Periodic Reporting for period 1 - ReinforceBG (Interrogating basal ganglia reinforcement with deep brain stimulation in Parkinson’s disease.)
Berichtszeitraum: 2023-03-01 bis 2025-08-31
Dopamine and the basal ganglia have been conserved over more than 500 million years of evolution. They are fundamental to animal and human behaviour. Parkinson’s disease (PD) is associated with loss of dopaminergic innervation to the basal ganglia. Over 6 million people suffer from the debilitating symptoms of PD that span disturbance of emotion, cognition and movement. There is a pressing need to understand the pathogenesis of these symptoms, but an integrated account of dopamine and basal ganglia function is lacking. This constitutes a significant roadblock to scientific and therapeutic advances. To overcome this roadblock, ReinforceBG poses the novel unconventional hypothesis that loss of dopamine in PD does not impair movement per se but leads to chronic negative reinforcement of neural population dynamics. Conversely, in the healthy state, transient dopamine signals may stabilize cortex–basal ganglia activity to facilitate reentry and refinement of cortical output. To address this hypothesis, ReinforceBG will combine invasive electrocorticography and local field potential recordings with closed-loop deep brain stimulation in PD patients. Aim 1 will investigate how basal ganglia pathways coordinate neuromuscular adaptation. Aim 2 will shed light on basal ganglia reinforcement in multiple behavioural domains, including movement, gait, speech, and spatial navigation in virtual reality. Aim 3 will develop a neuroprosthetic brain-computer interface that aims to modulate basal ganglia reinforcement. ReinforceBG deviates from outdated models on pro- vs. antikinetic “Go” and “NoGo” pathways and promises a holistic reinforcement-centred view of basal ganglia function. It will leverage the unprecedented spatiotemporal precision of neuromodulation for the development of an innovative brain circuit intervention that modulates neural reinforcement in real time. This opens new horizons for the interdisciplinary treatment of brain disorders affecting the dopaminergic system.
We have established invasive brain signal recordings combined with invasive electrical neurostimulation using both cortical and and subcortical neural interfaces in people living with Parkinson's disease. More than 50 patients undergoing brain surgery have been recruited and have successfully participated in our project. The first results from our study elucidate the propagation of brain circuit activity from cortex to subcortex and its' susceptibility to therapeutic interference using cortical brain stimulation and evoked response patterns in the depth of the brain and at the level of muscle activation. Moreover, we have used individualized precision phenotyping of motor output, combining high-density electromyography (EMG) and kinematic recordings for closed-loop neurostimulation. Based on this, our results show that subthalamic neuromodulation can reinforce and invigorate ongoing motor programs and kinematics. Finally, we have developed a computational framework for invasive brain signal decoding that was already used an validated across a variety of human behaviors and neuropsychiatric symptoms including, movement intention, movement speed, emotional processing and epileptic seizure activity. Using this computational framework we have implemented the first generalized adaptive DBS paradigm, based on pretrained movement decoders, stimulating only when patients intend or perform movements.
We identified beta oscillations as the most widely distributed cortical rhythm and demonstrated their integration into dopamine-sensitive cortico-subcortical networks using invasive recordings and connectomics. This establishes beta activity as a systems-level marker of dopaminergic modulation across motor and non-motor domains. Combining invasive electrophysiology with functional connectomics, we showed that both dopamine and DBS modulate cortex–basal ganglia networks by suppressing pathological long-range synchrony, despite differing local effects. In Parkinson’s disease, we found that both therapies accelerate neural dynamics underlying volitional action by shifting information flow from beta to theta rhythms, shortening the delay between intention and movement execution. We developed a generalizable machine learning platform integrating brain signal decoding with structural connectivity, validated across movement, emotion, and seizure domains in diverse patient populations. Finally, we implemented a motor state-dependent closed-loop DBS algorithm that selectively enhanced movement speed with minimal stimulation, demonstrating that state-aware neuromodulation can outperform continuous paradigms by targeting pathological dynamics with high temporal specificity.