Beta-band activity in the subthalamic local field potential (LFP) is correlated with Parkinson’s disease (PD) symptom severity and is the therapeutic target of deep brain stimulation (DBS). While beta fluctuations in PD patients are well characterized on shorter timescales, it is not known how beta activity evolves around the diurnal cycle, outside a clinical setting. Here, we obtained chronic recordings (34 ± 13 days) of subthalamic beta power in PD patients implanted with the Percept DBS device during high-frequency DBS and analysed their diurnal properties as well as sensitivity to artifacts. Time of day explained 41 ± 9% of the variance in beta power (p < 0.001 in all patients), with increased beta during the day and reduced beta at night. Certain movements affected LFP quality, which may have contributed to diurnal patterns in some patients. Future DBS algorithms may benefit from taking such diurnal and artifactual fluctuations in beta power into account.

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinson’s disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex – basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex – basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

Deep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance. To characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS. We recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation. Our findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity. Our results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.

Background: Deep brain stimulation (DBS) is an effective treatment option for patients with Parkinson's disease (PD). However, clinical programming remains challenging with segmented electrodes.Objective: Using novel sensing‐enabled neurostimulators, we investigated local field potentials (LFPs) and their modulation by DBS to assess whether electrophysiological biomarkers may facilitate clinical programming in chronically implanted patients.Methods: Sixteen patients (31 hemispheres) with PD implanted with segmented electrodes in the subthalamic nucleus and a sensing‐enabled neurostimulator were included in this study. Recordings were conducted 3 months after DBS surgery following overnight withdrawal of dopaminergic medication. LFPs were acquired while stimulation was turned OFF and during a monopolar review of both directional and ring contacts. Directional beta power and stimulation‐induced beta power suppression were computed. Motor performance, as assessed by a pronation‐supination task, clinical programming and electrode placement were correlated to directional beta power and stimulation‐induced beta power suppression.Results: Better motor performance was associated with stronger beta power suppression at higher stimulation amplitudes. Across directional contacts, differences in directional beta power and the extent of stimulation‐induced beta power suppression predicted motor performance. However, within individual hemispheres, beta power suppression was superior to directional beta power in selecting the contact with the best motor performance. Contacts clinically activated for chronic stimulation were associated with stronger beta power suppression than non‐activated contacts.Conclusions: Our results suggest that stimulation‐induced β power suppression is superior to directional β power in selecting the clinically most effective contact. In sum, electrophysiological biomarkers may guide programming of directional DBS systems in PD patients. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Finely tuned gamma (FTG) oscillations from the subthalamic nucleus (STN) and cortex in Parkinson’s disease (PD) patients undergoing deep brain stimulation (DBS) are often associated with dyskinesia. Recently it was shown that DBS entrains gamma activity at 1:2 of the stimulation frequency; however, the functional role of this signal is not yet fully understood. We recorded local field potentials from the STN in 19 chronically implanted PD patients on dopaminergic medication during DBS, at rest, and during repetitive movements. Here we show that high-frequency DBS induced 1:2 gamma entrainment in 15/19 patients. Spontaneous FTG was present in 8 patients; in five of these patients dyskinesia occurred or were enhanced with entrained gamma activity during stimulation. Further, there was a significant increase in the power of 1:2 entrained gamma activity during movement in comparison to rest, while patients with entrainment had faster movements compared to those without. These findings argue for a functional relevance of the stimulation-induced 1:2 gamma entrainment in PD patients as a prokinetic activity that, however, is not necessarily promoting dyskinesia. DBS-induced entrainment can be a promising neurophysiological biomarker for identifying the optimal amplitude during closed-loop DBS.

Brain–computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson’s disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.