DBSsync: combining intracranial and multimodal data to investigate new biomarkers in Parkinson's disease

Implanted deep brain stimulation devices are now capable of chronically recording activity from intracranial brain areas during stimulation. This new type of data has the potential to increase our understanding of disease-related brain activity and its modulation in response to therapy or other types of stimuli. With the innovative approach of adaptive deep brain stimulation now clinically available, multimodal characterization of neural biomarkers becomes of utmost importance to define optimal feedback signals for adaptive brain stimulation and allow for better fine-tuning of stimulation parameters. To investigate these biomarkers, we developed DBSsync, a paradigm and an open-source Python toolbox with its graphical user interface for temporally precise synchronization of intracranial recordings with external data, allowing for multimodal research protocols. DBSsync achieves a temporal precision of 8 ms and incorporates cardiac artifact removal methods to facilitate intracranial data preprocessing, thus enabling the integration and precise synchronization of multiple brain signals with external sensors and various behavioral timeline data.