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Wang X, Yan C, Yang PY, Xia Z, Cai XL, Wang Y, Kwok SC, Chan RCK. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry Clin Neurosci 2024; 78:157-168. [PMID: 38013639 DOI: 10.1111/pcn.13625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 11/01/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.
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Affiliation(s)
- Xuan Wang
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao Yan
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
| | | | - Zheng Xia
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Xin-Lu Cai
- Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Sze Chai Kwok
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Phylo-Cognition Laboratory, Division of Natural and Applied Sciences, Data Science Research Center, Duke Kunshan University, Kunshan, China
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Xie Y, Li C, Guan M, Zhang T, Ma C, Wang Z, Ma Z, Wang H, Fang P. Low-frequency rTMS induces modifications in cortical structural connectivity - functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations. Hum Brain Mapp 2024; 45:e26614. [PMID: 38375980 PMCID: PMC10878014 DOI: 10.1002/hbm.26614] [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: 08/17/2023] [Revised: 01/09/2024] [Accepted: 01/19/2024] [Indexed: 02/21/2024] Open
Abstract
Auditory verbal hallucinations (AVH) are distinctive clinical manifestations of schizophrenia. While low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated potential in mitigating AVH, the precise mechanisms by which it operates remain obscure. This study aimed to investigate alternations in structural connectivity and functional connectivity (SC-FC) coupling among schizophrenia patients with AVH prior to and following treatment with 1 Hz rTMS that specifically targets the left temporoparietal junction. Initially, patients exhibited significantly reduced macroscopic whole brain level SC-FC coupling compared to healthy controls. Notably, SC-FC coupling increased significantly across multiple networks, including the somatomotor, dorsal attention, ventral attention, frontoparietal control, and default mode networks, following rTMS treatment. Significant alternations in SC-FC coupling were noted in critical nodes comprising the somatomotor network and the default mode network, such as the precentral gyrus and the ventromedial prefrontal cortex, respectively. The alternations in SC-FC coupling exhibited a correlation with the amelioration of clinical symptom. The results of our study illuminate the intricate relationship between white matter structures and neuronal activity in patients who are receiving low-frequency rTMS. This advances our understanding of the foundational mechanisms underlying rTMS treatment for AVH.
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Affiliation(s)
- Yuanjun Xie
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
- Department of Radiology, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Chenxi Li
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Muzhen Guan
- Department of Mental HealthXi'an Medical CollegeXi'anChina
| | - Tian Zhang
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Chaozong Ma
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Zhongheng Wang
- Department of Psychiatry, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Zhujing Ma
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Huaning Wang
- Department of Psychiatry, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Peng Fang
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent PerceptionXi'anChina
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Mehta UM, Ithal D, Roy N, Shekhar S, Govindaraj R, Ramachandraiah CT, Bolo NR, Bharath RD, Thirthalli J, Venkatasubramanian G, Gangadhar BN, Keshavan MS. Posterior Cerebellar Resting-State Functional Hypoconnectivity: A Neural Marker of Schizophrenia Across Different Stages of Treatment Response. Biol Psychiatry 2024:S0006-3223(24)00076-3. [PMID: 38336217 DOI: 10.1016/j.biopsych.2024.01.027] [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: 08/02/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response. METHODS In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined. RESULTS Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate-corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen's d range -0.95 to -1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment. CONCLUSIONS Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.
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Affiliation(s)
- Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India.
| | - Dhruva Ithal
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Neelabja Roy
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Shreshth Shekhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Ramajayam Govindaraj
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Nicolas R Bolo
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
| | - Rose Dawn Bharath
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Jagadisha Thirthalli
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Bangalore N Gangadhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Matcheri S Keshavan
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
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Panjaitan NSD, Lestari CSW, Maha MS. Thoughtful Response on "MRI-based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma" [Letter]. J Multidiscip Healthc 2023; 16:275-276. [PMID: 36726481 PMCID: PMC9885877 DOI: 10.2147/jmdh.s405040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Affiliation(s)
- Novaria Sari Dewi Panjaitan
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia,Correspondence: Novaria Sari Dewi Panjaitan, Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Jl. Raya Bogor No. 490, Cibinong – Bogor Km. 46, Cibinong, West Java, Indonesia, Email
| | - Christina Safira Whinie Lestari
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia
| | - Masri Sembiring Maha
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia
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