From Striatum to Prescription: An Evidence-Based and Bayesian Framework for Neuromotor Rehabilitation in Parkinson's Disease.

Parkinson's disease (PD) involves progressive basal ganglia dysfunction, with hyperexcitability of striatal indirect-pathway D2 medium spiny neurons (D2-MSNs) linked to motor impairment. Neuromotor rehabilitation is an important therapy, but its efficacy varies across interventions. This thematic review examines eleven rehabilitative strategies for PD, spanning forced and voluntary exercise (FE and VE respectively), non-invasive brain stimulation (rTMS, tDCS), and technology-based approaches such as exoskeletons, augmented reality, and dual-task training, within a framework distinguishing striatal recalibration from compensation via alternative motor networks. To formalize this distinction, a Bayesian ranking framework combines neurobiological plausibility with clinical evidence quality, identifying three functional clusters: a high-recalibration cluster (FE, p ≈ 0.80; LSVT BIG, p ≈ 0.62), an intermediate-uncertainty cluster (rTMS, HIIT, tDCS, treadmill, resistance training, Tai Chi/dance; 0.40-0.56), and a bypass/compensatory cluster (augmented reality, exoskeletons, dual-task training; p ≤ 0.33). This distinction between direct modulation of basal ganglia circuitry and recruitment of alternative motor networks, including the lateral premotor cortex, parieto-premotor circuits, and cerebello-thalamo-cortical pathways, supports a precision rehabilitation approach in PD.