Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS

<i>Objective.</i>This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.<i>Approach.</i>vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.<i>Main results.</i>Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.<i>Significance.</i>By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.

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Publication Details

Journal
Journal of Neural Engineering
Published
2025-03-06
DOI
https://doi.org/10.1088/1741-2552/adbd78
Citations
6
Primary Topic
Genetic Neurodegenerative Diseases
Type
article
Field-Weighted Citation Impact
5.45

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article

Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS

Kriti Kacker, Noam Harel, Jennifer L. Collinger, David Putrino et al.
6 citations
Journal of Neural Engineering
Genetic Neurodegenerative Diseases
5.45
article

Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS

Kriti Kacker, Noam Harel, Jennifer L. Collinger, David Putrino, James Bennett, Peter E. Yoo, Douglas J. Weber, Shahram Majidi, David Lacomis, Nicholas L. Opie, Ariel K. Feldman, Raul G. Nogueira, Adam Fry, Thomas J. Oxley, Nikole Chetty
article en
6 citations

Abstract

<i>Objective.</i>This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.<i>Approach.</i>vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.<i>Main results.</i>Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.<i>Significance.</i>By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.

Journal of Neural EngineeringVol. 22(2)
The Royal Melbourne Hospital (AU), The University of Melbourne (AU), University of Pittsburgh (US), Center for the Neural Basis of Cognition (US), James J. Peters VA Medical Center (US), University of Pittsburgh Medical Center (US), Synchronoss (United States) (US), Carnegie Mellon University (US), Icahn School of Medicine at Mount Sinai (US)
National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 4%
Genetic Neurodegenerative Diseases
5.45
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