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Lian J, Xu L, Song T, Peng Z, Zhang Z, An X, Chen S, Zhong X, Shao Y. Reduced Resting-State EEG Power Spectra and Functional Connectivity after 24 and 36 Hours of Sleep Deprivation. Brain Sci 2023; 13:949. [PMID: 37371427 DOI: 10.3390/brainsci13060949] [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: 05/12/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period.
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Affiliation(s)
- Jie Lian
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Lin Xu
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Tao Song
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Zheyuan Zhang
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xin An
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Shufang Chen
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xiao Zhong
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing 100084, China
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Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder. Neural Plast 2022; 2022:4714763. [PMID: 36199291 PMCID: PMC9529483 DOI: 10.1155/2022/4714763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/08/2022] [Indexed: 12/03/2022] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.
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Ma K, Huang S, Zhang D. Diagnosis of Mild Cognitive Impairment with Ordinal Pattern Kernel. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1030-1040. [PMID: 35404822 DOI: 10.1109/tnsre.2022.3166560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Mild cognitive impairment (MCI) belongs to the prodromal stage of Alzheimer's disease (AD). Accurate diagnosis of MCI is very important for possibly deferring AD progression. Graph kernels, which measure the similarity between paired brain connectivity networks, have been widely used to diagnose brain diseases (e.g., MCI) and yielded promising classification performance. However, most of the existing graph kernels are based on unweighted graphs, and neglect the valuable weighted information of the edges in brain connectivity networks where edge weights convey the strengths of fiber connection or temporal correlation between paired brain regions. Accordingly, in this paper, we propose a new graph kernel called ordinal pattern kernel for measuring brain connectivity network similarity and apply it to brain disease classification tasks. Different from the existing graph kernels which measure the topological similarity of the unweighted graphs, our proposed ordinal pattern kernel can not only calculate the similarity of paired brain connectivity networks, but also capture the ordinal pattern relationship of edge weights in brain connectivity networks. To appraise the effectiveness of our proposed method, we perform extensive experiments in functional magnetic resonance imaging data of brain disease from Alzheimer's Disease Neuroimaging Initiative database. The experimental results show that our proposed ordinal pattern kernel outperforms the state-of-the-art graph kernels in the classification tasks of MCI.
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He H, Ding S, Jiang C, Wang Y, Luo Q, Wang Y. Information Flow Pattern in Early Mild Cognitive Impairment Patients. Front Neurol 2021; 12:706631. [PMID: 34858306 PMCID: PMC8631864 DOI: 10.3389/fneur.2021.706631] [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: 05/07/2021] [Accepted: 10/11/2021] [Indexed: 12/05/2022] Open
Abstract
Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI. Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state. Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures. Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.
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Affiliation(s)
- Haijuan He
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Shuang Ding
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Chunhui Jiang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Yuanyuan Wang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Qiaoya Luo
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Yunling Wang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
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Tao W, Li H, Li X, Huang R, Shao W, Guan Q, Zhang Z. Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis. Front Aging Neurosci 2021; 13:687530. [PMID: 34322011 PMCID: PMC8312851 DOI: 10.3389/fnagi.2021.687530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
People with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are both at high risk for Alzheimer’s disease (AD). Behaviorally, both SCD and aMCI have subjective reports of cognitive decline, but the latter suffers a more severe objective cognitive impairment than the former. However, it remains unclear how the brain develops from SCD to aMCI. In the current study, we aimed to investigate the topological characteristics of the white matter (WM) network that can successfully identify individuals with SCD or aMCI from healthy control (HC) and to describe the relationship of pathological changes between these two stages. To this end, three groups were recruited, including 22 SCD, 22 aMCI, and 22 healthy control (HC) subjects. We constructed WM network for each subject and compared large-scale topological organization between groups at both network and nodal levels. At the network level, the combined network indexes had the best performance in discriminating aMCI from HC. However, no indexes at the network level can significantly identify SCD from HC. These results suggested that aMCI but not SCD was associated with anatomical impairments at the network level. At the nodal level, we found that the short-path length can best differentiate between aMCI and HC subjects, whereas the global efficiency has the best performance in differentiating between SCD and HC subjects, suggesting that both SCD and aMCI had significant functional integration alteration compared to HC subjects. These results converged on the idea that the neural degeneration from SCD to aMCI follows a gradual process, from abnormalities at the nodal level to those at both nodal and network levels.
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Affiliation(s)
- Wuhai Tao
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Hehui Li
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rong Huang
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Ma K, Liu Y, Shao W, Sun J, Li J, Fang X, Li J, Wang Z, Zhang D. Brain Functional Interaction of Acupuncture Effects in Diarrhea-Dominant Irritable Bowel Syndrome. Front Neurosci 2021; 14:608688. [PMID: 33384580 PMCID: PMC7770184 DOI: 10.3389/fnins.2020.608688] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 11/11/2020] [Indexed: 01/30/2023] Open
Abstract
Acupuncture is a traditional Chinese medicine treatment that has widely been used to modulate gastrointestinal dysfunction caused by irritable bowel syndrome (IBS) and to alleviate the resulting pain. Recent studies have shown that gastrointestinal dysfunction caused by IBS is associated with dysregulation of the brain's central and peripheral nervous system, while functional magnetic resonance imaging (fMRI) helps explore functional abnormality of the brain. However, previous studies rarely used fMRI to study the correlations between brain functional connection, interaction, or segregation (e.g., network degree and clustering coefficient) and acupuncture stimulation in IBS. To bridge this knowledge gap, we study the changed brain functional connection, interaction, and segregation before and after acupuncture stimulation for diarrhea-dominant IBS (IBS-D) with the help of complex network methods based on fMRI. Our results indicate that the abnormal functional connections (FCs) in the right hippocampus, right superior occipital gyrus, left lingual gyrus, left middle occipital gyrus, and the cerebellum, and abnormal network degree in right middle occipital gyrus, where normal controls are significantly different from IBS-D patients, are improved after acupuncture stimulation. These changed FCs and the network degree before and after acupuncture stimulation have significant correlations with the changed clinical information including IBS symptom severity score (r = −0.54, p = 0.0065) and IBS quality of life (r = 0.426, p = 0.038). We conclude that the changes of the brain functional connection, interaction, and segregation in the hippocampus, middle and superior occipital gyrus, cerebellum, and the lingual gyrus may be related to acupuncture stimulation. The abnormal functional connection, interaction, and segregation in IBS-D may be improved after acupuncture stimulation.
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Affiliation(s)
- Kai Ma
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Yongkang Liu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wei Shao
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jianhua Sun
- Department of Acupuncture and Rehabilitation, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jing Li
- Department of Acupuncture and Moxibustion, Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiaokun Fang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jing Li
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhongqiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Daoqiang Zhang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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Zhang Y, Ma K, Yang Y, Yin Y, Hou Z, Zhang D, Yuan Y. Predicting Response to Group Cognitive Behavioral Therapy in Asthma by a Small Number of Abnormal Resting-State Functional Connections. Front Neurosci 2020; 14:575771. [PMID: 33328851 PMCID: PMC7732460 DOI: 10.3389/fnins.2020.575771] [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: 06/24/2020] [Accepted: 10/27/2020] [Indexed: 11/13/2022] Open
Abstract
Group cognitive behavioral therapy (GCBT) is a successful psychotherapy for asthma. However, response varies considerably among individuals, and identifying biomarkers of GCBT has been challenging. Thus, the aim of this study was to predict an individual's potential response by using machine learning algorithms and functional connectivity (FC) and to improve the personalized treatment of GCBT. We use the lasso method to make the feature selection in the functional connections between brain regions, and we utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and pre-GCBT patients (size = 20), pre-GCBT patients (size = 10) and post-GCBT patients (size = 10), and post-GCBT patients (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls and pre- and post-GCBT patients. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in post-GCBT patients. At last, linear support vector regression was applied to predict the therapeutic effect of GCBT. After feature selection and significant analysis, five discriminated FC regarding neuroimaging biomarkers of GCBT were discovered, which are also correlated with clinical symptoms. Using these discriminated functional connections, we could accurately classify the patients before and after GCBT (classification accuracy, 80%) and predict the therapeutic effect of GCBT in asthma (predicted accuracy, 67.8%). The findings in this study would provide a novel sight toward GCBT response prediction and further confirm neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT. CLINICAL TRIAL REGISTRATION The brain mechanisms of group cognitive behavioral therapy to improve the symptoms of asthma (Registration number: Chi-CTR-15007442, http://www.chictr.org.cn/index.aspx).
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Affiliation(s)
- Yuqun Zhang
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Kai Ma
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Yuan Yang
- Department of Respiratory, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Daoqiang Zhang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Xu X, Li W, Mei J, Tao M, Wang X, Zhao Q, Liang X, Wu W, Ding D, Wang P. Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns. Front Aging Neurosci 2020; 12:28. [PMID: 32140102 PMCID: PMC7042199 DOI: 10.3389/fnagi.2020.00028] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 01/28/2020] [Indexed: 12/28/2022] Open
Abstract
Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer’s disease (AD). Brain functional connectome data (i.e., functional connections, global and nodal graph metrics) based on resting-state functional magnetic resonance imaging (rs-fMRI) provides numerous information about brain networks and has been used to discriminate normal controls (NCs) from subjects with MCI. In this paper, Student’s t-tests and group-least absolute shrinkage and selection operator (group-LASSO) were used to extract functional connections with significant differences and the most discriminative network nodes, respectively. Based on group-LASSO, the middle temporal, inferior temporal, lingual, posterior cingulate, and middle frontal gyri were the most predominant brain regions for nodal observation in MCI patients. Nodal graph metrics (within-module degree, participation coefficient, and degree centrality) showed the maximum discriminative ability. To effectively combine the multipattern information, we employed the multiple kernel learning support vector machine (MKL-SVM). Combined with functional connectome information, the MKL-SVM achieved a good classification performance (area under the receiving operating characteristic curve = 0.9728). Additionally, the altered brain connectome pattern revealed that functional connectivity was generally decreased in the whole-brain network, whereas graph theory topological attributes of some special nodes in the brain network were increased in MCI patients. Our findings demonstrate that optimal feature selection and combination of all connectome features (i.e., functional connections, global and nodal graph metrics) can achieve good performance in discriminating NCs from MCI subjects. Thus, the combination of functional connections and global and nodal graph metrics of brain networks can predict the occurrence of MCI and contribute to the early clinical diagnosis of AD.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Weikai Li
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jian Mei
- Physical Education College, Soochow University, Suzhou, China
| | - Mengling Tao
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiangbin Wang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qianhua Zhao
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Wanqing Wu
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ding Ding
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
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Ma K, Yu J, Shao W, Xu X, Zhang Z, Zhang D. Functional Overlaps Exist in Neurological and Psychiatric Disorders: A Proof from Brain Network Analysis. Neuroscience 2020; 425:39-48. [PMID: 31794696 DOI: 10.1016/j.neuroscience.2019.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 11/10/2019] [Accepted: 11/11/2019] [Indexed: 11/26/2022]
Abstract
Psychopath and neuropath often exhibit similar symptoms in clinical functional performances. However, few studies ever demonstrate the existence of overlapped brain functional mechanism between neurological and psychiatric disorders. Accordingly, in this paper, we have made an attempt to verify the existence of functional overlaps among neurological and psychiatric disorders through brain network analysis. Specifically, our findings suggest that functional overlaps exist in mild cognitive impairment (MCI), Alzheimer's disease (AD) and major depressive disorder (MDD), as well as in epilepsy, attention deficit hyperactivity disorder (ADHD) and schizophrenia. In these overlapped functions, we also find that the brain regions of neuropsychopathic disorders exhibit different cooperative patterns at different levels of brain activities. For example, strong-strong cooperative patterns were observed at high levels of brain activities in epilepsy, ADHD and schizophrenia.
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Affiliation(s)
- Kai Ma
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu Province 210016, China
| | - Jintai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Wei Shao
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu Province 210016, China
| | - Xijia Xu
- Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province 210002, China
| | - Daoqiang Zhang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu Province 210016, China.
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