Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

By C. Chestek, V. Gilja, Paul Nuyujukian, Justin D. Foster, Joline M. Fan, Matthew T. Kaufman, Mark M. Churchland, Zuley Rivera-Alvidrez, John P. Cunningham, Stephen I. Ryu and 1 more. Journal of Neural Engineering, 2011

350 citations recorded by the source corpus.

Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3104b6d64bfb9c9d32331e60e7a940de978e001b

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Citations in the corpus

High-performance brain-to-text communication via handwriting (2021, result): “, 2019), and that neural decoding performance is comparable (within 5%) to using sorted units (Chestek et al., 2011; Christie et al., 2014) – although see also (Todorova et al.”
Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings (2020, positive): “Moreover, high-quality signals can be recorded for more than 8 weeks even with relatively rigid and large devices, such as wire tetrodes (35, 36), Utah arrays (37, 38), traditional silicon probes (39–41), and Neuropixels probes (6, 25, 42).”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, neutral): “However, the neural activity that provides inputs to these decoders typically changes over weeks and months, leading to degraded BCI performance 39,40 .”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “Groups that rely on this probe usually resort to the utilization of a threshold-crossing method to detect useful neuronal activity (134, 803), a maneuver that discards wellestablished neuronal recording quality criteria (580) and increases the likelihood of recording noise instead of neuronal spikes”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “Promising results have been obtained by invasive methods such as Peripheral Nerve Interfaces (Urbanchek et al., 2012), Cortical Interfaces (Chestek et al., 2011) or Targeted Muscle Reinnervation (TMR; Kuiken et al., 2009).”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…that can be simultaneously recorded from (Berényi et al., 2014; Buzsáki et al., 2015; Du et al., 2011; Viventi et al., 2011), as well as make the recordings more stable (Alivisatos et al., 2013; Chestek et al., 2011; Cogan, 2008; Felix et al., 2013; Guitchounts et al., 2013; Zhou et al., 2014).”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, background): “…along with other recent reports ( Gilja et al., 2012 ; Simeral et al., 2011 ; Nuyujukian et al., 2015 ; Gilja et al., 2015 ; Hochberg et al., 2012 ; Chestek et al., 2011 ; Bishop et al., 2014 ; Flint et al., 2013 ; Nuyujukian et al., 2014 ), demonstrates that intracortical BCIs may be useful for…”