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Pan N, Qin K, Patino LR, Tallman MJ, Lei D, Lu L, Li W, Blom TJ, Bruns KM, Welge JA, Strawn JR, Gong Q, Sweeney JA, Singh MK, DelBello MP. Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study. J Child Psychol Psychiatry 2024; 65:1072-1086. [PMID: 38220469 PMCID: PMC11246494 DOI: 10.1111/jcpp.13946] [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] [Accepted: 11/26/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal, p = .022) and clustering coefficient (Cp, p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal: p < .001; Cp: p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Luis R. Patino
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Maxwell J. Tallman
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Thomas J. Blom
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Kaitlyn M. Bruns
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jeffrey A. Welge
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - John A. Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Manpreet K. Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, California, USA
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Cao P, Li Y, Dong Y, Tang Y, Xu G, Si Q, Chen C, Yao Y, Li R, Sui Y. Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder. J Affect Disord 2024; 363:269-281. [PMID: 39053628 DOI: 10.1016/j.jad.2024.07.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/25/2024] [Accepted: 07/14/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms. METHODS Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups. RESULTS Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively. CONCLUSION This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.
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Affiliation(s)
- Peiyu Cao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yuting Li
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huzhou Third People's Hospital, Huzhou 313000, Zhejiang, China
| | - Yingbo Dong
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yilin Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Guoxin Xu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Qi Si
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huai'an No. 3 People's Hospital, Huai'an 223001, Jiangsu, China
| | - Congxin Chen
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210000, Jiangsu, China
| | - Ye Yao
- Nanjing Medical University, Nanjing 210000, Jiangsu, China
| | - Runda Li
- Vanderbilt University, Nashville 37240, TN, USA
| | - Yuxiu Sui
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China.
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Li Q, Xing Y, Zhu Z, Fei X, Tang Y, Lu J. Effects of computerized cognitive training on functional brain networks in patients with vascular cognitive impairment and no dementia. CNS Neurosci Ther 2024; 30:e14779. [PMID: 38828650 PMCID: PMC11145123 DOI: 10.1111/cns.14779] [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: 01/14/2024] [Revised: 04/21/2024] [Accepted: 05/08/2024] [Indexed: 06/05/2024] Open
Abstract
AIMS Previous neuroimaging studies of vascular cognitive impairment, no dementia (VCIND), have reported functional alterations, but far less is known about the effects of cognitive training on functional connectivity (FC) of intrinsic connectivity networks (ICNs) and how they relate to intervention-related cognitive improvement. This study provides comprehensive research on the changes in intra- and inter-brain functional networks in patients with VCIND who received computerized cognitive training, with a focus on the underlying mechanisms and potential therapeutic strategies. METHODS We prospectively collected 60 patients with VCIND who were randomly divided into the training group (N = 30) receiving computerized cognitive training and the control group (N = 30) receiving fixed cognitive training. Functional MRI scans and cognitive assessments were performed at baseline, at the 7-week training, and at the 6-month follow-up. Utilizing templates for ICNs, the study employed a linear mixed model to compare intra- and inter-network FC changes between the two groups. Pearson correlation was applied to calculate the relationship between FC and cognitive function. RESULTS We found significantly decreased intra-network FC within the default mode network (DMN) following computerized cognitive training at Month 6 (p = 0.034), suggesting a potential loss of functional specialization. Computerized training led to increased functional coupling between the DMN and sensorimotor network (SMN) (p = 0.01) and between the language network (LN) and executive control network (ECN) at Month 6 (p < 0.001), indicating compensatory network adaptations in patients with VCIND. Notably, the intra-LN exhibited enhanced functional specialization after computerized cognitive training (p = 0.049), with significant FC increases among LN regions, which correlated with improvements in neuropsychological measures (p < 0.05), emphasizing the targeted impact of computerized cognitive training on language abilities. CONCLUSIONS This study provides insights into neuroplasticity and adaptive changes resulting from cognitive training in patients with VCIND, with implications for potential therapeutic strategies.
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Affiliation(s)
- Qiong‐Ge Li
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Yi Xing
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Zu‐De Zhu
- Collaborative Innovation Center for Language AbilityJiangsu Normal UniversityXuzhouChina
| | - Xiao‐Lu Fei
- Department of Information, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Yi Tang
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
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Wang Y, Shu Y, Cai G, Guo Y, Gao J, Chen Y, Lv L, Zeng X. Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients. Sci Rep 2024; 14:11682. [PMID: 38778225 PMCID: PMC11111766 DOI: 10.1038/s41598-024-62635-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.
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Affiliation(s)
- Yuanyuan Wang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yongqiang Shu
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Guoqian Cai
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yu Guo
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junwei Gao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ye Chen
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lianjiang Lv
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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Yuan H, Li X, Wei B. Modeling default mode network patterns via a universal spatio-temporal brain attention skip network. Neuroimage 2024; 287:120522. [PMID: 38253216 DOI: 10.1016/j.neuroimage.2024.120522] [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: 09/26/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.
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Affiliation(s)
- Hang Yuan
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Xiang Li
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China.
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6
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Wang XK, Yang C, Dong WQ, Zhang QR, Ma SZ, Zang YF, Yuan LX. Impaired segregation of the attention deficit hyperactivity disorder related pattern in children. J Psychiatr Res 2024; 170:111-121. [PMID: 38134720 DOI: 10.1016/j.jpsychires.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/05/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear. METHODS Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7-14. Relationships between the measures and inattention symptoms were also investigated. RESULTS Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10-8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10-5). Both ADHD-C (p = 2.16 × 10-6) and ADHD-I (p = 2.87 × 10-6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores. CONCLUSIONS This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes.
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Affiliation(s)
- Xing-Ke Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Chen Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Wen-Qiang Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Qiu-Rong Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Sheng-Zhi Ma
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China; TMS Center, Deqing Hospital of Hangzhou Normal University, Deqing, Zhejiang, China
| | - Li-Xia Yuan
- School of Physics, Zhejiang University, Hangzhou, China.
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Li D, Hao J, Hao J, Cui X, Niu Y, Xiang J, Wang B. Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder. Brain Sci 2023; 13:1646. [PMID: 38137094 PMCID: PMC10741828 DOI: 10.3390/brainsci13121646] [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: 10/19/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 12/24/2023] Open
Abstract
An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas-Fernandez-Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects' laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality.
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Affiliation(s)
- Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, China; (J.H.)
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Zhou L, Xie Y, Wang R, Fan Y, Wu Y. Dynamic segregation and integration of brain functional networks associated with emotional arousal. iScience 2023; 26:106609. [PMID: 37250309 PMCID: PMC10214403 DOI: 10.1016/j.isci.2023.106609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/12/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.
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Affiliation(s)
- Lv Zhou
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yong Xie
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Rong Wang
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yongchen Fan
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Ying Wu
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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Wang X, Chang Z, Wang R. Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia. Commun Biol 2023; 6:279. [PMID: 36932140 PMCID: PMC10023794 DOI: 10.1038/s42003-023-04637-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.
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Affiliation(s)
- Xinrui Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Zhao Chang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
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Carhart-Harris RL, Chandaria S, Erritzoe DE, Gazzaley A, Girn M, Kettner H, Mediano PAM, Nutt DJ, Rosas FE, Roseman L, Timmermann C, Weiss B, Zeifman RJ, Friston KJ. Canalization and plasticity in psychopathology. Neuropharmacology 2023; 226:109398. [PMID: 36584883 DOI: 10.1016/j.neuropharm.2022.109398] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/01/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022]
Abstract
This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call 'TEMP' for 'Temperature or Entropy Mediated Plasticity', and another, we call 'canalization', which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to 'precision' or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of 'plasticity' describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that 'pathological' phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP - combined with gently guiding psychological support - can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications. This article is part of the Special Issue on "National Institutes of Health Psilocybin Research Speaker Series".
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Affiliation(s)
- R L Carhart-Harris
- Psychedelics Division - Neuroscape, Department of Neurology, University of California, San Francisco, USA; Centre for Psychedelic Research, Imperial College London, UK.
| | - S Chandaria
- Centre for Psychedelic Research, Imperial College London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK; Institute of Philosophy, School of Advanced Study, University of London, UK
| | - D E Erritzoe
- Centre for Psychedelic Research, Imperial College London, UK; CNWL-Imperial Psychopharmacology and Psychedelic Research Clinic (CIPPRS), UK
| | - A Gazzaley
- Psychedelics Division - Neuroscape, Department of Neurology, University of California, San Francisco, USA
| | - M Girn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - H Kettner
- Psychedelics Division - Neuroscape, Department of Neurology, University of California, San Francisco, USA; Centre for Psychedelic Research, Imperial College London, UK
| | - P A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, UK
| | - D J Nutt
- Centre for Psychedelic Research, Imperial College London, UK
| | - F E Rosas
- Centre for Psychedelic Research, Imperial College London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK; Department of Informatics, University of Sussex, UK; Centre for Complexity Science, Imperial College London, UK
| | - L Roseman
- Centre for Psychedelic Research, Imperial College London, UK; CNWL-Imperial Psychopharmacology and Psychedelic Research Clinic (CIPPRS), UK
| | - C Timmermann
- Centre for Psychedelic Research, Imperial College London, UK; CNWL-Imperial Psychopharmacology and Psychedelic Research Clinic (CIPPRS), UK
| | - B Weiss
- Centre for Psychedelic Research, Imperial College London, UK; CNWL-Imperial Psychopharmacology and Psychedelic Research Clinic (CIPPRS), UK
| | - R J Zeifman
- Centre for Psychedelic Research, Imperial College London, UK; NYU Langone Center for Psychedelic Medicine, NYU Grossman School of Medicine, USA
| | - K J Friston
- Wellcome Centre for Human Neuroimaging, University College London, UK
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