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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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Cong Z, Yang L, Zhao Z, Zheng G, Bao C, Zhang P, Wang J, Zheng W, Yao Z, Hu B. Disrupted dynamic brain functional connectivity in male cocaine use disorder: Hyperconnectivity, strongly-connected state tendency, and links to impulsivity and borderline traits. J Psychiatr Res 2024; 176:218-231. [PMID: 38889552 DOI: 10.1016/j.jpsychires.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 05/28/2024] [Accepted: 06/08/2024] [Indexed: 06/20/2024]
Abstract
Cocaine use is a major public health problem with serious negative consequences at both the individual and societal levels. Cocaine use disorder (CUD) is associated with cognitive and emotional impairments, often manifesting as alterations in brain functional connectivity (FC). This study employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine dynamic FC in 38 male participants with CUD and 31 matched healthy controls. Using group spatial independent component analysis (group ICA) combined with sliding window approach, we identified two recurring distinct connectivity states: the strongly-connected state (state 1) and weakly-connected state (state 2). CUD patients exhibited significant increased mean dwell and fraction time in state 1, and increased transitions from state 2 to state 1, demonstrated significant strongly-connected state tendency. Our analysis revealed abnormal FC patterns that are state-dependent and state-shared in CUD patients. This study observed hyperconnectivity within the default mode network (DMN) and between DMN and other networks, which varied depending on the state. Furthermore, after adjustment for multiple comparisons, we found significant correlations between these altered dynamic FCs and clinical measures of impulsivity and borderline personality disorder. The disrupted FC and repetitive effects of precuneus and angular gyrus across correlations suggested that they might be the important hub of neural circuits related behaviorally and mentally in CUD. In summary, our study highlighted the potential of these disrupted FC as neuroimaging biomarkers and therapeutic targets, and provided new insights into the understanding of the neurophysiologic mechanisms of CUD.
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Affiliation(s)
- Zhaoyang Cong
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China; State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Guowei Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150006, China
| | - Cong Bao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China; School of Medical Technology, Beijing Institute of Technology, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China.
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Wang Y, Han Z, Wang C, Liu J, Guo J, Miao P, Wei Y, Wu L, Wang X, Wang P, Zhang Y, Cheng J, Fan S. Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis. Comput Biol Med 2024:108712. [PMID: 38906761 DOI: 10.1016/j.compbiomed.2024.108712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/10/2024] [Accepted: 06/03/2024] [Indexed: 06/23/2024]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconveniencethis may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zongli Han
- Department of Neurosurgery, Peking University Shenzhen Hospital, Futian District Shenzhen Guangdong, P.R. China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Guo
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Siyuan Fan
- Cardiovascular Center, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Qu J, Tian M, Zhu R, Song C, Wu Y, Xu G, Liu Y, Wang D. Aberrant dynamic functional network connectivity in progressive supranuclear palsy. Neurobiol Dis 2024; 195:106493. [PMID: 38579913 DOI: 10.1016/j.nbd.2024.106493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/07/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND The clinical symptoms of progressive supranuclear palsy (PSP) may be mediated by aberrant dynamic functional network connectivity (dFNC). While earlier research has found altered functional network connections in PSP patients, the majority of those studies have concentrated on static functional connectivity. Nevertheless, in this study, we sought to evaluate the modifications in dynamic characteristics and establish the correlation between these disease-related changes and clinical variables. METHODS In our study, we conducted a study on 53 PSP patients and 65 normal controls. Initially, we employed a group independent component analysis (ICA) to derive resting-state networks (RSNs), while employing a sliding window correlation approach to produce dFNC matrices. The K-means algorithm was used to cluster these matrices into distinct dynamic states, and then state analysis was subsequently employed to analyze the dFNC and temporal metrics between the two groups. Finally, we made a correlation analysis. RESULTS PSP patients showed increased connectivity strength between medulla oblongata (MO) and visual network (VN) /cerebellum network (CBN) and decreased connections were found between default mode network (DMN) and VN/CBN, subcortical cortex network (SCN) and CBN. In addition, PSP patients spend less fraction time and shorter dwell time in a diffused state, especially the MO and SCN. Finally, the fraction time and mean dwell time in the distributed connectivity state (state 2) is negatively correlated with duration, bulbar and oculomotor symptoms. DISCUSSION Our findings were that the altered connectivity was mostly concentrated in the CBN and MO. In addition, PSP patients had different temporal dynamics, which were associated with bulbar and oculomotor symptoms in PSPRS. It suggest that variations in dynamic functional network connectivity properties may represent an essential neurological mechanism in PSP.
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Affiliation(s)
- Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Min Tian
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Yiming Liu
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China.
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China; Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Ji'nan, China; Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Ji'nan, China.
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Wang Y, Shu Y, Cai G, Guo Y, Gao J, Chen Y, Lv L, Zeng X. Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients. Sci Rep 2024; 14:11682. [PMID: 38778225 PMCID: PMC11111766 DOI: 10.1038/s41598-024-62635-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.
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Affiliation(s)
- Yuanyuan Wang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yongqiang Shu
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Guoqian Cai
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yu Guo
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junwei Gao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ye Chen
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lianjiang Lv
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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Huang W, Fang X, Li S, Mao R, Ye C, Liu W, Deng Y, Lin G. Abnormal characteristic static and dynamic functional network connectivity in idiopathic normal pressure hydrocephalus. CNS Neurosci Ther 2024; 30:e14178. [PMID: 36949617 PMCID: PMC10915979 DOI: 10.1111/cns.14178] [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: 11/22/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
AIMS Idiopathic Normal pressure hydrocephalus (iNPH) is a neurodegenerative disease characterized by gait disturbance, dementia, and urinary dysfunction. The neural network mechanisms underlying this phenomenon is currently unknown. METHODS To investigate the resting-state functional connectivity (rs-FC) abnormalities of iNPH-related brain connectivity from static and dynamic perspectives and the correlation of these abnormalities with clinical symptoms before and 3-month after shunt. We investigated both static and dynamic functional network connectivity (sFNC and dFNC, respectively) in 33 iNPH patients and 23 healthy controls (HCs). RESULTS The sFNC and dFNC of networks were generally decreased in iNPH patients. The reduction in sFNC within the default mode network (DMN) and between the somatomotor network (SMN) and visual network (VN) were related to symptoms. The temporal properties of dFNC and its temporal variability in state-4 were sensitive to the identification of iNPH and were correlated with symptoms. The temporal variability in the dorsal attention network (DAN) increased, and the average instantaneous FC was altered among networks in iNPH. These features were partially associated with clinical symptoms. CONCLUSION The dFNC may be a more sensitive biomarker for altered network function in iNPH, providing us with extra information on the mechanisms of iNPH.
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Affiliation(s)
- Wenjun Huang
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Xuhao Fang
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Shihong Li
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Renling Mao
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Chuntao Ye
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Wei Liu
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Yao Deng
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Guangwu Lin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
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Yao W, Zhou H, Zhang X, Chen H, Bai F. Inflammation affects dynamic functional network connectivity pattern changes via plasma NFL in cognitive impairment patients. CNS Neurosci Ther 2024; 30:e14391. [PMID: 37545369 PMCID: PMC10848064 DOI: 10.1111/cns.14391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/03/2023] [Accepted: 07/26/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Plasma neurofilament light chain (NFL) is a biomarker of inflammation and neurodegenerative diseases such as Alzheimer's disease (AD). However, the underlying neural mechanisms by which NFL affects cognitive function remain unclear. In this study, we investigated the effects of inflammation on cognitive integrity in patients with cognitive impairment through the functional interaction of plasma NFL with large-scale brain networks. METHODS This study included 29 cognitively normal, 55 LowNFL patients, and 55 HighNFL patients. Group independent component analysis (ICA) was applied to the resting-state fMRI data, and 40 independent components (IC) were extracted for the whole brain. Next, the dynamic functional network connectivity (dFNC) of each subject was estimated using the sliding-window method and k-means clustering, and five dynamic functional states were identified. Finally, we applied mediation analysis to investigate the relationship between plasma NFL and dFNC indicators and cognitive scales. RESULTS The present study explored the dynamics of whole-brain FNC in controls and LowNFL and HighNFL patients and highlighted the temporal properties of dFNC states in relation to psychological scales. A potential mechanism for the association between dFNC indicators and NFL levels in cognitively impaired patients. CONCLUSIONS Our findings suggested the decreased ability of information processing and communication in the HighNFL group, which helps us to understand their abnormal cognitive functions clinically. Characteristic changes in the inflammation-coupled dynamic brain network may provide alternative biomarkers for the assessment of disease severity in cognitive impairment patients.
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Affiliation(s)
- Weina Yao
- Department of NeurologyZhongnan Hospital of Wuhan UniversityWuhanChina
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital Clinical College of Wuhan UniversityNanjingChina
| | - Huijuan Zhou
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Xiao Zhang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Feng Bai
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital Clinical College of Wuhan UniversityNanjingChina
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjingChina
- Department of NeurologyNanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjingChina
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Wei HL, Wei C, Yu YS, Yu X, Chen Y, Li J, Zhang H, Chen X. Dysfunction of the triple-network model is associated with cognitive impairment in patients with cerebral small vessel disease. Heliyon 2024; 10:e24701. [PMID: 38298689 PMCID: PMC10828708 DOI: 10.1016/j.heliyon.2024.e24701] [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: 05/05/2023] [Revised: 11/29/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose This study aimed to demonstrate the correlations between the altered functional connectivity patterns in the triple-network model and cognitive impairment in patients with cerebral small vascular disease (CSVD). Methods Resting-state functional magnetic resonance imaging data were obtained from 22 patients with CSVD and 20 healthy controls. The resting-state data were analyzed using independent component analysis and functional network connectivity (FNC) analysis to explore the functional alterations in the intrinsic triple-network model including the salience network (SN), default mode network (DMN), and central executive network (CEN), and their correlations with the cognitive deficits and clinical observations in the patients with CSVD. Results Compared to the healthy controls, the patients with CSVD exhibited increased connectivity patterns in the CEN-DMN and decreased connectivity patterns in the DMN-SN, CEN-SN, intra-SN, and intra-DMN. Significant negative correlations were detected between the intra-DMN connectivity pattern and the Montreal Cognitive Assessment (MoCA) total scores (r = -0.460, p = 0.048) and MoCA abstraction scores (r = -0.565, p = 0.012), and a positive correlation was determined between the intra-SN connectivity pattern and the MoCA abstraction scores (r = 0.491, p = 0.033). Conclusions Our study findings suggest that the functional alterations in the triple-network model are associated with the cognitive deficits in patients with CSVD and shed light on the importance of the triple-network model in the pathogenesis of CSVD.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Cunsheng Wei
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xiaorong Yu
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yuan Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xuemei Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
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Lyu W, Wu Y, Huang H, Chen Y, Tan X, Liang Y, Ma X, Feng Y, Wu J, Kang S, Qiu S, Yap PT. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals. Cogn Neurodyn 2023; 17:1525-1539. [PMID: 37969945 PMCID: PMC10640562 DOI: 10.1007/s11571-022-09899-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/11/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022] Open
Abstract
An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu China
| | - Haoming Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yuna Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei Province, Jingzhou, Hubei China
| | - Yue Feng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Jinjian Wu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shangyu Kang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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Huang C, Zhou X, Ren M, Zhang W, Wan K, Yin J, Li M, Li Z, Zhu X, Sun Z. Altered dynamic functional network connectivity and topological organization variance in patients with white matter hyperintensities. J Neurosci Res 2023; 101:1711-1727. [PMID: 37469210 DOI: 10.1002/jnr.25230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/14/2023] [Accepted: 07/01/2023] [Indexed: 07/21/2023]
Abstract
White matter hyperintensities (WMHs) of presumed vascular origin are important imaging biomarkers of cerebral small vessel disease (CSVD). Previous studies have verified abnormal functional brain networks in CSVD. However, most of these studies rely on static functional connectivity, and only a few focus on the varying severity of the WMHs. Hence, our study primarily explored the disrupted dynamic functional network connectivity (dFNC) and topological organization variance in patients with WMHs. This study included 38 patients with moderate WMHs, 47 with severe WMHs, and 68 healthy controls (HCs). Ten independent components were chosen using independent component analysis based on resting-state functional magnetic resonance imaging. The dFNC of each participant was estimated using sliding windows and k-means clustering. We identified three reproducible dFNC states. Among them, patients with WMHs had a significantly higher occurrence in the sparsely connected State 1, but a lower occurrence and shorter duration in the positive and stronger connected State 3. Regarding topological organization variance, patients with WMHs showed higher variance in local efficiency but not global efficiency compared to HCs. Among the WMH subgroups, patients with severe WMHs showed similar but more obvious alterations than those with moderate WMHs. These altered network characteristics indicated an imbalance between the functional segregation and integration of brain networks, which was correlated with global cognition, memory, executive functions, and visuospatial abilities. Our study confirmed aberrant dFNC state metrics and topological organization variance in patients with moderate-to-severe WMHs; thus, it might provide a new pathway for exploring the pathogenesis of cognitive impairment.
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Affiliation(s)
- Chaojuan Huang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mengmeng Ren
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wei Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ke Wan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiabin Yin
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingxu Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhiwei Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoqun Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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11
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Jin H, Ranasinghe KG, Prabhu P, Dale C, Gao Y, Kudo K, Vossel K, Raj A, Nagarajan SS, Jiang F. Dynamic functional connectivity MEG features of Alzheimer's disease. Neuroimage 2023; 281:120358. [PMID: 37699440 PMCID: PMC10865998 DOI: 10.1016/j.neuroimage.2023.120358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.
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Affiliation(s)
- Huaqing Jin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kamalini G Ranasinghe
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Pooja Prabhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Corby Dale
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Keith Vossel
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Fei Jiang
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
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12
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Guo Y, Xu X, Li T, Chen H, Wang X, Huang W, Liu T, Kong Q, Chen F. Dynamic functional connectivity changes associated with decreased memory performance in betel quid dependence. Addict Biol 2023; 28:e13329. [PMID: 37753571 DOI: 10.1111/adb.13329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 07/17/2023] [Accepted: 08/15/2023] [Indexed: 09/28/2023]
Abstract
The temporal variability of the dynamic functional connectivity (dFC) has been suggested as a useful metric for studying abnormal cognitive function. This study aimed to explore the associations between the temporal properties of dFC and memory performance in betel quid dependence (BQD). Sixty-four BQD individuals and 47 gender- and age-matched healthy controls (HCs) underwent functional magnetic resonance imaging and a series of neuropsychological assessments. The dFC was constructed by calculating the Pearson correlation coefficients within a sliding window and was clustered into three functional connectivity states using k-means clustering. The dFC temporal properties derived from the cluster results were compared between the BQD and HC groups. The results showed that States 1 and 3 featured more frequent and weak connectivity, and State 2 featured less frequent and strong connectivity. There were significant differences for mean dwell time (MDT) in State 3 (p = 0.022) and fraction of time in State 2 (p = 0.018) between the BQD and HC groups. Pearson correlation analyses showed that the MDT in State 1 was negatively correlated with long delay free recall and short delay free recall, and the MDT in State 3 was positively correlated with false positive of long delay recall. Our findings provide strong evidence that MDT match the memory performance and suggest new insights into the pathophysiological mechanism of memory disorders in BQD individuals.
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Affiliation(s)
- Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Xiaoling Xu
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Tiansheng Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Huijuan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Tao Liu
- Department of Geriatric Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qingle Kong
- MR Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
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13
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Chen H, Xu J, Lv W, Hu Z, Ke Z, Qin R, Xu Y. Altered static and dynamic functional network connectivity related to cognitive decline in individuals with white matter hyperintensities. Behav Brain Res 2023; 451:114506. [PMID: 37230298 DOI: 10.1016/j.bbr.2023.114506] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/08/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
White matter hyperintensities (WMH) of assumed vascular origin are common in elderly individuals and are closely associated with cognitive decline. However, the underlying neural mechanisms of WMH-related cognitive impairment remain unclear. After strict screening, 59 healthy controls (HC, n = 59), 51 patients with WMH and normal cognition (WMH-NC, n = 51) and 68 patients with WMH and mild cognitive impairment (WMH-MCI, n = 68) were included in the final analyses. All individuals underwent multimodal magnetic resonance imaging (MRI) and cognitive evaluations. We investigated the neural mechanism underlying WMH-related cognitive impairment based on static and dynamic functional network connectivity (sFNC and dFNC) approaches. Finally, the support vector machine (SVM) method was performed to identify WMH-MCI individuals. The sFNC analysis indicated that functional connectivity within the visual network (VN) could mediate the impairment of information processing speed related to WMH (indirect effect: 0.24; 95% CI: 0.03, 0.88 and indirect effect: 0.05; 95% CI: 0.001, 0.14). WMH may regulate the dFNC between the higher-order cognitive network and other networks and enhance the dynamic variability between the left frontoparietal network (lFPN) and the VN to compensate for the decline in high-level cognitive functions. The SVM model achieved good prediction ability for WMH-MCI patients based on the above characteristic connectivity patterns. Our findings shed light on the dynamic regulation of brain network resources to maintain cognitive processing in individuals with WMH. Crucially, dynamic reorganization of brain networks could be regarded as a potential neuroimaging biomarker for identifying WMH-related cognitive impairment.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Jingxian Xu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Weiping Lv
- Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhihong Ke
- Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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14
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Zuo Q, Hu J, Zhang Y, Pan J, Jing C, Chen X, Meng X, Hong J. Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis. COMPUTER MODELING IN ENGINEERING & SCIENCES : CMES 2023; 137:2129-2147. [PMID: 38566839 PMCID: PMC7615791 DOI: 10.32604/cmes.2023.028732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven models. Specifically, the label distribution is estimated to regularize the latent space learned by the graph encoder, which can make the learning process stable and the learned representation robust. Also, the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions. The typical topological properties and discriminative features can be preserved entirely. Furthermore, the generated brain functional networks improve the prediction performance using different classifiers, which can be applied to analyze other cognitive diseases. Attempts on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed model can generate good brain functional networks. The classification results show adding generated data can achieve the best accuracy value of 85.33%, sensitivity value of 84.00%, specificity value of 86.67%. The proposed model also achieves superior performance compared with other related augmented models. Overall, the proposed model effectively improves cognitive disease diagnosis by generating diverse brain functional networks.
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Affiliation(s)
- Qiankun Zuo
- School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Junhua Hu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
| | - Yudong Zhang
- School of Computing and Mathematic Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Junren Pan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Changhong Jing
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xuhang Chen
- Faculty of Science and Technology, University of Macau, Macau, 999078, China
| | - Xiaobo Meng
- School of Geophysics, Chengdu University of Technology, Chengdu, 610059, China
| | - Jin Hong
- Laboratory of Artificial Intelligence and 3D Technologies for Cardiovascular Diseases, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041, China
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15
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Sendi MS, Zendehrouh E, Fu Z, Liu J, Du Y, Mormino E, Salat DH, Calhoun VD, Miller RL. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect 2023; 13:334-343. [PMID: 34102870 PMCID: PMC10442683 DOI: 10.1089/brain.2020.0847] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | | | - David H. Salat
- Harvard Medical School, Cambridge, Massachusetts, USA
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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Mahmood U, Fu Z, Calhoun V, Plis S. GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING. PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING. ICASSP (CONFERENCE) 2023; 2023:10.1109/icassp49357.2023.10097126. [PMID: 37266485 PMCID: PMC10231935 DOI: 10.1109/icassp49357.2023.10097126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a 'glass-box' deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain's intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson's correlation coefficients.
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Affiliation(s)
- Usman Mahmood
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia Institute of Technology, Department of Electrical and Computer Engineering, Atlanta, GA, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
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Li Z, Wang Z, Cao D, You R, Hu J. Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke. Brain Res 2023:148406. [PMID: 37201623 DOI: 10.1016/j.brainres.2023.148406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Dynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). METHOD Resting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. RESULTS Four dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. CONCLUSIONS AIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.
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Affiliation(s)
- Zhongming Li
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
| | - Zhimin Wang
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Dairong Cao
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ruixiong You
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jianping Hu
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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18
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Ge C, Wang X, Wang Y, Lei L, Song G, Qian M, Wang S. PKCε inhibition prevents ischemia‑induced dendritic spine impairment in cultured primary neurons. Exp Ther Med 2023; 25:152. [PMID: 36911376 PMCID: PMC9995843 DOI: 10.3892/etm.2023.11851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Brain ischemia is an independent risk factor for Alzheimer's disease (AD); however, the mechanisms underlining ischemic stroke and AD remain unclear. The present study aimed to investigate the function of the ε isoform of protein kinase C (PKCε) in brain ischemia-induced dendritic spine dysfunction to elucidate how brain ischemia causes AD. In the present study, primary hippocampus and cortical neurons were cultured while an oxygen-glucose deprivation (OGD) model was used to simulate brain ischemia. In the OGD cell model, in vitro kinase activity assay was performed to investigate whether the PKCε kinase activity changed after OGD treatment. Confocal microscopy was performed to investigate whether inhibiting PKCε kinase activity protects dendritic spine morphology and function. G-LISA was used to investigate whether small GTPases worked downstream of PKCε. The results showed that PKCε kinase activity was significantly increased following OGD treatment in primary neurons, leading to dendritic spine dysfunction. Pre-treatment with PKCε-inhibiting peptide, which blocks PKCε activity, significantly rescued dendritic spine function following OGD treatment. Furthermore, PKCε could activate Ras homolog gene family member A (RhoA) as a downstream molecule, which mediated OGD-induced dendritic spine morphology changes and caused dendritic spine dysfunction. In conclusion, the present study demonstrated that the PKCε/RhoA signalling pathway is a novel mechanism mediating brain ischemia-induced dendritic spine dysfunction. Developing therapeutic targets for this pathway may protect against and prevent brain ischemia-induced cognitive impairment and AD.
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Affiliation(s)
- Chenjie Ge
- Department of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, P.R. China
| | - Xuefeng Wang
- WuXi AppTec Co., Ltd., Shanghai 200131, P.R. China
| | - Yunhong Wang
- WuXi AppTec Co., Ltd., Shanghai 200131, P.R. China
| | - Lilei Lei
- Department of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, P.R. China
| | - Guohua Song
- Department of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, P.R. China
| | - Mincai Qian
- Department of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, P.R. China
| | - Shiliang Wang
- Department of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, P.R. China
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Hajjar I, Okafor M, Choi JD, Moore E, Abrol A, Calhoun VD, Goldstein FC. Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12393. [PMID: 36777093 PMCID: PMC9899764 DOI: 10.1002/dad2.12393] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 02/08/2023]
Abstract
Introduction Advances in natural language processing (NLP), speech recognition, and machine learning (ML) allow the exploration of linguistic and acoustic changes previously difficult to measure. We developed processes for deriving lexical-semantic and acoustic measures as Alzheimer's disease (AD) digital voice biomarkers. Methods We collected connected speech, neuropsychological, neuroimaging, and cerebrospinal fluid (CSF) AD biomarker data from 92 cognitively unimpaired (40 Aβ+) and 114 impaired (63 Aβ+) participants. Acoustic and lexical-semantic features were derived from audio recordings using ML approaches. Results Lexical-semantic (area under the curve [AUC] = 0.80) and acoustic (AUC = 0.77) scores demonstrated higher diagnostic performance for detecting MCI compared to Boston Naming Test (AUC = 0.66). Only lexical-semantic scores detected amyloid-β status (p = 0.0003). Acoustic scores associated with hippocampal volume (p = 0.017) while lexical-semantic scores associated with CSF amyloid-β (p = 0.007). Both measures were significantly associated with 2-year disease progression. Discussion These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression. Highlights This study derived lexical-semantic and acoustics features as Alzheimer's disease (AD) digital biomarkers.These features were derived from audio recordings using machine learning approaches.Voice biomarkers detected cognitive impairment and amyloid-β status in early stages of AD.Voice biomarkers may predict Alzheimer's disease progression.These markers significantly mapped to functional connectivity in AD-susceptible brain regions.
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Affiliation(s)
- Ihab Hajjar
- Department of NeurologyUniversity of Texas SouthwesternDallasTexasUSA,Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Maureen Okafor
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Jinho D. Choi
- Department of Computer ScienceEmory UniversityAtlantaGeorgiaUSA
| | - Elliot Moore
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
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Sang L, Wang L, Zhang J, Qiao L, Li P, Zhang Y, Wang Q, Li C, Qiu M. Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients. Neurobiol Aging 2023; 122:45-54. [PMID: 36481660 DOI: 10.1016/j.neurobiolaging.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022]
Abstract
Alterations in the temporal evolution of brain states in the process of cognitive impairment aggravation due to subcortical ischemic vascular disease (SIVD) is not understood. The dynamic functional connectivity was investigated to identify the abnormal temporal properties of brain states associated with cognitive impairment caused by SIVD. Eighteen patients with subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND), 19 dementia patients (SIVaD) and 26 normal controls were enrolled. We found that the occupancy rate and mean lifetime of brain states were associated with cognitive performance. SIVCIND had a higher occupancy rate and longer mean lifetime in weakly connected states than normal controls. SIVaD had similar but more extensive changes in the temporal properties of brain states. In addition, switching from weakly connected states to more strongly connected states was more difficult in SIVCIND and SIVaD patients than in normal controls, especially in SIVaD patients. The results revealed that not only the transition to but also maintenance in strongly connected states became increasingly difficult when SIVD-related cognitive impairment progressed into a more severe stage.
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Affiliation(s)
- Linqiong Sang
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Liang Qiao
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Ye Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Qiannan Wang
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing, China.
| | - Mingguo Qiu
- Department of Medical Imaging, School of Biomedical Engineering, Army Medical University, Chongqing, China.
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Chen Z, Liu H, Wei XE, Wang Q, Liu Y, Hao L, Lin C, Xiao L, Rong L. Aberrant dynamic functional network connectivity in vestibular migraine patients without peripheral vestibular lesion. Eur Arch Otorhinolaryngol 2023; 280:2993-3003. [PMID: 36707433 DOI: 10.1007/s00405-023-07847-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/17/2023] [Indexed: 01/29/2023]
Abstract
PURPOSE This study aimed to investigate changes in dynamic functional network connectivity (FNC) in patients with vestibular migraine (VM) and explore their relationship with clinical manifestations. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were scanned from 35 VM patients without peripheral vestibular lesion and 40 age-, sex- and education-matched healthy controls (HC). Independent component analysis (ICA), sliding window (SW) and k-means clustering analysis were performed to explore the difference in FNC and temporal characteristics between two groups. Additionally, Pearson's partial correlation analysis was adopted to investigate the relationship between clinical manifestations and rs-fMRI results in patients with VM. RESULTS Compared with HC, patients with VM showed increased FNC in pairs of extrastriate visual network (eVN)-ventral attention network (VAN), eVN-default mode network (DMN) and eVN-left frontoparietal network (lFPN), and exhibited decreased FNC in pairs of VAN-auditory network (AuN). The altered FNC was correlated with clinical manifestations of patients with VM. Additionally, we found increased mean dwell time and fractional windows in state 2 in VM patients compared with HC. Mean dwell time was positively correlated with headache impact test-6 (HIT-6) scores, fractional windows was positively associated with dizziness handicap inventory (DHI) scores. CONCLUSION Our results indicated that patients with VM showed altered FNC primarily between sensory networks and networks related to cognitive, emotional and attention implementation, with more time spent in a state characterized by positive FNC between sensor cortex system and dorsal attention network (DAN). These findings could help reinforce the understanding on the neural mechanisms of VM.
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Affiliation(s)
- Zhengwei Chen
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Haiyan Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Xiu-E Wei
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Quan Wang
- Medical Imaging Department, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Yueji Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Lei Hao
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Cunxin Lin
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China
| | - Lijie Xiao
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China.
| | - Liangqun Rong
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Xuzhou, 221006, Jiangsu Province, China.
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Alqahtani MS, Abbas M, Alshahrani MY, Alabdullh K, Alqarni A, Alqahtani FF, Jambi LK, Alkhayat A. Effects of COVID-19 on Synaptic and Neuronal Degeneration. Brain Sci 2023; 13:brainsci13010131. [PMID: 36672112 PMCID: PMC9856402 DOI: 10.3390/brainsci13010131] [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: 11/29/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/13/2023] Open
Abstract
Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.
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Affiliation(s)
- Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
- BioImaging Unit, Space Research Centre, University of Leicester, Michael Atiyah Building, Leicester LE1 7RH, UK
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
- Electronics and Communications Department, College of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt
- Correspondence:
| | - Mohammad Y. Alshahrani
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Khulud Alabdullh
- Radiology Department, King Abdullah Hospital Bisha, Bisha 61922, Saudi Arabia
| | - Amjad Alqarni
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Fawaz F. Alqahtani
- Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran 55461, Saudi Arabia
| | - Layal K. Jambi
- Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi Arabia
| | - Adnan Alkhayat
- Department of Hematopathology, King Fahad Central Hospital, Gizan 82666, Saudi Arabia
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Huang J, Wang M, Ju H, Shi Z, Ding W, Zhang D. SD-CNN: A static-dynamic convolutional neural network for functional brain networks. Med Image Anal 2023; 83:102679. [PMID: 36423466 DOI: 10.1016/j.media.2022.102679] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/14/2022] [Accepted: 10/29/2022] [Indexed: 11/13/2022]
Abstract
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.
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Affiliation(s)
- Jiashuang Huang
- School of Information Science and Technology, Nantong University, Nantong, 226019, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Mingliang Wang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Hengrong Ju
- School of Information Science and Technology, Nantong University, Nantong, 226019, China
| | - Zhenquan Shi
- School of Information Science and Technology, Nantong University, Nantong, 226019, China
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019, China.
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
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Mahmood U, Fu Z, Ghosh S, Calhoun V, Plis S. Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI. Neuroimage 2022; 264:119737. [PMID: 36356823 PMCID: PMC9844250 DOI: 10.1016/j.neuroimage.2022.119737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/01/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022] Open
Abstract
Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.
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Affiliation(s)
- Usman Mahmood
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA.
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA; Georgia Institute of Technology, Department of Electrical and Computer Engineering, Atlanta, GA, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA
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25
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Pupíková M, Šimko P, Lamoš M, Gajdoš M, Rektorová I. Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS. Sci Rep 2022; 12:20754. [PMID: 36456622 PMCID: PMC9715685 DOI: 10.1038/s41598-022-25016-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
Transcranial direct current stimulation (tDCS) has the potential to modulate cognitive training in healthy aging; however, results from various studies have been inconsistent. We hypothesized that inter-individual differences in baseline brain state may contribute to the varied results. We aimed to explore whether baseline resting-state dynamic functional connectivity (rs-dFC) and/or conventional resting-state static functional connectivity (rs-sFC) may be related to the magnitude of cognitive aftereffects of tDCS. To achieve this aim, we used data from our double-blind randomized sham-controlled cross-over tDCS trial in 25 healthy seniors in which bifrontal tDCS combined with cognitive training had induced significant behavioral aftereffects. We performed a backward regression analysis including rs-sFC/rs-dFC measures to explain the variability in the magnitude of tDCS-induced improvements in visual object-matching task (VOMT) accuracy. Rs-dFC analysis revealed four rs-dFC states. The occurrence rate of a rs-dFC state 4, characterized by a high correlation between the left fronto-parietal control network and the language network, was significantly associated with tDCS-induced VOMT accuracy changes. The rs-sFC measure was not significantly associated with the cognitive outcome. We show that flexibility of the brain state representing readiness for top-down control of object identification implicated in the studied task is linked to the tDCS-enhanced task accuracy.
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Affiliation(s)
- Monika Pupíková
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Patrik Šimko
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Lamoš
- Brain and Mind Research Program, Central European Institute of Technology - CEITEC, Masaryk university, Brno, Czech Republic
| | - Martin Gajdoš
- Multimodal and Functional Neuroimaging Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
| | - Irena Rektorová
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic.
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
- International Clinical Research Center, ICRC, St Anne's University Hospital and Faculty of Medicine, Brno, Czech Republic.
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Dautricourt S, Gonneaud J, Landeau B, Calhoun VD, de Flores R, Poisnel G, Bougacha S, Ourry V, Touron E, Kuhn E, Demintz-King H, Marchant NL, Vivien D, de la Sayette V, Lutz A, Chételat G, Arenaza-Urquijo EM, Allais F, André C, Asselineau J, Bejanin A, Champetier P, Chételat G, Chocat A, Dautricourt S, de Flores R, Delarue M, Egret S, Felisatti F, Devouge EF, Frison E, Gonneaud J, Heidmann M, Tran TH, Kuhn E, le Du G, Landeau B, Lefranc V, Lutz A, Mezenge F, Moulinet I, Ourry V, Palix C, Paly L, Poisnel G, Quillard A, Rauchs G, Rehel S, Requier F, Touron E, Vivien D, Ware C, Lugo SB, Klimecki O, Vuilleumier P, Barnhofer T, Collette F, Salmon E, de la Sayette V, Delamillieure P, Batchelor M, Beaugonin A, Gheysen F, Demnitz-King H, Marchant N, Whitfield T, Schimmer C, Wirth M. Dynamic functional connectivity patterns associated with dementia risk. Alzheimers Res Ther 2022; 14:72. [PMID: 35606867 PMCID: PMC9128270 DOI: 10.1186/s13195-022-01006-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/06/2022] [Indexed: 12/03/2022]
Abstract
Background This study assesses the relationships between dynamic functional network connectivity (DFNC) and dementia risk. Methods DFNC of the default mode (DMN), salience (SN), and executive control networks was assessed in 127 cognitively unimpaired older adults. Stepwise regressions were performed with dementia risk and protective factors and biomarkers as predictors of DFNC. Results Associations were found between times spent in (i) a “weakly connected” state and lower self-reported engagement in early- and mid-life cognitive activity and higher LDL cholesterol; (ii) a “SN-negatively connected” state and higher blood pressure, higher depression score, and lower body mass index (BMI); (iii) a “strongly connected” state and higher self-reported engagement in early-life cognitive activity, Preclinical Alzheimer’s cognitive composite-5 score, and BMI; and (iv) a “DMN-negatively connected” state and higher self-reported engagement in early- and mid-life stimulating activities and lower LDL cholesterol and blood pressure. The lower number of state transitions was associated with lower brain perfusion. Conclusion DFNC states are differentially associated with dementia risk and could underlie reserve. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01006-7.
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Luo Q, Chen J, Li Y, Wu Z, Lin X, Yao J, Yu H, Wu H, Peng H. Aberrant static and dynamic functional connectivity of amygdala subregions in patients with major depressive disorder and childhood maltreatment. Neuroimage Clin 2022; 36:103270. [PMID: 36451372 PMCID: PMC9668673 DOI: 10.1016/j.nicl.2022.103270] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
Major depressive disorder (MDD) with childhood maltreatment is a heterogeneous clinical phenotype of depression with prominent features of brain disconnectivity in areas linked to maltreatment-related emotion processing (e.g., the amygdala). However, static and dynamic alterations of functional connectivity in amygdala subregions have not been investigated in MDD with childhood maltreatment. Here, we explored whether amygdala subregions (i.e., medial amygdala [MeA] and lateral amygdala [LA]) exhibited static functional connectivity (sFC) and dynamic functional connectivity (dFC) disruption, and whether these disruptions were related to childhood maltreatment. We compared sFC and dFC patterns in MDD with childhood maltreatment (n = 48), MDD without childhood maltreatment (n = 30), healthy controls with childhood maltreatment (n = 57), and healthy controls without childhood maltreatment (n = 46). The bilateral MeA and LA were selected as the seeds in the FC analysis. The results revealed a functional connectivity disruption pattern in maltreated MDD patients, characterized by sFC and dFC abnormalities involving the MeA, LA, and theory of mind-related brain areas including the middle occipital area, middle frontal gyrus, superior medial frontal gyrus, angular gyrus, supplementary motor areas, middle temporal gyrus, middle cingulate gyrus, and calcarine gyrus. Significant correlations were detected between impaired dFC patterns and childhood maltreatment. Furthermore, the dFC disruption pattern served as a moderator in the relationship between sexual abuse and depression severity. Our findings revealed neurobiological features of childhood maltreatment, providing new evidence regarding vulnerability to psychiatric disorders.
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Affiliation(s)
- Qianyi Luo
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Juran Chen
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Yuhong Li
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Zhiyao Wu
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Xinyi Lin
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Jiazheng Yao
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Huiwen Yu
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | - Huawang Wu
- Department of Radiology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China,Corresponding authors at: Department of Radiology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China (H. Wu); Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China (H. Peng).
| | - Hongjun Peng
- Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China,Corresponding authors at: Department of Radiology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China (H. Wu); Department of Clinical Psychology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China (H. Peng).
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Schlemm E, Frey BM, Mayer C, Petersen M, Fiehler J, Hanning U, Kühn S, Twerenbold R, Gallinat J, Gerloff C, Thomalla G, Cheng B. Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease. Biol Psychiatry 2022; 92:592-602. [PMID: 35691727 DOI: 10.1016/j.biopsych.2022.03.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 01/02/2023]
Abstract
BACKGROUND Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment. METHODS We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level-dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function. RESULTS Data were available for 979 participants (ages 45-74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (-) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN- = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN-. Time spent in DMN-related brain states was associated with executive function. CONCLUSIONS Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.
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Affiliation(s)
- Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
| | - Benedikt M Frey
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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29
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Li Y, Yu Z, Wu P, Chen J. Ability of an altered functional coupling between resting-state networks to predict behavioral outcomes in subcortical ischemic stroke: A longitudinal study. Front Aging Neurosci 2022; 14:933567. [PMID: 36185473 PMCID: PMC9520312 DOI: 10.3389/fnagi.2022.933567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/16/2022] [Indexed: 11/22/2022] Open
Abstract
Stroke can be viewed as an acute disruption of an individual's connectome caused by a focal or widespread loss of blood flow. Although individuals exhibit connectivity changes in multiple functional networks after stroke, the neural mechanisms that underlie the longitudinal reorganization of the connectivity patterns are still unclear. The study aimed to determine whether brain network connectivity patterns after stroke can predict longitudinal behavioral outcomes. Nineteen patients with stroke with subcortical lesions underwent two sessions of resting-state functional magnetic resonance imaging scanning at a 1-month interval. By independent component analysis, the functional connectivity within and between multiple brain networks (including the default mode network, the dorsal attention network, the limbic network, the visual network, and the frontoparietal network) was disrupted after stroke and partial recovery at the second time point. Additionally, regression analyses revealed that the connectivity between the limbic and dorsal attention networks at the first time point showed sufficient reliability in predicting the clinical scores (Fugl-Meyer Assessment and Neurological Deficit Scores) at the second time point. The overall findings suggest that functional coupling between the dorsal attention and limbic networks after stroke can be regarded as a biomarker to predict longitudinal clinical outcomes in motor function and the degree of neurological functional deficit. Overall, the present study provided a novel opportunity to improve prognostic ability after subcortical strokes.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Zeyun Yu
- Acupuncture and Tuina School/Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ping Wu
- Acupuncture and Tuina School/Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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30
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Xu X, Chen YC, Yin X, Zuo T, Feng G, Xu K. Dynamic functional connections in leukoaraiosis patients without cognitive impairment: A pilot study. Front Aging Neurosci 2022; 14:944485. [PMID: 36118700 PMCID: PMC9476943 DOI: 10.3389/fnagi.2022.944485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Leukoaraiosis (LA) is a major public issue that affects elderly adults. However, the underlying neuropathological mechanism of LA without cognitive impairment requires examination. The present study aimed to explore the dynamic functional network connectivity (dFNC) in LA patients without cognitive impairment. Methods Twenty-three patients with LA and 20 well-matched healthy controls were recruited for the present study. Each subject underwent magnetic resonance imaging (MRI) scanning and cognition evaluations. Spatial independent component analysis was conducted to evaluate dynamic functional connectivity. The differences in dFNC were determined and correlated with cognitive performance. Results Compared with controls, LA without cognitive impairment showed aberrant dFNC in State 1, involving increased connectivity in the default mode network (DMN) with the executive control network (ECN). In addition, decreased connectivity in the DMN with the salience network (SN) was found in State 3. Furthermore, the decreased number of transitions between states was positively associated with the visuospatial/executive score (Spearman's rho = 0.452, p = 0.031), and the longer mean dwell time in State 1 was negatively associated with the Montreal Cognitive Assessment (MoCA) score (Spearman's rho = – 0.420, p = 0.046). Conclusion These findings enrich our understanding of the neural mechanisms underlying LA and may serve as a potential imaging biomarker for investigating and recognizing the LA at an early stage.
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Affiliation(s)
- Xingru Xu
- Department of Radiology, Affiliated Lianyungang Traditional Chinese Medicine Hospital of Kangda College of Nanjing Medical University, Lianyungang, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Taosheng Zuo
- Department of Radiology, Affiliated Lianyungang Traditional Chinese Medicine Hospital of Kangda College of Nanjing Medical University, Lianyungang, China
| | - Guangkui Feng
- Department of Neurology, Affiliated Lianyungang Traditional Chinese Medicine Hospital of Kangda College of Nanjing Medical University, Lianyungang, China
- *Correspondence: Guangkui Feng
| | - Kaixi Xu
- Department of Radiology, Affiliated Lianyungang Traditional Chinese Medicine Hospital of Kangda College of Nanjing Medical University, Lianyungang, China
- Kaixi Xu
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31
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Di Nardo F, Manara R, Canna A, Trojsi F, Velletrani G, Sinisi AA, Cirillo M, Tedeschi G, Esposito F. Dynamic spectral signatures of mirror movements in the sensorimotor functional connectivity network of patients with Kallmann syndrome. Front Neurosci 2022; 16:971809. [PMID: 36117618 PMCID: PMC9477102 DOI: 10.3389/fnins.2022.971809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
In Kallmann syndrome (KS), the peculiar phenomenon of bimanual synkinesis or mirror movement (MM) has been associated with a spectral shift, from lower to higher frequencies, of the resting-state fMRI signal of the large-scale sensorimotor brain network (SMN). To possibly determine whether a similar frequency specificity exists across different functional connectivity SMN states, and to capture spontaneous transitions between them, we investigated the dynamic spectral changes of the SMN functional connectivity in KS patients with and without MM symptom. Brain MRI data were acquired at 3 Tesla in 39 KS patients (32 without MM, KSMM-, seven with MM, KSMM+) and 26 age- and sex-matched healthy control (HC) individuals. The imaging protocol included 20-min rs-fMRI scans enabling detailed spectro-temporal analyses of large-scale functional connectivity brain networks. Group independent component analysis was used to extract the SMN. A sliding window approach was used to extract the dynamic spectral power of the SMN functional connectivity within the canonical physiological frequency range of slow rs-fMRI signal fluctuations (0.01–0.25 Hz). K-means clustering was used to determine (and count) the most recurrent dynamic states of the SMN and detect the number of transitions between them. Two most recurrent states were identified, for which the spectral power peaked at a relatively lower (state 1) and higher (state 2) frequency. Compared to KS patients without MM and HC subjects, the SMN of KS patients with MM displayed significantly larger spectral power changes in the slow 3 canonical sub-band (0.073–0.198 Hz) and significantly fewer transitions between state 1 (less recurrent) and state 2 (more recurrent). These findings demonstrate that the presence of MM in KS patients is associated with reduced spontaneous transitions of the SMN between dynamic functional connectivity states and a higher recurrence and an increased spectral power change of the high-frequency state. These results provide novel information about the large-scale brain functional dynamics that could help to understand the pathologic mechanisms of bimanual synkinesis in KS syndrome and, potentially, other neurological disorders where MM may also occur.
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Affiliation(s)
- Federica Di Nardo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Renzo Manara
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Antonietta Canna
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Gianluca Velletrani
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonio Agostino Sinisi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
- *Correspondence: Fabrizio Esposito,
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32
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Li C, Li Y, Wu J, Wu M, Peng F, Chao Q. Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2022; 89:519-533. [DOI: 10.3233/jad-220282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.
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Affiliation(s)
- Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
- National Engineering Research Center for Healthcare Devices. Guangzhou, Guangdong, P.R. China
- The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, Shaanxi, P. R. China
| | - Jianqian Wu
- School of Public Policy and Adiminstration, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
| | - Fang Peng
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
| | - Qiuling Chao
- School of Public Policy and Adiminstration, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
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Abstract
Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.
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34
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Du K, Chen P, Zhao K, Qu Y, Kang X, Liu Y. Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites. BMC Bioinformatics 2022; 23:280. [PMID: 35836122 PMCID: PMC9284684 DOI: 10.1186/s12859-022-04776-x] [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: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/29/2022] Open
Abstract
Background The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity. Results In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = −0.38, P < 0.001; three classes classification: R = −0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls. Conclusions The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.
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Affiliation(s)
- Kai Du
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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35
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Yin W, Zhou X, Li C, You M, Wan K, Zhang W, Zhu W, Li M, Zhu X, Qian Y, Sun Z. The Clustering Analysis of Time Properties in Patients With Cerebral Small Vessel Disease: A Dynamic Connectivity Study. Front Neurol 2022; 13:913241. [PMID: 35795790 PMCID: PMC9251301 DOI: 10.3389/fneur.2022.913241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis study aimed to investigate the dynamic functional connectivity (DFC) pattern in cerebral small vessel disease (CSVD) and explore the relationships between DFC temporal properties and cognitive impairment in CSVD.MethodsFunctional data were collected from 67 CSVD patients, including 35 patients with subcortical vascular cognitive impairment (SVCI) and 32 cognitively unimpaired (CU) patients, as well as 35 healthy controls (HCs). The DFC properties were estimated by k-means clustering analysis. DFC strength analysis was used to explore the regional functional alterations between CSVD patients and HCs. Correlation analysis was used for DFC properties with cognition and SVD scores, respectively.ResultsThe DFC analysis showed three distinct connectivity states (state I: sparsely connected, state II: strongly connected, state III: intermediate pattern). Compared to HCs, CSVD patients exhibited an increased proportion in state I and decreased proportion in state II. Besides, CSVD patients dwelled longer in state I while dwelled shorter in state II. CSVD subgroup analyses showed that state I frequently occurred and dwelled longer in SVCI compared with CSVD-CU. Also, the internetwork (frontal-parietal lobe, frontal-occipital lobe) and intranetwork (frontal lobe, occipital lobe) functional activities were obviously decreased in CSVD. Furthermore, the fractional windows and mean dwell time (MDT) in state I were negatively correlated with cognition in CSVD but opposite to cognition in state II.ConclusionPatients with CSVD accounted for a higher proportion and dwelled longer mean time in the sparsely connected state, while presented lower proportion and shorter mean dwell time in the strongly connected state, which was more prominent in SVCI. The changes in the DFC are associated with altered cognition in CSVD. This study provides a better explanation of the potential mechanism of CSVD patients with cognitive impairment from the perspective of DFC.
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Affiliation(s)
- Wenwen Yin
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chenchen Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mengzhe You
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ke Wan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wei Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenhao Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingxu Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoqun Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Zhongwu Sun
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Miller RL, Vergara VM, Pearlson GD, Calhoun VD. Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs. Front Neurosci 2022; 16:770468. [PMID: 35516809 PMCID: PMC9063321 DOI: 10.3389/fnins.2022.770468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/24/2022] [Indexed: 11/28/2022] Open
Abstract
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.
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Affiliation(s)
- Robyn L. Miller
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- *Correspondence: Robyn L. Miller,
| | - Victor M. Vergara
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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Petracca M. Editorial: Multi-Modal Imaging in Neurological Conditions: Translational Applications. Front Neurol 2022; 13:855122. [PMID: 35242103 PMCID: PMC8885811 DOI: 10.3389/fneur.2022.855122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Maria Petracca
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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38
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Saha DK, Calhoun VD, Du Y, Fu Z, Kwon SM, Sarwate AD, Panta SR, Plis SM. Privacy-preserving quality control of neuroimaging datasets in federated environments. Hum Brain Mapp 2022; 43:2289-2310. [PMID: 35243723 PMCID: PMC8996357 DOI: 10.1002/hbm.25788] [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: 05/05/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.
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Affiliation(s)
- Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Soo Min Kwon
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Anand D Sarwate
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Sandeep R Panta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
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Fu Z, Sui J, Espinoza R, Narr K, Qi S, Sendi MSE, Abbot CC, Calhoun VD. Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:312-322. [PMID: 34303848 PMCID: PMC8783932 DOI: 10.1016/j.bpsc.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. METHODS In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. RESULTS Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. CONCLUSIONS These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Katherine Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Christopher C. Abbot
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States,Corresponding author: Dr. Christopher C. Abbott, Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States, , Phone: 505-272-0406
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States
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Yang F, Jiang X, Yue F, Wang L, Boecker H, Han Y, Jiang J. Exploring dynamic functional connectivity alterations in the preclinical stage of Alzheimer's disease: an exploratory study from SILCODE. J Neural Eng 2022; 19. [PMID: 35147522 DOI: 10.1088/1741-2552/ac542d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Exploring functional connectivity (FC) alterations is important for the understanding of underlying neuronal network alterations in subjective cognitive decline (SCD). The objective of this study was to prove that dynamic FC can better reflect the changes of brain function in individuals with SCD compared to static FC, and further to explore the association between FC alterations and amyloid pathology in the preclinical stage of Alzheimer's disease (AD). METHODS 101 normal control (NC) subjects, 97 SCDs, and 55 cognitive impairment (CI) subjects constituted the whole-cohort. Of these, 29 NCs and 52 SCDs with amyloid images were selected as the sub-cohort. First, independent components (ICs) were identified by independent component analysis and static and dynamic FC were calculated by pairwise correlation coefficient between ICs. Second, FC alterations were identified through group comparison, and seed-based dynamic FC analysis was done. Analysis of variance (ANOVA) was used to compare the seed-based dynamic FC maps and measure the group or amyloid effects. Finally, correlation analysis was conducted between the altered dynamic FC and amyloid burden. RESULTS The results showed that 42 ICs were revealed. Significantly altered dynamic FC included those between the salience/ventral attention network, the default mode network, and the visual network. Specifically, the thalamus/caudate (IC 25) drove the hub role in the group differences. In the seed-based dynamic FC analysis, the dynamic FC between the thalamus/caudate and the middle temporal/frontal gyrus was observed to be higher in the SCD and CI groups. Moreover, a higher dynamic FC between the thalamus/caudate and visual cortex was observed in the amyloid positive group. Finally, the altered dynamic FC was associated with the amyloid global standardized uptake value ratio (SUVr). CONCLUSION Our findings suggest SCD-related alterations could be more reflected by dynamic FC than static FC, and the alterations are associated with global SUVr.
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Affiliation(s)
- Fan Yang
- Shanghai University, Shangda Road, Baoshan district, Shanghai, Shanghai, 200444, CHINA
| | - Xueyan Jiang
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Feng Yue
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Luyao Wang
- Shanghai University, Shangda road, Baoshan district, shanghai, Shanghai, 200444, CHINA
| | - Henning Boecker
- University Hospital Bonn, Positron Emission Tomography (PET) Group, Bonn, Germany, Bonn, Nordrhein-Westfalen, 53127, GERMANY
| | - Ying Han
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Jiehui Jiang
- Shanghai University, Shangda road, Baoshan district, Shanghai, Shanghai, 200444, CHINA
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Huang J, Cheng R, Liu X, Chen L, Luo T. Abnormal static and dynamic functional connectivity of networks related to cognition in patients with subcortical ischemic vascular disease. Neuroradiology 2022; 64:1201-1211. [DOI: 10.1007/s00234-022-02895-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/20/2021] [Indexed: 12/01/2022]
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Altunkaya S, Huang SM, Hsu YH, Yang JJ, Lin CY, Kuo LW, Tu MC. Dissociable Functional Brain Networks Associated With Apathy in Subcortical Ischemic Vascular Disease and Alzheimer’s Disease. Front Aging Neurosci 2022; 13:717037. [PMID: 35185511 PMCID: PMC8851472 DOI: 10.3389/fnagi.2021.717037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 12/27/2021] [Indexed: 01/10/2023] Open
Abstract
Few studies have investigated differences in functional connectivity (FC) between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD), especially in relation to apathy. Therefore, the aim of this study was to compare apathy-related FC changes among patients with SIVD, AD, and cognitively normal subjects. The SIVD group had the highest level of apathy as measured using the Apathy Evaluation Scale-clinician version (AES). Dementia staging, volume of white matter hyperintensities (WMH), and the Beck Depression Inventory were the most significant clinical predictors for apathy. Group-wise comparisons revealed that the SIVD patients had the worst level of “Initiation” by factor analysis of the AES. FCs from four resting state networks (RSNs) were compared, and the connectograms at the level of intra- and inter-RSNs revealed dissociable FC changes, shared FC in the dorsal attention network, and distinct FC in the salient network across SIVD and AD. Neuronal correlates for “Initiation” deficits that underlie apathy were explored through a regional-specific approach, which showed that the right inferior frontal gyrus, left middle frontal gyrus, and left anterior insula were the critical hubs. These findings broaden the disconnection theory by considering the effect of FC interactions across multiple RSNs on apathy formation.
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Affiliation(s)
- Sabri Altunkaya
- Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Turkey
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yen-Hsuan Hsu
- Department of Psychology, National Chung Cheng University, Chiayi, Taiwan
- Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chaiyi, Taiwan
| | - Jir-Jei Yang
- Department of Radiology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
| | - Chien-Yuan Lin
- GE Healthcare, GE Medical Systems Taiwan, Ltd., Taipei, Taiwan
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Min-Chien Tu
- Department of Neurology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien, Taiwan
- *Correspondence: Min-Chien Tu,
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43
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Zhao L, Zeng W, Shi Y, Nie W. Dynamic effective connectivity network based on change points detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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von Schwanenflug N, Krohn S, Heine J, Paul F, Prüss H, Finke C. OUP accepted manuscript. Brain Commun 2022; 4:fcab298. [PMID: 35169701 PMCID: PMC8833311 DOI: 10.1093/braincomms/fcab298] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/13/2021] [Accepted: 01/03/2022] [Indexed: 11/21/2022] Open
Abstract
Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti-N-methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti-N-methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Josephine Heine
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Clinical Research Center, Charité—Universitätsmedizin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Harald Prüss
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Centre for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Carsten Finke
- Correspondence to: Carsten Finke Charitéplatz 1 10117 Berlin, Germany E-mail:
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NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data. Neuroinformatics 2022; 20:91-108. [PMID: 33948898 PMCID: PMC8566325 DOI: 10.1007/s12021-021-09525-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2021] [Indexed: 01/05/2023]
Abstract
The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially for clinical data or rare disorders). In addition, aggregating data across multiple large studies results in a huge amount of duplicated technical debt and the resources required can be challenging or impossible for an individual site to build. Training on the data distributed across organizations can result in models that generalize much better than models trained on data from any of organizations alone. While there are approaches for decentralized sharing, these often do not provide the highest possible guarantees of sample privacy that only cryptography can provide. In addition, such approaches are often focused on probabilistic solutions. In this paper, we propose an approach that leverages the potential of datasets spread among a number of data collecting organizations by performing joint analyses in a secure and deterministic manner when only encrypted data is shared and manipulated. The approach is based on secure multiparty computation which refers to cryptographic protocols that enable distributed computation of a function over distributed inputs without revealing additional information about the inputs. It enables multiple organizations to train machine learning models on their joint data and apply the trained models to encrypted data without revealing their sensitive data to the other parties. In our proposed approach, organizations (or sites) securely collaborate to build a machine learning model as it would have been trained on the aggregated data of all the organizations combined. Importantly, the approach does not require a trusted party (i.e. aggregator), each contributing site plays an equal role in the process, and no site can learn individual data of any other site. We demonstrate effectiveness of the proposed approach, in a range of empirical evaluations using different machine learning algorithms including logistic regression and convolutional neural network models on human structural and functional magnetic resonance imaging datasets.
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Xu Y, Shang H, Lu H, Zhang J, Yao L, Long Z. Altered Dynamic Functional Connectivity in Subcortical Ischemic Vascular Disease With Cognitive Impairment. Front Aging Neurosci 2021; 13:758137. [PMID: 34955812 PMCID: PMC8704998 DOI: 10.3389/fnagi.2021.758137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/17/2021] [Indexed: 11/25/2022] Open
Abstract
Subcortical ischemic vascular disease (SIVD) can cause cognitive impairment and affect the static functional connectivity of resting functional magnetic resonance imaging (fMRI). Numerous previous studies have demonstrated that functional connectivities (FCs) fluctuate dynamically over time. However, little is known about the impact of cognitive impairment on brain dynamic functional connectivity (DFC) in SIVD patients with MCI. In the present study, the DFC analysis method was applied to the resting functional magnetic resonance imaging (fMRI) data of 37 SIVD controls (SIVD-Control) without cognitive impairment, 34 SIVD patients with amnestic MCI (SIVD-aMCI) and 30 SIVD patients with nonamnestic MCI (SIVD-naMCI). The results indicated that the cognitive impairment of SIVD mainly reduced the mean dwell time of State 3 with overall strong positive connections. The reduction degree of SIVD-aMCI was larger than that of SIVD-naMCI. The memory/execution function impairment of SIVD also changed the relationship between the mean dwell time of State 3 and the behavioral performance of the memory/execution task from significant to non-significant correlation. Moreover, SIVD-aMCI showed significantly lower system segregation of FC states than SIVD-Control and SIVD-naMCI. The system segregation of State 5 with overall weak connections was significantly positive correlated with the memory performance. The results may suggest that the mean dwell time of State 3 and the system segregation of State 5 may be used as important neural measures of cognitive impairments of SIVD.
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Affiliation(s)
- Yuanhang Xu
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huajie Shang
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Hui Lu
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Junying Zhang
- BABRI Centre, Beijing Normal University, Beijing, China.,Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Li Yao
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Diez-Cirarda M, Gabilondo I, Ibarretxe-Bilbao N, Gómez-Esteban JC, Kim J, Lucas-Jiménez O, Del Pino R, Peña J, Ojeda N, Mihaescu A, Valli M, Acera MA, Cabrera-Zubizarreta A, Gómez-Beldarrain MA, Strafella AP. Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson's disease. NPJ Parkinsons Dis 2021; 7:117. [PMID: 34916518 PMCID: PMC8677758 DOI: 10.1038/s41531-021-00257-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/19/2021] [Indexed: 01/09/2023] Open
Abstract
Alterations in time-varying functional connectivity (FC) have been found in Parkinson's disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.
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Affiliation(s)
- Maria Diez-Cirarda
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada.
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.
| | - Iñigo Gabilondo
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
| | - Naroa Ibarretxe-Bilbao
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Juan Carlos Gómez-Esteban
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- Neurology Department, Cruces University Hospital, Barakaldo, Spain
| | - Jinhee Kim
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Olaia Lucas-Jiménez
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Rocio Del Pino
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Javier Peña
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Natalia Ojeda
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Alexander Mihaescu
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Mikaeel Valli
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Maria Angeles Acera
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | | | | | - Antonio P Strafella
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada.
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Zhang P, Jiang Y, Liu G, Han J, Wang J, Ma L, Hu W, Zhang J. Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study. J Headache Pain 2021; 22:147. [PMID: 34895135 PMCID: PMC8903588 DOI: 10.1186/s10194-021-01354-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/06/2021] [Indexed: 12/20/2022] Open
Abstract
Background Accumulating studies have indicated a wide range of brain alterations with respect to the structure and function of classic trigeminal neuralgia (CTN). Given the dynamic nature of pain experience, the exploration of temporal fluctuations in interregional activity covariance may enhance the understanding of pain processes in the brain. The present study aimed to characterize the temporal features of functional connectivity (FC) states as well as topological alteration in CTN. Methods Resting-state functional magnetic resonance imaging and three-dimensional T1-weighted images were obtained from 41 CTN patients and 43 matched healthy controls (HCs). After group independent component analysis, sliding window based dynamic functional network connectivity (dFNC) analysis was applied to investigate specific FC states and related temporal properties. Then, the dynamics of the whole brain topological organization were estimated by calculating the coefficient of variation of graph-theoretical properties. Further correlation analyses were performed between all these measurements and clinical data. Results Two distinct states were identified. Of these, the state 2, characterized by complicated coupling between default mode network (DMN) and cognitive control network (CC) and tight connections within DMN, was expressed more in CTN patients and presented as increased fractional windows and dwell time. Moreover, patients switched less frequently between states than HCs. Regarding the dynamic topological analysis, disruptions in global graph-theoretical properties (including network efficiency and small-worldness) were observed in patients, coupled with decreased variability in nodal efficiency of anterior cingulate cortex (ACC) in the salience network (SN) and the thalamus and caudate nucleus in the subcortical network (SC). The variation of topological properties showed negative correlation with disease duration and attack frequency. Conclusions The present study indicated disrupted flexibility of brain topological organization under persistent noxious stimulation and further highlighted the important role of “dynamic pain connectome” regions (including DMN/CC/SN) in the pathophysiology of CTN from the temporal fluctuation aspect. Additionally, the findings provided supplementary evidence for current knowledge about the aberrant cortical-subcortical interaction in pain development. Supplementary Information The online version contains supplementary material available at 10.1186/s10194-021-01354-z.
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Affiliation(s)
- Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Yanli Jiang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Guangyao Liu
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jiao Han
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Laiyang Ma
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China. .,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, P. R. China.
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49
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Dynamic functional network connectivity reveals the brain functional alterations in lung cancer patients after chemotherapy. Brain Imaging Behav 2021; 16:1040-1048. [PMID: 34718941 DOI: 10.1007/s11682-021-00575-9] [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: 03/29/2021] [Accepted: 09/28/2021] [Indexed: 10/19/2022]
Abstract
This study aimed to investigate alterations of brain functional network connectivity (FNC) in lung cancer patients after chemotherapy and explore links between these FNC differences and cognitive impairment. Twenty-two lung cancer patients receiving chemotherapy and 26 healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing. Group independent component analysis (GICA) was applied to rs-fMRI data to extract whole-brain resting state networks (RSNs). Static and dynamic FNC (dFNC) were constructed to reveal RSNs connectivity alterations between lung cancer patients and HCs group, and the correlations between the group differences in RSNs and cognitive performance were analyzed. Our findings revealed that chemotherapeutics can produce widespread connectivity abnormalities in RSNs, mainly focused on default mode network (DMN) and executive control network. Furthermore, the dFNC analysis help identify network configurations of each state and capture more chemotherapy-induced disorders of interactions between and within RSNs, which mainly includes sensorimotor network, attentional network and auditory network. In addition, after chemotherapy, the lung cancer patients spend shorter mean dwell time (MDT) in state 2. The decreased dFNC between DMN [independent component 5 (IC5)] and DMN (IC6) in the lung cancer patients after chemotherapy in state 4 was negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r=-0.447, p=0.042). The dFNC analysis enrich our understanding of the neural mechanisms underlying the chemobrain, and suggested that the temporal dynamics of FNC could be a potential effective method to detect cognitive changes in lung cancer patients receiving chemotherapy.
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50
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Xu W, Song Y, Chen S, Xue C, Hu G, Qi W, Ma W, Lin X, Chen J. An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment. Front Neurol 2021; 12:649233. [PMID: 34630270 PMCID: PMC8492914 DOI: 10.3389/fneur.2021.649233] [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: 01/06/2021] [Accepted: 07/28/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Subcortical vascular cognitive impairment (sVCI), caused by cerebral small vessel disease, accounts for the majority of vascular cognitive impairment, and is characterized by an insidious onset and impaired memory and executive function. If not recognized early, it inevitably develops into vascular dementia. Several quantitative studies have reported the consistent results of brain regions in sVCI patients that can be used to predict dementia conversion. The purpose of the study was to explore the exact abnormalities within the brain in sVCI patients by combining the coordinates reported in previous studies. Methods: The PubMed, Embase, and Web of Science databases were thoroughly searched to obtain neuroimaging articles on the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity in sVCI patients. According to the activation likelihood estimation (ALE) algorithm, a meta-analysis based on coordinate and functional connectivity modeling was conducted. Results: The quantitative meta-analysis included 20 functional imaging studies on sVCI patients. Alterations in specific brain regions were mainly concentrated in the frontal lobes including the middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, and precentral gyrus; parietal lobes including the precuneus, angular gyrus, postcentral gyrus, and inferior parietal lobule; occipital lobes including the lingual gyrus and cuneus; temporal lobes including the fusiform gyrus and middle temporal gyrus; and the limbic system including the cingulate gyrus. These specific brain regions belonged to important networks known as the default mode network, the executive control network, and the visual network. Conclusion: The present study determined specific abnormal brain regions in sVCI patients, and these brain regions with specific changes were found to belong to important brain functional networks. The findings objectively present the exact abnormalities within the brain, which help further understand the pathogenesis of sVCI and identify them as potential imaging biomarkers. The results may also provide a basis for new approaches to treatment.
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Affiliation(s)
- Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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