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Li L, Liang Z, Li G, Xu H, Yang X, Liu X, Zhang X, Wang J, Zhang Z, Zhou Y. Unveiling convergent and divergent intrinsic brain network alternations in depressed adolescents engaged in non-suicidal self-injurious behaviors with and without suicide attempts. CNS Neurosci Ther 2024; 30:e14684. [PMID: 38739217 PMCID: PMC11090151 DOI: 10.1111/cns.14684] [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: 12/11/2023] [Revised: 01/28/2024] [Accepted: 02/19/2024] [Indexed: 05/14/2024] Open
Abstract
AIMS Limited understanding exists regarding the neurobiological mechanisms underlying non-suicidal self-injury (NSSI) and suicide attempts (SA) in depressed adolescents. The maturation of brain network is crucial during adolescence, yet the abnormal alternations in depressed adolescents with NSSI or NSSI+SA remain poorly understood. METHODS Resting-state functional magnetic resonance imaging data were collected from 114 depressed adolescents, classified into three groups: clinical control (non-self-harm), NSSI only, and NSSI+SA based on self-harm history. The alternations of resting-state functional connectivity (RSFC) were identified through support vector machine-based classification. RESULTS Convergent alterations in NSSI and NSSI+SA predominantly centered on the inter-network RSFC between the Limbic network and the three core neurocognitive networks (SalVAttn, Control, and Default networks). Divergent alterations in the NSSI+SA group primarily focused on the Visual, Limbic, and Subcortical networks. Additionally, the severity of depressive symptoms only showed a significant correlation with altered RSFCs between Limbic and DorsAttn or Visual networks, strengthening the fact that increased depression severity alone does not fully explain observed FC alternations in the NSSI+SA group. CONCLUSION Convergent alterations suggest a shared neurobiological mechanism along the self-destructiveness continuum. Divergent alterations may indicate biomarkers differentiating risk for SA, informing neurobiologically guided interventions.
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Affiliation(s)
- Linling Li
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Zhen Liang
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Guohua Li
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Hong Xu
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Xing Yang
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Xia Liu
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Xin Zhang
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Jianhong Wang
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
| | - Zhiguo Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Peng Cheng Laboratory, Shenzhen, China
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, China
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Wu Y, Chu Z, Chen X, Zhu Y, Xu X, Shen Z. Functional connectivity between the habenula and posterior default mode network contributes to the response of the duloxetine effect in major depressive disorder. Neuroreport 2024; 35:380-386. [PMID: 38526956 DOI: 10.1097/wnr.0000000000002019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
This study aims to investigate the functional connectivity (FC) changes of the habenula (Hb) among patients with major depressive disorder (MDD) after 12 weeks of duloxetine treatment (MDD12). Patients who were diagnosed with MDD for the first time and were drug-naïve were recruited at baseline as cases. Healthy controls (HCs) matched for sex, age, and education level were also recruited at the same time. At baseline, all participants underwent resting-state functional MRI. FC analyses were performed using the Hb seed region of interest, and three groups including HCs, MDD group and MDD12 group were compared using whole-brain voxel-wise comparisons. Compared to the HCs, the MDD group had decreased FC between the Hb and the right anterior cingulate cortex at baseline. Compared to the HCs, the FC between the Hb and the left medial superior frontal gyrus decreased in the MDD12 group. Additionally, the FC between the left precuneus, bilateral cuneus and Hb increased in the MDD12 group than that in the MDD group. No significant correlation was found between HDRS-17 and the FC between the Hb, bilateral cuneus, and the left precuneus in the MDD12 group. Our study suggests that the FC between the post-default mode network and Hb may be the treatment mechanism of duloxetine and the treatment mechanisms and the pathogenesis of depression may be independent of each other.
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Affiliation(s)
- Yanru Wu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University
| | - Zhaosong Chu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University
| | - Xianyu Chen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University
| | - Yun Zhu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University
- Yunnan Clinical Research Center for Mental Disorders
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University
- Yunnan Clinical Research Center for Mental Disorders
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Kampaite A, Gustafsson R, York EN, Foley P, MacDougall NJJ, Bastin ME, Chandran S, Waldman AD, Meijboom R. Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review. PLoS One 2024; 19:e0299634. [PMID: 38551913 PMCID: PMC10980255 DOI: 10.1371/journal.pone.0299634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 02/13/2024] [Indexed: 04/01/2024] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterised by neuroinflammation and neurodegeneration. Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting MS (pwRRMS). An increased understanding of brain changes and mechanisms underlying fatigue and depression in RRMS could lead to more effective interventions and enhancement of quality of life. To elucidate the relationship between depression and fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: studied participants with RRMS (n ≥ 20; ≥ 18 years old) and differentiated between MS subtypes; published between 2001-01-01 and 2023-01-18; used fatigue and depression assessments validated for MS; included brain structural, functional magnetic resonance imaging (fMRI) or diffusion MRI (dMRI). Sixty studies met the criteria: 18 dMRI (15 fatigue, 5 depression) and 22 fMRI (20 fatigue, 5 depression) studies. The literature was heterogeneous; half of studies reported no correlation between brain connectivity measures and fatigue or depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression. Fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, ventral tegmental area, striatum, default mode and attention networks, and supramarginal, precentral, and postcentral gyri. Qualitative analysis suggests structural and functional connectivity changes, possibly due to axonal and/or myelin loss, in the cortico-thalamic-basal-ganglial and cortico-limbic network may underlie fatigue and depression in pwRRMS, respectively, but the overall results were inconclusive, possibly explained by heterogeneity and limited number of studies. This highlights the need for further studies including advanced MRI to detect more subtle brain changes in association with depression and fatigue. Future studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrates underlying these symptoms in pwRRMS.
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Affiliation(s)
- Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Rebecka Gustafsson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter Foley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Niall J. J. MacDougall
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
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Ravan M, Noroozi A, Sanchez MM, Borden L, Alam N, Flor-Henry P, Colic S, Khodayari-Rostamabad A, Minuzzi L, Hasey G. Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers. J Affect Disord 2024; 346:285-298. [PMID: 37963517 DOI: 10.1016/j.jad.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 11/02/2023] [Accepted: 11/07/2023] [Indexed: 11/16/2023]
Abstract
BACKGROUND Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. METHODS We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. RESULTS Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. LIMITATIONS The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. CONCLUSIONS DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.
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Affiliation(s)
- Maryam Ravan
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA.
| | - Amin Noroozi
- Department of Digital, Technologies, and Arts, Staffordshire University, Staffordshire, England, UK
| | - Mary Margarette Sanchez
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | - Lee Borden
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | - Nafia Alam
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | | | - Sinisa Colic
- Department of Electrical Engineering, University of Toronto, Canada
| | | | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Gary Hasey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
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Zhang M, Long D, Chen Z, Fang C, Li Y, Huang P, Chen F, Sun H. Multi-view graph network learning framework for identification of major depressive disorder. Comput Biol Med 2023; 166:107478. [PMID: 37776730 DOI: 10.1016/j.compbiomed.2023.107478] [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: 07/04/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 10/02/2023]
Abstract
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures' high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.
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Affiliation(s)
- Mengda Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Dan Long
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhaoqing Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Chunhao Fang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - You Li
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Pinpin Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Fengnong Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou, China.
| | - Hongwei Sun
- School of Automation, Hangzhou Dianzi University, Hangzhou, China.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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7
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Gao Y, Guo X, Zhong Y, Liu X, Tian S, Deng J, Lin X, Bao Y, Lu L, Wang G. Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder. J Affect Disord 2023; 332:136-142. [PMID: 36990286 DOI: 10.1016/j.jad.2023.03.080] [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: 06/15/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Gaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals. METHODS This study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time. RESULTS Relative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients. CONCLUSIONS These results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.
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Affiliation(s)
- Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Yi Zhong
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiaoxin Liu
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Shanshan Tian
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Jiahui Deng
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiao Lin
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Yanpin Bao
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China.
| | - Lin Lu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China; Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China.
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8
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Li Y, Chu T, Liu Y, Zhang H, Dong F, Gai Q, Shi Y, Ma H, Zhao F, Che K, Mao N, Xie H. Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network. J Affect Disord 2023; 323:10-20. [PMID: 36403803 DOI: 10.1016/j.jad.2022.11.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/09/2022] [Accepted: 11/07/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD. METHOD Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively. RESULT The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction). CONCLUSION We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.
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Affiliation(s)
- Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, PR China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, PR China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Fanghui Dong
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong 264000, PR China
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Feng Zhao
- Compute Science and Technology, Shandong Technology and Business University Yantai, Shandong 264000, PR China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
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10
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Zhu M, Quan Y, He X. The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network. Front Hum Neurosci 2023; 17:1094592. [PMID: 36778038 PMCID: PMC9908753 DOI: 10.3389/fnhum.2023.1094592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/12/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The early diagnosis of major depressive disorder (MDD) is very important for patients that suffer from severe and irreversible consequences of depression. It has been indicated that functional connectivity (FC) analysis based on functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers for clinical diagnosis. However, previous studies mainly focus on brain disease classification in small sample sizes, which may lead to dramatic divergences in classification accuracy. Methods This paper attempts to address this limitation by applying the deep graph convolutional neural network (DGCNN) method on a large multi-site MDD dataset. The resting-state fMRI data are acquired from 830 MDD patients and 771 normal controls (NC) shared by the REST-meta-MDD consortium. Results The DGCNN model trained with the binary network after thresholding, identified MDD patients from normal controls and achieved an accuracy of 72.1% with 10-fold cross-validation, which is 12.4%, 9.8%, and 7.6% higher than SVM, RF, and GCN, respectively. Moreover, the process of dataset reading and model training is faster. Therefore, it demonstrates the advantages of the DGCNN model with low time complexity and sound classification performance. Discussion Based on a large, multi-site dataset from MDD patients, the results expressed that DGCNN is not an extremely accurate method for MDD diagnosis. However, there is an improvement over previous methods with our goal of better understanding brain function and ultimately providing a biomarker or diagnostic capability for MDD diagnosis.
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Affiliation(s)
- Manyun Zhu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yu Quan
- Information Center of Shengjing Hospital of China Medical University, Shenyang, China
| | - Xuan He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,*Correspondence: Xuan He,
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11
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Sun J, Ma Y, Guo C, Du Z, Chen L, Wang Z, Li X, Xu K, Luo Y, Hong Y, Yu X, Xiao X, Fang J, Lu J. Distinct patterns of functional brain network integration between treatment-resistant depression and non treatment-resistant depression: A resting-state functional magnetic resonance imaging study. Prog Neuropsychopharmacol Biol Psychiatry 2023; 120:110621. [PMID: 36031163 DOI: 10.1016/j.pnpbp.2022.110621] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/13/2022] [Accepted: 08/21/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Previous neuroimaging has paid little attention to the differences in brain network integration between patients with treatment-resistant depression(TRD) and non-TRD (nTRD), and the relationship between their impaired brain network integration and clinical symptoms has not been elucidated. METHOD Eighty one major depressive disorder (MDD) patients (40 in TRD, 41 in nTRD) and 40 healthy controls (HCs) were enrolled for the functional magnetic resonance imaging (fMRI) scans. A seed-based functional connectivity (FC) method was used to investigate the brain network abnormalities of default mode network (DMN), affective network (AN), salience network (SN) and cognitive control network (CCN) for the MDD. Finally, the correlation was analyzed between the abnormal FCs and 17-item Hamilton Rating Scale for Depression scale (HAMD-17) scores. RESULTS Compared with the HC group, the FCs in DMN, AN, SN, CCN were altered in both the TRD and nTRD groups. Compared with the nTRD group, FC alterations in the AN and CCN were more abnormal in the TRD group, and the FC alterations were generally decreased at the SN in the TRD group. In addition, the FC values of right dorsolateral prefrontal cortices and left caudate nucleus in the TRD group and the FC values of right subgenual anterior cingulate cortex and left middle temporal gyrus in the nTRD group were positively correlated with HAMD-17 scale scores. CONCLUSIONS Abnormal FCs are present in four brain networks (DMN, AN, SN, CCN) in both the TRD and nTRD groups. Except of DMN, FCs in AN, SN and CCN maybe underlay the neurobiological mechanism in differentiating TRD from nTRD.
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Affiliation(s)
- Jifei Sun
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Yue Ma
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Chunlei Guo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Zhongming Du
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700 Beijing, China
| | - Limei Chen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Zhi Wang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Xiaojiao Li
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Ke Xu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Yi Luo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Yang Hong
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China
| | - Xue Yu
- Beijing First Hospital of Integrated Chinese and Western Medicine, 100026 Beijing, China
| | - Xue Xiao
- Beijing First Hospital of Integrated Chinese and Western Medicine, 100026 Beijing, China
| | - Jiliang Fang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053 Beijing, China.
| | - Jie Lu
- Xuanwu Hospital, Capital Medical University, 100053 Beijing, China.
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12
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Venkatapathy S, Votinov M, Wagels L, Kim S, Lee M, Habel U, Ra IH, Jo HG. Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity. Front Psychiatry 2023; 14:1125339. [PMID: 37032921 PMCID: PMC10077869 DOI: 10.3389/fpsyt.2023.1125339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.
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Affiliation(s)
- Sujitha Venkatapathy
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
| | - Mikhail Votinov
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - Lisa Wagels
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - Sangyun Kim
- AI Convergence Research Section, Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea
| | - Munseob Lee
- AI Convergence Research Section, Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - In-Ho Ra
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
| | - Han-Gue Jo
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
- *Correspondence: Han-Gue Jo
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13
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Stoyanov D, Khorev V, Paunova R, Kandilarova S, Simeonova D, Badarin A, Hramov A, Kurkin S. Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14045. [PMID: 36360924 PMCID: PMC9656256 DOI: 10.3390/ijerph192114045] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/14/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
AIM This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression. METHOD AND SUBJECTS We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups. RESULTS AND DISCUSSION Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects. CONCLUSION The study provides supportive evidence for impaired functional connectivity networks in MDE patients.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Denitsa Simeonova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Artem Badarin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
| | - Alexander Hramov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
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14
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Zhao F, Gao T, Cao Z, Chen X, Mao Y, Mao N, Ren Y. Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal. Front Comput Neurosci 2022; 16:1046310. [PMID: 36387303 PMCID: PMC9647659 DOI: 10.3389/fncom.2022.1046310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2023] Open
Abstract
Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Tianyu Gao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhi Cao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
- College of Oceanography and Space Informatics, China University of Petroleum (Huadong), Qingdao, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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15
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Hou A, Pang X, Zhang X, Peng Y, Li D, Wang H, Zhang Q, Liang M, Gao F. Widespread aberrant functional connectivity throughout the whole brain in obstructive sleep apnea. Front Neurosci 2022; 16:920765. [PMID: 35979339 PMCID: PMC9377518 DOI: 10.3389/fnins.2022.920765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/01/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Obstructive sleep apnea (OSA) is a sleep-related breathing disorder with high prevalence and is associated with cognitive impairment. Previous neuroimaging studies have reported abnormal brain functional connectivity (FC) in patients with OSA that might contribute to their neurocognitive impairments. However, it is unclear whether patients with OSA have a characteristic pattern of FC changes that can serve as a neuroimaging biomarker for identifying OSA. Methods A total of 21 patients with OSA and 21 healthy controls (HCs) were included in this study and scanned using resting-state functional magnetic resonance imaging (fMRI). The automated anatomical labeling (AAL) atlas was used to divide the cerebrum into 90 regions, and FC between each pair of regions was calculated. Univariate analyses were then performed to detect abnormal FCs in patients with OSA compared with controls, and multivariate pattern analyses (MVPAs) were applied to classify between patients with OSA and controls. Results The univariate comparisons did not detect any significantly altered FC. However, the MVPA showed a successful classification between patients with OSA and controls with an accuracy of 83.33% (p = 0.0001). Furthermore, the selected FCs were associated with nearly all brain regions and widely distributed in the whole brain, both within and between, many resting-state functional networks. Among these selected FCs, 3 were significantly correlated with the apnea-hypopnea index (AHI) and 2 were significantly correlated with the percentage of time with the saturation of oxygen (SaO2) below 90% of the total sleep time (%TST < 90%). Conclusion There existed widespread abnormal FCs in the whole brain in patients with OSA. This aberrant FC pattern has the potential to serve as a neurological biomarker of OSA, highlighting its importance for understanding the complex neural mechanism underlying OSA and its cognitive impairment.
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Affiliation(s)
- Ailin Hou
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Xueming Pang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Xi Zhang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Dongyue Li
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - He Wang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin, China
- *Correspondence: Quan Zhang,
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
- Meng Liang,
| | - Feng Gao
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
- Feng Gao,
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16
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Aili X, Wang W, Zhang A, Jiao Z, Li X, Rao B, Li R, Li H. Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads. Front Neurol 2022; 13:825177. [PMID: 35812120 PMCID: PMC9263507 DOI: 10.3389/fneur.2022.825177] [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: 11/30/2021] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveEven with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads.MethodsIn this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well.ResultsAt the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus.ConclusionThe results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders.
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Affiliation(s)
- Xire Aili
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Aidong Zhang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Zengxin Jiao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Xing Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- Bo Rao
| | - Ruili Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Ruili Li
| | - Hongjun Li
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Hongjun Li
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17
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Hsieh H, Xu Q, Yang F, Zhang Q, Hao J, Liu G, Liu R, Yu Q, Zhang Z, Xing W, Bernhardt BC, Lu G, Zhang Z. Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures. J Clin Med 2022; 11:jcm11061612. [PMID: 35329938 PMCID: PMC8951449 DOI: 10.3390/jcm11061612] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/18/2022] [Accepted: 03/09/2022] [Indexed: 02/04/2023] Open
Abstract
This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.
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Affiliation(s)
- Hsinyu Hsieh
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qiang Xu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Fang Yang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qirui Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Jingru Hao
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Gaoping Liu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Ruoting Liu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qianqian Yu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Zixuan Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University/Changzhou First People’s Hospital, Changzhou 213004, China;
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada;
| | - Guangming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
- Correspondence:
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Pinto B, Conde T, Domingues I, Domingues MR. Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review. Int J Mol Sci 2022; 23:ijms23042032. [PMID: 35216147 PMCID: PMC8874755 DOI: 10.3390/ijms23042032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 11/30/2022] Open
Abstract
Major depressive disorder (MDD), also called depression, is a serious disease that impairs the quality of life of patients and has a high incidence, affecting approximately 3.8% of the world population. Its diagnosis is very subjective and is not supported by measurable biomarkers mainly due to the lack of biochemical markers. Recently, disturbance of lipid profiling has been recognized in MDD, in animal models of MDD or in depressed patients, which may contribute to unravel the etiology of the disease and find putative new biomarkers, for a diagnosis or for monitoring the disease and therapeutics outcomes. In this review, we provide an overview of current knowledge of lipidomics analysis, both in animal models of MDD (at the brain and plasma level) and in humans (in plasma and serum). Furthermore, studies of lipidomics analyses after antidepressant treatment in rodents (in brain, plasma, and serum), in primates (in the brain) and in humans (in plasma) were reviewed and give evidence that antidepressants seem to counteract the modification seen in lipids in MDD, giving some evidence that certain altered lipid profiles could be useful MDD biomarkers for future precision medicine.
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Affiliation(s)
- Bruno Pinto
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Tiago Conde
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
- Institute of Biomedicine—iBiMED, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Inês Domingues
- Centre for Environmental and Marine Studies, CESAM, Department of Biology, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - M. Rosário Domingues
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
- Correspondence:
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Rao B, Cheng H, Xu H, Peng Y. Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study. Front Neurol 2022; 13:790607. [PMID: 35185761 PMCID: PMC8847279 DOI: 10.3389/fneur.2022.790607] [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: 10/07/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022] Open
Abstract
Objective The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons. Methods A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP. Results The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere. Conclusion The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Hua Cheng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu
| | - Yun Peng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
- Yun Peng
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Chen W, Hu H, Wu Q, Chen L, Zhou J, Chen HH, Xu XQ, Wu FY. Altered Static and Dynamic Interhemispheric Resting-State Functional Connectivity in Patients With Thyroid-Associated Ophthalmopathy. Front Neurosci 2021; 15:799916. [PMID: 34938158 PMCID: PMC8685321 DOI: 10.3389/fnins.2021.799916] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/17/2021] [Indexed: 11/16/2022] Open
Abstract
Purpose: Thyroid-associated ophthalmopathy (TAO) is a debilitating and sight-threatening autoimmune disease that severely impairs patients' quality of life. Besides the most common ophthalmic manifestations, the emotional and psychiatric disturbances are also usually observed in clinical settings. This study was to investigate the interhemispheric functional connectivity alterations in TAO patients using resting-state functional magnetic resonance imaging (rs-fMRI). Methods: Twenty-eight TAO patients and 22 healthy controls (HCs) underwent rs-fMRI scans. Static and dynamic voxel-mirrored homotopic connectivity (VMHC) values were calculated and compared between the two groups. A linear support vector machine (SVM) classifier was used to examine the performance of static and dynamic VMHC differences in distinguishing TAOs from HCs. Results: Compared with HCs, TAOs showed decreased static VMHC in lingual gyrus (LG)/calcarine (CAL), middle occipital gyrus, postcentral gyrus, superior parietal lobule, inferior parietal lobule, and precuneus. Meanwhile, TAOs demonstrated increased dynamic VMHC in orbitofrontal cortex (OFC). In TAOs, static VMHC in LG/CAL was positively correlated with visual acuity (r = 0.412, P = 0.036), whilst dynamic VMHC in OFC was positively correlated with Hamilton Anxiety Rating Scale (HARS) score (r = 0.397, P = 0.044) and Hamilton Depression Rating Scale (HDRS) score (r = 0.401, P = 0.042). The SVM model showed good performance in distinguishing TAOs from HCs (area under the curve, 0.971; average accuracy, 94%). Conclusion: TAO patients had altered static and dynamic VMHC in the occipital, parietal, and orbitofrontal areas, which could serve as neuroimaging prediction markers of TAO.
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Affiliation(s)
- Wen Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qian Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huan-Huan Chen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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