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Background: 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. Purpose/Hypothesis: 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. Study Type: A systematic review and diagnostic meta‐analysis. Population/Subjects: Sixty‐seven studies with 6,285 patients were included. Field Strength/Sequence: 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. Assessment: The authors independently assessed the study quality using the CLAIM and QUADAS‐2 criteria and extracted data on diagnostic accuracy measures. Statistical Tests: Sensitivity, specificity, accuracy, and area under the curve were pooled using random‐effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. Results: 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. Data Conclusion: 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. Level of Evidence: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: One of the most useful tools for identifying sleep disturbances is neuroimaging, especially magnetic resonance imaging (MRI). This review research was to look at the role of MRI findings in movement disorders and sleep disturbances., Methods: This review collects all MRI data on movement disorders and sleep disruptions. Between 2000 and 2022, PubMed and Google Scholar were utilized to find original English publications and reviews. According to the inclusion and exclusion criteria, around 100 publications were included. We only looked at research that explored MRI modality together with movement problems, sleep disorders, and brain area involvement. Most of the information focuses on movement irregularities and sleep interruptions., Results: Movement disorders such as Parkinson's disease (PD), Huntington's disease (HD), neuromuscular diseases, rapid eye movement (REM) sleep behavior movement disorder (RBD), cerebellar movement disorders, and brainstem movement disorders are assessed using MRI-based neuroimaging techniques. Some of the brain areas were associated with disorders in movement abnormalities and related sleep disturbances. This review found that many people with mobility disorders also have sleep problems. Some brain areas' malfunctions may cause motor and sleep issues., Conclusion: Neuroimaging helps us understand the sleep difficulties associated with movement disorders by examining the structural and functional implications of movement disorders and sleep disturbances., Competing Interests: None., (AJNMMI Copyright © 2023.)