Data for: Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques
Data files
Sep 27, 2023 version files 11.65 GB
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device_data.zip
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experimental_data.zip
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pmc_acsf1.mat
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pmc_acsf2.mat
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pmc_gaba1.mat
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pmc_gaba2.mat
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pmc_gaba3.mat
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pmc_gaba4.mat
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pmc_noic1.mat
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pmc_noic2.mat
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pmc_noic3.mat
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pmc_noic4.mat
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pmc_sal1.mat
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pmc_sal2.mat
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pmc_sal3.mat
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processed_data.zip
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put_gaba.mat
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put_noic1.mat
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put_noic2.mat
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put_noic3.mat
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README.md
Abstract
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.
README: 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.
- Contains 30 .mat files in the format of fluidic infusion data from 10-100 nl/min
- 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)
- Contains 24 .txt files containing impedance spectroscopy data (frequency vs. impedance)
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
- 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.
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.
Methods
All details regarding data collection and preprocessing can be found in the associated manuscript.
Usage notes
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