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Luo Y, Zhu T, Zhang Y, Fan J, Zuo X, Feng X, Gong J, Yao D, Wang J, Luo C. Association of core brain networks with antipsychotic therapeutic effects in first-episode schizophrenia. Cereb Cortex 2025; 35:bhaf088. [PMID: 40298442 DOI: 10.1093/cercor/bhaf088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 03/10/2025] [Accepted: 03/20/2025] [Indexed: 04/30/2025] Open
Abstract
Elucidating neurobiological mechanisms underlying the heterogeneity of antipsychotic treatment will be of great value for precision medicine in schizophrenia, yet there has been limited progress. We combined static and dynamic functional connectivity (FC) analysis to examine the abnormal communications among core brain networks [default-mode network (DMN), central executive network (CEN), salience network (SN), primary network (PN), and subcortical network (SCN) in clinical subtypes of schizophrenia (responders and nonresponders to antipsychotic monotherapy). Resting-state functional magnetic resonance imaging data were collected from 79 first-episode schizophrenia and 90 healthy controls. All patients received antipsychotic monotherapy for up to 12 weeks and underwent a second scan. We found that significantly reduced static FC in CEN-DMN/SN and SN-SCN were observed in nonresponders after treatment, whereas almost no difference was observed in responders. The nonresponders showed significantly higher dynamic FC in PN-DMN/SN than responders at baseline. Further, the baseline FC in core brain networks were treated as moderators involved in symptom relief and distinguished response subtypes with high classification accuracy. Collectively, the current work highlights the potential of communications among five core brain networks in searching biomarkers of antipsychotic monotherapy response and neuroanatomical subtypes, advancing the understanding of antipsychotic treatment mechanisms in schizophrenia.
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Affiliation(s)
- Yuling Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
| | - Tianyuan Zhu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600, Wanping South Road, Shanghai 200030, P.R. China
| | - Yu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
| | - Jiamin Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
| | - Xiaojun Zuo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
| | - Xiaorong Feng
- School of Computer Science, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu 610225, P. R. China
| | - Jinnan Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
- School of Computer Science, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu 610225, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
- China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600, Wanping South Road, Shanghai 200030, P.R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
- China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-Tech District, Chengdu 610054, P. R. China
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Zhao L, Zou R, Jin L. Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity. Int J Neural Syst 2025; 35:2550001. [PMID: 39560445 DOI: 10.1142/s0129065725500017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2024]
Abstract
Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic screening, face challenges in identifying secondary cases due to symptom overlap with other disorders. This study introduces a novel PKD recognition method utilizing a resting-state electroencephalogram (EEG) functional connectivity matrix and a deep learning architecture (AT-1CBL). Resting-state EEG data from 44 PKD patients and 44 healthy controls (HCs) were collected using a 128-channel EEG system. Functional connectivity matrices were computed and transformed into graph data to examine brain network property differences between PKD patients and controls through graph theory. Source localization was conducted to explore neural circuit differences in patients. The AT-1CBL model, integrating 1D-CNN and Bi-LSTM with attentional mechanisms, achieved a classification accuracy of 93.77% on phase lag index (PLI) features in the Theta band. Graph theoretic analysis revealed significant phase synchronization impairments in the Theta band of the functional brain network in PKD patients, particularly in the distribution of weak connections compared to HCs. Source localization analyses indicated greater differences in functional connectivity in sensorimotor regions and the frontal-limbic system in PKD patients, suggesting abnormalities in motor integration related to clinical symptoms. This study highlights the potential of deep learning models based on EEG functional connectivity for accurate and cost-effective PKD diagnosis, supporting the development of portable EEG devices for clinical monitoring and diagnosis. However, the limited dataset size may affect generalizability, and further exploration of multimodal data integration and advanced deep learning architectures is necessary to enhance the robustness of PKD diagnostic models.
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Affiliation(s)
- Liang Zhao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - Renling Zou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - Linpeng Jin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
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3
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Di Camillo F, Grimaldi DA, Cattarinussi G, Di Giorgio A, Locatelli C, Khuntia A, Enrico P, Brambilla P, Koutsouleris N, Sambataro F. Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis. Psychiatry Clin Neurosci 2024; 78:732-743. [PMID: 39290174 PMCID: PMC11612547 DOI: 10.1111/pcn.13736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. METHODS We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. RESULTS A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. CONCLUSIONS Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.
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Affiliation(s)
| | | | - Giulia Cattarinussi
- Department of Neuroscience (DNS)University of PadovaPaduaItaly
- Padova Neuroscience CenterUniversity of PadovaPaduaItaly
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
| | | | - Clara Locatelli
- Department of Mental Health and AddictionsASST Papa Giovanni XXIIIBergamoItaly
| | - Adyasha Khuntia
- Department of Psychiatry and PsychotherapyLudwig‐Maximilian UniversityMunichGermany
- International Max Planck Research School for Translational Psychiatry (IMPRS‐TP)MunichGermany
- Max‐Planck‐Institute of PsychiatryMunichGermany
| | - Paolo Enrico
- Department of Psychiatry and PsychotherapyLudwig‐Maximilian UniversityMunichGermany
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
- Department of Neurosciences and Mental HealthFondazione IRCSS Ca’ Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Paolo Brambilla
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
- Department of Neurosciences and Mental HealthFondazione IRCSS Ca’ Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Nikolaos Koutsouleris
- Max‐Planck‐Institute of PsychiatryMunichGermany
- Department of PsychiatryMunich University HospitalMunichGermany
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
| | - Fabio Sambataro
- Department of Neuroscience (DNS)University of PadovaPaduaItaly
- Padova Neuroscience CenterUniversity of PadovaPaduaItaly
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Vicente-Querol MA, Fernández-Caballero A, González P, González-Gualda LM, Fernández-Sotos P, Molina JP, García AS. Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces. Int J Neural Syst 2023; 33:2350053. [PMID: 37746831 DOI: 10.1142/s0129065723500533] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Facial affect recognition is a critical skill in human interactions that is often impaired in psychiatric disorders. To address this challenge, tests have been developed to measure and train this skill. Recently, virtual human (VH) and virtual reality (VR) technologies have emerged as novel tools for this purpose. This study investigates the unique contributions of different factors in the communication and perception of emotions conveyed by VHs. Specifically, it examines the effects of the use of action units (AUs) in virtual faces, the positioning of the VH (frontal or mid-profile), and the level of immersion in the VR environment (desktop screen versus immersive VR). Thirty-six healthy subjects participated in each condition. Dynamic virtual faces (DVFs), VHs with facial animations, were used to represent the six basic emotions and the neutral expression. The results highlight the important role of the accurate implementation of AUs in virtual faces for emotion recognition. Furthermore, it is observed that frontal views outperform mid-profile views in both test conditions, while immersive VR shows a slight improvement in emotion recognition. This study provides novel insights into the influence of these factors on emotion perception and advances the understanding and application of these technologies for effective facial emotion recognition training.
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Affiliation(s)
- Miguel A Vicente-Querol
- Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, Albacete 02071, Spain
| | - Antonio Fernández-Caballero
- Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Biomedical Research Networking Centre in Mental Health, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Pascual González
- Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Biomedical Research Networking Centre in Mental Health, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Luz M González-Gualda
- Servicio de Salud Mental, Complejo Hospitalario, Universitario de Albacete, Albacete 02004, Spain
| | - Patricia Fernández-Sotos
- Biomedical Research Networking Centre in Mental Health, Instituto de Salud Carlos III, Madrid 28029, Spain
- Servicio de Salud Mental, Complejo Hospitalario, Universitario de Albacete, Albacete 02004, Spain
| | - José P Molina
- Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
| | - Arturo S García
- Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, Albacete 02071, Spain
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
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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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Sreeraj VS, Shivakumar V, Bhalerao GV, Kalmady SV, Narayanaswamy JC, Venkatasubramanian G. Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia. Asian J Psychiatr 2023; 82:103459. [PMID: 36682158 DOI: 10.1016/j.ajp.2023.103459] [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: 10/16/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. METHODS Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. RESULTS Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. CONCLUSION ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.
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Affiliation(s)
- Vanteemar S Sreeraj
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.
| | - Venkataram Shivakumar
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | | | - Sunil V Kalmady
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | | | - Ganesan Venkatasubramanian
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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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: 8] [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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Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning. Comput Biol Med 2022; 146:105511. [DOI: 10.1016/j.compbiomed.2022.105511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/11/2022]
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9
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Vicente-Querol MA, Fernandez-Caballero A, Molina JP, Gonzalez-Gualda LM, Fernandez-Sotos P, Garcia AS. Facial Affect Recognition in Immersive Virtual Reality: Where Is the Participant Looking? Int J Neural Syst 2022; 32:2250029. [DOI: 10.1142/s0129065722500290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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10
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Wei J, Wang X, Cui X, Wang B, Xue J, Niu Y, Wang Q, Osmani A, Xiang J. Functional Integration and Segregation in a Multilayer Network Model of Patients with Schizophrenia. Brain Sci 2022; 12:brainsci12030368. [PMID: 35326324 PMCID: PMC8946586 DOI: 10.3390/brainsci12030368] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022] Open
Abstract
Research has shown that abnormal brain networks in patients with schizophrenia appear at different frequencies, but the relationship between these different frequencies is unclear. Therefore, it is necessary to use a multilayer network model to evaluate the integration of information from different frequency bands. To explore the mechanism of integration and separation in the multilayer network of schizophrenia, we constructed multilayer frequency brain network models in 50 patients with schizophrenia and 69 healthy subjects, and the entropy of the multiplex degree (EMD) and multilayer clustering coefficient (MCC) were calculated. The results showed that the ability to integrate and separate information in the multilayer network of patients was significantly higher than that of normal people. This difference was mainly reflected in the default mode network, sensorimotor network, subcortical network, and visual network. Among them, the subcortical network was different in both MCC and EMD outcomes. Furthermore, differences were found in the posterior cingulate gyrus, hippocampus, amygdala, putamen, pallidum, and thalamus. The thalamus and posterior cingulate gyrus were associated with the patient’s symptom scores. Our results showed that the cross-frequency interaction ability of patients with schizophrenia was significantly enhanced, among which the subcortical network was the most active. This interaction may serve as a compensation mechanism for intralayer dysfunction.
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Affiliation(s)
- Jing Wei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
- School of Information, Shanxi University of Finance and Economics, Taiyuan 030024, China
| | - Xiaoyue Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Qianshan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Arezo Osmani
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
- Correspondence:
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Zandbagleh A, Mirzakuchaki S, Daliri MR, Premkumar P, Sanei S. Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity. Int J Neural Syst 2022; 32:2250013. [PMID: 35236254 DOI: 10.1142/s0129065722500137] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sattar Mirzakuchaki
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Preethi Premkumar
- Division of Psychology, School of Applied Sciences, London Southbank University, London, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, UK
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