Subthalamic DBS engages right-lateralized frontal control to improve gait adaptation in Parkinson's disease.

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BackgroundSuccessful walking requires continuous adaptation to changing environmental demands, a process that is frequently impaired in Parkinson's disease (PD). The neural mechanisms by which subthalamic nucleus deep brain stimulation (STN-DBS) modulates adaptive gait control remain unclear.ObjectiveTo characterize the clinical, kinematic, metabolic, and cortical electrophysiological correlates of gait adaptation in PD and their modulation by STN-DBS during dynamic obstacle avoidance.MethodsWe evaluated gait-adaptation kinematics and electroencephalography (EEG) in twelve PD patients during an immersive virtual-reality overground walking task under active and paused STN-DBS. Brain metabolism was assessed with three [18F]fluorodeoxyglucose positron-emission tomography scans acquired after rest and after gait adaptation under paused and active STN-DBS. Eight age-matched healthy participants completed the same gait-adaptation task for comparative kinematic analyses.ResultsDuring gait adaptation with paused stimulation, patients showed increased metabolic activity in the cerebellum and sensorimotor cortex. Active STN-DBS selectively increased thalamic and superior frontal gyrus (SFG) metabolism while reducing cerebellar uptake. Right-lateralized SFG metabolism correlated with gait adaptation performance, and DBS-induced shifts toward greater right SFG activity were associated with gait adaptation improvement. This association was independent of baseline clinical asymmetry, electrode location, and structural connectivity to the SFG. STN-DBS amplitude asymmetry independently predicted right-lateralized SFG metabolism. EEG provided complementary evidence of lateralized network modulation, with frontal theta-band asymmetry paralleling the PET findings.ConclusionOur findings suggest that gait adaptation in PD is associated with lateralized thalamo-cortical activity involving the SFG, while STN-DBS modulates activity within this network, informing mechanisms of gait control and future image-guided programming.

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