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

Abstract

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 requires further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from 4 intracortical arrays in 3 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 3 animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for 3 of 4 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.

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

“…We used two measures of stability in this study (see "Stability of ensemble tuning" section for details on the second measure). The first approach is based on single feature decoders (SFDs), which had been used earlier to evaluate single-unit stability over 1-2 d (Chestek et al, 2007). SFDs are constructed by treating each feature of each input signal as an independent input and calculating the ability of that single feature to predict the output.…”

“…However, it has been debated whether this high behavioral stability requires highly stable activity at the neuronal level (Chestek et al, 2007;Ganguly and Carmena, 2009;Stevenson et al, 2011) or if stability is a network-level phenomenon that arises despite unstable representations in single neurons (Cohen and Nicolelis, 2004;Carmena et al, 2005;Rokni et al, 2007). Here, we found evidence for stability of both MSPs and LFPs at the single-feature level.…”

“…Two prior studies found lower stability in ensemble-level, offline decoding (Flint et al, 2013;Perge et al, 2014) than we saw previously with online BMI control (Flint et al, 2013) or here using single-feature analyses. In addition, movement representations may change with learning of a task (Cohen and Nicolelis, 2004;Chestek et al, 2007;Mandelblat-Cerf et al, 2009). Ganguly and Carmena (2009) showed that this phenomenon also occurs when learning a BMI task using a small ensemble of neurons while requiring the hand to remain stationary.…”

“…Ganguly and Carmena (2009) showed that this phenomenon also occurs when learning a BMI task using a small ensemble of neurons while requiring the hand to remain stationary. In our study, as in Chestek et al (2007), monkeys performed overlearned control tasks. In addition, our BMI decoders were biomimetic (trained on actual reaches) and the monkeys' arms were not required to remain stationary during BMI control.…”

“…However, it remains unclear whether movement representations at the single-neuron level are stable over long time periods (months to years). Prior studies have found conflicting evidence of single-unit stability, with some showing variability (Carmena et al, 2005;Rokni et al, 2007) and others showing stable properties over the course of a few hours (Stevenson et al, 2011), days (Chestek et al, 2007), or weeks (Ganguly and Carmena, 2009). Recently, numerous studies have used unsorted threshold crossings (multiunit spikes, or MSPs) in both scientific and neuroprosthetic experiments (Fraser et al, 2009;Chestek et al, 2011;Flint et al, 2013;Todorova et al, 2014), yet the level of stability of these signals has not been investigated.…”