Fabio Di Camillo, David Antonio Grimaldi, Giulia Cattarinussi, Annabella Di Giorgio, Clara Locatelli, Adyasha Khuntia, Paolo Enrico, Paolo Brambilla, Nikolaos Koutsouleris, Fabio Sambataro
{"title":"基于磁共振成像的精神分裂症谱系障碍机器学习分类:荟萃分析","authors":"Fabio Di Camillo, David Antonio Grimaldi, Giulia Cattarinussi, Annabella Di Giorgio, Clara Locatelli, Adyasha Khuntia, Paolo Enrico, Paolo Brambilla, Nikolaos Koutsouleris, Fabio Sambataro","doi":"10.1111/pcn.13736","DOIUrl":null,"url":null,"abstract":"BackgroundRecent 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.MethodsWe 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.ResultsA 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.ConclusionsMultivariate 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.","PeriodicalId":20938,"journal":{"name":"Psychiatry and Clinical Neurosciences","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Magnetic resonance imaging–based machine learning classification of schizophrenia spectrum disorders: a meta‐analysis\",\"authors\":\"Fabio Di Camillo, David Antonio Grimaldi, Giulia Cattarinussi, Annabella Di Giorgio, Clara Locatelli, Adyasha Khuntia, Paolo Enrico, Paolo Brambilla, Nikolaos Koutsouleris, Fabio Sambataro\",\"doi\":\"10.1111/pcn.13736\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BackgroundRecent 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.MethodsWe 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.ResultsA 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.ConclusionsMultivariate 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.\",\"PeriodicalId\":20938,\"journal\":{\"name\":\"Psychiatry and Clinical Neurosciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychiatry and Clinical Neurosciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/pcn.13736\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychiatry and Clinical Neurosciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/pcn.13736","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Magnetic resonance imaging–based machine learning classification of schizophrenia spectrum disorders: a meta‐analysis
BackgroundRecent 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.MethodsWe 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.ResultsA 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.ConclusionsMultivariate 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.
期刊介绍:
PCN (Psychiatry and Clinical Neurosciences)
Publication Frequency:
Published 12 online issues a year by JSPN
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All manuscripts undergo peer review by anonymous reviewers, an Editorial Board Member, and the Editor
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Manuscripts are accepted based on quality, originality, and significance to the readership
Authors must confirm that the manuscript has not been published or submitted elsewhere and has been approved by each author