Emotional symptoms and cognitive function outcomes of subthalamic stimulation in Parkinson's disease depend on location of active contacts and the volume of tissue activated.

BACKGROUND: Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), that can improve patients' motor and non-motor symptoms. However, there are differences in the improvement of patients' emotional symptoms and cognitive function. OBJECTIVE: To investigate the impact of active contact location and the volume of tissue activated (VTA) on patients' emotional symptoms and cognitive function in STN-DBS in PD. METHODS: A total of 185 PD patients were included in this study. We evaluated them using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale, Hamilton Anxiety Scale (HAM-A), Hamilton Depression Scale (HAM-D), Montreal Cognitive Assessment (MoCA), and Mini-Mental State Examination (MMSE) scales at the preoperative, 1- and 12-month postoperative time points. Leads were positioned in standard space using the Lead-DBS toolbox, and VTA was calculated for analysis. RESULTS: When the lead active contact was closer to the ventral side of the STN, the patients' HAM-A improvement rate was higher, and when the active contact was closer to the anterior and dorsal sides of the STN, the patients' MoCA improvement rate was higher. Stimulation of the sensorimotor zone was more favorable to the improvement of HAM-A and HAM-D in patients. And, the stimulation of the associative zone was more favorable to the improvement of MoCA in patients. CONCLUSION: Our results provide evidence that the 12-month outcomes of cognitive function and emotional symptoms in PD patients with STN-DBS were closely related to the specific location of the active contacts in the STN and influenced by the VTA.

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation.

BACKGROUND: This study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS). METHODS: Fourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model. RESULTS: Compared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders. CONCLUSION: The results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

Balance response to levodopa predicts balance improvement after bilateral subthalamic nucleus deep brain stimulation in Parkinson's disease.

The effect of subthalamic nucleus deep brain stimulation (STN-DBS) on balance function in patients with Parkinson's disease (PD) and the potential outcome predictive factors remains unclear. We retrospectively included 261 PD patients who underwent STN-DBS and finished the 1-month follow-up (M1) assessment in the explorative set for identifying postoperative balance change predictors, and 111 patients who finished both the M1 and 12-month follow-up (M12) assessment in the validation set for verifying the identified factors. Motor and balance improvement were evaluated through the UPDRS-III and the Berg balance scale (BBS) and pull test (PT), respectively. Candidate predictors of balance improvement included age, disease duration, motor subtypes, baseline severity of PD, cognitive status, motor and balance response to levodopa, and stimulation parameters. In the off-medication condition, STN-DBS significantly improved BBS and PT performance in both the M1 and M12, in both datasets. While in the on-medication condition, no significant balance improvement was observed. Higher preoperative BBS response to levodopa was significantly associated with larger postoperative off-medication, but not on-medication, BBS (p < 0.001) and PT (p < 0.001) improvement in both the M1 and M12. BBS subitems 8, 9, 11, 13, and 14 were the major contributors to the prediction of balance improvement after STN-DBS. STN-DBS improves short-term off-medication, but not on-medication, balance function assessed through BBS and PT. Preoperative BBS response to levodopa best predicts postoperative off-medication balance improvement. For patients who manifested severe balance problems, a levodopa challenge test on BBS or the short version of BBS is recommended.

The amplitude of low-frequency fluctuation predicts levodopa treatment response in patients with Parkinson's disease.

INTRODUCTION: Levodopa has become the main therapy for motor symptoms of Parkinson's disease (PD). This study aimed to test whether the amplitude of low-frequency fluctuation (ALFF) computed by fMRI could predict individual patient's response to levodopa treatment. METHODS: We included 40 patients. Treatment efficacy was defined based on motor symptoms improvement from the state of medication off to medication on, as assessed by the Unified Parkinson's Disease Rating Scale score III. Two machine learning models were constructed to test the prediction ability of ALFF. First, the ensemble method was implemented to predict individual treatment responses. Second, the categorical boosting (CatBoost) classification was used to predict individual levodopa responses in patients classified as moderate and superior responders, according to the 50% threshold of improvement. The age, disease duration and treatment dose were controlled as covariates. RESULTS: No significant difference in clinical data were observed between moderate and superior responders. Using the ensemble method, the regression model showed a significant correlation between the predicted and the observed motor symptoms improvement (r = 0.61, p < 0.01, mean absolute error = 0.11 ± 0.02), measured as a continuous variable. The use of the Catboost algorithm revealed that ALFF was able to differentiate between moderate and superior responders (area under the curve = 0.90). The mainly contributed regions for both models included the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia. CONCLUSION: Both continuous and binary ALFF values have the potential to serve as promising predictive markers of dopaminergic therapy response in patients with PD.