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Guthrie AJ, Paredes-Echeverri S, Bleier C, Adams C, Millstein DJ, Ranford J, Perez DL. Mechanistic studies in pathological health anxiety: A systematic review and emerging conceptual framework. J Affect Disord 2024; 358:222-249. [PMID: 38718945 DOI: 10.1016/j.jad.2024.05.029] [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: 08/05/2023] [Revised: 04/02/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Pathological health anxiety (PHA) (e.g., hypochondriasis and illness anxiety disorder) is common in medical settings and associated with increased healthcare costs. However, the psychological and neurobiological mechanisms contributing to the development and maintenance of PHA are incompletely understood. METHODS We performed a systematic review to characterize the mechanistic understanding of PHA. PubMed, PsycINFO, and Embase databases were searched to find articles published between 1/1/1990 and 12/31/2022 employing a behavioral task and/or physiological measures in individuals with hypochondriasis, illness anxiety disorder, and PHA more broadly. RESULTS Out of 9141 records identified, fifty-seven met inclusion criteria. Article quality varied substantially across studies, and was overall inadequate. Cognitive, behavioral, and affective findings implicated in PHA included health-related attentional and memory recall biases, a narrow health concept, threat confirming thought patterns, use of safety-seeking behaviors, and biased explicit and implicit affective processing of health-related information among other observations. There is initial evidence supporting a potential overestimation of interoceptive stimuli in those with PHA. Neuroendocrine, electrophysiology, and brain imaging research in PHA are particularly in their early stages. LIMITATIONS Included articles evaluated PHA categorically, suggesting that sub-threshold and dimensional health anxiety considerations are not contextualized. CONCLUSIONS Within an integrated cognitive-behavioral-affective and predictive processing formulation, we theorize that sub-optimal illness and health concepts, altered interoceptive modeling, biased illness-based predictions and attention, and aberrant prediction error learning are mechanisms relevant to PHA requiring more research. Comprehensively investigating the pathophysiology of PHA offers the potential to identify adjunctive diagnostic biomarkers and catalyze new biologically-informed treatments.
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Affiliation(s)
- Andrew J Guthrie
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sara Paredes-Echeverri
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Cristina Bleier
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Caitlin Adams
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel J Millstein
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica Ranford
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Occupational Therapy, Massachusetts General Hospital, Boston, MA, USA
| | - David L Perez
- Functional Neurological Disorder Unit, Division of Behavioral Neurology and Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Division of Neuropsychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Li H, Zhang W, Song H, Zhuo L, Yao H, Sun H, Liu R, Feng R, Tang C, Lui S. Altered temporal lobe connectivity is associated with psychotic symptoms in drug-naïve adolescent patients with first-episode schizophrenia. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02485-9. [PMID: 38832962 DOI: 10.1007/s00787-024-02485-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 05/23/2024] [Indexed: 06/06/2024]
Abstract
Research on individuals with a younger onset age of schizophrenia is important for identifying neurobiological processes derived from the interaction of genes and the environment that lead to the manifestation of schizophrenia. Schizophrenia has long been recognized as a disorder of dysconnectivity, but it is largely unknown how brain connectivity changes are associated with psychotic symptoms. Twenty-one adolescent-onset schizophrenia (AOS) patients and 21 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging. Regional homogeneity (ReHo) was used to investigate local brain connectivity alterations in AOS. Regions with significant ReHo changes in patients were selected as "seeds" for further functional connectivity (FC) analysis and Granger causality analysis (GCA), and associations of the obtained functional brain measures with psychotic symptoms in patients with AOS were examined. Compared with HCs, AOS patients showed significantly increased ReHo in the right middle temporal gyrus (MTG), which was positively correlated with PANSS-positive scores, PSYRATS-delusion scores and auditory hallucination scores. With the MTG as the seed, lower connectivity with the bilateral postcentral gyrus (PCG) and higher connectivity with the right precuneus were observed in patients. The reduced FC between the right MTG and bilateral PCG was significantly and positively correlated with hallucination scores. GCA indicated decreased Granger causality from the right MTG to the left middle frontal gyrus (MFG) and from the right MFG to the right MTG in AOS patients, but such effects did not significantly associate with psychotic symptoms. Abnormalities in the connectivity within the MTG and its connectivity with other networks were identified and were significantly correlated with hallucination and delusion ratings. This region may be a key neural substrate of psychotic symptoms in AOS.
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Affiliation(s)
- Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Song
- Department of Psychiatry, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Lihua Zhuo
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Hongchao Yao
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ruishan Liu
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Ruohan Feng
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Chungen Tang
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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3
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Cai B, Zhu Y, Liu D, Li Y, Bueber M, Yang X, Luo G, Su Y, Grivel MM, Yang LH, Qian M, Stone WS, Phillips MR. Use of the Chinese version of the MATRICS Consensus Cognitive Battery to assess cognitive functioning in individuals with high risk for psychosis, first-episode schizophrenia and chronic schizophrenia: a systematic review and meta-analysis. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 45:101016. [PMID: 38699289 PMCID: PMC11064724 DOI: 10.1016/j.lanwpc.2024.101016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 05/05/2024]
Abstract
More than one hundred studies have used the mainland Chinese version of the MATRICS Consensus Cognitive Battery (MCCB) to assess cognition in schizophrenia, but the results of these studies, the quality of the reports, and the strength of the evidence provided in the reports have not been systematically assessed. We identified 114 studies from English-language and Chinese-language databases that used the Chinese MCCB to assess cognition in combined samples of 7394 healthy controls (HC), 392 individuals with clinical high risk for psychosis (CHR-P), 4922 with first-episode schizophrenia (FES), 1549 with chronic schizophrenia (CS), and 2925 with schizophrenia of unspecified duration. The mean difference (MD) of the composite MCCB T-score (-13.72) and T-scores of each of the seven cognitive domains assessed by MCCB (-14.27 to -7.92) were significantly lower in individuals with schizophrenia than in controls. Meta-analysis identified significantly greater cognitive impairment in FES and CS than in CHR-P in six of the seven domains and significantly greater impairment in CS than FES in the reasoning and problem-solving domain (i.e., executive functioning). The only significant covariate of overall cognitive functioning in individuals with schizophrenia was a negative association with the severity of psychotic symptoms. These results confirm the construct validity of the mainland Chinese version of MCCB. However, there were significant limitations in the strength of the evidence provided about CHR-P (small pooled sample sizes) and the social cognition domain (inconsistency of results across studies), and the quality of many reports (particularly those published in Chinese) was rated 'poor' due to failure to report sample size calculations, matching procedures or methods of handling missing data. Moreover, almost all studies were cross-sectional studies limited to persons under 60 with at least nine years of education, so longitudinal studies of under-educated, older individuals with schizophrenia are needed.
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Affiliation(s)
- Bing Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yikang Zhu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dongyang Liu
- School of Public Health of Guangxi Medical University, Nanning, Guangxi, China
| | - Yaxi Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Marlys Bueber
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuezhi Yang
- The Fifth People's Hospital, Nanning, Guangxi, China
| | - Guoshuai Luo
- Mental Health Center of Tianjin Medical University, Tianjin Anding Hospital, Tianjin, China
| | - Ying Su
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Margaux M. Grivel
- Department of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, NY, USA
| | - Lawrence H. Yang
- Department of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Min Qian
- Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - William S. Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michael R. Phillips
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
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Fang J, Lv Y, Xie Y, Tang X, Zhang X, Wang X, Yu M, Zhou C, Qin W, Zhang X. Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:18. [PMID: 38365896 PMCID: PMC10873412 DOI: 10.1038/s41537-024-00432-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.
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Affiliation(s)
- Jin Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yiding Lv
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaowei Tang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiaobin Zhang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Miao Yu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
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Huang FF, Yang XY, Luo J, Yang XJ, Meng FQ, Wang PC, Li ZJ. Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods. BMC Psychiatry 2023; 23:792. [PMID: 37904114 PMCID: PMC10617132 DOI: 10.1186/s12888-023-05299-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/23/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. METHODS Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. RESULTS SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. CONCLUSION SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.
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Affiliation(s)
- Fang-Fang Huang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Henan, China
| | - Xiang-Yun Yang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jia Luo
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiao-Jie Yang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Fan-Qiang Meng
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Peng-Chong Wang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zhan-Jiang Li
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 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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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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8
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Cai M, Wang R, Liu M, Du X, Xue K, Ji Y, Wang Z, Zhang Y, Guo L, Qin W, Zhu W, Fu J, Liu F. Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:93. [PMID: 36347874 PMCID: PMC9643538 DOI: 10.1038/s41537-022-00311-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/31/2022] [Indexed: 06/06/2023]
Abstract
Neuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Rui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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9
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Gao Z, Xiao Y, Zhang Y, Zhu F, Tao B, Tang X, Lui S. Comparisons of resting-state brain activity between insomnia and schizophrenia: a coordinate-based meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:80. [PMID: 36207333 PMCID: PMC9547062 DOI: 10.1038/s41537-022-00291-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 09/19/2022] [Indexed: 11/05/2022]
Abstract
Growing evidence shows that insomnia is closely associated with schizophrenia (SCZ), but the neural mechanism under the association remains unclear. A direct comparison of the patterns of resting-state brain activities would help understand the above question. Using meta-analytic approach, 11 studies of insomnia vs. healthy controls (HC) and 39 studies of SCZ vs. HC were included to illuminate the common and distinct patterns between insomnia and SCZ. Results showed that SCZ and insomnia shared increased resting-state brain activities in frontolimbic structures including the right medial prefrontal gyrus (mPFC) and left parahippocampal gyrus. SCZ additionally revealed greater increased activities in subcortical areas including bilateral putamen, caudate and right insula and greater decreased activities in precentral gyrus and orbitofrontal gyrus. Our study reveals both shared and distinct activation patterns in SCZ and insomnia, which may provide novel insights for understanding the neural basis of the two disorders and enlighten the possibility of the development of treatment strategies for insomnia in SCZ in the future.
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Affiliation(s)
- Ziyang Gao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ye Zhang
- grid.412901.f0000 0004 1770 1022Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Fei Zhu
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bo Tao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Tang
- grid.412901.f0000 0004 1770 1022Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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10
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Qiu X, Zhang R, Wen L, Jiang F, Mao H, Yan W, Xie S, Pan X. Alterations in Spontaneous Brain Activity in Drug-Naïve First-Episode Schizophrenia: An Anatomical/Activation Likelihood Estimation Meta-Analysis. Psychiatry Investig 2022; 19:606-613. [PMID: 36059049 PMCID: PMC9441467 DOI: 10.30773/pi.2022.0074] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/21/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The etiology of schizophrenia is unknown and is associated with abnormal spontaneous brain activity. There are no consistent results regarding the change in spontaneous brain activity of people with schizophrenia. In this study, we determined the specific changes in the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) in patients with drug-naïve first-episode schizophrenia (Dn-FES). METHODS A comprehensive search of databases such as PubMed, Web of Science, and Embase was conducted to find articles on resting-state functional magnetic resonance imaging using ALFF/fALFF and ReHo in schizophrenia patients compared to healthy controls (HCs) and then, anatomical/activation likelihood estimation was performed. RESULTS Eighteen eligible studies were included in this meta-analysis. Compared to the spontaneous brain activity of HCs, we found changes in spontaneous brain activity in Dn-FES based on these two methods, mainly including the frontal lobe, putamen, lateral globus pallidus, insula, cerebellum, and posterior cingulate cortex. CONCLUSION We found that widespread abnormalities of spontaneous brain activity occur in the early stages of the onset of schizophrenia and may provide a reference for the early intervention of schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Wen
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Fuli Jiang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hongjun Mao
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinming Pan
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
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11
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Wu J, Wu J, Guo R, Chu L, Li J, Zhang S, Ren H. The decreased connectivity in middle temporal gyrus can be used as a potential neuroimaging biomarker for left temporal lobe epilepsy. Front Psychiatry 2022; 13:972939. [PMID: 36032260 PMCID: PMC9399621 DOI: 10.3389/fpsyt.2022.972939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 07/08/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE We aimed to explore voxel-mirrored homotopic connectivity (VMHC) abnormalities between the two brain hemispheres in left temporal lobe epilepsy (lTLE) patients and to determine whether these alterations could be leveraged to guide lTLE diagnosis. MATERIALS AND METHODS Fifty-eight lTLE patients and sixty healthy controls (HCs) matched in age, sex, and education level were recruited to receive resting state functional magnetic resonance imaging (rs-fMRI) scan. Then VHMC analyses of bilateral brain regions were conducted based on the results of these rs-fMRI scans. The resultant imaging data were further analyzed using support vector machine (SVM) methods. RESULTS Compared to HCs, patients with lTLE exhibited decreased VMHC values in the bilateral middle temporal gyrus (MTG) and middle cingulum gyrus (MCG), while no brain regions in these patients exhibited increased VMHC values. SVM analyses revealed the diagnostic accuracy of reduced bilateral MTG VMHC values to be 75.42% (89/118) when differentiating between lTLE patients and HCs, with respective sensitivity and specificity values of 74.14% (43/58) and 76.67% (46/60). CONCLUSION Patients with lTLE exhibit abnormal VMHC values corresponding to the impairment of functional coordination between homotopic regions of the brain. These altered MTG VMHC values may also offer value as a robust neuroimaging biomarker that can guide lTLE patient diagnosis.
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Affiliation(s)
- Jinlong Wu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Jun Wu
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruimin Guo
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Linkang Chu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jun Li
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Zhang
- Liyuan Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
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