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Lin J, Huang J, Wu Y, Zhou L, Qiao C, Xie J, Hu C. Exploring the neural link between childhood maltreatment and depression: a default mode network rs-fMRI study. Front Psychiatry 2024; 15:1450051. [PMID: 39345924 PMCID: PMC11427261 DOI: 10.3389/fpsyt.2024.1450051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Accepted: 08/30/2024] [Indexed: 10/01/2024] Open
Abstract
Background Childhood maltreatment (CM) is increasingly recognized as a significant risk factor for major depressive disorder (MDD), yet the neural mechanisms underlying the connection between CM and depression are not fully understood. This study aims to deepen our understanding of this relationship through neuroimaging, exploring how CM correlates with depression. Methods The study included 56 MDD patients (33 with CM experiences and 23 without) and 23 healthy controls. Participants were assessed for depression severity, CM experiences, and underwent resting-state functional MRI scans. Independent Component Analysis was used to examine differences in functional connectivity (FC) within the Default Mode Network (DMN) among the groups. Results MDD patients with CM experiences exhibited significantly stronger functional connectivity in the left Superior Frontal Gyrus (SFG) and right Anterior Cingulate Cortex (ACC) within the DMN compared to both MDD patients without CM experiences and healthy controls. FC in these regions positively correlated with Childhood Trauma Questionnaire scores. Receiver Operating Characteristic (ROC) curve analysis underscored the diagnostic value of FC in the SFG and ACC for identifying MDD related to CM. Additionally, MDD patients with CM experiences showed markedly reduced FC in the left medial Prefrontal Cortex (mPFC) relative to MDD patients without CM experiences, correlating negatively with Childhood Trauma Questionnaire scores. Conclusion Our findings suggest that increased FC in the ACC and SFG within the DMN is associated with CM in MDD patients. This enhanced connectivity in these brain regions is key to understanding the predisposition to depression related to CM.
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Affiliation(s)
- Jian Lin
- Department of Clinical Psychiatry, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Jialing Huang
- Department of Clinical Psychiatry, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Yun Wu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Linqi Zhou
- School of the Fourth Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Changyuan Qiao
- School of the Fourth Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jian Xie
- Department of Clinical Psychiatry, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Changchun Hu
- Department of Clinical Psychiatry, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
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Xue C, Zheng D, Ruan Y, Guo W, Hu J. Alteration in temporal-cerebellar effective connectivity can effectively distinguish stable and progressive mild cognitive impairment. Front Aging Neurosci 2024; 16:1442721. [PMID: 39267723 PMCID: PMC11390694 DOI: 10.3389/fnagi.2024.1442721] [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: 06/02/2024] [Accepted: 08/20/2024] [Indexed: 09/15/2024] Open
Abstract
Background Stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI) represent two distinct subtypes of mild cognitive impairment (MCI). Early and effective diagnosis and accurate differentiation between sMCI and pMCI are crucial for administering targeted early intervention and preventing cognitive decline. This study investigated the intrinsic dysconnectivity patterns in sMCI and pMCI based on degree centrality (DC) and effective connectivity (EC) analyses, with the goal of uncovering shared and distinct neuroimaging mechanisms between subtypes. Methods Resting-state functional magnetic resonance imaging combined with DC analysis was used to explore the functional connectivity density in 42 patients with sMCI, 31 patients with pMCI, and 82 healthy control (HC) participants. Granger causality analysis was used to assess changes in EC based on the significant clusters found in DC. Furthermore, correlation analysis was conducted to examine the associations between altered DC/EC values and cognitive function. Receiver operating characteristic curve analysis was performed to determine the accuracy of abnormal DC and EC values in distinguishing sMCI from pMCI. Results Compared with the HC group, both pMCI and sMCI groups exhibited increased DC in the left inferior temporal gyrus (ITG), left posterior cerebellum lobe (CPL), and right cerebellum anterior lobe (CAL), along with decreased DC in the left medial frontal gyrus. Moreover, the sMCI group displayed reduced EC from the right CAL to bilateral CPL, left superior temporal gyrus, and bilateral caudate compared with HC. pMCI demonstrated elevated EC from the right CAL to left ITG, which was linked to episodic memory and executive function. Notably, the EC from the right CAL to the right ITG effectively distinguished sMCI from pMCI, with sensitivity, specificity, and accuracy of 0.5806, 0.9512, and 0.828, respectively. Conclusion This study uncovered shared and distinct alterations in DC and EC between sMCI and pMCI, highlighting their involvement in cognitive function. Of particular significance are the unidirectional EC disruptions from the cerebellum to the temporal lobe, which serve as a discriminating factor between sMCI and pMCI and provide a new perspective for understanding the temporal-cerebellum. These findings offer novel insights into the neural circuit mechanisms involving the temporal-cerebellum connection in MCI.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Darui Zheng
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yiming Ruan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenxuan Guo
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Hu
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 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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Izzi JVR, Ferreira RF, Girardi VA, Pena RFO. Identifying Effective Connectivity between Stochastic Neurons with Variable-Length Memory Using a Transfer Entropy Rate Estimator. Brain Sci 2024; 14:442. [PMID: 38790421 PMCID: PMC11119028 DOI: 10.3390/brainsci14050442] [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: 03/23/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.
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Affiliation(s)
- João V. R. Izzi
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Ricardo F. Ferreira
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Victor A. Girardi
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Rodrigo F. O. Pena
- Department of Biological Sciences, Florida Atlantic University, Jupiter, FL 33458, USA
- Stiles-Nicholson Brain Institute, Florida Atlantic University, Jupiter, FL 33458, USA
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Zhang F, Yang Y, Xin Y, Sun Y, Wang C, Zhu J, Tang T, Zhang J, Xu K. Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats. Epilepsy Behav 2023; 143:109212. [PMID: 37172446 DOI: 10.1016/j.yebeh.2023.109212] [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: 03/08/2023] [Accepted: 04/01/2023] [Indexed: 05/15/2023]
Abstract
Responsive neurostimulation (RNS) has shown promising but limited efficacy in the treatment of drug-resistant epilepsy. The clinical utility of RNS is hindered by the incomplete understanding of the mechanism behind its therapeutic effects. Thus, assessing the acute effects of responsive stimulation (AERS) based on intracranial EEG recordings in the temporal lobe epilepsy rat model may provide a better understanding of the potential therapeutic mechanisms underlying the antiepileptic effect of RNS. Furthermore, clarifying the correlation between AERS and seizure severity may help guide the optimization of RNS parameter settings. In this study, RNS with high (130 Hz) and low frequencies (5 Hz) was applied to the subiculum (SUB) and CA1. To quantify the changes induced by RNS, we calculated the AERS during synchronization by Granger causality and analyzed the band power ratio in the classic power band after different stimulations were delivered in the interictal and seizure onset periods, respectively. This demonstrates that only targets combined with an appropriate stimulation frequency could be efficient for seizure control. High-frequency stimulation of CA1 significantly shortened the ongoing seizure duration, which may be causally related to increased synchronization after stimulation. Both high-frequency stimulation of the CA1 and low-frequency stimulation delivered to the SUB reduced seizure frequency, and the reduced seizure risk may correlate with the change in power ratio near the theta band. It indicated that different stimulations may control seizures in diverse manners, perhaps with disparate mechanisms. More focus should be placed on understanding the correlation between seizure severity and synchronization and rhythm around theta bands to simplify the process of parameter optimization.
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Affiliation(s)
- Fang Zhang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yufang Yang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yanjie Xin
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yuting Sun
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Chang Wang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Junming Zhu
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Department of Neurosurgery, Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Zhejiang, China
| | - Tao Tang
- Zhejiang Lab, Hangzhou 311100, China
| | - Jianmin Zhang
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Department of Neurosurgery, Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Zhejiang, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
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Onoda K, Akama H. Complex of global functional network as the core of consciousness. Neurosci Res 2023; 190:67-77. [PMID: 36535365 DOI: 10.1016/j.neures.2022.12.007] [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: 05/09/2022] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.
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Affiliation(s)
- Keiichi Onoda
- Department of Psychology, Otemon Gakuin University, Ibaraki, Osaka 567-8502, Japan.
| | - Hiroyuki Akama
- Department of Life Science and Technology, Tokyo Institute of Technology, Meguro, Tokyo 152-8550, Japan
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Ye Y, Wang C, Lan X, Li W, Fu L, Zhang F, Liu H, Wu K, Zhou Y, Ning Y. Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder. Neuroimage Clin 2022; 36:103230. [PMID: 36274375 PMCID: PMC9668631 DOI: 10.1016/j.nicl.2022.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy. METHODS In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups. RESULTS Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission. CONCLUSION These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.
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Affiliation(s)
- Yanxiang Ye
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Chengyu Wang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Weicheng Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Ling Fu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Fan Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Haiyan Liu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
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Zhang S, Yang W, Li M, Wang S, Zhang J, Liu J, Yuan K. Partial recovery of the left DLPFC-right insula circuit with reduced craving in abstinent heroin users: a longitudinal study. Brain Imaging Behav 2022; 16:2647-2656. [PMID: 36136203 DOI: 10.1007/s11682-022-00721-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
The phenomenon of brain recovery after long-term abstinence has been reported in substance use disorders. However, few longitudinal studies have been conducted to observe the potential recovery in heroin users, and little is known about the neural mechanism underlying the decreased craving after prolonged abstinence. The 8-month longitudinal study was carried out in 29 heroin users and 30 healthy controls. By choosing the L_DLPFC, which was activated by the heroin cue as the seeding region, different brain connection patterns were compared between healthy controls and heroin users by using Granger causality analysis (GCA) at baseline. Then, a paired t test was employed to detect the potential recovery of L_DLPFC circuits after prolonged abstinence. The visual analog scale (VAS) and trail-making test-A (TMT-A) were adopted to investigate craving and cognitive control impairment, respectively. The neuroimaging changes were then correlated with behavioral improvements. Similar analyses were applied for the mirrored right DLPFC to verify the lateralization hypothesis of the DLPFC in addiction. In the longitudinal study, enhanced GCA coefficients were observed in the L_DLPFC-R_insula circuit of heroin users after long-term abstinence and were associated with craving score changes. At baseline, decreased GCA coefficients from the left DLPFC to the bilateral SMA and right putamen, together with the reduced GCA strength from the bilateral OFC to the left DLPFC, were found between HUs and HCs. Our findings extended the brain recovery phenomenon into the field of heroin and suggested that the increased regulation of the L_DLPFC over the insula after prolonged abstinence was important for craving inhibition.
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Affiliation(s)
- Shan Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Wenhan Yang
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China
| | - Minpeng Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Shicong Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Jun Zhang
- Hunan Judicial Police Academy, Changsha, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China.
| | - Kai Yuan
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China. .,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China. .,Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China. .,International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.
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Cheung EYW, Chau ACM, Shea YF, Chiu PKC, Kwan JSK, Mak HKF. Level of Amyloid-β (Aβ) Binding Leading to Differential Effects on Resting State Functional Connectivity in Major Brain Networks. Biomedicines 2022; 10:biomedicines10092321. [PMID: 36140422 PMCID: PMC9496530 DOI: 10.3390/biomedicines10092321] [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: 07/06/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
Introduction: Amyloid-β protein (Aβ) is one of the biomarkers for Alzheimer’s disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aβ accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN). Methods: 58 participants (27 Hi Aβ (HiAmy) and 31 low Aβ (LowAmy)) and 25 healthy controls (HC) were recruited. [18F]flutemetamol PET/CT was performed for diseased groups, and MRI scanning was done for all participants. Voxel-by-voxel correlation analysis was done for both groups in all networks. Results: In HiAmy, IFC was reduced in all networks except SN. A negative correlation in DMN, CEN, SRN and SMN suggests high Aβ related to IFC reduction; However, a positive correlation in SN suggests high Aβ related to an increase in IFC. In LowAmy, IFC increased in CEN, SMN, SN and SRN. Positive correlation in all major brain networks. Conclusion: The level of Aβ accumulated demonstrated differential effects on IFC in various brain networks. As the treatment to reduce Aβ plaque deposition is available in the market, it may be an option for the HiAmy group to improve their IFC in major brain networks.
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Affiliation(s)
- Eva Y. W. Cheung
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- School of Medical Health and Sciences, Tung Wah College, 19/F, 31 Wylie Road, Ho Man Tin, Hong Kong
- Correspondence: (E.Y.W.C.); (H.K.F.M.)
| | - Anson C. M. Chau
- Medical Radiation Science, Allied Health and Human Performance Unit, University of South Australia, City East Campus, Bonython Jubilee Building, 1-26, Adelaide, SA 5001, Australia
| | - Yat-Fung Shea
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong
| | - Patrick K. C. Chiu
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong
| | - Joseph S. K. Kwan
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Henry K. F. Mak
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong
- Alzheimer’s Disease Research Network, The University of Hong Kong, Hong Kong
- Correspondence: (E.Y.W.C.); (H.K.F.M.)
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Qin Y, Hu Z, Chen Y, Liu J, Jiang L, Che Y, Han C. Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1093. [PMID: 36010760 PMCID: PMC9407608 DOI: 10.3390/e24081093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/06/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.
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11
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Latency structure of BOLD signals within white matter in resting-state fMRI. Magn Reson Imaging 2022; 89:58-69. [PMID: 34999161 PMCID: PMC9851671 DOI: 10.1016/j.mri.2021.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 01/22/2023]
Abstract
PURPOSE Previous studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter. METHODS We used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-covariances between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states. RESULTS Projections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states. CONCLUSIONS These findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.
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Zhang W, Guo L, Liu D. Concurrent interactions between prefrontal cortex and hippocampus during a spatial working memory task. Brain Struct Funct 2022; 227:1735-1755. [DOI: 10.1007/s00429-022-02469-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 01/28/2022] [Indexed: 11/02/2022]
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13
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Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain". Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110147. [PMID: 33096157 DOI: 10.1016/j.pnpbp.2020.110147] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/21/2020] [Accepted: 10/16/2020] [Indexed: 01/27/2023]
Abstract
Although accumulating neuroimaging studies have reported that social behavior deficits in children with autism spectrum disorders (ASD) are commonly attributed to the dysfunction of social brain regions underlying social cognition, the dynamic interaction within the social brain network and its association with social deficits remain unclear. Here, resting-state functional magnetic resonance imaging data obtained from Autism Brain Imaging Data Exchange (I and II) were analyzed in 105 children with ASD and 102 demographically matched typically developing controls (TDCs) (age range: 7-12 years old). Term-based meta-analysis combined the prior reference and anatomical labeling were used to define the regions of interests of the social brain network, and multivariate Granger causality analysis with blind deconvolution was employed to assess the effective connectivity within the social brain network in the ASD and TDC groups. Between-group comparison revealed significantly attenuated effective connectivity from the medial prefrontal cortex (mPFC) to the bilateral amygdala in children with the ASD group compared with TDC group. In addition, raw values of the effective connectivity from the mPFC to the bilateral amygdala were used to predict social deficits in ASD. Our findings indicate the impaired mPFC-amygdala pathway and its association with social deficits in children with ASD and provide a new perspective into the neuropathology of the developing autistic brain.
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14
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Tavares LCS, Tort ABL. Hippocampal-prefrontal interactions during spatial decision-making. Hippocampus 2021; 32:38-54. [PMID: 34843143 DOI: 10.1002/hipo.23394] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/04/2021] [Accepted: 11/15/2021] [Indexed: 12/28/2022]
Abstract
The hippocampus has been linked to memory encoding and spatial navigation, while the prefrontal cortex is associated with cognitive functions such as decision-making. These regions are hypothesized to communicate in tasks that demand both spatial navigation and decision-making processes. However, the electrophysiological signatures underlying this communication remain to be better elucidated. To investigate the dynamics of the hippocampal-prefrontal interactions, we have analyzed their local field potentials and spiking activity recorded from rats performing a spatial alternation task on a figure eight-shaped maze. We found that the phase coherence of theta peaked around the choice point area of the maze. Moreover, Granger causality revealed a hippocampus → prefrontal cortex directionality of information flow at theta frequency, peaking at starting areas of the maze, and on the reverse direction at delta frequency, peaking near the turn onset. Additionally, the patterns of phase-amplitude cross-frequency coupling within and between the regions also showed spatial selectivity, and hippocampal theta and prefrontal delta modulated not only gamma amplitude but also inter-regional gamma synchrony. Finally, we found that the theta rhythm dynamically modulated neurons in both regions, with the highest modulation at the choice area; interestingly, prefrontal cortex neurons were more strongly modulated by the hippocampal theta rhythm than by their local field rhythm. In all, our results reveal maximum electrophysiological interactions between the hippocampus and the prefrontal cortex near the decision-making period of the spatial alternation task, corroborating the hypothesis that a dynamic interplay between these regions takes place during spatial decisions.
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Affiliation(s)
- Lucas C S Tavares
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil.,Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal, Brazil
| | - Adriano B L Tort
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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15
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16
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Li J, Liu J, Zhong Y, Wang H, Yan B, Zheng K, Wei L, Lu H, Li B. Causal Interactions Between the Default Mode Network and Central Executive Network in Patients with Major Depression. Neuroscience 2021; 475:93-102. [PMID: 34487819 DOI: 10.1016/j.neuroscience.2021.08.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 11/15/2022]
Abstract
Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.
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Affiliation(s)
- Jiaming Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China; School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Jian Liu
- Network Center, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Yufang Zhong
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Huaning Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Baoyu Yan
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Kaizhong Zheng
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Lei Wei
- Network Center, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
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17
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Chen S, Song Y, Xu W, Hu G, Ge H, Xue C, Gao J, Qi W, Lin X, Chen J. Impaired Memory Awareness and Loss Integration in Self-Referential Network Across the Progression of Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 83:111-126. [PMID: 34250942 DOI: 10.3233/jad-210541] [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] [Indexed: 01/17/2023]
Abstract
BACKGROUND Anosognosia, or unawareness of memory deficits, is a common manifestation of Alzheimer's disease (AD), but greatly variable in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) subjects. Self-referential network (SRN) is responsible for self-referential processing and considered to be related to AD progression. OBJECTIVE Our aim is to explore connectivity changes of SRN and its interaction with memory-related network and primary sensorimotor network (SMN) in the AD spectrum. METHODS About 444 Alzheimer's Disease Neuroimaging Initiative subjects (86 cognitively normal [CN]; 156 SCD; 146 aMCI; 56 AD) were enrolled in our study. The independent component analysis (ICA) method was used to extract the SRN, SMN, and memory-related network from all subjects. The alteration of functional connectivity (FC) within SRN and its connectivity with memory-related network/SMN were compared among four groups and further correlation analysis between altered FC and memory awareness index as well as episodic memory score were performed. RESULTS Compared with CN group, individuals with SCD exhibited hyperconnectivity within SRN, while aMCI and AD patients showed hypoconnectivity. Furthermore, aMCI patients and AD patients both showed the interruption of the FC between the SRN and memory-related network compared to CN group. Pearson correlation analysis showed that disruptive FC within SRN and its interaction with memory-related network were related to memory awareness index and episodic memory scores. CONCLUSION In conclusion, impaired memory awareness and episodic memory in the AD spectrum are correlated to the disconnection within SRN and its interaction with memory-related network.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yu Song
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Honglin Ge
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ju Gao
- Department of Geriatric Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xingjian Lin
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
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Raut RV, Snyder AZ, Mitra A, Yellin D, Fujii N, Malach R, Raichle ME. Global waves synchronize the brain's functional systems with fluctuating arousal. SCIENCE ADVANCES 2021; 7:7/30/eabf2709. [PMID: 34290088 PMCID: PMC8294763 DOI: 10.1126/sciadv.abf2709] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/04/2021] [Indexed: 05/04/2023]
Abstract
We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure ("functional connectivity") of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.
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Affiliation(s)
- Ryan V Raut
- Department of Radiology, Washington University, St. Louis, MO 63110, USA.
| | - Abraham Z Snyder
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Anish Mitra
- Department of Psychiatry, Stanford University, Stanford, CA 94305, USA
| | - Dov Yellin
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Rafael Malach
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Marcus E Raichle
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
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19
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Yao W, Chen H, Luo C, Sheng X, Zhao H, Xu Y, Bai F. Hyperconnectivity of Self-Referential Network as a Predictive Biomarker of the Progression of Alzheimer's Disease. J Alzheimers Dis 2021; 80:577-590. [PMID: 33579849 DOI: 10.3233/jad-201376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Self-referential processing is associated with the progression of Alzheimer's disease (AD), and cerebrospinal fluid (CSF) proteins have become accepted biomarkers of AD. OBJECTIVE Our objective in this study was to focus on the relationships between the self-referential network (SRN) and CSF pathology in AD-spectrum patients. METHODS A total of 80 participants, including 20 cognitively normal, 20 early mild cognitive impairment (EMCI), 20 late MCI (LMCI), and 20 AD, were recruited for this study. Independent component analysis was used to explore the topological SRN patterns, and the abnormalities of this network were identified at different stages of AD. Finally, CSF pathological characteristics (i.e., CSF Aβ, t-tau, and p-tau) that affected the abnormalities of the SRN were further determined during the progression of AD. RESULTS Compared to cognitively normal subjects, AD-spectrum patients (i.e., EMCI, LMCI, and AD) showed a reversing trend toward an association between CSF pathological markers and the abnormal SRN occurring during the progression of AD. However, a certain disease state (i.e., the present LMCI) with a low concentration of CSF tau could evoke more hyperconnectivity of the SRN than other patients with progressively increasing concentrations of CSF tau (i.e., EMCI and AD), and this fluctuation of CSF tau was more sensitive to the hyperconnectivity of the SRN than the dynamic changes of CSF Aβ. CONCLUSION The integrity of the SRN was closely associated with CSF pathological characteristics, and these findings support the view that the hyperconnectivity of the SRN will play an important role in monitoring the progression of the pre-dementia state to AD.
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Affiliation(s)
- Weina Yao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, 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 of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, 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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20
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Robust Autoregression with Exogenous Input Model for System Identification and Predicting. ELECTRONICS 2021. [DOI: 10.3390/electronics10060755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting-state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp-ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts.
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21
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Identifying Individuals Using EEG-Based Brain Connectivity Patterns. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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22
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Liu G, Jiao K, Zhong Y, Hao Z, Wang C, Xu H, Teng C, Song X, Xiao C, Fox PT, Zhang N, Wang C. The alteration of cognitive function networks in remitted patients with major depressive disorder: an independent component analysis. Behav Brain Res 2020; 400:113018. [PMID: 33301816 DOI: 10.1016/j.bbr.2020.113018] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/22/2020] [Accepted: 11/11/2020] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Dysfunctional connectivity of resting-state functional networks has been observed in patients with major depressive disorder (MDD), particularly in cognitive function networks including the central executive network (CEN), default mode network (DMN) and salience network (SN). Findings from studies examining how aberrant functional connectivity (FC) changed after antidepressant treatment, however, have been inconsistent. Thus, the purpose of the present study was to explore potential mechanisms of altered cognitive function networks during resting-state between remitted major depressive disorder (rMDD) patients and healthy controls (HCs) and furthermore, the relationship between dysfunctional connectivity patterns in rMDD and clinical symptoms. METHODOLOGY In this study, 19 HCs and 19 rMDD patients were recruited for resting-state functional magnetic resonance imaging (fMRI) scanning. FC was evaluated with independent component analysis for CEN, DMN and SN. Two sample t tests were conducted to compare differences between rMDD and HCs. A Pearson correlation analysis was also performed to examine the relationship between connectivity of networks and cognitive function scores and clinical symptoms. RESULTS Compared to healthy controls, remitted patients showed lower connectivity in CEN, mostly in the superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior parietal lobule (IPL) and part of the supramarginal gyrus (SMG). Conversely, the bilateral insula, part of the SMG (a key node of the CEN) and dorsal anterior cingulate cortex (dACC) of the DMN showed higher connectivity in rMDD patients. Pearson correlation results demonstrated that connectivity of the right IPL in CEN was positively correlated with cognitive function scores, and connectivity of the left insula was negatively correlated with BDI scores. CONCLUSIONS Though rMDD patients reached the standard of clinal remission, unique impairments of FC in cognitive function networks remained. Aberrant FC between cognitive function networks responsible for executive control was observed in rMDD and may be associated with residual clinical symptoms.
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Affiliation(s)
- Gang Liu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Kaili Jiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Zhengzhou Ninth People's Hospital, Zhengzhou, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing 210097, China
| | - Ziyu Hao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Chiyue Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huazhen Xu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Changjun Teng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiu Song
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Peter T Fox
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; South Texas Veterans Healthcare System, University of Texas Health San Antonio, United States; Research Imaging Institute, University of Texas Health San Antonio, United States
| | - Ning Zhang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
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Qi H, Hu Y, Lv Y, Wang P. Primarily Disrupted Default Subsystems Cause Impairments in Inter-system Interactions and a Higher Regulatory Burden in Alzheimer's Disease. Front Aging Neurosci 2020; 12:593648. [PMID: 33262699 PMCID: PMC7686542 DOI: 10.3389/fnagi.2020.593648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/26/2020] [Indexed: 12/03/2022] Open
Abstract
Background: Intrinsically organized large-scale brain networks and their interactions support complex cognitive function. Investigations suggest that the default network (DN) is the earliest disrupted network and that the frontoparietal control network (FPCN) and dorsal attention network (DAN) are subsequently impaired in Alzheimer's disease (AD). These large-scale networks comprise different subsystems (DN: medial temporal lobe (MTL), dorsomedial prefrontal cortex (DM) subsystems and a Core; FPCN: FPCNA and FPCNB). Our previous research has indicated that different DN subsystems are not equally damaged in AD. However, changes in the patterns of interactions among these large-scale network subsystems and the underlying cause of the alterations in AD remain unclear. We hypothesized that disrupted DN subsystems cause specific impairments in inter-system interactions and a higher regulatory burden for the FPCNA. Method: To test this hypothesis, Granger causality analysis (GCA) was performed to explore effective functional connectivity (FC) pattern of these networks. The regional information flow strength (IFS) was calculated and compared across groups to explore changes in the subsystems and their inter-system interactions and the relationship between them. To investigate specific inter-system changes, we summed the inter-system IFS and performed correlation analyses of the bidirectional inter-system IFS, which was compared across groups. Additionally, correlation analyses of dynamic effective FC patterns were performed to reveal alterations in the temporal co-evolution of sets of inter-subsystem interactions. Furthermore, we used partial correlation analysis to quantify the FPCN's regulatory effects. Finally, we applied a support vector machine (SVM) linear classifier to probe which network most effectively discriminated patients from controls. Results: Compared with controls, AD patients showed a decreased intra-DN regional IFS, which was significantly related to the inter-network's IFS. The IFS between the DN subsystems and FPCN subsystems/DAN decreased. Critically, the correlation values of the decreased bidirectional IFS between the DN subsystems and FPCNA diminished. Additionally, the Core and DM play pivotal roles in disordered temporal co-evolution. Furthermore, the FPCNA showed enhanced regulation of the Core. Finally, the MTL subsystem and Core were effective at discriminating patients from controls. Conclusion: The predominantly disrupted DN subsystems caused impaired inter-system interactions and created a higher regulatory burden for the FPCNA.
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Affiliation(s)
- Huihui Qi
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingru Lv
- Department of Imaging, Huashan Hospital, Fudan University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital Affiliated With Tongji University, Shanghai, China
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Feng Y, Wang YF, Zheng LJ, Shi Z, Huang W, Zhang LJ. Network-level functional connectivity alterations in chemotherapy treated breast cancer patients: a longitudinal resting state functional MRI study. Cancer Imaging 2020; 20:73. [PMID: 33066822 PMCID: PMC7565338 DOI: 10.1186/s40644-020-00355-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/07/2020] [Indexed: 01/18/2023] Open
Abstract
Background Previous studies have found abnormal structural and functional brain alterations in breast cancer survivors undergoing chemotherapy. However, the network-level brain changes following chemotherapy remain unknown. The purpose of this study was to investigate the dynamic changes of large-scale within- and between-network functional connectivity in chemotherapy-treated breast cancer patients. Methods Seventeen breast cancer patients were evaluated with resting state functional MRI (rs-fMRI), neuropsychological tests and blood examination before postoperative chemotherapy (t0), one week after completing chemotherapy (t1) and six months after completing chemotherapy (t2). Nineteen age- and education level-matched healthy controls (HC) were also recruited. Independent components analysis (ICA) was performed to assess network component using rs-fMRI data. The functional network changes were then correlated with cognitive assessment scores and blood biochemical indexes. Results One-way repeated measures ANOVA revealed significantly changed within-network functional connectivity in the anterior and posterior default mode network (ADMN and PDMN), left and right frontoparietal network (LFPN and RFPN), visual network and self-referential network. Post-hoc test showed that decreased within-network functional connectivity in ADMN, PDMN, LFPN, RFPN, SRN and central network one week after chemotherapy and increased six months after chemotherapy (all P < 0.05). As for the between-network functional connectivity, the PDMN- sensorimotor network connectivity showed the same tendency. Most of these within- and between-network functional connectivity changes were negatively associated with blood biochemical indexes and cognitive assessment scores (all P < 0.05). Conclusions These results indicated that chemotherapy may induce widespread abnormalities in resting state networks, which may serve as a potential biomarker of chemotherapy related cognitive impairment, providing insights for further functional recovery treatment.
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Affiliation(s)
- Yun Feng
- Department of Medical Imaging, Jinling Clinical Hospital, Nanjing Medical University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China.,Department of Medical Imaging, Medical Imaging Center, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, 223300, Jiangsu, China
| | - Yun Fei Wang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China
| | - Li Juan Zheng
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China
| | - Zhao Shi
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China
| | - Wei Huang
- Department of Medical Imaging, Medical Imaging Center, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, 223300, Jiangsu, China
| | - Long Jiang Zhang
- Department of Medical Imaging, Jinling Clinical Hospital, Nanjing Medical University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China. .,Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China. .,Department of Medical Imaging, Jinling Clinical Hospital, Southern Medical University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China.
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25
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Beyond traditional approaches: a partial directed coherence with graph theory-based mental load assessment using EEG modality. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05408-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Abnormalities of effective connectivity and white matter microstructure in the triple network in patients with schizophrenia. Psychiatry Res 2020; 290:113019. [PMID: 32474067 DOI: 10.1016/j.psychres.2020.113019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 11/23/2022]
Abstract
Disorganized communication among large-scale brain networks, especially in the salience network, default mode network and central executive network, have been consistently reported in schizophrenia (SZ) patients. However, abnormal patterns of the effective connectivity and abnormalities in the white matter of these networks remains unclear in patients with SZ. Fifty-six SZ patients and fifty-five healthy controls were enrolled in the present study and underwent resting state functional magnetic resonance and diffusion tensor imaging. Twelve main nodes within the triple networks were defined by independent components analysis. Effective connectivity between these main nodes was computed using Granger causality analysis. Voxel-based analysis of the diffusion tensor imaging data was conducted to explore white matter changes. The SZ patients showed abnormal effective connectivity between the anterior cingulate cortex and the dorsolateral prefrontal cortex. The abnormal white matter showed decreased fractional anisotropy localized in the bilateral anterior corona radiate and left superior long fasciculus in patients with SZ. These findings shed light on the importance of the triple network in the pathogenesis of SZ, which may facilitate the understanding of SZ.
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Liu T, Bai Y, Ma L, Ma X, Wei W, Zhang J, Roberts N, Wang M. Altered Effective Connectivity of Bilateral Hippocampus in Type 2 Diabetes Mellitus. Front Neurosci 2020; 14:657. [PMID: 32655364 PMCID: PMC7325692 DOI: 10.3389/fnins.2020.00657] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/27/2020] [Indexed: 02/05/2023] Open
Abstract
Patients with type 2 diabetes mellitus (T2DM) experience cognitive deficits but the underlying pathophysiologic mechanisms are not known. We therefore applied Granger causality analysis of resting-state functional magnetic resonance imaging to study the effective connectivity (EC) of the hippocampus in patients with T2DM. Eighty six patients with T2DM and 84 matched healthy controls (HC) were recruited. The directional EC between anatomically defined seeds in left hippocampus (LHIP) and right hippocampus (RHIP) and other brain regions was compared between T2DM and HC and Pearson correlation analysis was performed to determine whether alterations in EC were related to clinical characteristics of diabetes. Compared with HC, patients with T2DM had altered EC between LHIP and RHIP and the default mode network (DMN), occipital cortex and cerebellum. In addition, for LHIP only duration of diabetes positively correlated with decreased inflow from right postcentral gyrus and right parietal lobe, glycosylated hemoglobin (HbA1c) negatively correlated with decreased inflow from right thalamus (r = -0.255, p = 0.018) and Montreal Cognitive Assessment (MoCA) negatively correlated with decreased inflow from left inferior parietal lobe (r = -0.206, p = 0.05). The altered EC between hippocampus and DMN is interpreted to be related to cognitive deficits in patients with T2DM particularly affecting memory and learning.
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Affiliation(s)
- Taiyuan Liu
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Yan Bai
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Lun Ma
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiaoyue Ma
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Wei Wei
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Junran Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Neil Roberts
- The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Meiyun Wang
- Henan Key Laboratory of Neurological Imaging, Department of Medical Imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
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Shi Q, Chen H, Jia Q, Yuan Z, Wang J, Li Y, Han Z, Mo D, Zhang Y. Altered Granger Causal Connectivity of Resting-State Neural Networks in Patients With Leukoaraiosis-Associated Cognitive Impairment-A Cross-Sectional Study. Front Neurol 2020; 11:457. [PMID: 32655471 PMCID: PMC7325959 DOI: 10.3389/fneur.2020.00457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/29/2020] [Indexed: 12/17/2022] Open
Abstract
Background: The purpose of this study was to provide an imaging reference for the measurement of disease progression, as well as to reveal the pathogenesis of leukoaraiosis (LA). Methods: Eighty-seven subjects were divided into three groups: LA patients with vascular dementia (LA-VaD) (20 subjects: 14 female, 6 male), LA patients with vascular cognitive impairment nondementia (LA-VCIND) (32 subjects: 14 male, 18 female), and normal controls (NC) (35 subjects: 14 male, 21 female). A multivariate Granger causality analysis (mGCA) was applied to the resting-state networks (RSNs) to evaluate the possible effective connectivity within the resting-state networks retrieved by independent component analysis (ICA) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Results: Ten RSNs were identified: the primary visual network, secondary visual network, auditory network, sensorimotor network, anterior default mode network, posterior default mode network, salience network, dorsal attention network, left working memory network, and the right working memory network. Using independent component analysis, significant average Z scores were found in the anterior default mode network, salience network, dorsal attention network, and right working memory network between LA-VAD and NC groups. The functional connectivity (FC) strength of the networks was different between the NC, LA-VCIND, and LA-VaD groups. Effective connectivity between RSNs was compensated by either increased or decreased effective connectivity changes in these three groups. Conclusions: The components of resting-state networks kept changing as the disease progressed. Meanwhile, the activation intensity increased at the early stage of LA and decreased as patients' cognitive impairment aggravated. Furthermore, the direction and strength of connections between these networks changed and remodeled differently. These suggest that the human brain compensates for specific functional changes at different stages.
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Affiliation(s)
- Qingli Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurology, Beijing Pinggu Hospital, Beijing, China
| | - Hongyan Chen
- Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qian Jia
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zinan Yuan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinfang Wang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, China
| | - Yuexiu Li
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Zaizhu Han
- State Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dapeng Mo
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
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He W, Liu H, Liu Z, Wu Q. Electrical status epilepticus in sleep affects intrinsically connected networks in patients with benign childhood epilepsy with centrotemporal spikes. Epilepsy Behav 2020; 106:107032. [PMID: 32220803 DOI: 10.1016/j.yebeh.2020.107032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/14/2020] [Accepted: 03/04/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND Although outcomes of benign childhood epilepsy with centrotemporal spikes (BECTS) are frequently excellent, some atypical forms of BECTS, especially electrical status epilepticus in sleep (ESES), are characterized by worse outcomes and negative impacts on cognitive development. METHODS To explore specific ESES-related brain networks in patients with BECTS, we used resting-state functional magnetic resonance imaging (fMRI) to scan patients with BECTS with ESES (n = 9), patients with BECTS without ESES (n = 17), and healthy controls (n = 36). Unbiased seed-based whole-brain functional connectivity (FC) was adopted to explore the connectivity mode of three resting-state cerebral networks: the default mode network (DMN), salience network (SN), and central executive network (CEN). RESULTS Compared with the other two groups, patients with BECTS with ESES showed FC in the SN or in the CEN decreased, but not in the DMN. Moreover, we found the FC in the CEN in patients with BECTS without ESES decreased when compared with controls. Our currently intrinsically defined anticorrelated networks strength was disrupted in BECTS and connote greater deactivation than the results from FC for a seed region in children with BECTS. CONCLUSION These results indicated that children with BECTS with ESES showed brain activity altered in the CEN and the SN. The difference of impairment in the SN and CEN may lead to improve the understanding of the underlying neuropathophysiology, and to assess the activity of patients with BECTS with ESES, which is crucial for measuring disease activity, improving patient care, and assessing the effect of antiepilepsy therapy.
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Affiliation(s)
- Wen He
- Radiology Department of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Renmin Middle Road 253rd, Guangzhou 510220, China
| | - Hongsheng Liu
- Radiology Department of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Renmin Middle Road 253rd, Guangzhou 510220, China.
| | - Zhenqing Liu
- Radiology Department of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Renmin Middle Road 253rd, Guangzhou 510220, China
| | - Qianqian Wu
- Radiology Department of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Renmin Middle Road 253rd, Guangzhou 510220, China
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Hua M, Peng Y, Zhou Y, Qin W, Yu C, Liang M. Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis. Prog Neuropsychopharmacol Biol Psychiatry 2020; 99:109837. [PMID: 31830509 DOI: 10.1016/j.pnpbp.2019.109837] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 11/22/2019] [Accepted: 12/06/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Numerous neuroimaging studies have revealed that schizophrenia was characterized by wide-spread dysconnection among brain regions during rest measured by functional connectivity (FC). In contrast with FC, effective connectivity (EC) provides information about directionality of brain connections and is thus valuable in mechanistic investigation of schizophrenic brain. However, a systematic characterization of whole-brain resting-state EC (rsEC) and how it captures different information compared with resting-state FC (rsFC) in schizophrenia are still lacking. AIMS To systematically characterize the abnormalities of rsEC, compared with rsFC, in schizophrenia, and to test its discriminative power as a neuroimaging marker for schizophrenia diagnosis. METHOD Whole-brain rsEC and rsFC networks were constructed using resting-state fMRI data and compared between 103 patients with schizophrenia and 110 healthy participants. Pattern classifications between patients and controls based on whole-brain rsEC and rsFC were further performed using multivariate pattern analysis. RESULTS We identified 17 rsEC significantly disrupted (mostly decreased) in patients, among which all were associated with the thalamus and 15 were from limbic areas (including hippocampus, parahippocampus and cingulate cortex) to the thalamus. In contrast, abnormal rsFC were widely distributed in the whole brain. The classification accuracies for distinguishing patients and controls using whole-brain rsEC and rsFC patterns were 78.6% and 82.7%, respectively, and was further improved to 84.5% when combining rsEC and rsFC. CONCLUSIONS Schizophrenia is featured by disrupted 'limbic areas-to-thalamus' rsEC, in contrast with diffusively altered rsFC. Moreover, both rsEC and rsFC contain valuable and complementary information which may be used as diagnostic markers for schizophrenia.
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Affiliation(s)
- Minghui Hua
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yuan Zhou
- CAS Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunshui Yu
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China; Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.
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Jiang F, Fang JW, Ye YQ, Tian YJ, Zeng XJ, Zhong YL. Altered effective connectivity of primary visual cortex in primary angle closure glaucoma using Granger causality analysis. Acta Radiol 2020; 61:508-519. [PMID: 31390872 DOI: 10.1177/0284185119867644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Previous neuroimaging studies demonstrated that primary angle closure glaucoma patients were associated with abnormal intrinsic brain activity in primary visual cortex (V1). Purpose The purpose of this study was to investigate the effective connectivity patterns of V1 in patients with primary angle closure glaucoma. Material and Methods Thirty-seven patients with primary angle closure glaucoma (20 men, 17 women) and 36 healthy controls (20 men, 16 women) closely matched for age, sex, and education, underwent resting-state MRI scans. A voxel-wise Granger causality analysis method was performed to explore different effective connectivity pattern of V1 between the two groups. Results Compared with healthy controls, patients with primary angle closure glaucoma showed decreased effective connectivity from the left V1 to left cuneus and increased effective connectivity from the left V1 to left precentral gyrus and right supplementary motor area. Meanwhile, patients with primary angle closure glaucoma showed decreased effective connectivity from left precentral gyrus to left V1 and right frontal middle gyrus to left V1. In addition, patients with primary angle closure glaucoma showed a decreased effective connectivity from the right V1 to left cuneus/calcarine and increased effective connectivity from the right V1 to left inferior frontal gyrus and right caudate. Meanwhile, patients with primary angle closure glaucoma showed decreased effective connectivity from right middle frontal gyrus/precentral gyrus to right V1 and left precentral gyrus to right V1. Conclusion Our results highlighted that patients with primary angle closure glaucoma had abnormal effective connectivity between V1 and higher visual area, motor cortices, somatosensory cortices, and frontal lobe, which indicated that they might present with abnormal top-down modulations, visual imagery, vision-motor function, and vision-related higher cognition function.
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Affiliation(s)
- Fei Jiang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, PR China
| | - Jian-Wen Fang
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, PR China
| | - Yin-Quan Ye
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, PR China
| | - Yan-Jin Tian
- Medical College of Nanchang University, Nanchang, Jiangxi Province, PR China
| | - Xian-Jun Zeng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi Province, PR China
| | - Yu-Lin Zhong
- Department of Ophthalmology, The Affiliated Hospital of JiuJiang University, Jiujiang, Jiangxi Province, PR China
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Qian S, Yan S, Zhou C, Shi Z, Wang Z, Xiong Y, Zhou Y. Resting-state brain activity predicts selective attention deficits during hyperthermia exposure. Int J Hyperthermia 2020; 37:220-230. [PMID: 32126849 DOI: 10.1080/02656736.2020.1735536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
Purpose: Environmental hyperthermia exerts detrimental effect on attention performance that might increase the probability of accidents for high risk occupation. Previously, we reported aberrant activations and selective attention deficits under task performing during hyperthermia. However, whether resting-state baseline during hyperthermia would contribute to the reported selective attention deficits remains unclear.Materials and methods: Here, we investigated the resting-state activity within two attention subsystems named dorsal attention network (DAN) and ventral attention network (VAN) using the conjoint analysis of functional connectivity (FC) and regional cerebral blood flow (CBF). Blood oxygenation level dependent (BOLD) and 3 D arterial spin labeling data were obtained from 25 healthy male participants under two simulated thermal conditions: normothermic (25 °C for 1 h) and hyperthermic condition (50 °C for 1 h).Results: Paired comparisons on the FC and CBF showed decreased activity in the bilateral frontal eye field (FEF) and intraparietal sulcus (IPS) in the DAN but increased activity in the ventral frontal cortex (VFC) in the VAN. The CBF-FC correlation analysis further confirmed decreased CBF-FC coupling in the bilateral FEF in the DAN and increased coupling in the VFC in the VAN. Additionally, the left IPS and FEF in the DAN showed altered CBF per unit functional connectivity in the CBF/FC ratio analysis. Multiple regression analysis revealed that the selectively altered performances were predicted by alterations of the multiple metrics within the DAN and VAN.Conclusions: These findings suggested that altered resting-state brain activity within the attention networks might provide potential neural basis of the selective deficits for different cognitive-demand attention tasks under hyperthermia.
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Affiliation(s)
- Shaowen Qian
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China.,Department of Medical Imaging, Jinan Military General Hospital, Jinan, People's Republic of China
| | - Sumei Yan
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
| | - Chang Zhou
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
| | - Zhiyue Shi
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
| | - Zhaoqun Wang
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
| | - Ying Xiong
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
| | - Yi Zhou
- Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, Army Medical University, Chongqing, People's Republic of China
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Sex differences in resting state network (RSN) functional connections with mild cognitive impairment (MCI) progression. Neurosci Lett 2020; 724:134891. [PMID: 32145308 DOI: 10.1016/j.neulet.2020.134891] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/15/2019] [Accepted: 03/03/2020] [Indexed: 11/20/2022]
Abstract
OBJECTIVE Sex plays an important role in many diseases. The purpose of current study is to explore whether there are different lesion patterns in the RSN functional connections between males and females with MCI progression, and identify the differences in brain network changes due to sex. METHODS Resting state fMRI data included 37 normal controls (NC), 39 early MCI (EMCI) patients and 37 late MCI (LMCI) patients were collected, and network model based on graph theory was performed to compare the differences of brain network at different stages caused by sex from three aspects: functional connectivity between ROIs, intra-functional connectivity within RSN and inter-functional connectivity between RSN and white matter (WM). RESULTS Sex plays a role in the changes of RSN functional connectivity, including the default mode network (DMN), the sensory-motor network (SMN), the dorsal attention network (DAN) and the executive control network (CON). The female SMN is more vulnerable and the damage of functional connectivity between DAN and WM is more serious. CONCLUSIONS There are different lesion patterns in the RSN functional connections between males and females in the progression of MCI, which suggests that we should take full account of sex differences when conducting MCI progress studies and developing more effective biomarkers to promote the progress of cognitive impairment and dementia.
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Detecting cognitive impairment in HIV-infected individuals using mutual connectivity analysis of resting state functional MRI. J Neurovirol 2020; 26:188-200. [PMID: 31912459 DOI: 10.1007/s13365-019-00823-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 10/29/2019] [Accepted: 12/03/2019] [Indexed: 01/03/2023]
Abstract
It is estimated that more than 50% of the individuals affected with Human Immunodeficiency Virus (HIV) present deficits in multiple cognitive domains, collectively known as HIV-associated neurocognitive disorder (HAND). Early stages of brain injury may be clinically silent but potentially measurable via neuroimaging. A total of 40 subjects (20 HIV positive and 20 age-matched controls) volunteered for the study. All subjects underwent a standard battery of neuropsychological tests used for the clinical diagnosis of HAND. Fourteen HIV+ and five healthy subjects showed signs of neurological impairment. Connectivity was computed using mutual connectivity analysis (MCA) with generalized radial basis function neural network, a framework for quantifying non-linear connectivity as well as conventional correlation from 160 regional time-series that were extracted based on the Dosenbach (DOS) atlas. We subsequently applied graph theoretic as well as network analysis approaches for characterizing the connectivity matrices obtained and localizing between-group differences. We focused on trying to detect cognitive impairment using the subset of 29 (14 subjects with HAND and 15 cognitively normal controls) subjects. For the global analysis, significant differences (p < 0.05) were seen in the variance in degree, modularity and Smallworldness. Regional analysis revealed changes occurring mainly in portions of the lateral occipital cortex and the cingulate cortex. Furthermore, using Network Based Statistics (NBS), we uncovered an affected sub-network of 19 nodes comprising predominantly of regions of the default mode network. Similar analysis using the conventional correlation method revealed no significant results at a global scale, while regional analysis shows some differences spread across resting state networks. These results suggest that there is a subtle reorganization occurring in the topology of brain networks in HAND, which can be captured using improved connectivity analysis.
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Fan Y, Li Z, Duan X, Xiao J, Guo X, Han S, Guo J, Yang S, Li J, Cui Q, Liao W, Chen H. Impaired interactions among white-matter functional networks in antipsychotic-naive first-episode schizophrenia. Hum Brain Mapp 2020; 41:230-240. [PMID: 31571346 PMCID: PMC7267955 DOI: 10.1002/hbm.24801] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 08/27/2019] [Accepted: 09/09/2019] [Indexed: 12/18/2022] Open
Abstract
Schizophrenia has been conceptualized as a disorder arising from structurally pathological alterations to white-matter fibers in the brain. However, few studies have focused on white-matter functional changes in schizophrenia. Considering that converging evidence suggests that white-matter resting state functional MRI (rsfMRI) signals can effectively depict neuronal activity and psychopathological status, this study examined white-matter network-level interactions in antipsychotic-naive first-episode schizophrenia (FES) to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia. We recruited 42 FES patients (FESs) and 38 healthy controls (HCs), all of whom underwent rsfMRI. We identified 11 white-matter functional networks, which could be further classified into deep, middle, and superficial layers of networks. We then examined network-level interactions among these 11 white-matter functional networks using coefficient Granger causality analysis. We employed group comparisons on the influences among 11 networks using network-based statistic. Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in FESs compared with HCs. Additionally, an extra failure of suppression within superficial networks (including the frontoparietal network, temporofrontal network, and the orbitofrontal network) was observed in FESs. We additionally recruited an independent cohort (13 FESs and 13 HCs) from another center to examine the replicability of our findings across centers. Similar replication results further verified the white-matter functional network interaction model of schizophrenia. The novel findings of impaired interactions among white-matter functional networks in schizophrenia indicate that the pathophysiology of schizophrenia may also lie in white-matter functional abnormalities.
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Affiliation(s)
- Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Zehan Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- School of Life Science and Technology, Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
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Wang X, Wang R, Li F, Lin Q, Zhao X, Hu Z. Large-Scale Granger Causal Brain Network based on Resting-State fMRI data. Neuroscience 2019; 425:169-180. [PMID: 31794821 DOI: 10.1016/j.neuroscience.2019.11.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 01/09/2023]
Abstract
The causal connections among small-scale regions based on resting-state fMRI data have been extensively studied and a lot of achievements have been demonstrated. However, the causal connection among large-scale regions was seldom discussed. In this paper, we applied global Granger causality analysis to construct the causal connections in the whole-brain network among 103 healthy subjects (33 M/66F, ages 20-23) based on a resting-state fMRI dataset. We further explored four large-scale cognitive networks which have been widely known: central executive network (CEN), default mode network (DMN), dorsal attention network (DAN) and salience network (SN). These four cognitive networks are particularly important for understanding higher cognitive functions and dysfunction. Based on the above research, Out-In degree were introduced to identify the driving and driven hubs. Studying the driving and driven hub of brain network is of great significance for assessing the functional mechanism of the brain network. There were 817 directed edges identified as significant among the 8010 possible causal connections; seven driving hubs and ten driven hubs were identified in the whole-brain network. In CEN, dorsolateral prefrontal cortex (DlPFC) and superior parietal cortex (SPC) were the driven and driving hubs, respectively; in DMN, they were posterior cingulate cortex (PCC) and medial prefrontal cortex (MPFC); in DAN, they were frontal eye fields (FEF) and intraparietal sulcus (IPS); and in SN, they were frontoinsular cortex (FIC) and medial frontal cortex (MFC). These findings may provide insights into our understanding of human brain function mechanisms and the diagnosis of brain diseases.
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Affiliation(s)
- Xuewei Wang
- College of Science, Zhejiang University of Technology, Hangzhou, China
| | - Ru Wang
- College of Science, Zhejiang University of Technology, Hangzhou, China
| | - Fei Li
- College of Science, Zhejiang University of Technology, Hangzhou, China
| | - Qiang Lin
- College of Science, Zhejiang University of Technology, Hangzhou, China
| | - Xiaohu Zhao
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
| | - Zhenghui Hu
- College of Science, Zhejiang University of Technology, Hangzhou, China.
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Liu Z, Liu J, Yuan H, Liu T, Cui X, Tang Z, Du Y, Wang M, Lin Y, Tian J. Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 17:441-452. [PMID: 31786312 PMCID: PMC6943769 DOI: 10.1016/j.gpb.2019.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 08/09/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R = 0.81 and the mean absolute error (MAE) = 1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100080, China
| | - Jiangang Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China
| | - Huijuan Yuan
- Department of Endocrinology and Metabolism, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou 450003, China
| | - Taiyuan Liu
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou 450003, China
| | - Xingwei Cui
- Cooperative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou 450003, China
| | - Zhenchao Tang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai 264209, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou 450003, China.
| | - Yusong Lin
- Cooperative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou 450003, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100080, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710126, China.
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Jiao K, Xu H, Teng C, Song X, Xiao C, Fox PT, Zhang N, Wang C, Zhong Y. Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression. Behav Brain Res 2019; 379:112381. [PMID: 31770543 DOI: 10.1016/j.bbr.2019.112381] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 11/19/2019] [Accepted: 11/22/2019] [Indexed: 01/19/2023]
Abstract
BACKGROUND Cognitive dysfunctions, such as impaired cognitive control, are frequently observed in patients with major depressive disorder (MDD). Although the cognitive control network (CCN) is widely considered a core feature of major depressive disorder (MDD), the relationship between cognitive dysfunction and symptom dimensions remains unclear. This study investigated differences in resting-state functional connectivity of the cognitive control network (CCN) between first-episode medication-naive MDD patients and remitted MDD. METHODS We collected resting-state functional MRI (rs-fMRI) data from 22 first-episode medication-naive major depressive disorder (fMDD) patients, 20 patients previously diagnosed with MDD in the remitted phase of depression (rMDD), and 20 healthy controls (HC). The CCN was derived from fMRI images using independent component analysis (ICA), a data-driven image analysis method. RESULTS Changes in functional connectivity (FC) within the CCN was mainly attenuated in the right dorsolateral prefrontal cortex and the left inferior parietal lobule, while strengthened in the right dorsal anterior cingulate cortex and the right insula in both fMDD and rMDD groups. Compared with the fMDD group, the rMDD group had decreased FC in the bilateral insula and the right dorsolateral prefrontal cortex. Further analysis explored that the FC in the bilateral insula, the right dorsal anterior cingulate cortex and the right inferior parietal lobule were correlated positively cognitive disturbance factor scores in both patients groups. CONCLUSIONS These findings are in agreement with the previous findings that the cognitive control network are impaired in MDD. Furthermore, our results suggest that the alteration of CCN might underpin the cognitive disturbance and the distinct patterns of the CCN between fMDD and rMDD patients may be an important target for effective cognitive remediation in MDD.
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Affiliation(s)
- Kaili Jiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huazhen Xu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Changjun Teng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiu Song
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Peter T Fox
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; South Texas Veterans Healthcare System, University of Texas Health Science Center at San Antonio, United States; Research Imaging Institute, University of Texas Health San Antonio, United States
| | - Ning Zhang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, China.
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing, China.
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Han S, Wang Y, Liao W, Duan X, Guo J, Yu Y, Ye L, Li J, Chen X, Chen H. The distinguishing intrinsic brain circuitry in treatment-naïve first-episode schizophrenia: Ensemble learning classification. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.061] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Liao W, Fan YS, Yang S, Li J, Duan X, Cui Q, Chen H. Preservation Effect: Cigarette Smoking Acts on the Dynamic of Influences Among Unifying Neuropsychiatric Triple Networks in Schizophrenia. Schizophr Bull 2019; 45:1242-1250. [PMID: 30561724 PMCID: PMC6811814 DOI: 10.1093/schbul/sby184] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The high prevalence of cigarette smoking in schizophrenia (SZ) is generally explained by the self-medication theory. However, its neurobiological mechanism remains unclear. The impaired dynamic of influences among unifying neuropsychiatric triple networks in SZ, including the central executive network (CEN), the default mode network (DMN), and the salience network (SN), might explain the nature of their syndromes, whereas smoking could regulate the dynamics within networks. Therefore, this study examined whether cigarette smoking could elicit a distinct improvement in the dynamics of triple networks in SZ and associated with the alleviation of symptoms. METHODS Four groups were recruited, namely, SZ smoking (n = 22)/nonsmoking (n = 25), and healthy controls smoking (n = 22)/nonsmoking (n = 21). All participants underwent a resting-state functional magnetic resonance imaging (fMRI). The dynamics among unifying neuropsychiatric triple networks were measured using Granger causality analysis on the resting-sate fMRI signal. Interaction effects between SZ and smoking on dynamics were detected using 2-way analysis of covariance, correcting for sex, age, and education level. RESULTS Whereas smoking reduced SN→DMN dynamic in healthy controls, it preserved the dynamic in SZ, thus suggesting a preservation effect. Moreover, smoking additionally increased DMN→CEN dynamic in SZ. CONCLUSIONS This finding from neural pathways shed new insights into the prevailing self-medication hypothesis in SZ. More broadly, this study elaborates on the neurobiological dynamics that may assist in the treatment of the symptomatology of SZ.
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Affiliation(s)
- Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
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Kronman CA, Kern KL, Nauer RK, Dunne MF, Storer TW, Schon K. Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults. Hippocampus 2019; 30:526-541. [PMID: 31647603 DOI: 10.1002/hipo.23169] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 07/27/2019] [Accepted: 09/17/2019] [Indexed: 01/17/2023]
Abstract
Rodent and human studies examining the relationship between aerobic exercise, brain structure, and brain function indicate that the hippocampus (HC), a brain region critical for episodic memory, demonstrates striking plasticity in response to exercise. Beyond the hippocampal memory system, human studies also indicate that aerobic exercise and cardiorespiratory fitness (CRF) are associated with individual differences in large-scale brain networks responsible for broad cognitive domains. Examining network activity in large-scale resting-state brain networks may provide a link connecting the observed relationships between aerobic exercise, hippocampal plasticity, and cognitive enhancement within broad cognitive domains. Previously, CRF has been associated with increased functional connectivity of the default mode network (DMN), specifically in older adults. However, how CRF relates to the magnitude and directionality of connectivity, or effective connectivity, between the HC and other DMN nodes remains unknown. We used resting-state fMRI and conditional Granger causality analysis (CGCA) to test the hypothesis that CRF positively predicts effective connectivity between the HC and other DMN nodes in healthy young adults. Twenty-six participants (ages 18-35 years) underwent a treadmill test to determine CRF by estimating its primary determinant, maximal oxygen uptake (V. O2max ), and a 10-min resting-state fMRI scan to examine DMN effective connectivity. We identified the DMN using group independent component analysis and examined effective connectivity between nodes using CGCA. Linear regression analyses demonstrated that CRF significantly predicts causal influence from the HC to the ventromedial prefrontal cortex, posterior cingulate cortex, and lateral temporal cortex and to the HC from the dorsomedial prefrontal cortex. The observed relationship between CRF and hippocampal effective connectivity provides a link between the rodent literature, which demonstrates a relationship between aerobic exercise and hippocampal plasticity, and the human literature, which demonstrates a relationship between aerobic exercise and CRF and the enhancement of broad cognitive domains including, but not limited to, memory.
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Affiliation(s)
- Corey A Kronman
- Graduate Medical Sciences, Boston University School of Medicine, Boston, Massachusetts
| | - Kathryn L Kern
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts
| | - Rachel K Nauer
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts.,Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.,Center for Memory and Brain, Boston University, Boston, Massachusetts.,Center for Systems Neuroscience, Boston University, Boston, Massachusetts
| | - Matthew F Dunne
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts.,Center for Systems Neuroscience, Boston University, Boston, Massachusetts
| | - Thomas W Storer
- Men's Health, Aging, and Metabolism Unit, Brigham and Women's Hospital, Boston, Massachusetts
| | - Karin Schon
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts.,Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.,Center for Memory and Brain, Boston University, Boston, Massachusetts.,Center for Systems Neuroscience, Boston University, Boston, Massachusetts
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Ding JR, Zhu F, Hua B, Xiong X, Wen Y, Ding Z, Thompson PM. Presurgical localization and spatial shift of resting state networks in patients with brain metastases. Brain Imaging Behav 2019; 13:408-420. [PMID: 29611075 DOI: 10.1007/s11682-018-9864-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.
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Affiliation(s)
- Ju-Rong Ding
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China. .,Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
| | - Fangmei Zhu
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, People's Republic of China
| | - Bo Hua
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Xingzhong Xiong
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Yuqiao Wen
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, People's Republic of China.
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
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Abstract
Statistics plays three important roles in brain studies. They are (1) the study of differences between brains in distinctive populations; (2) the study of the variability in the structure and functioning of the brain; and (3) the study of data reduction on large-scale brain data. I discuss these concepts using examples from past and ongoing research in brain connectivity, brain information flow, information extraction from large-scale neuroimaging data, and neural predictive modeling. Having dispensed with the past, I attempt to present a few areas where statistical science facilitates brain decoding and to write prospectively, in the light of present knowledge and in the quest for artificial intelligence, about questions that statistical and neurobiological communities could work closely together to address in the future.
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Abstract
Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and through simulations, the present paper proves that genetic algorithms initialized with classical solutions are able to easily break the symmetry of random search and progress towards specific modeling.
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Li S, Hu N, Zhang W, Tao B, Dai J, Gong Y, Tan Y, Cai D, Lui S. Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity. Front Psychiatry 2019; 10:482. [PMID: 31354545 PMCID: PMC6639431 DOI: 10.3389/fpsyt.2019.00482] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 06/19/2019] [Indexed: 02/05/2023] Open
Abstract
Background: Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. Methods: We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Results: Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Conclusions: Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.
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Affiliation(s)
- Siyi Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Tao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Dai
- Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China
| | - Yao Gong
- Department of Geriatric Psychiatry, The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Youguo Tan
- Department of Psychiatry, Zigong Mental Health Center, Zigong, China
| | - Duanfang Cai
- Department of Psychiatry, Zigong Mental Health Center, Zigong, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Klugah-Brown B, Luo C, Peng R, He H, Li J, Dong L, Yao D. Altered structural and causal connectivity in frontal lobe epilepsy. BMC Neurol 2019; 19:70. [PMID: 31023252 PMCID: PMC6485093 DOI: 10.1186/s12883-019-1300-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 04/11/2019] [Indexed: 01/09/2023] Open
Abstract
Background Albeit the few resting-state fMRI neuroimaging studies in frontal lobe epilepsy (FLE) patients, these studies focused on functional connectivity. The aim of this current study was to examine the effective connectivity based on voxel-based morphometry in FLE patients. Methods Resting-state structural and functional magnetic resonance imaging (fMRI) data were acquired from 19 FLE patients and 19 age and gender-matched healthy controls using the 3.0 Tesla magnetic resonance imaging (3.0 T MRI). The investigations were done by acquiring the structural information through voxel-based morphometry, then based on the seed obtained, Granger causality analysis was used to evaluate the causal flow of the designated seed to and from other significant voxels. Results Our results showed altered structural and effective connectivity. Compared with healthy controls, FLE patients showed reduced grey matter volume in bilateral putamen and right caudate as well as altered causality with increased, and decreased causal outflow from the right caudate (seed region) to inferior frontal gyrus-triangular, from bilateral putamen (seed regions) to right middle frontal gyrus and frontal gyrus medial-orbital representing the frontal executive areas, respectively. Also, significantly increased and decreased inflow from left calcarine to right caudate and from cerebellum_6 and vermis_6 to bilateral putamen, respectively. Moreover, we found that the causal alterations to and from the seed regions (from vermis_6 to right putamen and from left putamen to right middle frontal gyrus) negatively correlated with clinical scores (duration of epilepsy). Conclusions The findings point to the impairment within the executive and motor-controlled system including the cerebellum, frontal, caudate and putamen regions in FLE patients. These results would therefore enhance our understanding of structural and effective mechanisms in FLE.
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Affiliation(s)
- Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China.
| | - Rui Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, People's Republic of China
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Chén OY, Cao H, Reinen JM, Qian T, Gou J, Phan H, De Vos M, Cannon TD. Resting-state brain information flow predicts cognitive flexibility in humans. Sci Rep 2019; 9:3879. [PMID: 30846746 PMCID: PMC6406001 DOI: 10.1038/s41598-019-40345-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/07/2019] [Indexed: 11/25/2022] Open
Abstract
The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain's directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.
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Affiliation(s)
- Oliver Y Chén
- Department of Psychology, Yale University, New Haven, CT, USA.
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Hengyi Cao
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jenna M Reinen
- Department of Psychology, Yale University, New Haven, CT, USA
- IBM Watson Research, New York, NY, USA
| | - Tianchen Qian
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics, The City University of New York, New York, NY, USA
- Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Huy Phan
- Department of Engineering Science, University of Oxford, Oxford, UK
- School of Computing, University of Kent, Canterbury, UK
| | - Maarten De Vos
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
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Ding JR, Ding X, Hua B, Xiong X, Wen Y, Ding Z, Wang Q, Thompson P. Altered connectivity patterns among resting state networks in patients with ischemic white matter lesions. Brain Imaging Behav 2018; 12:1239-1250. [PMID: 29134612 PMCID: PMC6290724 DOI: 10.1007/s11682-017-9793-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
White matter lesions (WMLs) have been associated with cognitive and motor decline. Resting state networks (RSNs) are spatially coherent patterns in the human brain and their interactions sustain our daily function. Therefore, investigating the altered intra- and inter-network connectivity among the RSNs may help to understand the association of WMLs with impaired cognitive and motor function. Here, we assessed alterations in functional connectivity patterns based on six well-defined RSNs-the default mode network (DMN), dorsal attention network (DAN), frontal-parietal control network (FPCN), auditory network (AN), sensory motor network (SMN) and visual network (VN)-in 15 patients with ischemic WMLs and 15 controls. In the patients, Spearman's correlation analysis was further performed between these alterations and cognitive test scores, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Our results showed wide alterations of inter-network connectivity mainly involving the SMN, DMN, FPCN and DAN, and some alterations correlated with cognitive test scores in the patients. The reduced functional connectivities in the SMN-AN, SMN-VN, FPCN-AN, DAN-VN pairs may account for the cognitive and motor decline in patients with ischemic WMLs, while the increased functional connectivities in the DMN-AN, DMN-FPCN and DAN-FPCN pairs may reflect a functional network reorganization after damage to white matter. It is unexpected that altered intra-network connectivities were found within the AN and VN, which may explain the impairments in verbal fluency and information retrieval associated with WMLs. This study highlights the importance of functional connectivity in understanding how WMLs influence cognitive and behavior dysfunction.
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Affiliation(s)
- Ju-Rong Ding
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, China.
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
| | - Xin Ding
- Department of Neurology, Chengdu Military General Hospital, Chengdu, China
| | - Bo Hua
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Xingzhong Xiong
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Yuqiao Wen
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Qingsong Wang
- Department of Neurology, Chengdu Military General Hospital, Chengdu, China
| | - Paul Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
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Bi XA, Sun Q, Zhao J, Xu Q, Wang L. Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment. Front Neurosci 2018; 12:413. [PMID: 29970984 PMCID: PMC6018085 DOI: 10.3389/fnins.2018.00413] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 05/30/2018] [Indexed: 01/02/2023] Open
Abstract
Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Junxia Zhao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Liqin Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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Ning Y, Zheng R, Li K, Zhang Y, Lyu D, Jia H, Ren Y, Zou Y. The altered Granger causality connection among pain-related brain networks in migraine. Medicine (Baltimore) 2018; 97:e0102. [PMID: 29517685 PMCID: PMC5882438 DOI: 10.1097/md.0000000000010102] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Numerous fMRI studies have confirmed functional abnormalities in resting-state brain networks in migraine patients. However, few studies focusing on causal relationships of pain-related brain networks in migraine have been conducted. This study aims to explore the difference of Granger causality connection among pain-related brain networks in migraine without aura (MWoA) patients.Twenty two MWoA patients and 17 matched healthy subjects were recruited to undergo resting-state fMRI scanning. Independent component analysis was used to extract pain-related brain networks, and Granger causality analysis to characterize the difference of Granger causality connection among pain-related brain networks was employed.Seven pain-related brain networks were identified, and MwoA patients showed more complex Granger causality connections in comparison with healthy subjects. Two-sample t test results displayed that there was the significant difference between right-frontoparietal network (RFPN) and executive control network (ECN).This study indicates that the specific intrinsic brain Granger causality connectivity among pain-related networks in MwoA patients are affected after long-term migraine attacks.
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Affiliation(s)
- Yanzhe Ning
- Department of Neurology and Stroke Center, Dongzhimen Hospital, the First Affiliated Hospital of Beijing University of Chinese Medicine
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University
| | - Ruwen Zheng
- Department of Acupuncture and Moxibustion, Dongfang Hospital, The Second Affiliated Hospital of Beijing University of Chinese Medicine
| | - Kuangshi Li
- Department of Internal Medicine, Gulou Hospital of Traditional Chinese Medicine of Beijing
| | - Yong Zhang
- Department of Neurology and Stroke Center, Dongzhimen Hospital, the First Affiliated Hospital of Beijing University of Chinese Medicine
| | - Diyang Lyu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University
| | - Hongxiao Jia
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University
| | - Yi Ren
- Department of Neurology and Stroke Center, Dongzhimen Hospital, the First Affiliated Hospital of Beijing University of Chinese Medicine
| | - Yihuai Zou
- Department of Neurology and Stroke Center, Dongzhimen Hospital, the First Affiliated Hospital of Beijing University of Chinese Medicine
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