DOI: 10.5281/zenodo.7737573
title: Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"

description: This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex". Data collection methods: Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey. The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37). <br> Abstract: Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.