Closed-loop DBS triggered by real-time movement and tremor decoding based on thalamic LFPs for essential tremor

Abstract

High frequency Deep Brain Stimulation (DBS) targeting the motor thalamus is an effective therapy for essential tremor (ET). However, since tremor mainly affects periods of voluntary movements and sustained postures in ET, conventional continuous stimulation may deliver unnecessary current to the brain. Here we tried to decode movement states based on local field potentials (LFPs) recorded from motor thalamus and zona incerta in real-time to trigger the switching on and off of DBS in three patients with ET. Patient-specific models were first identified using thalamic LFPs recorded while the patient performed movements that tended to trigger tremor in everyday life. During the real-time test, LFPs were continuously recorded to decode movements and tremor, and the detection triggered stimulation. Results show that voluntary movements can be detected with a mean sensitivity ranging from 76.8% to 88.6% and a false positive rate ranging from 16.0% to 23.1% Postural tremor was detected with similar accuracy. The closed-loop DBS triggered by tremor detection suppressed intention tremor by 90.5% with a false positive rate of 20.3%. Clinical Relevance-This is the first study on closed-loop DBS triggered by real-time movement and tremor decoding based solely on thalamic LFPs. The results suggest that responsive DBS based on movement and tremor detection can be achieved without any requirement for external sensors or additional electrocorticography strips.

Movement Decoding using Spatio-Spectral Features of Cortical and Subcortical Local Field Potentials

“…The most common approach for movement decoding from intracranial recordings is based on the extraction of band-power features at specific frequency ranges, such as in the beta ( [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz) and gamma ([30-100] Hz) bands, after band-pass filtering the signal. This strategy has been applied by several studies using either subcortical STN-LFPs [7,8] or cortical ECoG signals [11,14].…”

“…The implementation of such methodologies within the field of aDBS may allow for the development of more sophisticated and subject-specific brain decoding algorithms [18]. Recent studies have shown how machine learning can be integrated in the context of aDBS [14,[19][20][21]. Interestingly, all these studies have based the decoding model in frequency-domain features extracted from a single frequency band.…”

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

“…An additional limitation was the relatively small amount of available data, which was constrained by the intraoperative setting (see table 1). For deep learning approaches we hypothesize better performances with an increased dataset size, which in the future will be obtained through longer term recordings, either with externalized leads (He et al, 2020) or sensing enabled implantable devices (Gilron et al, 2020;Opri et al, 2020a).…”

“…We also tested the combination of all ECoG channels, all STN-LFP channels and all ECoG and STN-LFP channels combined, which could not reach the individual best channel performance. Decoding of movement parameters may be a crucial first step in clinical real-time BCI in movement disorders (He et al, 2020;Opri et al, 2020a) and other motor system diseases such as stroke and spinal cord injury.…”

“…Treatment can be aligned to changes of therapeutic demand, paving the way for a precision medicine approach toward individualized symptom-specific DBS (He et al, 2020;Neumann and Rodriguez-Oroz, 2021;Neumann et al, 2019a;Opri et al, 2020a). To make sensing algorithms more deliberate and specific (instead of mere beta power estimation), machine-learning can integrate multivariate feature sets for adaptive DBS control.…”