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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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Sun S, Xiao S, Guo Z, Gong J, Tang G, Huang L, Wang Y. Meta-analysis of cortical thickness reduction in adult schizophrenia. J Psychiatry Neurosci 2023; 48:E461-E470. [PMID: 38123240 PMCID: PMC10743639 DOI: 10.1503/jpn.230081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/17/2023] [Accepted: 09/11/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Numerous neuroimaging studies using surface-based morphometry analyses have reported altered cortical thickness among patients with schizophrenia, but the results have been inconsistent. We sought to provide a whole-brain meta-analysis, which may help enhance the spatial accuracy of identification. METHODS We conducted a meta-analysis of whole-brain studies that explored cortical thickness alteration among adult patients with schizophrenia, including first-episode patients with schizophrenia, and patients with chronic schizophrenia, compared with healthy controls by using the seed-based d mapping with permutation of subject images (SDM-PSI) software. RESULTS A systematic literature search identified 25 studies (33 data sets) of cortical thickness, including 2008 patients with schizophrenia and 2004 healthy controls. Overall, patients with schizophrenia showed decreased cortical thickness in the right inferior frontal gyrus (IFG) and bilateral insula extending to the superior temporal gyrus (STG). Subgroup meta-analysis reported that patients with chronic schizophrenia showed decreased cortical thickness in the right insula extending to the right IFG. There was no significant cortical thickness difference between first-episode patients with schizophrenia and healthy controls. LIMITATIONS The results of meta-regression analyses should be viewed cautiously since they were driven by a small number of studies or did not overlap with the between-group differences found in the primary analyses. CONCLUSION The meta-analysis suggested robust cortical thickness reduction in the IFG, insula and STG among adult patients with schizophrenia, particularly in those with chronic schizophrenia. The results provide useful insights to understanding the underlying pathophysiology of schizophrenia.
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Affiliation(s)
- Shilin Sun
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Shu Xiao
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Zixuan Guo
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Jiaying Gong
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Guixian Tang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Li Huang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Ying Wang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
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3
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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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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Shi Y, Shen Z, Zeng W, Luo S, Zhou L, Wang N. A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI. Front Hum Neurosci 2023; 17:1164685. [PMID: 37250690 PMCID: PMC10213427 DOI: 10.3389/fnhum.2023.1164685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
At present, fMRI studies mainly focus on the entire low-frequency band (0. 01-0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency-based dynamic functional connectivity (dFC) analysis method was proposed in this study, which was then applied to a schizophrenia study. First, three frequency bands (Conventional: 0.01-0.08 Hz, Slow-5: 0.0111-0.0302 Hz, and Slow-4: 0.0302-0.0820 Hz) were obtained using Fast Fourier Transform. Next, the fractional amplitude of low-frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by the sliding time window method at four window-widths. Finally, recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of patients with schizophrenia and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with the conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.
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Affiliation(s)
- Yuhu Shi
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Zehao Shen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Sizhe Luo
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lili Zhou
- Surgery Department of Tongji University Affiliated Yangpu Central Hospital, Shanghai, China
| | - Nizhuan Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 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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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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8
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Fu Y, Niu M, Gao Y, Dong S, Huang Y, Zhang Z, Zhuo C. Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia. J Neural Eng 2022; 19. [PMID: 36579785 DOI: 10.1088/1741-2552/acabe7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.
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Affiliation(s)
- Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Meng Niu
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, People's Republic of China
| | - Yuanhang Gao
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Shunjie Dong
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Yanyan Huang
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- School of Physics, Hangzhou Normal University, Hangzhou, People's Republic of China.,Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China.,Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Hangzhou, People's Republic of China
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Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 PMCID: PMC9793282 DOI: 10.1016/j.heliyon.2022.e12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
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10
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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11
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Chitneni A, Rupp A, Ghorayeb J, Abd-Elsayed A. Early Detection of Diabetic Peripheral Neuropathy by fMRI: An Evidence-Based Review. Brain Sci 2022; 12:brainsci12050557. [PMID: 35624944 PMCID: PMC9139132 DOI: 10.3390/brainsci12050557] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/24/2022] [Accepted: 04/26/2022] [Indexed: 01/13/2023] Open
Abstract
With the significant rise in the prevalence of diabetes worldwide, diabetic peripheral neuropathy (DPN) remains the most common complication among type 1 and 2 diabetics. The adverse sequelae of DPN, which include neuropathic pain, diabetic foot ulcers and lower-limb amputations, significantly impact quality of life and are major contributors to the biopsychosocial and economic burden of diabetes at the individual, societal and health system levels. Because DPN is often diagnosed in the late stages of disease progression by electromyography (EMG), and neuropathic pain as a result of DPN is difficult to treat, the need for earlier detection is crucial to better ascertain and manage the condition. Among the various modalities available to aid in the early detection of DPN, functional magnetic resonance imaging (fMRI) has emerged as a practical tool in DPN imaging due to its noninvasive radiation-free nature and its ability to relate real-time functional changes reflecting the local oxygen consumption of regions of the CNS due to external stimuli. This review aims to summarize the current body of knowledge regarding the utility of fMRI in detecting DPN by observing central nervous system (CNS) activity changes among individuals with DPN when compared to controls. The evidence to date points toward a tendency for increased activity in various central neuroanatomical structures that can be detected by fMRI and positively correlates with diabetic neuropathic pain.
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Affiliation(s)
- Ahish Chitneni
- Department of Rehabilitation and Regenerative Medicine, NewYork-Presbyterian Hospital—Columbia and Cornell, New York, NY 10065, USA
- Correspondence: (A.C.); (A.A.-E.); Tel.: +1-608-263-6039 (A.A.-E.)
| | - Adam Rupp
- Department of Physical Medicine and Rehabilitation, University of Kansas Health System, Kansas City, MO 66160, USA;
| | - Joe Ghorayeb
- Department of Physical Medicine and Rehabilitation, University of Medicine & Health Sciences, New York, NY 10001, USA;
| | - Alaa Abd-Elsayed
- Department of Anesthesia, Division of Pain Medicine, School of Medicine & Public Health, University of Wisconsin, Madison, WI 53726, USA
- Correspondence: (A.C.); (A.A.-E.); Tel.: +1-608-263-6039 (A.A.-E.)
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12
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Richardson B, Swenson S, Hamilton J, Leonard K, Delis F, Gold M, Blum K, Thanos PK. Chronic neuroleptic treatment combined with a high fat diet elevated [3H] flunitrazepam binding in the cerebellum. Prog Neuropsychopharmacol Biol Psychiatry 2022; 112:110407. [PMID: 34320402 DOI: 10.1016/j.pnpbp.2021.110407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/21/2021] [Accepted: 07/22/2021] [Indexed: 01/29/2023]
Abstract
Clinical and preclinical studies have shown dysfunctions in genetic expression and neurotransmission of γ-Aminobutyric acid (GABA), GABAA receptor subunits, and GABA-synthesizing enzymes GAD67 and GAD65 in schizophrenia. It is well documented that there is significant weight gain after chronic neuroleptic treatment in humans. While there are limited studies on the effects of diet on GABA signaling directly, a change in diet has been used clinically as an adjunct to treatment for schizophrenic relief. In this study, rats chronically consumed either a chow diet (CD) or a 60% high-fat diet (HFD) and drank from bottles that contained one of the following solutions: water, haloperidol (1.5 mg/kg), or olanzapine (10 mg/kg) for four weeks. Rats were then euthanized and their brains were processed for GABAA in-vitro receptor autoradiography using [3H] flunitrazepam. A chronic HFD treatment yielded significantly increased [3H] flunitrazepam binding in the rat cerebellum independent of neuroleptic treatment. The desynchronization between the prefrontal cortex and the cerebellum is associated with major cognitive and motor dysfunctions commonly found in schizophrenic symptomatology, such as slowed reaction time, motor dyscoordination, and prefrontal activations related to speech fluency and cognitive alertness. These data support the notion that there is a dietary effect on GABA signaling within the cerebellum, as well as the importance of considering nutritional intervention methods as an adjunct treatment for patients chronically treated with neuroleptics. Finally, we indicate that future studies involving the analysis of individual patient's genetic profiles will further assist towards a precision medicine approach to the treatment of schizophrenia.
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Affiliation(s)
- Brittany Richardson
- Behavioral Neuropharmacology and Neuroimaging Laboratory on Addictions (BNNLA), Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Department of Psychology, University at Buffalo, Buffalo, NY, USA
| | - Sabrina Swenson
- Behavioral Neuropharmacology and Neuroimaging Laboratory on Addictions (BNNLA), Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - John Hamilton
- Behavioral Neuropharmacology and Neuroimaging Laboratory on Addictions (BNNLA), Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Department of Psychology, University at Buffalo, Buffalo, NY, USA
| | - Ken Leonard
- Department of Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Foteini Delis
- Department of Pharmacology, University at Ioannina, Ioannina, Greece
| | - Mark Gold
- Washington University in St Louis, School of Medicine, St. Louis, MS, USA
| | - Ken Blum
- Western University Health Sciences, Graduate School of Biomedical Sciences, Pomona, CA, USA
| | - Panayotis K Thanos
- Behavioral Neuropharmacology and Neuroimaging Laboratory on Addictions (BNNLA), Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Department of Psychology, University at Buffalo, Buffalo, NY, USA.
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13
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Schneider H. Artificial Intelligence in Schizophrenia. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Cruz-Martinez C, Reyes-Garcia CA, Vanello N. A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106509. [PMID: 34800805 DOI: 10.1016/j.cmpb.2021.106509] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/29/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified. METHODS We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels' generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers. RESULTS According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works. CONCLUSIONS The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.
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Affiliation(s)
- Claudia Cruz-Martinez
- Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Computer Science Department, Puebla, Mexico.
| | - Carlos A Reyes-Garcia
- Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Computer Science Department, Puebla, Mexico.
| | - Nicola Vanello
- University of Pisa, Department of Information Engineering, Pisa, Italy.
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15
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Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021; 11:1402. [PMID: 34441336 PMCID: PMC8392727 DOI: 10.3390/diagnostics11081402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer's disease (AD), Parkinson's disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.
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Affiliation(s)
- Zhao Zhang
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
| | - Guangfei Li
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
| | - Yong Xu
- Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China;
| | - Xiaoying Tang
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
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16
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Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, Ren K. Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging. DISEASE MARKERS 2021; 2021:9963824. [PMID: 34211615 PMCID: PMC8208855 DOI: 10.1155/2021/9963824] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Yanfei Li
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Xiang Yao
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Siyuan Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
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17
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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18
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Artificial Intelligence in Schizophrenia. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_214-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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19
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Steardo L, Carbone EA, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A, De Fazio P, Steardo L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry 2020; 11:588. [PMID: 32670113 PMCID: PMC7326270 DOI: 10.3389/fpsyt.2020.00588] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/08/2020] [Indexed: 01/06/2023] Open
Abstract
Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.
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Affiliation(s)
- Luca Steardo
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Renato de Filippis
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Claudia Pisanu
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Pasquale De Fazio
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Luca Steardo
- Department of Physiology and Pharmacology, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Rome, Italy.,Department of Psychiatry, Giustino Fortunato University, Benevento, Italy
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20
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Abstract
BACKGROUND This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. METHODS We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. RESULTS Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. CONCLUSIONS Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
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Affiliation(s)
- Adrian B R Shatte
- Federation University, School of Science, Engineering & Information Technology,Melbourne,Australia
| | - Delyse M Hutchinson
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
| | - Samantha J Teague
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
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21
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Kaur P, Sharma M. Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis. J Med Syst 2019; 43:204. [DOI: 10.1007/s10916-019-1341-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 05/03/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
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22
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de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, De Fazio P. Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr Dis Treat 2019; 15:1605-1627. [PMID: 31354276 PMCID: PMC6590624 DOI: 10.2147/ndt.s202418] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 04/09/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. OBJECTIVES A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. METHODS We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. RESULTS Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). CONCLUSION The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.
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Affiliation(s)
- Renato de Filippis
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Raffaele Gaetano
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Antonella Bruni
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Valentina Pugliese
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Pasquale De Fazio
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
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