical Neuroscience and Neuroimaging, Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Andras Attila Horvath
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
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3
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Zhou Y, Si X, Chao YP, Chen Y, Lin CP, Li S, Zhang X, Sun Y, Ming D, Li Q. Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network. Front Aging Neurosci 2022; 14:866230. [PMID: 35774112 PMCID: PMC9237212 DOI: 10.3389/fnagi.2022.866230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance. Methods Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier. Results (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI. Conclusion Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.
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Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
- Institute of Applied Psychology, Tianjin University, Tianjin, China
- *Correspondence: Xiaopeng Si,
| | - Yi-Ping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
- Dong Ming,
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin, China
- Qiang Li,
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4
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Sheng C, Yang K, He B, Li T, Wang X, Du W, Hu X, Jiang J, Jiang X, Jessen F, Han Y. Cross-Cultural Longitudinal Study on Cognitive Decline (CLoCODE) for Subjective Cognitive Decline in China and Germany: A Protocol for Study Design. J Alzheimers Dis 2022; 87:1319-1333. [PMID: 35431240 DOI: 10.3233/jad-215452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: Subjective cognitive decline (SCD) is considered as the first symptomatic manifestation of Alzheimer’s disease (AD), which is also affected by different cultural backgrounds. Establishing cross-cultural prediction models of SCD is challenging. Objective: To establish prediction models of SCD available for both the Chinese and European populations. Methods: In this project, 330 SCD from China and 380 SCD from Germany are intended to be recruited. For all participants, standardized assessments, including clinical, neuropsychological, apolipoprotein E (APOE) genotype, blood, and multi-parameter magnetic resonance imaging (MRI) at baseline will be conducted. Participants will voluntarily undergo amyloid positron emission tomography (PET) and are classified into amyloid-β (Aβ) positive SCD (SCD+) and Aβ negative SCD (SCD-). First, baseline data of all SCD individuals between the two cohorts will be compared. Then, key features associated with brain amyloidosis will be extracted in SCD+ individuals, and the diagnosis model will be established using the radiomics method. Finally, the follow-up visits will be conducted every 12 months and the primary outcome is the conversion to mild cognitive impairment or dementia. After a 4-year follow-up, we will extract factors associated with the conversion risk of SCD using Cox regression analysis. Results: At present, 141 SCD from China and 338 SCD from Germany have been recruited. Initial analysis showed significant differences in demographic information, neuropsychological tests, and regional brain atrophy in SCD compared with controls in both cohorts. Conclusion: This project may be of great value for future implications of SCD studies in different cultural backgrounds. Trial registration: ClinicalTrials.gov, NCT04696315. Registered 3 January 2021.
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Affiliation(s)
- Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Kun Yang
- Evidence-Based Medicine Center, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Beiqi He
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaochen Hu
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Xueyan Jiang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- German Center for Neurodegenerative Disease, Clinical Research Group, Bonn, Germany
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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5
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Chen Z, Zhou J, Wu D, Ji C, Luo B, Wang K. Altered executive control network connectivity in anti-NMDA receptor encephalitis. Ann Clin Transl Neurol 2021; 9:30-40. [PMID: 34923775 PMCID: PMC8791804 DOI: 10.1002/acn3.51487] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 11/24/2022] Open
Abstract
Objective The goal of this study was to examine whether the static functional connectivity (FC) of the executive control network (ECN) and the temporal properties of dynamic FC states in the ECN can characterize the underlying nature of anti‐N‐methyl‐d‐aspartate (anti‐NMDA) receptor encephalitis and their correlations with cognitive functions. Methods In total, 21 patients with anti‐NMDA receptor encephalitis past the acute stage and 23 healthy controls (HCs) underwent a set of neuropsychological tests and participated in a resting‐state fMRI study to analyse the static FC of the ECN and the temporal properties of dynamic FC states in the ECN. In addition, correlation analyses were performed to determine the correlations between the FC metrics and cognitive performance. Results Patients with anti‐NMDA receptor encephalitis past the acute stage showed significant cognitive impairments compared to HCs. In accord with the results of neuropsychological tests, static intrinsic FC alterations and changed dynamic FC metrics of ECN were observed in the patients. Importantly, we observed significant correlations between altered ECN metrics and working memory, information processing speed, executive function performance in the patients. Interpretation Our findings suggest that cognitive impairments in patients with anti‐NMDA receptor encephalitis past the acute stage are likely related to altered static and dynamic ECN connectivity. These observations may enhance our understanding of the pathophysiological mechanisms underlying cognitive function in this population.
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Affiliation(s)
- Zhongqin Chen
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jintao Zhou
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Dengchang Wu
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Caihong Ji
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Benyan Luo
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kang Wang
- Department of Neurology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Wang K, Wu D, Ji C, Luo B, Wang C, Chen Z. Abnormal Brain Activation During Verbal Memory Encoding in Postacute Anti-N-Methyl-d-Aspartate Receptor Encephalitis. Brain Connect 2021; 12:660-669. [PMID: 34514848 PMCID: PMC9527060 DOI: 10.1089/brain.2021.0046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background: Patients with postacute anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis are often left with permanent memory impairments. Given that NMDA receptors are essential to memory encoding, and encoding processes have been suggested to contribute to the success of memory retrieval, we investigate whether postacute anti-NMDA receptor encephalitis leads to abnormal brain activation during verbal memory encoding and its potential effects on subsequent memory retrieval performance. Methods: To address this issue, this study recruited 21 adult patients with anti-NMDA receptor encephalitis past the acute stage and 22 healthy controls (HCs). Functional magnetic resonance imaging (fMRI) data were collected when they completed an episodic memory task. Results: At the neural level, the patients showed higher brain activation than the HCs in the bilateral hippocampus/parahippocampus (HG/PHG), right superior temporal gyrus (STG), and right thalamus during memory encoding. At the behavioral level, the patients showed worse memory retrieval performance than the HCs. Importantly, greater brain activation in the left HG/PHG during memory encoding was significantly associated with worse memory retrieval performance among the patients. Conclusion: Our findings indicate that postacute anti-NMDA receptor encephalitis is likely related to altered brain activation during memory encoding. Particularly, less memory retrieval performance often observed in patients with postacute anti-NMDA receptor encephalitis may result from abnormal activation in HG during encoding. These observations may enhance our understanding of NMDA receptor dysfunction in the human brain. Impact statement Patients with anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis are often left with permanent memory impairments. In this study, brain activation during verbal memory encoding and its potential effects on subsequent memory retrieval performance are addressed using 21 adult patients with postacute anti-NMDA receptor encephalitis and 22 healthy controls. Greater brain activation in the left hippocampus/parahippocampus during memory encoding was significantly associated with worse memory retrieval performance among the patients. These observations enhance our understanding of NMDA receptor dysfunction in the human brain.
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Affiliation(s)
- Kang Wang
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Dengchang Wu
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Caihong Ji
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Benyan Luo
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chunjie Wang
- Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Zhongqin Chen
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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7
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Sheng C, Sun Y, Wang M, Wang X, Liu Y, Pang D, Liu J, Bi X, Du W, Zhao M, Li Y, Li X, Jiang J, Han Y. Combining Visual Rating Scales for Medial Temporal Lobe Atrophy and Posterior Atrophy to Identify Amnestic Mild Cognitive Impairment from Cognitively Normal Older Adults: Evidence Based on Two Cohorts. J Alzheimers Dis 2021; 77:323-337. [PMID: 32716355 DOI: 10.3233/jad-200016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice. OBJECTIVE To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls. METHODS This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments. RESULTS Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058). CONCLUSION The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings.
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Affiliation(s)
- Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Xiaoni Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yi Liu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Dongqing Pang
- Department of Neurology, the First Hospital of Tsinghua University, Beijing, China
| | - Jiaqi Liu
- Department of Neurology, the First Hospital of Tsinghua University, Beijing, China
| | - Xiaoxia Bi
- Department of Neurology, the First Hospital of Tsinghua University, Beijing, China
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yuxia Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
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8
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Li Y, Cong L, Hou T, Chang L, Zhang C, Tang S, Han X, Wang Y, Wang X, Kalpouzos G, Du Y, Qiu C. Characterizing Global and Regional Brain Structures in Amnestic Mild Cognitive Impairment Among Rural Residents: A Population-Based Study. J Alzheimers Dis 2021; 80:1429-1438. [PMID: 33682713 DOI: 10.3233/jad-201372] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Structural brain magnetic resonance imaging (MRI) scans may provide reliable neuroimaging markers for defining amnestic mild cognitive impairment (aMCI). Objective: We sought to characterize global and regional brain structures of aMCI among rural-dwelling older adults with limited education in China. Methods: This population-based study included 180 participants (aged≥65 years, 42 with aMCI and 138 normal controls) in the Shandong Yanggu Study of Aging and Dementia during 2014–2016. We defined aMCI following the Petersen’s criteria. Global and regional brain volumes were automatically segmented on MRI scans and compared using a region-of-interest approach. Data were analyzed using general linear regression models. Results: Multi-adjusted β-coefficient (95% confidence interval) of brain volumes (cm3) associated with aMCI was –12.07 (–21.49, –2.64) for global grey matter (GM), –18.31 (–28.45, –8.17) for global white matter (WM), 28.17 (12.83, 44.07) for cerebrospinal fluid (CSF), and 2.20 (0.24, 4.16) for white matter hyperintensities (WMH). Furthermore, aMCI was significantly associated with lower GM volumes in bilateral superior temporal gyri, thalamus and right cuneus, and lower WM volumes in lateral areas extending from the frontal to the parietal, temporal, and occipital lobes, as well as right hippocampus (p < 0.05). Conclusion: Brain structure of older adults with aMCI is characterized by reduced global GM and WM volumes, enlarged CSF volume, increased WMH burden, reduced GM volumes in bilateral superior temporal gyri, thalamus, and right cuneus, and widespread reductions of lateral WM volumes.
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Affiliation(s)
- Yuanjing Li
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
| | - Lin Cong
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Tingting Hou
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Liguo Chang
- Liaocheng Third People’s Hospital, Liaocheng, Shandong, P. R. China
| | - Chuanchen Zhang
- Department of Medical Imaging, Liaocheng People’s Hospital and Department of Medical Imaging, Liaocheng Brain Hospital, Liaocheng, Shandong, P. R. China
| | - Shi Tang
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Xiaolei Han
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
| | - Yongxiang Wang
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Xiang Wang
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Grégoria Kalpouzos
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Yifeng Du
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China
| | - Chengxuan Qiu
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, P. R. China
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
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9
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Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease. Neuroinformatics 2021; 19:57-78. [PMID: 32524428 DOI: 10.1007/s12021-020-09469-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .
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10
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He C, Bai Y, Wang Z, Fan D, Wang Q, Liu X, Zhang H, Zhang H, Zhang Z, Yao H, Xie C. Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity. Neuroimage 2020; 224:117428. [PMID: 33038536 DOI: 10.1016/j.neuroimage.2020.117428] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 01/20/2023] Open
Abstract
Childhood maltreatment (CM) is regarded as an important risk factor for major depressive disorder (MDD). However, the neural links corresponding to the process of early CM experience producing brain alterations and then leading to depression later remain unclear. To explore the neural basis of the effects of CM on MDD and the potential role of microRNA-9 (miR-9) in these processes, we recruited 40 unmedicated MDD patients and 34 healthy controls (HCs) to complete resting-state fMRI scans and peripheral blood miR-9 tests. The neural substrates of CM, miR-9, and depression, as well as their interactive effects on intrinsic amygdala functional connectivity (AFC) networks were investigated in MDD patients. Two-step mediation analysis was separately employed to explore whether AFC strength mediates the association among CM severity, miR-9 levels, and depression. A support vector classifier (SVC) model of machine learning was used to distinguish MDD patients from HCs. MDD patients showed higher miR-9 levels that were negatively correlated with CM scores and depressive severity. Overlapping effects of CM, miR-9, and depressive severity on bilateral AFC networks in MDD patients were primarily located in the prefrontal-striatum pathway and limbic system. The connection of amygdala to prefrontal-limbic circuits could mediate the effects of CM severity on the miR-9 levels, as well as the impacts of miR-9 levels on the severity of depression in MDD patients. Furthermore, the SVC model, which integrated miR-9 levels, CM severity, and AFC strength in prefrontal-limbic regions, had good power in differentiating MDD patients from HCs (accuracy 85.1%). MiR-9 may play a crucial role in the process of CM experience-produced brain changes targeting prefrontal-limbic regions and that subsequently leads to depression. The present neuroimaging-epigenetic results provide new insight into our understanding of MDD pathophysiology.
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Affiliation(s)
- Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Ying Bai
- Department of Pharmacology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Xinyi Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Haisan Zhang
- Department of Radiology, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China; Xinxiang Key Laboratory of Multimodal Brain Imaging, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China; Psychology School of Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China.
| | - Honghong Yao
- Department of Pharmacology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Life Sciences, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China.
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11
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Feng C, Zhu Z, Cui Z, Ushakov V, Dreher JC, Luo W, Gu R, Wu X, Krueger F. Prediction of trust propensity from intrinsic brain morphology and functional connectome. Hum Brain Mapp 2020; 42:175-191. [PMID: 33001541 PMCID: PMC7721234 DOI: 10.1002/hbm.25215] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/31/2020] [Accepted: 09/09/2020] [Indexed: 01/08/2023] Open
Abstract
Trust forms the basis of virtually all interpersonal relationships. Although significant individual differences characterize trust, the driving neuropsychological signatures behind its heterogeneity remain obscure. Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures. Our multivariate prediction analyses revealed that trust propensity was predicted by gray matter volume and node strength across multiple regions. The gray matter volume of identified regions further enabled the classification of individuals from an independent sample with the propensity to trust or distrust. Our modular and functional decoding analyses showed that the contributing regions were part of three large‐scale networks implicated in calculus‐based trust strategy, cost–benefit calculation, and trustworthiness inference. These findings do not only deepen our neuropsychological understanding of individual differences in trust propensity, but also provide potential biomarkers in predicting trust impairment in neuropsychiatric disorders.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zhiyuan Zhu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vadim Ushakov
- National Research Center, Kurchatov Institute, Moscow, Russia.,National Research Nuclear University MEPhI, Moscow Engineering Physics Institute, Moscow, Russia
| | - Jean-Claude Dreher
- Neuroeconomics, Reward and Decision Making Laboratory, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Bron, France
| | - Wenbo Luo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xia Wu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, Virginia, USA.,Department of Psychology, George Mason University, Fairfax, Virginia, USA
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12
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Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020; 216:262-271. [PMID: 31826827 DOI: 10.1016/j.schres.2019.11.046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.
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13
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Li Y, Cui Z, Liao Q, Dong H, Zhang J, Shen W, Zhou W. Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling. Addict Biol 2019; 24:1254-1262. [PMID: 30623517 DOI: 10.1111/adb.12705] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/14/2018] [Accepted: 11/17/2018] [Indexed: 01/15/2023]
Abstract
Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of brain disorders. To evaluate its significance in detecting methamphetamine (MA) dependence, this study used a multivariate pattern classification algorithm, ie, a support vector machine (SVM), to construct classifiers for discriminating MA-dependent subjects from normal controls. Forty-five MA-dependent subjects, 45 normal controls, and 36 heroin-dependent subjects were enrolled. Classifiers trained with ASL-CBF data from the left or right cerebrum showed significant hemispheric asymmetry in their cross-validated prediction performance (P < 0.001 for accuracy, sensitivity, specificity, kappa, and area under the curve [AUC] of the receiver operating characteristics [ROC] curve). A classifier trained with ASL-CBF data from all cerebral regions (bilateral hemispheres and corpus callosum) was able to differentiate MA-dependent subjects from normal controls with a cross-validated prediction accuracy, sensitivity, specificity, kappa, and AUC of 89%, 94%, 84%, 0.78, and 0.95, respectively. The discrimination map extracted from this classifier covered multiple brain circuits that either constitute a network related to drug abuse and addiction or could be impaired in MA-dependence. The cerebral regions contribute most to classification include occipital lobe, insular cortex, postcentral gyrus, corpus callosum, and inferior frontal cortex. This classifier was also specific to MA-dependence rather than substance use disorders in general (ie, 55.56% accuracy for heroin dependence). These results support the future utilization of ASL with an SVM-based classifier for the diagnosis of MA-dependence and could help improve the understanding of MA-related neuropathology.
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Affiliation(s)
- Yadi Li
- Department of Radiology, Ningbo Medical Center Lihuili HospitalNingbo University Ningbo China
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of MedicineUniversity of Pennsylvania Philadelphia Pennsylvania USA
| | - Qi Liao
- Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of PathophysiologyMedical School of Ningbo University Ningbo China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Center Lihuili HospitalNingbo University Ningbo China
| | - Jianbing Zhang
- Laboratory of Behavioral Neuroscience, Ningbo Addiction Research and Treatment Center Ningbo China
| | - Wenwen Shen
- Laboratory of Behavioral Neuroscience, Ningbo Addiction Research and Treatment Center Ningbo China
| | - Wenhua Zhou
- Laboratory of Behavioral Neuroscience, Ningbo Addiction Research and Treatment Center Ningbo China
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14
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Abstract
BACKGROUND Excessive worry is a defining feature of generalized anxiety disorder and is present in a wide range of other psychiatric conditions. Therefore, individualized predictions of worry propensity could be highly relevant in clinical practice, with respect to the assessment of worry symptom severity at the individual level. METHODS We applied a multivariate machine learning approach to predict dispositional worry based on microstructural integrity of white matter (WM) tracts. RESULTS We demonstrated that the machine learning model was able to decode individual dispositional worry scores from microstructural properties in widely distributed WM tracts (mean absolute error = 10.46, p < 0.001; root mean squared error = 12.82, p < 0.001; prediction R2 = 0.17, p < 0.001). WM tracts that contributed to worry prediction included the posterior limb of internal capsule, anterior corona radiate, and cerebral peduncle, as well as the corticolimbic pathways (e.g. uncinate fasciculus, cingulum, and fornix) already known to be critical for emotion processing and regulation. CONCLUSIONS The current work thus elucidates potential neuromarkers for clinical assessment of worry symptoms across a wide range of psychiatric disorders. In addition, the identification of widely distributed pathways underlying worry propensity serves to better improve the understanding of the neurobiological mechanisms associated with worry.
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Affiliation(s)
- Chunliang Feng
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA
| | - Dazhi Cheng
- Department of Pediatric Neurology, Capital Institute of Pediatrics, Beijing 100020, China
| | - Rui Xu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Ruolei Gu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
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15
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Liang S, Li Y, Zhang Z, Kong X, Wang Q, Deng W, Li X, Zhao L, Li M, Meng Y, Huang F, Ma X, Li XM, Greenshaw AJ, Shao J, Li T. Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion Imaging Study. Schizophr Bull 2019; 45:591-599. [PMID: 29947804 PMCID: PMC6483586 DOI: 10.1093/schbul/sby091] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia.
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Affiliation(s)
- Sugai Liang
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China,West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yinfei Li
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China,West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhong Zhang
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangzhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Qiang Wang
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Deng
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China,West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Mingli Li
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yajing Meng
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Feng Huang
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaohong Ma
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xin-min Li
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Andrew J Greenshaw
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Junming Shao
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Li
- Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China,West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China,To whom correspondence should be addressed; West China Mental Health Centre, West China Hospital, Sichuan University, No. 28th Dianxin Nan Str., Chengdu, Sichuan 610041, China; tel.: 86-28-85423561, fax: 86-28-85422632, e-mail:
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16
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Cui Z, Su M, Li L, Shu H, Gong G. Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume. Cereb Cortex 2018; 28:1656-1672. [PMID: 28334252 PMCID: PMC6669415 DOI: 10.1093/cercor/bhx061] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 02/19/2017] [Accepted: 02/23/2017] [Indexed: 12/23/2022] Open
Abstract
Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.
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Affiliation(s)
- Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mengmeng Su
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liangjie Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hua Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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17
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Cheng JX, Zhang HY, Peng ZK, Xu Y, Tang H, Wu JT, Xu J. Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis. Transl Neurodegener 2018; 7:10. [PMID: 29719719 PMCID: PMC5921324 DOI: 10.1186/s40035-018-0115-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/10/2018] [Indexed: 02/06/2023] Open
Abstract
Background Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD). Methods Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches. Results We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks. Conclusions Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.
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Affiliation(s)
- Jia-Xing Cheng
- Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Hong-Ying Zhang
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Zheng-Kun Peng
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Yao Xu
- Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Hui Tang
- Medical Experimental Center, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Jing-Tao Wu
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Jun Xu
- 4Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, 100050 China.,5Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, School of Medicine, Yangzhou University, Yangzhou, 225001 Jiangsu China
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18
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Bi XA, Xu Q, Luo X, Sun Q, Wang Z. Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment. Front Psychiatry 2018; 9:340. [PMID: 30090075 PMCID: PMC6068241 DOI: 10.3389/fpsyt.2018.00340] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 07/09/2018] [Indexed: 12/15/2022] Open
Abstract
The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xianhao Luo
- College of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zhigang Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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19
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Gallaway PJ, Miyake H, Buchowski MS, Shimada M, Yoshitake Y, Kim AS, Hongu N. Physical Activity: A Viable Way to Reduce the Risks of Mild Cognitive Impairment, Alzheimer's Disease, and Vascular Dementia in Older Adults. Brain Sci 2017; 7:E22. [PMID: 28230730 PMCID: PMC5332965 DOI: 10.3390/brainsci7020022] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 01/23/2017] [Accepted: 02/03/2017] [Indexed: 11/17/2022] Open
Abstract
A recent alarming rise of neurodegenerative diseases in the developed world is one of the major medical issues affecting older adults. In this review, we provide information about the associations of physical activity (PA) with major age-related neurodegenerative diseases and syndromes, including Alzheimer's disease, vascular dementia, and mild cognitive impairment. We also provide evidence of PA's role in reducing the risks of these diseases and helping to improve cognitive outcomes in older adults. Finally, we describe some potential mechanisms by which this protective effect occurs, providing guidelines for future research.
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Affiliation(s)
- Patrick J Gallaway
- Department of Nutritional Sciences, The University of Arizona, Tucson, AZ 85721-0038, USA.
| | - Hiroji Miyake
- Nishinomiya Kyoritsu Neurosurgical Hospital, Hyogo 663-8211, Japan.
| | - Maciej S Buchowski
- Department of Medicine, Vanderbilt University, Nashville, TN 37232-5280, USA.
| | - Mieko Shimada
- Chiba Prefectural University of Health Sciences, Chiba 261-0014, Japan.
| | - Yutaka Yoshitake
- National Institute of Fitness & Sport in Kanoya, Kagoshima 891-2311, Japan.
| | - Angela S Kim
- Department of Nutritional Sciences, The University of Arizona, Tucson, AZ 85721-0038, USA.
| | - Nobuko Hongu
- Department of Nutritional Sciences, The University of Arizona, Tucson, AZ 85721-0038, USA.
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Liu J, Liang P, Yin L, Shu N, Zhao T, Xing Y, Li F, Zhao Z, Li K, Han Y. White Matter Abnormalities in Two Different Subtypes of Amnestic Mild Cognitive Impairment. PLoS One 2017; 12:e0170185. [PMID: 28107493 PMCID: PMC5249194 DOI: 10.1371/journal.pone.0170185] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 12/30/2016] [Indexed: 12/04/2022] Open
Abstract
White matter (WM) degeneration has been found during the course of cognitive decline in both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), however, it is unclear whether there are different WM microstructural abnormalities between two subtypes of aMCI, including single domain aMCI (aMCI-s) and multiple domain aMCI (aMCI-m). Thirty-two patients of aMCI single-domain (aMCI-s), twenty-three patients of aMCI multiple-domain (aMCI-m) and twenty-three healthy normal controls (NC) participated in this study. Neuropsychological measures and diffusion tensor imaging (DTI) data were acquired from each subject and tract-based spatial statistics (TBSS) was implemented. It was found that both aMCI groups showed significantly reduced fractional anisotropy (FA) in the right superior longitudinal fasciculus (SLF) than NC. It was also identified that, as compared to aMCI-m, aMCI-s showed significantly decreased FA in the left SLF, left uncinate fasciculus (UF) and left inferior longitudinal fasciculus (ILF), while significantly increased FA in the left anterior thalamic radiation (ATR). The correlation analysis showed that FA values in the regions with group difference were significantly correlated with cognitive functions as measured by Boston naming test and trail making test. These results suggested that the variations of aMCI may be differentiated by FA indexes and DTI may help to understand why specific signs and symptoms occur in patients.
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Affiliation(s)
- Jianghong Liu
- Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Peipeng Liang
- Department of Radiology, Xuan Wu Hospital, Capital Medical University, Beijing, China.,Beijing Key Lab of MRI and Brain Informatics, Beijing, China
| | - Linlin Yin
- Department of Pharmacology, Xuan Wu Hospital, Capital Medical University, Beijing Geriatric Medical Research Center, Key Laboratory for Neurodegenerative Disease of Ministry of Education, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yi Xing
- Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Fangyu Li
- Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Zhilian Zhao
- Department of Radiology, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuan Wu Hospital, Capital Medical University, Beijing, China.,Beijing Key Lab of MRI and Brain Informatics, Beijing, China
| | - Ying Han
- Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
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John M, Ikuta T, Ferbinteanu J. Graph analysis of structural brain networks in Alzheimer’s disease: beyond small world properties. Brain Struct Funct 2016; 222:923-942. [DOI: 10.1007/s00429-016-1255-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 06/17/2016] [Indexed: 01/07/2023]
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Konukoglu E, Coutu JP, Salat DH, Fischl B. Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease. Neuroimage 2016; 134:573-586. [PMID: 27103138 DOI: 10.1016/j.neuroimage.2016.04.038] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 03/26/2016] [Accepted: 04/15/2016] [Indexed: 11/25/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?"
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Affiliation(s)
- Ender Konukoglu
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
| | - Jean-Philippe Coutu
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - Bruce Fischl
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, USA
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