DOI: 10.5061/dryad.zkh1893ft
title: Data for: Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques

description: Recording and modulating neural activity in vivo enables investigations of
 the neurophysiology underlying behavior and disease, but there is a dearth
 of translational tools for simultaneous recording and localized
 receptor-specific modulation. We addressed this limitation by translating
 multifunctional fiber neurotechnology previously only available for rodent
 studies to enable cortical and subcortical neural recording and modulation
 in macaques. In the premotor cortex and putamen, we recorded single-neuron
 activity and local field potential oscillations during intracranial GABA
 infusions while a macaque performed a working memory task. We also
 recorded neuronal activity during saline, artificial cerebrospinal fluid,
 and no infusion control experiments. We characterized the dynamic effects
 of local infusions with multiple time series analysis techniques,
 including spectrotemporal analysis and state-space modeling. This dataset
 and accompanying software toolbox provide detailed insight into the
 electrophysiological effect of neurotransmitter receptor modulation in
 both cortical and subcortical structures in an awake macaque. 
description: All details regarding data
 collection and preprocessing can be found in the associated
 manuscript.
description: All data files are saved as .mat files, which can be opened with
 MatLab or python. The software is written in MatLab.
 The analysis software integrates with the following
 toolboxes: Chronux: http://chronux.org/ FOOOF: https://github.com/fooof-tools/fooof  Neurostat: https://github.com/iahncajigas/nSTAT PPSSM: https://github.com/josefinacmenendez/PPSSM
description: # Data for: Multifunctional fibers enable modulation of cortical and deep
 brain activity during cognitive behavior in macaques *** Simultaneous
 neuronal recordings and neuromodulation were enabled by multifunctional
 fiber neurotechnology. This dataset contains neurophysiological and
 behavioral data recorded from a macaque performing a working memory task.
 Recordings were performed in the premotor cortex or putamen. During the
 recordings, there was intracranial neuromodulation. There are 5 recording
 types and corresponding files * pmc\_gaba: Recordings in the premotor
 cortex with intracranial GABA infusion(s) (n = 4) * pmc\_sal: Recordings
 in the premotor cortex with intracranial saline infusion(s) (n = 3) *
 pmc\_acsf: Recordings in the premotor cortex with intracranial aCSF
 infusion(s) (n = 2) * pmc\_noic: Recordings in the premotor cortex with no
 intracranial infusion (n = 4) * put\_GABA: Recording in the putamen with
 intracranial GABA infusion * pmc\_noic: Recordings in the putamen with no
 intracranial infusion. Device data is stored in device\_data.zip
 Additionally, we provide preprocessed data in experimental\_data.zip and
 processed\_data.zip Further detail regarding experimental details can be
 found in the associated manuscript (Garwood, et al.) ## Description of
 experimental data and file structure Each data file contains 2 variables
 and 4 structures: Variables: * fs\_lfp: The sampling rate of local field
 potential (lfp) recordings * fs\_spike: The sampling rate of single unit
 activity recordings Structures: * infusion (when applicable; empty
 otherwise): Data related to the intracranial infusions * start: The start
 time(s) of intracranial infusion(s) in seconds, relative to the beginning
 of the recording * end: The end time(s) of intracranial infusion(s) in
 seconds, relative to the beginning of the recording * rate: The infusion
 rate(s) of intracranial infusion(s) in nL/min * drug: String corresponding
 to the drug infused * lfp: Data related to local field potential
 oscillations * lfp: 4xN matrix of LFP data * lfp\_elec: The LFP electrode
 used for subsequent analysis * sua: Data related to single unit activity *
 unit\_locs: Indices, relative to the beginning of the recording,
 corresponding to timepoints when each (sorted) unit fired an action
 potential * unit\_spikes: The corresponding (unsorted) event index for
 each sorted event, relative to the first event index * spike\_locs\_all:
 The indices of all valid spike events, relative to the beginning of the
 recording * spikes\_all: The (unsorted) waveforms of all spike events, in
 chronological order * spike\_length: The duration of each spike waveform
 (in ms) * spike\_threshold: The threshold used to define spike events,
 relative to the standard deviation of the raw spike data at baseline *
 noise\_thresh: The amplitude beyond which threshold crossings were
 considered to be an artifact (in uV) * task\_info: Data related to the
 behavioral task * fs\_task: Sampling rate used to convert trial indices to
 time (in sec) * trials: Trial start and end indices, relative to the
 beginning of the recording * samples: Start and stop indices corresponding
 to when the sample was visible on the screen * matches: Start and stop
 indices corresponding to when the match was visible on the screen. *
 correct\_trials: Binary variable, where 0 indicates incorrect trials, 1
 indicates correct trials * complete\_trials: Binary variable, where 0
 indicates incomplete trials, 1 indicates complete trials * no\_fix: Binary
 variable, where 0 indicates successful fixation, 1 indicates unsuccessful
 fixation * sample\_id: The ID of the sample (1-3) * correct\_loc: The
 location of the correct selection * reaction time: The time between when
 the match appeared and when a selection was made (in s) Note that one aCSF
 file was included in behavioral analysis but not electrophysiology
 analysis, because the infusion rate + volume exceeded that of the GABA
 recordings. We additionally provide the output of fooof analysis in
 experimental\_data.zip/data\_for\_fooof * The data files have the
 following naming convention: fooof\_output\_exptype\_6.mat where 6
 corresponds to the FOOOF parameter for the max number of spectral peaks in
 each spectra * Each data file contains fooof parameters estimated from 2N
 spectral samples, where samples 1:N occurred before intracranial infusions
 and samples N+1:2N occurred after intracranial infusions (Garwood 2023,
 Methods) * Each data file contains 6 variables: * aperiodic\_signal: two
 columns corresponding to the aperiodic exponent and aperiodic offset
 across 2N samples * fooof\_spectrum: 2Nxn\_freq matrix corresponding to
 the estimated spectra of 2N samples * fooof\_freq: n\_freq vector of
 frequencies corresponding to the columns in fooof\_spectrum (where
 frequencies between 59.5 and 60.5 Hz have been removed) * peak\_cf: 2Nx6
 matrix of center frequencies across up to 6 spectral peaks; if
 peak\_cf(n,p+1:6) = 0, only p spectral peaks were identified in that
 sample * peak\_pow: 2Nx6 matrix of power across up to 6 spectral peaks; if
 peak\_pow(n,p+1:6) = 0, only p spectral peaks were identified in that
 sample * session\_tags: 2Nx1 vector containing the session number
 corresponding to each spectral sample (also saved in the files
 session\_tags\_exptype.mat) * freqs.csv * contains the frequencies of the
 original spectral samples ## Description of device data and file structure
 Device data is organized into three subfolders: DMA, fluidic, and
 impedance. * DMA: Dynamic material analysis data * Contains DMA data for
 three fiber samples and one steel sample in .csv format. * fluidic:
 fluidic rate characterization data * Contains 30 .mat files in the format
 of fluidic infusion data from 10-100 nl/min * Naming convention:
 rate\_nlm\_trial\_x.mat where "nlm" refers to nanoliters/minute
 as the units of time * Each file contains two variables: time, and volume
 (cumulative volume infused over time). * Subfolder 'raw\_videos'
 contains the video recordings corresponding to each trial in the parent
 directory. * impedance: impedance spectroscopy data * Contains 24 .txt
 files containing impedance spectroscopy data (frequency vs. impedance) *
 Naming convention: devx\_elecy.txt or devx\_postAC\_elecy.txt (postAC
 indicates spectroscopy performed after autoclave sterilization) ##
 Description of preprocessed data and file structure Processed data folder
 includes spike sorting results and example SS-GLM and AR models *
 spike\_data: folder containing spike sorting results * Naming convention:
 spikes\_cluster\_location\_exptypex.mat where 'x' indicates that
 experiment number * Each file contains 3 variables: * s\_time: experiment
 time sampled at 30 kHz * spikes\_cluster: the spike cluster id of every
 threshold crossing * spikes\_locs: n\_units by 1 cell array containing the
 index of every spike of the corresponding unit. Single unit times are
 given by s\_time(spike\_locs{unit\_num}) * SSGLM models follow the naming
 convention location\_exptypex\_unity\_trialvariant\_Rz.mat where
 'x' indicates the experiment number, 'y' indicates the
 unit id, and 'z' indicates the model order. * Additional
 subscripts include '\_nohist.mat' indicating the model has no
 history terms and '\_stationary.mat', indicating that the
 parameters are stationary across trials. * Each file contains estimated
 SSGLM parameters (Garwood 2023, Methods, README\_code.md). Each parameter
 is an 1 by M cell array, where M corresponds to the number of models
 estimated for the corresponding trial variant. * gammahat: estimated
 values of gamma (history coefficients) on the final EM iteration * xK: the
 expected value of the state (effect of task subphase on firing rate in a
 given trial) * gammahatall: estimated values of gamma across all EM
 iterations * logll: log likelihood of the model * nIter: number of EM
 iterations performed * Qhat: estimated state noise variance (state noise
 covariance is assumed to be diagonal) on the final EM iteration * Qhat:
 estimated state noise variance across all EM iterations * WK: state
 covariance within trials\, W\_\(k\|K\) * WKu: state covariance across
 trials\, W\_\(k\,u\|K\) * stimCIs: optional SSGLM output; confidence
 intervals for state estimates derived with Monte Carlo; computationally
 intensive to compute, currently empty * stimulus: optional SSGLM output;
 median of the state estimates derived with Monte Carlo; computationally
 intensive to compute, currently empty * fitResults: optional output;
 extended fitResults if this is enabled in the nSTAT function,
 DecodingAlgorithms\_IG.m; currently empty to avoid unnecessary data
 storage. * AR models follow the naming convention
 location\_exptypex\_lfp\_trialvariant\_Rz.mat * Each file contains
 estimated AR parameters with and without task subphase covariates
 (variable names \_with\_encoding and \_without\_encoding). Each parameter
 is an 1 by M cell array, where M corresponds to the number of models
 estimated for the corresponding trial variant. * E: AR model residuals *
 V: AR model conditional variance * LL: AR model log likelihood * EstMdl:
 Matlab ARIMA structure for the estimated AR model ## Code/Software Code
 will be available in a permanently archived repository located at
 https://github.com/igarwood/NHP\_fibers See the main README\_code.md file
 in https://github.com/igarwood/NHP\_fibers MIT license.