{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:39:37Z","timestamp":1783075177056,"version":"3.54.6"},"reference-count":97,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose\/Hypothesis<\/jats:title><jats:p>We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>A systematic review and diagnostic meta\u2010analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population\/Subjects<\/jats:title><jats:p>Sixty\u2010seven studies with 6,285 patients were included.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>The authors independently assessed the study quality using the CLAIM and QUADAS\u20102 criteria and extracted data on diagnostic accuracy measures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Sensitivity, specificity, accuracy, and area under the curve were pooled using random\u2010effects models. Q statistics and the <jats:italic>I<\/jats:italic><jats:sup>2<\/jats:sup> index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>1<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29364","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:59:30Z","timestamp":1711587570000},"page":"2518-2546","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using <scp>MRI<\/scp> Data: A Systematic Review and Diagnostic Meta\u2010analysis"],"prefix":"10.1002","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1334-8826","authenticated-orcid":false,"given":"Sadegh","family":"Ghaderi","sequence":"first","affiliation":[{"name":"Department of Neuroscience and Addiction Studies, School of Advanced Technologies 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