Striatum'dan Reçeteye: Parkinson Hastalığında Nöromotor Rehabilitasyon için Kanıta Dayalı Ve Bayesian Çerçevesi

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Parkinson’s disease (PD) involves progressive basal ganglia dysfunction, with hyper-excitability 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 integrates neurobiological mecha-nisms and clinical evidence across diverse rehabilitative strategies in PD, including voluntary and forced exercise, high-intensity interval training (HIIT), treadmill and re-sistance training, Tai Chi and dance, Lee Silverman Voice Treatment BIG (LSVT BIG), repetitive transcranial magnetic stimulation [rTMS], transcranial direct current stimu-lation [tDCS]), exoskeleton-assisted training, augmented reality-based interventions (AR), multimodal cueing, and dual-task paradigms. These approaches are unified by striatal indirect-pathway recalibration as the main mechanism underlying rehabilitation efficacy in PD. To formalize evidence integration, a Bayesian ranking framework is proposed, combining neurobiological plausibility with clinical evidence quality. Ap-plied to eleven interventions, this framework identifies three functional clusters: a high-recalibration cluster including forced exercise (posterior probability P ≈ 0.80) and LSVT BIG (P ≈ 0.62); an intermediate uncertainty cluster comprising rTMS, HIIT, tDCS, treadmill, resistance training, and Tai Chi/dance (0.40 ≤ P ≤ 0.56); and a low-recalibration or compensatory bypass cluster including AR, exoskeletons, and dual-task training (P ≤ 0.33). This framework highlights an epistemic distinction between interventions that may directly modulate basal ganglia circuitry and those that primarily recruit alterna-tive motor networks, including the lateral premotor cortex, parieto-premotor circuits, and cerebello-thalamo-cortical pathways, supporting a precision rehabilitation ap-proach in PD.

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