Mechanisms of action underlying the efficacy of deep brain stimulation of the subthalamic nucleus in Parkinson's disease: central role of disease severity.

Despite consensus on some neurophysiological hallmarks of the Parkinsonian state (such as beta) band increase) a single mechanism is unlikely to explain the efficacy of deep brain stimulation (DBS) of the subthalamic nucleus (STN). Most experimental evidence to date correlates with an extreme degree of nigral neurodegeneration and not with different stages of PD progression. It seems inappropriate to combine substantially different patients - newly diagnosed, early fluctuators or advanced dyskinetic individuals - within the same group. An efficacious STN-DBS imposes a new activity pattern within brain circuits, favouring alpha- and gamma-like neuronal discharge, and restores the thalamo-cortical transmission pathway through axonal activation. In addition, stimulation via the dorsal contacts of the macro-electrode may affect cortical activation antidromically. However, basal ganglia (BG) modulation remains cardinal for 'OFF'-'ON' transition (as revealed by cGMP increase occurring during STN-DBS in the substantia nigra pars reticulata and internal globus pallidus). New research promises to clarify to what extent STN-DBS restores striato-centric bidirectional plasticity, and whether non-neuronal cellular actions (microglia, neurovascular) play a part. Future studies will assess whether extremely anticipated DBS or lesioning in selected patients are capable of providing neuroprotection to the synuclein-mediated alterations of synaptic efficiency. This review addresses these open issues through the specific mechanisms prevailing in a given disease stage. In patients undergoing early protocol, alteration in endogenous transmitters and recovery of plasticity are concurrent players. In advanced stages, re-modulation of endogenous band frequencies, disruption of pathological pattern and/or antidromic cortical activation are, likely, the prominent modes.

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation.

Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.