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Yang J, Xiao R, Liu Y, He C, Han L, Xu X, Chen M, Zhong J. Spatiotemporal consistency analysis of cerebral small vessel disease: an rs-fMRI study. Front Neurosci 2024; 18:1385960. [PMID: 38841094 PMCID: PMC11150806 DOI: 10.3389/fnins.2024.1385960] [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: 02/14/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Introduction Cerebral small vessel disease (SVD) affects older adults, but traditional approaches have limited the understanding of the neural mechanisms of SVD. This study aimed to explore the effects of SVD on brain regions and its association with cognitive decline using the four-dimensional (spatiotemporal) consistency of local neural activity (FOCA) method. Methods Magnetic resonance imaging data from 42 patients with SVD and 38 healthy controls (HCs) were analyzed using the FOCA values. A two-sample t test was performed to compare the differences in FOCA values in the brain between the HCs and SVD groups. Pearson correlation analysis was conducted to analyze the association of various brain regions with SVD scores. Results The results revealed that the FOCA values in the right frontal_inf_oper, right temporal_pole_sup, and default mode network decreased, whereas those in the temporal_inf, hippocampus, basal ganglia, and cerebellum increased, in patients with SVD. Most of these varying brain regions were negatively correlated with SVD scores. Discussion This study suggested that the FOCA approach might have the potential to provide useful insights into the understanding of the neurophysiologic mechanisms of patients with SVD.
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Affiliation(s)
- Jie Yang
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
| | - Rui Xiao
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
| | - Yujian Liu
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
- Sichuan Vocational College of Health and Rehabilitation, Zigong, China
| | - Chaoliang He
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
| | - Limei Han
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
- North Sichuan Medical College, Nanchong, China
| | - Xiaoya Xu
- Department of Neurology, Zigong First People’s Hospital, Zigong, China
| | - Meining Chen
- MR Research and Collaboration, Siemens Healthineers, Shanghai, China
| | - Jianquan Zhong
- Department of Radiology, Zigong First People’s Hospital, Zigong, China
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Jiang S, Pei H, Chen J, Li H, Liu Z, Wang Y, Gong J, Wang S, Li Q, Duan M, Calhoun VD, Yao D, Luo C. Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges. Int J Neural Syst 2024; 34:2450017. [PMID: 38372049 DOI: 10.1142/s0129065724500175] [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] [Indexed: 02/20/2024]
Abstract
Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Junxia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Zetao Liu
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yuehan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Jinnan Gong
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Computer Science, Chengdu University of Information Technology, Chengdu, P. R. China
| | - Sheng Wang
- Department of Neurology, Hainan Medical University, Hainan 571199, P. R. China
| | - Qifu Li
- Department of Neurology, Hainan Medical University, Hainan 571199, P. R. China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
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Jiang S, Wang Y, Pei H, Li H, Chen J, Yao Y, Li Q, Yao D, Luo C. Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures. CNS Neurosci Ther 2024; 30:e14672. [PMID: 38644561 PMCID: PMC11033329 DOI: 10.1111/cns.14672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 02/08/2024] [Accepted: 02/17/2024] [Indexed: 04/23/2024] Open
Abstract
AIMS Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism. METHODS Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups. RESULTS All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients. CONCLUSION Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
- Research Unit of NeuroInformationChinese Academy of Medical SciencesChengduP. R. China
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceCenter for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduP. R. China
| | - Yuehan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
| | - Junxia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
| | - Yutong Yao
- Department of NeurosurgeySichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduP. R. China
| | - Qifu Li
- Department of NeurologyHainan Medical UniversityHainanP. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
- Research Unit of NeuroInformationChinese Academy of Medical SciencesChengduP. R. China
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceCenter for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduP. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduP. R. China
- Research Unit of NeuroInformationChinese Academy of Medical SciencesChengduP. R. China
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceCenter for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduP. R. China
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Chen J, Jiang S, Lu B, Liao J, Yang Z, Li H, Pei H, Li J, Iturria-Medina Y, Yao D, Luo C. The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello-cerebral functional integration. Hum Brain Mapp 2024; 45:e26551. [PMID: 38063289 PMCID: PMC10789200 DOI: 10.1002/hbm.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/12/2023] [Accepted: 11/16/2023] [Indexed: 01/16/2024] Open
Abstract
The interaction between cerebellum and cerebrum participates widely in function from motor processing to high-level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello-cerebral circuit is still poorly understood. Resting-state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel-based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello-cerebral functional integration on the voxel-based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello-cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.
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Affiliation(s)
- Junxia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Bao Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Jiangyan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Zhihuan Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, P. R. China
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [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: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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Wang Y, Qin Y, Li H, Yao D, Sun B, Gong J, Dai Y, Wen C, Zhang L, Zhang C, Luo C, Zhu T. Acupuncture modulates the functional connectivity among the subcortical nucleus and fronto-parietal network in adolescents with internet addiction. Brain Behav 2023; 13:e3241. [PMID: 37721727 PMCID: PMC10636388 DOI: 10.1002/brb3.3241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Internet addiction (IA), recognized as a behavioral addiction, is emerging as a global public health problem. Acupuncture has been demonstrated to be effective in alleviating IA; however, the mechanism is not yet clear. To fill this knowledge gap, our study aimed to investigate the modulatory effects of acupuncture on the functional interactions among the addiction-related networks in adolescents with IA. METHODS Thirty individuals with IA and thirty age- and sex-matched healthy control subjects (HCs) were recruited. Subjects with IA were given a 40-day acupuncture treatment, and resting-state functional magnetic resonance imaging (fMRI) data were collected before and after acupuncture sessions. HCs received no treatment and underwent one fMRI scan after enrollment. The intergroup differences in functional connectivity (FC) among the subcortical nucleus (SN) and fronto-parietal network (FPN) were compared between HCs and subjects with IA at baseline. Then, the intragroup FC differences between the pre- and post-treatment were analyzed in the IA group. A multiple linear regression model was further employed to fit the FC changes to symptom relief in the IA group. RESULTS In comparison to HCs, subjects with IA exhibited significantly heightened FC within and between the SN and FPN at baseline. After 40 days of acupuncture treatment, the FC within the FPN and between the SN and FPN were significantly decreased in individuals with IA. Symptom improvement in subjects with IA was well fitted by the decrease in FC between the left midbrain and ventral prefrontal cortex and between the left thalamus and ventral anterior prefrontal cortex. CONCLUSION These findings confirmed the modulatory effects of acupuncture on the aberrant functional interactions among the SN and FPN, which may partly reflect the neurophysiological mechanism of acupuncture for IA.
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Affiliation(s)
- Yang Wang
- School of Sports Medicine and HealthChengdu Sport UniversityChengduChina
- Postdoctoral Workstation, Affiliated Sport Hospital of Chengdu Sport UniversityChengduChina
- School of Rehabilitation and Health PreservationChengdu University of TCMChengduChina
- College of Traditional Chinese MedicineChongqing Medical UniversityShapingbaChina
| | - Yun Qin
- Key Laboratory for NeuroInformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hui Li
- School of MedicineChengdu UniversityChengduChina
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Bo Sun
- Key Laboratory for NeuroInformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jinnan Gong
- Key Laboratory for NeuroInformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Computer ScienceChengdu University of Information TechnologyChengduChina
| | - Yu Dai
- Department of Chinese MedicineChengdu Eighth People's HospitalChengduChina
| | - Chao Wen
- Department of RehabilitationZigong Fifth People's HospitalZigongChina
| | - Lingrui Zhang
- Department of MedicineLeshan Vocational and Technical CollegeLeshanChina
| | - Chenchen Zhang
- Department of RehabilitationTCM Hospital of Longquanyi DistrictChengduChina
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformationChinese Academy of Medical SciencesBeijingChina
| | - Tianmin Zhu
- School of Rehabilitation and Health PreservationChengdu University of TCMChengduChina
- Library, Chengdu University of TCMChengduChina
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Li H, Shi H, Jiang S, Hou C, Wu H, Yao G, Yao D, Luo C. Atypical Hierarchical Connectivity Revealed by Stepwise Functional Connectivity in Aging. Bioengineering (Basel) 2023; 10:1166. [PMID: 37892896 PMCID: PMC10604600 DOI: 10.3390/bioengineering10101166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/18/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Hierarchical functional structure plays a crucial role in brain function. We aimed to investigate how aging affects hierarchical functional structure and to evaluate the relationship between such effects and molecular, microvascular, and cognitive features. We used resting-state functional magnetic resonance imaging (fMRI) data from 95 older adults (66.94 ± 7.23 years) and 44 younger adults (21.8 ± 2.53 years) and employed an innovative graph-theory-based analysis (stepwise functional connectivity (SFC)) to reveal the effects of aging on hierarchical functional structure in the brain. In the older group, an SFC pattern converged on the primary sensory-motor network (PSN) rather than the default mode network (DMN). Moreover, SFC decreased in the DMN and increased in the PSN at longer link-steps in aging, indicating a reconfiguration of brain hub systems during aging. Subsequent correlation analyses were performed between SFC values and molecular, microvascular features, and behavioral performance. Altered SFC patterns were associated with dopamine and serotonin, suggesting that altered hierarchical functional structure in aging is linked to the molecular fundament with dopamine and serotonin. Furthermore, increased SFC in the PSN, decreased SFC in the DMN, and accelerated convergence rate were all linked to poorer microvascular features and lower executive function. Finally, a mediation analysis among SFC features, microvascular features, and behavioral performance indicated that the microvascular state may influence executive function through SFC features, highlighting the interactive effects of SFC features and microvascular state on cognition.
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Affiliation(s)
- Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- 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, Chengdu 610054, China
| | - Hongru Shi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- 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, Chengdu 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
| | - Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- 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, Chengdu 610054, China
| | - Hanxi Wu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- 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, Chengdu 610054, China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.); (H.S.); (S.J.); (C.H.); (H.W.); (D.Y.)
- 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, Chengdu 610054, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
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Wang Z, Yang J, Zheng Z, Cao W, Dong L, Li H, Wen X, Luo C, Cai Q, Jian W, Yao D. Trait- and State-Dependent Changes in Cortical-Subcortical Functional Networks Across the Adult Lifespan. J Magn Reson Imaging 2023; 58:720-731. [PMID: 36637029 DOI: 10.1002/jmri.28599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND How the functional interactions of the basal ganglia/thalamus with the cerebral cortex and the cerebellum change over the adult lifespan in movie-watching and resting-state is less clear. PURPOSE To investigate the functional changes in the organization of the human cortical-subcortical functional networks over the adult lifespan using movie-watching and resting-state fMRI data. STUDY TYPE Cohort. SUBJECTS Healthy 467 adults (cross-sectional individuals aged 18-88 years) from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com). FIELD STRENGTH/SEQUENCE: fMRI using a gradient-echo echo-planar imaging (EPI) sequence at 3 T. ASSESSMENT Functional connectivities (FCs) of the subcortical subregions (i.e. the basal ganglia and thalamus) with both the cerebral cortex and cerebellum were examined in fMRI data acquired during resting state and movie-watching. And, fluid intelligence scores were also assessed. STATISTICAL TESTS Student's t-tests, false discovery rate (FDR) corrected. RESULTS As age increased, FCs that mainly within the basal ganglia and thalamus, and between the basal ganglia/thalamus and cortical networks (including the dorsal attention, ventral attention, and limbic networks) were both increased/decreased during movie-watching and resting states. However, FCs showed a state-dependent component with advancing age. During the movie-watching state, the FCs between the basal ganglia/thalamus and cerebellum/frontoparietal control networks were mainly increased with age, and the FCs in the somatomotor network were decreased with age. During the resting state, the FCs between the basal ganglia/thalamus and default mode/visual networks were mainly increased with age, and the FCs in the cerebellum were mainly decreased with age. Moreover, inverse relationships between FCs and fluid intelligence were mainly found in these network regions. DATA CONCLUSION Our study may suggest that changes in cortical-subcortical functional networks across the adult lifespan were both state-dependent and stable traits, and that aging fMRI studies should consider the effects of both physiological characteristics and individual situations. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 3.
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Affiliation(s)
- Ziqi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weifang Cao
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Wen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qingyan Cai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Jian
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
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9
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Pei H, Ma S, Yan W, Liu Z, Wang Y, Yang Z, Li Q, Yao D, Jiang S, Luo C, Yu L. Functional and structural networks decoupling in generalized tonic-clonic seizures and its reorganization by drugs. Epilepsia Open 2023; 8:1038-1048. [PMID: 37394869 PMCID: PMC10472403 DOI: 10.1002/epi4.12781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVE To investigate potential functional and structural large-scale network disturbances in untreated patients with generalized tonic-clonic seizures (GTCS) and the effects of antiseizure drugs. METHODS In this study, 41 patients with GTCS, comprising 21 untreated patients and 20 patients who received antiseizure medications (ASMs), and 29 healthy controls were recruited to construct large-scale brain networks based on resting-state functional magnetic resonance imaging and diffusion tensor imaging. Structural and functional connectivity and network-level weighted correlation probability (NWCP) were further investigated to identify network features that corresponded to response to ASMs. RESULTS Untreated patients showed more extensive enhancement of functional and structural connections than controls. Specifically, we observed abnormally enhanced connections between the default mode network (DMN) and the frontal-parietal network. In addition, treated patients showed similar functional connection strength to that of the control group. However, all patients exhibited similar structural network alterations. Moreover, the NWCP value was lower for connections within the DMN and between the DMN and other networks in the untreated patients; receiving ASMs could reverse this pattern. SIGNIFICANCE Our study identified alterations in structural and functional connectivity in patients with GTCS. The influence of ASMs may be more noticeable within the functional network; moreover, abnormalities in both the functional and structural coupling state may be improved by ASM treatment. Therefore, the coupling state of structural and functional connectivity may be used as an indicator of the efficacy of ASMs.
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Affiliation(s)
- Haonan Pei
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Shuai Ma
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- Neurology DepartmentSichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, The Affiliated Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Yan
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Zetao Liu
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Yuehan Wang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Zhihuan Yang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Qifu Li
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouChina
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Yu
- Neurology DepartmentSichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, The Affiliated Hospital of University of Electronic Science and Technology of ChinaChengduChina
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10
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Jiang S, Huang H, Zhou J, Li H, Duan M, Yao D, Luo C. Progressive trajectories of schizophrenia across symptoms, genes, and the brain. BMC Med 2023; 21:237. [PMID: 37400838 DOI: 10.1186/s12916-023-02935-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 06/12/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Schizophrenia is characterized by complex psychiatric symptoms and unclear pathological mechanisms. Most previous studies have focused on the morphological changes that occur over the development of the disease; however, the corresponding functional trajectories remain unclear. In the present study, we aimed to explore the progressive trajectories of patterns of dysfunction after diagnosis. METHODS Eighty-six patients with schizophrenia and 120 healthy controls were recruited as the discovery dataset. Based on multiple functional indicators of resting-state brain functional magnetic resonance imaging, we conducted a duration-sliding dynamic analysis framework to investigate trajectories in association with disease progression. Neuroimaging findings were associated with clinical symptoms and gene expression data from the Allen Human Brain Atlas database. A replication cohort of patients with schizophrenia from the University of California, Los Angeles, was used as the replication dataset for the validation analysis. RESULTS Five stage-specific phenotypes were identified. A symptom trajectory was characterized by positive-dominated, negative ascendant, negative-dominated, positive ascendant, and negative surpassed stages. Dysfunctional trajectories from primary and subcortical regions to higher-order cortices were recognized; these are associated with abnormal external sensory gating and a disrupted internal excitation-inhibition equilibrium. From stage 1 to stage 5, the importance of neuroimaging features associated with behaviors gradually shifted from primary to higher-order cortices and subcortical regions. Genetic enrichment analysis identified that neurodevelopmental and neurodegenerative factors may be relevant as schizophrenia progresses and highlighted multiple synaptic systems. CONCLUSIONS Our convergent results indicate that progressive symptoms and functional neuroimaging phenotypes are associated with genetic factors in schizophrenia. Furthermore, the identification of functional trajectories complements previous findings of structural abnormalities and provides potential targets for drug and non-drug interventions in different stages of schizophrenia.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Jingyu Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Mingjun Duan
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, 611731, Chengdu, Sichuan, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, 611731, Chengdu, Sichuan, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, 611731, Chengdu, Sichuan, People's Republic of China.
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11
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Dong L, Lai Y, Duan M, Qin Y, Luo C, Wang L, Wang Y, Cai X, Huang P, Cui H, Yao D. Rereferencing of clinical EEGs with nonunipolar mastoid reference to infinity reference by REST. Clin Neurophysiol 2023; 151:1-9. [PMID: 37116379 DOI: 10.1016/j.clinph.2023.03.361] [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: 09/09/2022] [Revised: 03/07/2023] [Accepted: 03/30/2023] [Indexed: 04/30/2023]
Abstract
OBJECTIVE Conventional electroencephalography (EEG) offline subtraction rereferencing is invalid for many clinical practices when adopting a specific nonunipolar recording montage (e.g., the ipsilateral mastoid (IM) and contralateral mastoid (CM)). Further comparative analyses would thus be blocked due to the lack of a uniform offline reference. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform nonunipolar mastoid montages into a computational zero reference at infinity (IR) offline. METHODS For EEG signals and power/connectivity configurations, simulation and clinical schizophrenia resting-state EEG datasets were used to investigate the performance of REST. RESULTS REST produced small absolute errors (signal level: 1.21-1.26; power: 0.0057-0.021; connectivity: 0.066-0.088) and high correlations (>0.9) between the IM/CM-IR and true IR references. Using clinical data with the IM online reference, REST revealed valuable changes in spectral and connectivity (P < 0.05) in schizophrenia patients, consistent with previous studies. CONCLUSIONS These results demonstrated that REST transformation could be adopted to resolve the offline rereferencing of clinical EEGs with specific nonunipolar mastoid references. SIGNIFICANCE REST could be an effective and robust resolution for nonunipolar clinical EEGs and could therefore retrieve these data for further analysis by deriving a favorable offline reference IR.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | - Yongxiu Lai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | - Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | - Liping Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Yongchao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Xiyu Cai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Pan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Huizhen Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China.
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12
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Hou C, Jiang S, Liu M, Li H, Zhang L, Duan M, Yao G, He H, Yao D, Luo C. Spatiotemporal dynamics of functional connectivity and association with molecular architecture in schizophrenia. Cereb Cortex 2023:7179746. [PMID: 37231204 DOI: 10.1093/cercor/bhad185] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/27/2023] Open
Abstract
Schizophrenia is a self-disorder characterized by disrupted brain dynamics and architectures of multiple molecules. This study aims to explore spatiotemporal dynamics and its association with psychiatric symptoms. Resting-state functional magnetic resonance imaging data were collected from 98 patients with schizophrenia. Brain dynamics included the temporal and spatial variations in functional connectivity density and association with symptom scores were evaluated. Moreover, the spatial association between dynamics and receptors/transporters according to prior molecular imaging in healthy subjects was examined. Patients demonstrated decreased temporal variation and increased spatial variation in perceptual and attentional systems. However, increased temporal variation and decreased spatial variation were revealed in higher order networks and subcortical networks in patients. Specifically, spatial variation in perceptual and attentional systems was associated with symptom severity. Moreover, case-control differences were associated with dopamine, serotonin and mu-opioid receptor densities, serotonin reuptake transporter density, dopamine transporter density, and dopamine synthesis capacity. Therefore, this study implicates the abnormal dynamic interactions between the perceptual system and cortical core networks; in addition, the subcortical regions play a role in the dynamic interaction among the cortical regions in schizophrenia. These convergent findings support the importance of brain dynamics and emphasize the contribution of primary information processing to the pathological mechanism underlying schizophrenia.
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Affiliation(s)
- Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Mei Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Lang Zhang
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
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13
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Zhou J, Guo X, Liu X, Luo Y, Chang X, He H, Duan M, Li S, Li Q, Tan Y, Yao G, Yao D, Luo C. Intrinsic Therapeutic Link between Recuperative Cerebellar Con-Nectivity and Psychiatry Symptom in Schizophrenia Patients with Comorbidity of Metabolic Syndrome. LIFE (BASEL, SWITZERLAND) 2023; 13:life13010144. [PMID: 36676092 PMCID: PMC9863013 DOI: 10.3390/life13010144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 01/06/2023]
Abstract
Components of metabolic syndrome might be predictors of the therapeutic outcome of psychiatric symptom in schizophrenia, whereas clinical results are inconsistent and an intrinsic therapeutic link between weaker psychiatric symptoms and emergent metabolic syndrome remains unclear. This study aims to reveal the relationship and illustrate potential mechanism by exploring the alteration of cerebellar functional connectivity (FC) in schizophrenia patients with comorbidity metabolic syndrome. Thirty-six schizophrenia patients with comorbidity of metabolic syndrome (SCZ-MetS), 45 schizophrenia patients without metabolic syndrome (SCZ-nMetS) and 39 healthy controls (HC) were recruited in this study. We constructed FC map of cerebello-cortical circuit and used moderation effect analysis to reveal complicated relationship among FC, psychiatric symptom and metabolic disturbance. Components of metabolic syndrome were significantly correlated with positive symptom score and negative symptom score. Importantly, the dysconnectivity between cognitive module of cerebellum and left middle frontal gyrus in SCZ-nMetS was recuperative increased in SCZ-MetS, and was significantly correlated with general symptom score. Finally, we observed significant moderation effect of body mass index on this correlation. The present findings further supported the potential relationship between emergence of metabolic syndrome and weaker psychiatric symptom, and provided neuroimaging evidence. The mechanism of intrinsic therapeutic link involved functional change of cerebello-cortical circuit.
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Affiliation(s)
- Jingyu Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Xiao Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Xiaoli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Yuling Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Shicai Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Qifu Li
- Department of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou 570102, China
| | - Ying Tan
- The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610093, China
- Research Unit of Neuroinformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu 610072, China
- Correspondence: (Y.T.); (G.Y.); (C.L.)
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610056, China
- Correspondence: (Y.T.); (G.Y.); (C.L.)
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Department of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou 570102, China
- Research Unit of Neuroinformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- Research Unit of Neuroinformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu 610072, China
- Correspondence: (Y.T.); (G.Y.); (C.L.)
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14
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Zhang Y, Cai X, Duan M, He H. The influence of high worry on static and dynamic insular functional connectivity. Front Neurosci 2023; 17:1062947. [PMID: 37025377 PMCID: PMC10070698 DOI: 10.3389/fnins.2023.1062947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/23/2023] [Indexed: 04/08/2023] Open
Abstract
Worry is a form of repetitive negative thought. High worry-proneness is one risk factor leading to anxiety disorder. Several types of research indicated that anxiety disorder was highly associated with disrupted interoception. The insula is consistently considered to play a key role in interoception. However, the relationship between worry and the interoception network is poorly investigated in worry-prone individuals. Thus, it is essential to identify the neural characteristic of high worry-proneness subjects. A total of 32 high worry-proneness (HWP) subjects and 25 low worry-proneness (LWP) subjects were recruited and underwent magnetic resonance imaging scanning. Six subregions of insula were chosen as regions of interest. Then, seed-based static and dynamic functional connectivity were calculated. Increased static functional connectivity was observed between the ventral anterior insula and inferior parietal lobule in HWP compared to LWP. Decreased static functional connectivity was found between the left ventral anterior insula and the pregenual anterior cingulate cortex. Decreased dynamic functional connectivity was also shown between the right posterior insula and the inferior parietal lobule in HWP. Moreover, a post-hoc test exploring the effect of changed function within the insular region confirmed that a significant positive relationship between static functional connectivity (ventral anterior insula-inferior parietal lobule) and dynamic functional connectivity (posterior insula-inferior parietal lobule) in LWP but not in HWP. Our results might suggest that deficient insular function may be an essential factor related to high worry in healthy subjects.
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Affiliation(s)
- Youxue Zhang
- School of Education and Psychology, Chengdu Normal University, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xueli Cai
- Psychological Research and Counseling Center, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Hui He,
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15
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Li H, Jiang S, Dong D, Hu J, He C, Hou C, He H, Huang H, Shen D, Pei H, Zhao G, Dong L, Yao D, Luo C. Vascular feature as a modulator of the aging brain. Cereb Cortex 2022; 32:5609-5621. [PMID: 35174854 DOI: 10.1093/cercor/bhac039] [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: 12/10/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/25/2023] Open
Abstract
The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.
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Affiliation(s)
- Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jian Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Chuan He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Dai Shen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Guocheng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
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16
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Niu J, Zheng Z, Wang Z, Xu L, Meng Q, Zhang X, Kuang L, Wang S, Dong L, Qiu J, Jiao Q, Cao W. Thalamo-cortical inter-subject functional correlation during movie watching across the adult lifespan. Front Neurosci 2022; 16:984571. [PMID: 36213738 PMCID: PMC9534554 DOI: 10.3389/fnins.2022.984571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
An increasing number of studies have shown that the functional interactions between the thalamus and cerebral cortices play an important role in cognitive function and are influenced by age. Previous studies have revealed age-related changes in the thalamo-cortical system within individuals, while neglecting differences between individuals. Here, we characterized inter-subject functional correlation (ISFC) between the thalamus and several cortical brain networks in 500 healthy participants aged 18–87 years old from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) cohort using movie-watching state fMRI data. General linear models (GLM) were performed to assess age-related changes in ISFC of thalamo-cortical networks and the relationship between ISFC and fluid intelligence. We found significant age-related decreases in ISFC between the posterior thalamus (e.g., ventral posterior nucleus and pulvinar) and the attentional network, sensorimotor network, and visual network (FDR correction with p < 0.05). Meanwhile, the ISFC between the thalamus (mainly the mediodorsal nucleus and ventral thalamic nuclei) and higher-order cortical networks, including the default mode network, salience network and control network, showed complex changes with age. Furthermore, the altered ISFC of thalamo-cortical networks was positively correlated with decreased fluid intelligence (FDR correction with p < 0.05). Overall, our results provide further evidence that alterations in the functional integrity of the thalamo-cortical system might play an important role in cognitive decline during aging.
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Affiliation(s)
- Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Zihao Zheng
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Ziqi Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Longchun Xu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Qingmin Meng
- Department of Interventional Radiology, Taian Central Hospital, Tai’an, China
| | - Xiaotong Zhang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Liangfeng Kuang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Shigang Wang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Li Dong
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfeng Qiu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Science, Tai’an, China
- *Correspondence: Weifang Cao,
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17
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Yang B, Ma J, Qiu W, Zhang J, Wang X. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Huang H, Zhang B, Mi L, Liu M, Chang X, Luo Y, Li C, He H, Zhou J, Yang R, Li H, Jiang S, Yao D, Li Q, Duan M, Luo C. Reconfiguration of Functional Dynamics in Cortico-Thalamo-Cerebellar Circuit in Schizophrenia Following High-Frequency Repeated Transcranial Magnetic Stimulation. Front Hum Neurosci 2022; 16:928315. [PMID: 35959244 PMCID: PMC9359206 DOI: 10.3389/fnhum.2022.928315] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/22/2022] [Indexed: 11/20/2022] Open
Abstract
Schizophrenia is a serious mental illness characterized by a disconnection between brain regions. Transcranial magnetic stimulation is a non-invasive brain intervention technique that can be used as a new and safe treatment option for patients with schizophrenia with drug-refractory symptoms, such as negative symptoms and cognitive impairment. However, the therapeutic effects of transcranial magnetic stimulation remain unclear and would be investigated using non-invasive tools, such as functional connectivity (FC). A longitudinal design was adopted to investigate the alteration in FC dynamics using a dynamic functional connectivity (dFC) approach in patients with schizophrenia following high-frequency repeated transcranial magnetic stimulation (rTMS) with the target at the left dorsolateral prefrontal cortex (DLPFC). Two groups of schizophrenia inpatients were recruited. One group received a 4-week high-frequency rTMS together with antipsychotic drugs (TSZ, n = 27), while the other group only received antipsychotic drugs (DSZ, n = 26). Resting-state functional magnetic resonance imaging (fMRI) and psychiatric symptoms were obtained from the patients with schizophrenia twice at baseline (t1) and after 4-week treatment (t2). The dynamics was evaluated using voxel- and region-wise FC temporal variability resulting from fMRI data. The pattern classification technique was used to verify the clinical application value of FC temporal variability. For the voxel-wise FC temporary variability, the repeated measures ANCOVA analysis showed significant treatment × time interaction effects on the FC temporary variability between the left DLPFC and several regions, including the thalamus, cerebellum, precuneus, and precentral gyrus, which are mainly located within the cortico-thalamo-cerebellar circuit (CTCC). For the ROI-wise FC temporary variability, our results found a significant interaction effect on the FC among CTCC. rTMS intervention led to a reduced FC temporary variability. In addition, higher alteration in FC temporal variability between left DLPFC and right posterior parietal thalamus predicted a higher remission ratio of negative symptom scores, indicating that the decrease of FC temporal variability between the brain regions was associated with the remission of schizophrenia severity. The support vector regression (SVR) results suggested that the baseline pattern of FC temporary variability between the regions in CTCC could predict the efficacy of high-frequency rTMS intervention on negative symptoms in schizophrenia. These findings confirm the potential relationship between the reduction in whole-brain functional dynamics induced by high-frequency rTMS and the improvement in psychiatric scores, suggesting that high-frequency rTMS affects psychiatric symptoms by coordinating the heterogeneity of activity between the brain regions. Future studies would examine the clinical utility of using functional dynamics patterns between specific brain regions as a biomarker to predict the treatment response of high-frequency rTMS.
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Affiliation(s)
- Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bei Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Mi
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Meiqing Liu
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuling Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Li
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingyu Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruikun Yang
- University of Science and Technology Beijing, Beijing, China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qifu Li
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
- *Correspondence: Qifu Li,
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
- Mingjun Duan,
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
- Cheng Luo,
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19
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Effects of transcranial direct current stimulation on brain changes and relation to cognition in patients with schizophrenia: a fMRI study. Brain Imaging Behav 2022; 16:2061-2071. [PMID: 35781191 DOI: 10.1007/s11682-022-00676-z] [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: 04/13/2022] [Indexed: 01/10/2023]
Abstract
We studied brain changes during an N-back task before and after 10 sessions of transcranial direct current stimulation (tDCS) and its relation to cognitive changes. This was a double-blind, sham-controlled, randomized study of tDCS in 27 patients with schizophrenia. They performed an N-back task in a 3 T scanner before and after receiving the 10 tDCS sessions. Cognitive performance outside the fMRI session was assessed using the MATRICS Consensus Cognitive Battery and other tests at baseline and several time points after 10 sessions of tDCS. During the N-back task performed during fMRI scans, comparing the 0-back vs. the 2-back task, the active tDCS group demonstrated a significantly increased activation in the right fusiform, left middle frontal, left inferior frontal gyrus (opercular part) and right inferior frontal gyrus (triangular part) and reduced activation in the left posterior cingulum gyrus with most of these results primarily due to increases in activation during the 0-back rather than 2-back task. There were also significant positive or negative correlations between some of the brain changes and cognitive performance. tDCS modulated prefrontal activation at low working memory load or attention mode, but default mode network at higher working memory load. Changes in brain activation measured during the N-back task were correlated with some dimensions of cognitive function immediately after 10 tDCS sessions and at follow-up times. The results support tDCS could offer a potential novel approach for modulating cortical activity and its relation to cognitive function.
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20
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Qin Y, Li S, Yao D, Luo C. Causality Analysis to the Abnormal Subcortical–Cortical Connections in Idiopathic-Generalized Epilepsy. Front Neurosci 2022; 16:925968. [PMID: 35844218 PMCID: PMC9280354 DOI: 10.3389/fnins.2022.925968] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Idiopathic generalized epilepsy (IGE) was characterized by 3–6 Hz generalized spike-wave discharges (GSWDs), and extensive altered interactions in subcortical-cortical circuit. However, the dynamics and the causal relationship among these interactions were less studied. Using resting-state functional magnetic resonance imaging (fMRI) data, the abnormal connections in the subcortical-cortical pathway in IGE were examined. Then, we proposed a novel method of granger causal analysis based on the dynamic functional connectivity, and the predictive effects among these abnormal connections were calculated. The results showed that the thalamus, and precuneus were key regions representing abnormal functional network connectivity (FNC) in the subcortical-cortical circuit. Moreover, the connectivity between precuneus and adjacent regions had a causal effect on the widespread dysfunction of the thalamocortical circuit. In addition, the connection between the striatum and thalamus indicated the modulation role on the cortical connection in epilepsy. These results described the causality of the widespread abnormality of the subcortical-cortical circuit in IGE in terms of the dynamics of functional connections, which provided additional evidence for understanding the potential modulation pattern of the abnormal epileptic pathway.
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Affiliation(s)
- Yun Qin
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Sipei Li
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Cheng Luo
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Cheng Luo,
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21
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Song Y, Wu H, Chen S, Ge H, Yan Z, Xue C, Qi W, Yuan Q, Liang X, Lin X, Chen J. Differential Abnormality in Functional Connectivity Density in Preclinical and Early-Stage Alzheimer's Disease. Front Aging Neurosci 2022; 14:879836. [PMID: 35693335 PMCID: PMC9177137 DOI: 10.3389/fnagi.2022.879836] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/27/2022] [Indexed: 12/23/2022] Open
Abstract
Background Both subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) have a high risk of progression to Alzheimer's disease (AD). While most of the available evidence described changes in functional connectivity (FC) in SCD and aMCI, there was no confirmation of changes in functional connectivity density (FCD) that have not been confirmed. Therefore, the purpose of this study was to investigate the specific alterations in resting-state FCD in SCD and aMCI and further assess the extent to which these changes can distinguish the preclinical and early-stage AD. Methods A total of 57 patients with SCD, 59 patients with aMCI, and 78 healthy controls (HC) were included. The global FCD, local FCD, and long-range FCD were calculated for each voxel to identify brain regions with significant FCD alterations. The brain regions with abnormal FCD were then used as regions of interest for FC analysis. In addition, we calculated correlations between neuroimaging alterations and cognitive function and performed receiver-operating characteristic analyses to assess the diagnostic effect of the FCD and FC alterations on SCD and aMCI. Results FCD mapping revealed significantly increased global FCD in the left parahippocampal gyrus (PHG.L) and increased long-range FCD in the left hippocampus for patients with SCD when compared to HCs. However, when compared to SCD, patients with aMCI showed significantly decreased global FCD and long-range FCD in the PHG.L. The follow-up FC analysis further revealed significant variations between the PHG.L and the occipital lobe in patients with SCD and aMCI. In addition, patients with SCD also presented significant changes in FC between the left hippocampus, the left cerebellum anterior lobe, and the inferior temporal gyrus. Moreover, changes in abnormal indicators in the SCD and aMCI groups were significantly associated with cognitive function. Finally, combining FCD and FC abnormalities allowed for a more precise differentiation of the clinical stages. Conclusion To our knowledge, this study is the first to investigate specific alterations in FCD and FC for both patients with SCD and aMCI and confirms differential abnormalities that can serve as potential imaging markers for preclinical and early-stage Alzheimer's disease (AD). Also, it adds a new dimension of understanding to the diagnosis of SCD and aMCI as well as the evaluation of disease progression.
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Affiliation(s)
- Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Xingjian Lin
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Jiu Chen
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22
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Walton LR, Verber M, Lee SH, Chao THH, Wightman RM, Shih YYI. Simultaneous fMRI and fast-scan cyclic voltammetry bridges evoked oxygen and neurotransmitter dynamics across spatiotemporal scales. Neuroimage 2021; 244:118634. [PMID: 34624504 PMCID: PMC8667333 DOI: 10.1016/j.neuroimage.2021.118634] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high temporal resolution and spatial specificity inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.
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Affiliation(s)
- Lindsay R Walton
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
| | - Matthew Verber
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - R Mark Wightman
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
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23
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Dong L, Zhao L, Zhang Y, Yu X, Li F, Li J, Lai Y, Liu T, Yao D. Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG. Brain Topogr 2021; 34:403-414. [PMID: 33950323 PMCID: PMC8195908 DOI: 10.1007/s10548-021-00844-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 04/25/2021] [Indexed: 11/30/2022]
Abstract
“Bad channels” are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform (https://webrain.uestc.edu.cn/). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China
| | - Lingling Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yufan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China
| | - Yongxiu Lai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China
| | - Tiejun Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. .,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China. .,School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China. .,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China.
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24
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Abstract
Basal ganglia, which include the striatum and thalamus, have key roles in motivation, emotion, motor function, also contribute to higher-order cognitive function. Previous researches have documented structural and functional alterations in basal ganglia in schizophrenia. While few studies have assessed asymmetries of these characters in basal ganglia of schizophrenia. The current study investigated this issue by using diffusion tensor imaging, anatomic T1-weight image and resting-state functional data from 88 chronic schizophrenic subjects and 92 healthy controls. The structural characteristic, including fractional anisotropy, mean diffusivity (MD) and volume, were extracted and quantified from the subregions of basal ganglia, including caudate, putamen, pallidum and thalamus, through automated atlas-based method. The resting-state functional maps of these regions were also calculated through seed-based functional connectivity. Then, the laterality indexes of structural and functional features were calculated. Compared with healthy controls, schizophrenic subjects showed increased left laterality of volume in striatum and reduced left laterality of volume in thalamus. Furthermore, the difference of laterality of subregions in thalamus is compensatory in schizophrenic subjects. Importantly, the severity of patients' positive symptom was negative corelated with reduced left laterality of volume in thalamus. Our findings provide preliminary evidence demonstrating that the possibility of aberrant laterality in neural pathways and connectivity patterns related to the basal ganglia in schizophrenia.
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25
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Wang Z, Zhang Y, Dong L, Zheng Z, Zhong D, Long X, Cai Q, Jian W, Zhang S, Wu W, Yao D. Effects of Morning Blue-Green 500 nm Light Therapy on Cognition and Biomarkers in Middle-Aged and Older Adults with Subjective Cognitive Decline and Mild Cognitive Impairment: Study Protocol for a Randomized Controlled Trial. J Alzheimers Dis 2021; 83:1521-1536. [PMID: 33843675 DOI: 10.3233/jad-201560] [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: 11/15/2022]
Abstract
BACKGROUND Given that there is no specific drug to treat Alzheimer's disease, non-pharmacologic interventions in people with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are one of the most important treatment strategies. OBJECTIVE To clarify the efficacy of blue-green (500 nm) light therapy on sleep, mood, and physiological parameters in patients with SCD and aMCI is an interesting avenue to explore. METHODS This is a monocentric, randomized, and controlled trial that will last for 4 weeks. We will recruit 150 individuals aged 45 years or older from memory clinics and divide them into 5 groups: SCD treatment (n = 30), SCD control (n = 30), aMCI treatment (n = 30), aMCI control (n = 30), and a group of healthy adult subjects (n = 30) as a normal control (NC). RESULTS The primary outcome is the change in subjective and objective cognitive performance between baseline and postintervention visits (4 weeks after baseline). Secondary outcomes include changes in performance assessing from baseline, postintervention to follow-up (3 months after the intervention), as well as sleep, mood, and physiological parameters (including blood, urine, electrophysiology, and neuroimaging biomarkers). CONCLUSION This study aims to provide evidence of the impact of light therapy on subjective and objective cognitive performance in middle-aged and older adults with SCD or aMCI. In addition, we will identify possible neurophysiological mechanisms of action underlying light therapy. Overall, this trial will contribute to the establishment of light therapy in the prevention of Alzheimer's disease.
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Affiliation(s)
- Ziqi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,The Memory Clinic of department of Neurology, Chengdu Western Hospital, Chengdu, China
| | - Yige Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Zihao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dayong Zhong
- Sichuan Provincial Revolutionary Disabled Soldiers Hospital, Chengdu, China
| | - Xunqin Long
- The Memory Clinic of department of Neurology, Chengdu Western Hospital, Chengdu, China
| | - Qingyan Cai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Jian
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Songge Zhang
- The Memory Clinic of department of Neurology, Chengdu Western Hospital, Chengdu, China
| | - Wenbin Wu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China.,School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
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26
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Zhang Y, Huang G, Liu M, Li M, Wang Z, Wang R, Yang D. Functional and structural connective disturbance of the primary and default network in patients with generalized tonic-clonic seizures. Epilepsy Res 2021; 174:106595. [PMID: 33993017 DOI: 10.1016/j.eplepsyres.2021.106595] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 01/27/2023]
Abstract
OBJECTIVE The present study aims to investigate the disturbance of functional and structural profiles of patients with generalized tonic-clonic seizures (GTCS). METHODS Resting-state fMRI and diffusion tensor imaging (DTI) data was collected from fifty-six patients and sixty-two healthy controls. Degree centrality (DC) of functional connectivity was first calculated and compared between groups using a two-sample t-test. Furthermore, the regions with significant alteration of DC in patients with GTCS were used as nodes to construct the brain network. Functional connectivity (FC) network was constructed using the Person's correlation analysis and structural connectivity (SC) network was obtained using deterministic tractography technology. Gray matter volume (GMV) and cortical thickness (CT) were computed and correlated with connective profiles. RESULTS The patients with GTCS showed increased DC in the primary network (PN), including bilateral precentral gyrus, supplementary motor areas (SMA), and visual cortex, and decreased DC in core regions of default mode network (DMN), bilateral anterior insular, and supramarginal gyrus. In the present study, 14 regions were identified to construct networks. In patients, the FC and SC were increased within the sensorimotor network (mainly linking with SMA) and decreased within DMN (mainly linking with the posterior cingulate cortex (PCC)). Except for the decreased FC and SC between cerebellum and SMA, patients demonstrated increased connectivity between DMN and PN. Besides, the insula demonstrated decreased FC with DMN and increased FC with PN, without significant SC alterations in patients with GTCS. Decreased GMV in bilateral thalamus and increased GMV in frontoparietal regions were found in patients. The decreased GMV of thalamus and increased GMV of SMA positively and negatively correlated with the FC between PCC and left superior frontal cortex, the FC between SMA and left precuneus respectively. CONCLUSION Hyper-connectivity within PN helps to understand the disturbance of primary functions, especially the motor abnormality in GTCS. The hypo-connectivity within DMN suggested abnormal network organization possibly related to epileptogenesis. Moreover, over-interaction between DMN and PN and unbalanced connectivity between them and insula provided potential evidence reflecting abnormal interactions between primary and high-order function systems.
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Affiliation(s)
- Yaodan Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China; Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's Hospital, Chengdu, PR China
| | - Gengzhen Huang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Meijun Liu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Mao Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Zhiqiang Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Rongyu Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Dongdong Yang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China.
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27
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He H, Cao H, Huang B, He M, Ma C, Yao D, Luo C, Yao G, Duan M. Functional abnormalities of striatum are related to the season-specific effect on schizophrenia. Brain Imaging Behav 2021; 15:2347-2355. [PMID: 33398777 DOI: 10.1007/s11682-020-00430-3] [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: 12/08/2020] [Indexed: 11/29/2022]
Abstract
Schizophrenia is a syndrome that is typically accompanied by delusions, hallucinations and cognitive impairments. Specifically, abundant evidences support the notion that more people diagnosed with schizophrenia are born during fall-winter than spring-summer. Although pathophysiological of schizophrenia might be associated with abnormal brain functional network, little is currently known the relationship between season and deficient brain functional network of schizophrenia. To investigate this issue, in this study 51 schizophrenic subjects and 72 healthy controls underwent MRI scanning to detect the brain functional mapping, each at spring-summer and fall-winter season throughout the year. The data-driven method was used to measure the blood oxygen metabolism variability (BOMV). Decreased BOMV in spring-summer while increased in fall-winter were observed within dopaminergic network of schizophrenic subjects, including striatum, thalamus, and hippocampus. The post hoc analysis exploring the coupling among changed BOMV regions, confirmed that a positive relationship, between pallidum and hippocampus existed in fall-winter healthy controls, but not in fall-winter schizophrenic subjects. These findings identified that seasonal effect on striatum might be associated with modulation of striatum-hippocampus. Our results provide a new insight into the role of season in understanding the pathophysiological of schizophrenia.
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Affiliation(s)
- Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Huan Cao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Binxin Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Manxi He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Chi Ma
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China. .,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, Chengdu, China.
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.
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28
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He H, Luo C, He C, He M, Du J, Biswal BB, Yao D, Yao G, Duan M. Altered Spatial Organization of Dynamic Functional Network Associates With Deficient Sensory and Perceptual Network in Schizophrenia. Front Psychiatry 2021; 12:687580. [PMID: 34421674 PMCID: PMC8374440 DOI: 10.3389/fpsyt.2021.687580] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/08/2021] [Indexed: 12/31/2022] Open
Abstract
Schizophrenia is currently thought as a disorder with dysfunctional communication within and between sensory and cognitive processes. It has been hypothesized that these deficits mediate heterogeneous and comprehensive schizophrenia symptomatology. In this study, we investigated as to how the abnormal dynamic functional architecture of sensory and cognitive networks may contribute to these symptoms in schizophrenia. We calculated a sliding-window-based dynamic functional connectivity strength (FCS) and amplitude of low-frequency fluctuation (ALFF) maps. Then, using group-independent component analysis, we characterized spatial organization of dynamic functional network (sDFN) across various time windows. The spatial architectures of FCS/ALFF-sDFN were similar with traditional resting-state functional networks and cannot be accounted by length of the sliding window. Moreover, schizophrenic subjects demonstrated reduced dynamic functional connectivity (dFC) within sensory and perceptual sDFNs, as well as decreased connectivity between these sDFNs and high-order frontal sDFNs. The severity of patients' positive and total symptoms was related to these abnormal dFCs. Our findings revealed that the sDFN during rest might form the intrinsic functional architecture and functional changes associated with psychotic symptom deficit. Our results support the hypothesis that the dynamic functional network may influence the aberrant sensory and cognitive function in schizophrenia, further highlighting that targeting perceptual deficits could extend our understanding of the pathophysiology of schizophrenia.
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Affiliation(s)
- Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chuan He
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Manxi He
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Jing Du
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education (MOE) Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
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29
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Zhang L, Chen D, Chen P, Li W, Li X. Dual-CNN based multi-modal sleep scoring with temporal correlation driven fine-tuning. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.08.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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30
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Jiang S, Pei H, Huang Y, Chen Y, Liu L, Li J, He H, Yao D, Luo C. Dynamic Temporospatial Patterns of Functional Connectivity and Alterations in Idiopathic Generalized Epilepsy. Int J Neural Syst 2020; 30:2050065. [PMID: 33161788 DOI: 10.1142/s0129065720500653] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The dynamic profile of brain function has received much attention in recent years and is also a focus in the study of epilepsy. The present study aims to integrate the dynamics of temporal and spatial characteristics to provide comprehensive and novel understanding of epileptic dynamics. Resting state fMRI data were collected from eighty-three patients with idiopathic generalized epilepsy (IGE) and 87 healthy controls (HC). Specifically, we explored the temporal and spatial variation of functional connectivity density (tvFCD and svFCD) in the whole brain. Using a sliding-window approach, for a given region, the standard variation of the FCD series was calculated as the tvFCD and the variation of voxel-wise spatial distribution was calculated as the svFCD. We found primary, high-level, and sub-cortical networks demonstrated distinct tvFCD and svFCD patterns in HC. In general, the high-level networks showed the highest variation, the subcortical and primary networks showed moderate variation, and the limbic system showed the lowest variation. Relative to HC, the patients with IGE showed weaken temporal and enhanced spatial variation in the default mode network and weaken temporospatial variation in the subcortical network. Besides, enhanced temporospatial variation in sensorimotor and high-level networks was also observed in patients. The hyper-synchronization of specific brain networks was inferred to be associated with the phenomenon responsible for the intrinsic propensity of generation and propagation of epileptic activities. The disrupted dynamic characteristics of sensorimotor and high-level networks might potentially contribute to the driven motion and cognition phenotypes in patients. In all, presently provided evidence from the temporospatial variation of functional interaction shed light on the dynamics underlying neuropathological profiles of epilepsy.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yang Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Linli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu P. R. China
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Altered Functional Integration in the Salience and Default Mode Networks in Euthymic Pediatric Bipolar Disorder. Neural Plast 2020; 2020:5853701. [PMID: 33133177 PMCID: PMC7568799 DOI: 10.1155/2020/5853701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 08/28/2020] [Accepted: 09/21/2020] [Indexed: 11/18/2022] Open
Abstract
Accumulating studies demonstrate emotional and cognitive dysregulation in the euthymic period of pediatric bipolar disorder (PBD). However, the relative contribution of functional integration in human brain to disturbed emotion and cognitive function in the euthymic PBD patients remains unclear. In this study, 16 euthymic PBD patients and 16 healthy controls underwent resting-state functional magnetic resonance imaging. A data-driven functional connectivity analysis was used to investigate functional connectivity changes of the euthymic PBD. Compared with healthy controls, the euthymic PBD exhibited greater global functional connectivity density in the left anterior insula and lower global functional connectivity density in the right temporoparietal junction, the left angular gyrus, and the bilateral occipital lobule. A distant functional connectivity analysis demonstrated altered integration within the salience and default mode networks in euthymic PBD. Correlation analysis found that altered functional connectivity of the salience network was related to the reduced performance in the backward digit span test, and altered functional connectivity of the default mode network was related to the Young Mania Rating Scale in euthymic PBD patients. Our findings indicated that disturbed functional integration in salience and default mode networks might shed light on the physiopathology associated with emotional and cognitive dysregulation in PBD.
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Qin Y, Zhang N, Chen Y, Tan Y, Dong L, Xu P, Guo D, Zhang T, Yao D, Luo C. How Alpha Rhythm Spatiotemporally Acts Upon the Thalamus-Default Mode Circuit in Idiopathic Generalized Epilepsy. IEEE Trans Biomed Eng 2020; 68:1282-1292. [PMID: 32976091 DOI: 10.1109/tbme.2020.3026055] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
GOAL Idiopathic generalized epilepsy (IGE) represents generalized spike-wave discharges (GSWD) and distributed changes in thalamocortical circuit. The purpose of this study is to investigate how the ongoing alpha oscillation acts upon the local temporal dynamics and spatial hyperconnectivity in epilepsy. METHODS We evaluated the spatiotemporal regulation of alpha oscillations in epileptic state based on simultaneous EEG-fMRI recordings in 45 IGE patients. The alpha-BOLD temporal consistency, as well as the effect of alpha power windows on dynamic functional connectivity strength (dFCS) was analyzed. Then, stable synchronization networks during GSWD were constructed, and the spatial covariation with alpha-based network integration was investigated. RESULTS Increased temporal covariation was demonstrated between alpha power and BOLD fluctuations in thalamus and distributed cortical regions in IGE. High alpha power had inhibition effect on dFCS in healthy controls, while in epilepsy, high alpha windows arose along with the enhancement of dFCS in thalamus, caudate and some default mode network (DMN) regions. Moreover, synchronization networks in GSWD-before, GSWD-onset and GSWD-after stages were constructed, and the connectivity strength in prominent hub nodes (precuneus, thalamus) was associated with the spatially disturbed alpha-based network integration. CONCLUSION The results indicated spatiotemporal regulation of alpha in epilepsy by means of the increased power and decreased coherence communication. It provided links between alpha rhythm and the altered temporal dynamics, as well as the hyperconnectivity in thalamus-default mode circuit. SIGNIFICANCE The combination between neural oscillations and epileptic representations may be of clinical importance in terms of seizure prediction and non-invasive interventions.
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Yao Y, He H, Duan M, Li S, Li C, Chen X, Yao G, Chang X, Shu H, Wang H, Luo C. The Effects of Music Intervention on Pallidum-DMN Circuit of Schizophrenia. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4107065. [PMID: 33015164 PMCID: PMC7525302 DOI: 10.1155/2020/4107065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 11/04/2019] [Accepted: 12/03/2019] [Indexed: 11/20/2022]
Abstract
Music intervention has been applied to improve symptoms of schizophrenic subjects as a complementary treatment in medicine. Although the psychiatric symptoms, especially for motivation and emotion, could be increased in schizophrenia, the underlying neural mechanisms remain poorly understood. We employed a longitudinal study to measure the alteration of striatum functional networks in schizophrenic subjects undergoing Mozart music listening using resting-state functional magnetic resonance imaging (fMRI). Forty-five schizophrenic inpatients were recruited and randomly assigned to two groups. Under the standard care with antipsychotic medication, one group received music intervention for 1 month and the other group is set as control. Both schizophrenic groups were compared to healthy subjects. Resting-state fMRI was acquired from schizophrenic subjects at baseline and after one-month music intervention and from healthy subjects at baseline. Striatum network was assessed through seed-based static and dynamic functional connectivity (FC) analyses. After music intervention, increased static FC was observed between pallidum and ventral hippocampus in schizophrenic subjects. Increased dynamic FCs were also found between pallidus and subregions of default mode network (DMN), including cerebellum crus and posterior cingulate cortex. Moreover, static pallidus-hippocampus FC increment was positively correlated with the improvement of negative symptoms in schizophrenic subjects. Together, these findings provided evidence that music intervention might have an effect on the FC of the striatum-DMN circuit and might be related to the remission of symptoms of schizophrenia.
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Affiliation(s)
- Yutong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shicai Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xi Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Haifeng Shu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hongming Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
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Jiang S, Luo C, Huang Y, Li Z, Chen Y, Li X, Pei H, Wang P, Wang X, Yao D. Altered Static and Dynamic Spontaneous Neural Activity in Drug-Naïve and Drug-Receiving Benign Childhood Epilepsy With Centrotemporal Spikes. Front Hum Neurosci 2020; 14:361. [PMID: 33005141 PMCID: PMC7485420 DOI: 10.3389/fnhum.2020.00361] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/07/2020] [Indexed: 11/13/2022] Open
Abstract
The present study aims to investigate intrinsic abnormalities of brain and the effect of antiepileptic treatment on brain activity in Benign childhood epilepsy with centrotemporal spikes (BECTS). Twenty-six drug-naïve patients (DNP) and 22 drug-receiving patients (DRP) with BECTS were collected in this study. Static amplitude of low frequency fluctuation (sALFF) and dynamic ALFF (dALFF) were applied to resting-state fMRI data. Functional connectivity (FC) analysis was further performed for affected regions identified by static and dynamic analysis. One-way analysis of variance and post hoc statistical analyses were performed for between-group differences. Abnormal sALFF and dALFF values were correlated with clinical features of patients. Compared with healthy controls (HC), DNP group demonstrated alterations of sALFF and/or dALFF in medial prefrontal cortex (MPFC), supplementary motor areas (SMA), cerebellum, hippocampus, pallidum and cingulate cortex, in which the values were close to normal in DRP. Notably, sALFF and dALFF showed specific sensitivity in detecting abnormalities in basal ganglia and cerebellum. Additionally, DRP showed additional changes in precuneus, inferior temporal gyrus, superior frontal gyrus and occipital visual cortex. Compared with HC, the DNP showed increased FC in default network and motion-related networks, and the DRP showed decreased FC in default network. The MPFC, hippocampus, SMA, basal ganglia and cerebellum are indicated to be intrinsically affected regions and effective therapeutic targets. And the FC profiles of default and motion-related networks might be potential core indicators for clinical treatment. This study revealed potential neuromodulatory targets and helped understand pathomechanism of BECTS. Static and dynamic analyses should be combined to investigate neuropsychiatric disorders.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yang Huang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pingfu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Wang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
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Wen X, He H, Dong L, Chen J, Yang J, Guo H, Luo C, Yao D. Alterations of local functional connectivity in lifespan: A resting-state fMRI study. Brain Behav 2020; 10:e01652. [PMID: 32462815 PMCID: PMC7375100 DOI: 10.1002/brb3.1652] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 04/08/2020] [Accepted: 04/13/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION As aging attracted attention globally, revealing changes in brain function across the lifespan was largely concerned. In this study, we aimed to reveal the changes of functional networks of the brain (via local functional connectivity, local FC) in lifespan and explore the mechanism underlying them. MATERIALS AND METHODS A total of 523 healthy participants (258 males and 265 females) aged 18-88 years from part of the Cambridge Center for Ageing and Neuroscience (CamCAN) were involved in this study. Next, two data-driven measures of local FC, local functional connectivity density (lFCD) and four-dimensional spatial-temporal consistency of local neural activity (FOCA), were calculated, and then, general linear models were used to assess the changes of them in lifespan. RESULTS Local functional connectivity (lFCD and FOCA) within visual networks (VN), sensorimotor network (SMN), and default mode network (DMN) decreased across the lifespan, while within basal ganglia network (BGN), local connectivity was increased across the lifespan. And, the fluid intelligence decreased within BGN while increased within VN, SMN, and DMN. CONCLUSION These results might suggest that the decline of executive control and intrinsic cognitive ability in the aging population was related to the decline of functional connectivity in VN, SMN, and DMN. Meanwhile, BGN might play a regulatory role in the aging process to compensate for the dysfunction of other functional systems. Our findings may provide important neuroimaging evidence for exploring the brain functional mechanism in lifespan.
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Affiliation(s)
- Xin Wen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Jacobsen S, Meiron O, Salomon DY, Kraizler N, Factor H, Jaul E, Tsur EE. Integrated Development Environment for EEG-Driven Cognitive-Neuropsychological Research. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:2200208. [PMID: 32431963 PMCID: PMC7233754 DOI: 10.1109/jtehm.2020.2989768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 03/23/2020] [Accepted: 04/17/2020] [Indexed: 11/29/2022]
Abstract
Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical “data-collection pipeline” of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.
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Affiliation(s)
- Shoham Jacobsen
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Oded Meiron
- 2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel
| | - David Yoel Salomon
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Nir Kraizler
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Hagai Factor
- 2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel
| | - Efraim Jaul
- 3Geriatric Skilled Nursing DepartmentHerzog Medical CenterJerusalem91120Israel
| | - Elishai Ezra Tsur
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
- 4Neuro-Biomorphic Engineering Laboratory (NBEL)Department of Mathematics and Computer ScienceThe Open University of IsraelRa'anana4353701Israel
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Cerebello-cerebral connectivity in idiopathic generalized epilepsy. Eur Radiol 2020; 30:3924-3933. [DOI: 10.1007/s00330-020-06674-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 12/17/2019] [Accepted: 01/24/2020] [Indexed: 12/24/2022]
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Luo Y, He H, Duan M, Huang H, Hu Z, Wang H, Yao G, Yao D, Li J, Luo C. Dynamic Functional Connectivity Strength Within Different Frequency-Band in Schizophrenia. Front Psychiatry 2020; 10:995. [PMID: 32116820 PMCID: PMC7029741 DOI: 10.3389/fpsyt.2019.00995] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 12/17/2019] [Indexed: 12/18/2022] Open
Abstract
As a complex psychiatric disorder, schizophrenia is interpreted as a "dysconnection" syndrome, which is linked to abnormal integrations in between distal brain regions. Recently, neuroimaging has been widely adopted to investigate how schizophrenia affects brain networks. Furthermore, some studies reported frequency dependence of the abnormalities of functional network in schizophrenia, however, dynamic functional connectivity with frequency dependence is rarely used to explore changes in the whole brain of patients with schizophrenia (SZ). Therefore, in the current study, dynamic functional connectivity strength (dFCS) was performed on resting-state functional magnetic resonance data from 96 SZ patients and 121 healthy controls (HCs) at slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz) frequency bands and further assessed whether the altered dFCS was correlated to clinical symptoms in SZ patients. Results revealed that decreased dFCS of schizophrenia were found in salience, auditory, sensorimotor, visual networks, while increased dFCS in cerebellum, basal ganglia, and prefrontal networks were observed across different frequency bands. Specifically, the thalamus subregion of schizophrenic patients exhibited enhanced dynamic FCS in slow-5 and slow-4, while reduced in slow-3. Moreover, in slow-5 and slow-4, significant interaction effects between frequency and group were observed in the left calcarine cortex, the bilateral inferior orbitofrontal gyrus, and anterior cingulum cortex (ACC). Furthermore, the altered dFCS of insula, thalamus (THA), calcarine cortex, orbitofrontal gyrus, and paracentral lobule were partial correlated with clinical symptoms of SZ patients in slow-5 and slow-4 bands. These results demonstrate the abnormalities of dFCS in schizophrenia patients is rely on different frequency bands and may provide potential implications for exploring the neuropathological mechanism of schizophrenia.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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Chen X, Xu K, Yang Y, Wang Q, Jiang H, Guo X, Chen X, Yang J, Luo C. Altered Default Mode Network Dynamics in Civil Aviation Pilots. Front Neurosci 2020; 13:1406. [PMID: 31992967 PMCID: PMC6971098 DOI: 10.3389/fnins.2019.01406] [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: 09/11/2019] [Accepted: 12/12/2019] [Indexed: 12/27/2022] Open
Abstract
Background Airlines occupy an increasingly important place in the economy of many countries. Because air disasters may cause substantial losses, comprehensive surveys of the psychophysiological mechanism of flying are needed; however, relatively few studies have focused on pilots. The default mode network (DMN) is an important intrinsic connectivity network involved in a range of functions related to flying. This study aimed to examine functional properties of the DMN in pilots. Method Resting-state functional magnetic resonance imaging data from 26 pilots and 24 controls were collected. Independent component analysis, a data-driven approach, was combined with functional connectivity analysis to investigate functional properties of the DMN in pilots. Results The pilot group exhibited increased functional integration in the precuneus/posterior cingulate cortex (PCC) and left middle occipital gyrus. Subsequent functional connectivity analysis identified enhanced functional connection between the precuneus/PCC and medial superior frontal gyrus. Conclusion The pilot group exhibited increased functional connections within the DMN. These findings highlight the importance of the DMN in the neurophysiological mechanism of flying.
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Affiliation(s)
- Xi Chen
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Kaijun Xu
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Yong Yang
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Quanchuan Wang
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Hao Jiang
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Xiangmei Guo
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Xipeng Chen
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Jiazhong Yang
- Institute of Aviation Human Factors and Ergonomics, Department of Aviation Psychology, Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Wen X, Dong L, Chen J, Xiang J, Yang J, Li H, Liu X, Luo C, Yao D. Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective. Front Neurosci 2020; 13:1435. [PMID: 32009894 PMCID: PMC6978665 DOI: 10.3389/fnins.2019.01435] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 12/19/2019] [Indexed: 01/09/2023] Open
Abstract
A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.
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Affiliation(s)
- Xin Wen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Yang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hechun Li
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Dong L, Liu X, Zhao L, Lai Y, Gong D, Liu T, Yao D. A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis. Front Neurosci 2019; 13:1068. [PMID: 31680810 PMCID: PMC6798171 DOI: 10.3389/fnins.2019.01068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 09/24/2019] [Indexed: 12/16/2022] Open
Abstract
In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.
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Affiliation(s)
- Li Dong
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lingling Zhao
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxiu Lai
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Diankun Gong
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Tiejun Liu
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Gong D, Li Y, Yan Y, Yao Y, Gao Y, Liu T, Ma W, Yao D. The high-working load states induced by action real-time strategy gaming: An EEG power spectrum and network study. Neuropsychologia 2019; 131:42-52. [PMID: 31100346 DOI: 10.1016/j.neuropsychologia.2019.05.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 05/01/2019] [Accepted: 05/02/2019] [Indexed: 01/19/2023]
Abstract
Action Real-time Strategy Gaming (ARSG) is a cognitively demanding task that requires attention, sensorimotor skills, high-level team coordination, and strategy-making abilities. Thus, ARSG can offer important, new insights into learning-related neural plasticity. However, little research has examined how the brain allocates cognitive resources in ARSG. By analyzing power spectrums and electroencephalograph (EEG) functional connectivity (FC) networks, this study compared multiple conditions (resting, movie watching, ARSG, and Life simulation gaming - LSG) in two experiments. Consistent with previous research, we found that brain waves appeared to be de-assimilated after activation. Furthermore, results showed that ARSG was associated with higher activation and workload as indicated by θ-waves, and required higher attention as reflected by β-waves. Furthermore, as participants began ARSG, the allocation of cognitive resource gradually prioritized the frontal area, which controls attention, decision-making, monitoring, and mnemonic processing, while participants also showed an enhanced ability to process information under the ARSG condition as indicated by network characteristics. These electrophysiological changes observed in ARSG were not found under LSG. Thus, this study applied both power spectrum and EEG FC networks analyses to ARSG research, revealing characteristics of brain waves in typical areas and how the brain gradually changes from low-working load states to high-working load states based on real-time EEG recordings.
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Affiliation(s)
- Diankun Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuening Yan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yutong Yao
- Faculty of Natural Science, University of Stirling, Stirling, UK
| | - Yu Gao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Tiejun Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Weiyi Ma
- School of Human Environmental Sciences, University of Arkansas, Fayetteville, AR, 72701, USA.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, 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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Qin Y, Jiang S, Zhang Q, Dong L, Jia X, He H, Yao Y, Yang H, Zhang T, Luo C, Yao D. BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy. NEUROIMAGE-CLINICAL 2019; 22:101759. [PMID: 30897433 PMCID: PMC6425117 DOI: 10.1016/j.nicl.2019.101759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/22/2019] [Accepted: 03/10/2019] [Indexed: 01/14/2023]
Abstract
Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.
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Affiliation(s)
- Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Qiqi Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoyan Jia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yutong Yao
- Faculty of natural science, University of Stirling, Stirling, United Kingdom
| | - Huanghao Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Jiang Y, Xia M, Li X, Tang Y, Li C, Huang H, Dong D, Jiang S, Wang J, Xu J, Luo C, Yao D. Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2019; 89:254-262. [PMID: 30248379 DOI: 10.1016/j.pnpbp.2018.09.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/10/2018] [Accepted: 09/19/2018] [Indexed: 12/30/2022]
Abstract
Although modified electroconvulsive therapy (MECT) has been employed as a treatment strategy and to resolve medication resistant symptoms in schizophrenia (SZ), its action mechanisms remain unclear. The insula has been demonstrated to associate with clinical symptoms and neuropathology in SZ. This study examined whether insular changes response to MECT outcomes in SZ. Forty-two SZ were divided into two groups according to their treatment strategies. One group (MSZ, n = 21) received 4-weeks MECT together with antipsychotics; another group (DSZ, n = 21) was treated only with antipsychotics. Twenty-three healthy controls (HC) were also included. Structural and functional MRI were scanned twice (baseline and after 4-week treatment) for SZ and once for HC. Firstly, the insula was divided into three subregions based on resting-state functional connectivity (FC). Subsequently, gray matter volume (GMV) and voxel-wise FC were assessed in each subregion. Finally, the relationship between insular changes and symptom improvements was also investigated. Compared with baseline, the DSZ group showed reduced GMV in insular subregions. In contrast, the MSZ group exhibited increased GMV in bilateral posterior insula (PIns); furthermore, the increase in the PIns was correlated with symptom improvements. Second, the decreased FC between right PIns and left orbitofrontal cortex, and left PIns and middle occipital gyrus was observed only in the MSZ group; moreover, these FC changes were associated with symptom improvements. The present study demonstrated that MECT induced insular changes, which may contribute to the mechanisms of MECT.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, Shanghai 200030, China
| | - Huan Huang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, Shanghai 200030, China.
| | - Jian Xu
- Department of Neurology, Nantong University Affiliated Mental Health Center, Jiangsu, Nantong 226005, 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, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Li H, Cao W, Zhang X, Sun B, Jiang S, Li J, Liu C, Yin W, Wu Y, Liu T, Yao D, Luo C. BOLD-fMRI reveals the association between renal oxygenation and functional connectivity in the aging brain. Neuroimage 2019; 186:510-517. [DOI: 10.1016/j.neuroimage.2018.11.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/19/2018] [Accepted: 11/20/2018] [Indexed: 01/23/2023] Open
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Altered Dynamic Functional Network Connectivity in Frontal Lobe Epilepsy. Brain Topogr 2018; 32:394-404. [DOI: 10.1007/s10548-018-0678-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 09/10/2018] [Indexed: 01/10/2023]
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