David Mikhael,Skyler Deutsch,Juhi Mehta,Sarah Wang,John Kornak,Philip A Starr,Jill L Ostrem,Doris D Wang,Ian O Bledsoe,Melanie A Morrison
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{"title":"术前功能连通性预测脑深部刺激后抗帕金森药物变化。","authors":"David Mikhael,Skyler Deutsch,Juhi Mehta,Sarah Wang,John Kornak,Philip A Starr,Jill L Ostrem,Doris D Wang,Ian O Bledsoe,Melanie A Morrison","doi":"10.1002/mds.70063","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nDeep brain stimulation (DBS) can effectively ameliorate motor symptoms in Parkinson's disease (PD), but patient outcomes remain variable. Clinical predictors lack reliability and only explain a small proportion of outcome variance, outlining a need for biomarkers that can enhance prediction accuracy. Functional magnetic resonance imaging (fMRI) could address this, offering insight into the relative impact of functional brain health on DBS outcomes.\r\n\r\nMETHODS\r\nPreoperative resting-state fMRI was retrospectively collected for 120 patients with PD and DBS targeting the subthalamic nucleus or pallidum. Motor network connectivity was computed, and clinical predictors extracted including age, sex, target, hemisphere treated, and preoperative medication responsiveness. Pre-to-post changes in antiparkinson medications defined outcomes. Regression analysis selected important connectivity features and evaluated additional outcome variance explained by fMRI when combined with clinical predictors. Last, classification analysis assessed fMRI feature specificity to PD compared to Huntington's disease and healthy controls.\r\n\r\nRESULTS\r\nAlongside clinical predictors, fMRI features explained more outcome variance (R2 adjusted = 0.36) than clinical predictors alone (R2 adjusted = 0.13). Five connectivity pairs bridging the cortico-basal ganglia-cerebellar network were predictive of outcomes and distinguished PD from controls and Huntington's disease with reasonable accuracy (67% and 73%). Exploratory analysis revealed that intracerebellar connectivity, although a less stable predictor of DBS outcomes, dramatically increased PD classification accuracy (≥98%).\r\n\r\nCONCLUSION\r\nPatient-specific motor connectivity enhances DBS outcome prediction and could aid detection and monitoring of basal ganglia disorders. Future work will validate and extend our models for multi-parameter MR prediction of standardized outcomes, toward the development of decision support tools for DBS. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.","PeriodicalId":213,"journal":{"name":"Movement Disorders","volume":"18 1","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Preoperative Functional Connectivity Predicts Antiparkinson Drug Change after Deep Brain Stimulation.\",\"authors\":\"David Mikhael,Skyler Deutsch,Juhi Mehta,Sarah Wang,John Kornak,Philip A Starr,Jill L Ostrem,Doris D Wang,Ian O Bledsoe,Melanie A Morrison\",\"doi\":\"10.1002/mds.70063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\r\\nDeep brain stimulation (DBS) can effectively ameliorate motor symptoms in Parkinson's disease (PD), but patient outcomes remain variable. Clinical predictors lack reliability and only explain a small proportion of outcome variance, outlining a need for biomarkers that can enhance prediction accuracy. Functional magnetic resonance imaging (fMRI) could address this, offering insight into the relative impact of functional brain health on DBS outcomes.\\r\\n\\r\\nMETHODS\\r\\nPreoperative resting-state fMRI was retrospectively collected for 120 patients with PD and DBS targeting the subthalamic nucleus or pallidum. Motor network connectivity was computed, and clinical predictors extracted including age, sex, target, hemisphere treated, and preoperative medication responsiveness. Pre-to-post changes in antiparkinson medications defined outcomes. Regression analysis selected important connectivity features and evaluated additional outcome variance explained by fMRI when combined with clinical predictors. 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Preoperative Functional Connectivity Predicts Antiparkinson Drug Change after Deep Brain Stimulation.
BACKGROUND
Deep brain stimulation (DBS) can effectively ameliorate motor symptoms in Parkinson's disease (PD), but patient outcomes remain variable. Clinical predictors lack reliability and only explain a small proportion of outcome variance, outlining a need for biomarkers that can enhance prediction accuracy. Functional magnetic resonance imaging (fMRI) could address this, offering insight into the relative impact of functional brain health on DBS outcomes.
METHODS
Preoperative resting-state fMRI was retrospectively collected for 120 patients with PD and DBS targeting the subthalamic nucleus or pallidum. Motor network connectivity was computed, and clinical predictors extracted including age, sex, target, hemisphere treated, and preoperative medication responsiveness. Pre-to-post changes in antiparkinson medications defined outcomes. Regression analysis selected important connectivity features and evaluated additional outcome variance explained by fMRI when combined with clinical predictors. Last, classification analysis assessed fMRI feature specificity to PD compared to Huntington's disease and healthy controls.
RESULTS
Alongside clinical predictors, fMRI features explained more outcome variance (R2 adjusted = 0.36) than clinical predictors alone (R2 adjusted = 0.13). Five connectivity pairs bridging the cortico-basal ganglia-cerebellar network were predictive of outcomes and distinguished PD from controls and Huntington's disease with reasonable accuracy (67% and 73%). Exploratory analysis revealed that intracerebellar connectivity, although a less stable predictor of DBS outcomes, dramatically increased PD classification accuracy (≥98%).
CONCLUSION
Patient-specific motor connectivity enhances DBS outcome prediction and could aid detection and monitoring of basal ganglia disorders. Future work will validate and extend our models for multi-parameter MR prediction of standardized outcomes, toward the development of decision support tools for DBS. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.