Gait initiation evaluation after deep brain stimulation for Parkinson's disease: A 7-year follow-up.

This study evaluated the long-term effects of deep brain stimulation of the subthalamic nucleus (DBS-STN) on gait initiation. Six Parkinson's disease (PD) patients who had undergone DBS-STN and 31 control subjects were evaluated. PD subjects were assessed at two different time periods: 11.3 ± 10.3 (P1) and 78.9 ± 10.6 (P2) months after surgery. Subjects under stimulation were tested in two conditions: without medication and with medication. Principal components (PC) analysis was separately applied on vertical, anterior-posterior and medial-lateral ground reaction force (GRF) from gait initiation, during the anticipatory postural adjustment (APA) phase. Three PC scores were chosen by the scree test for each GRF component. The higher loading factors pointed to major differences between controls and PD patients on maximum APA amplitude for vertical and anterior-posterior GRF. Friedman test showed a significant difference in standard distance among conditions (P = 0.006), with the post-hoc test recognizing differences only between P1 and P2 in the medication-on condition. All distances increased in the follow-up evaluation (P2), when considering the same medication condition, indicating a worsening in gait initiation after 7 years of follow-up.

Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network.

This study aims at applying an artificial neural network for the evaluation of the effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) on Parkinson disease (PD) patients with and without medication. A sample of 15 PD patients who have undergone STN DBS were evaluated under four test conditions: medication off and stimulation off (mof-sof), medication off and stimulation on (mof-son), medication on and stimulation off (mon-sof) and medication on and stimulation on (mon-son). A control group with 30 subjects was also evaluated. Principal component analysis (PCA) was applied on vertical ground reaction force (vGRF) and the first six principal component scores (PC score) were obtained in both groups. Those PCs scores were used as input in a probabilistic neural network (PNN). PNN presented satisfactory classification performance in the separation of controls and PD with 90.1% accuracy, 69.2% sensitivity and 100% specificity. The stimulation mof-son and mon-son conditions presented better results compared to mon-sof. In the mof-son condition, 41.7% were classified as normal, while further enhancement (63.3%) was given by the mon-son condition. These results indicated the potentiality of PNN to quantitatively evaluate treatment effects. Furthermore, STN DBS shows improvement on vGRF pattern in PD patients, most substantially when used with medication.