DOI: 10.5061/dryad.9p8cz8wm6
title: Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information

description: (This is the dataset used in Dynamic Structure Of Motor Cortical Neuron
 Co-Activity Carries Behaviorally Relevant Information, Abstract below)
 Skillful, voluntary movements are underpinned by computations performed by
 networks of interconnected neurons in the primary motor cortex (M1).
 Computations are reflected by patterns of co-activity between neurons.
 Using pairwise spike time statistics, co-activity can be summarized as a
 functional network (FN). Here, we show that the structure of FNs
 constructed from an instructed-delay reach task in non-human primates are
 behaviorally specific: low dimensional embedding and graph alignment
 scores show that FNs constructed from closer target reach directions are
 also closer in network space. Using short intervals across a trial we
 constructed temporal FNs and found that temporal FNs traverse a
 low-dimensional subspace in a reach-specific trajectory. Alignment scores
 show that FNs become separable and correspondingly decodable shortly after
 the instruction cue. Finally, we observe that reciprocal connections in
 FNs transiently decrease following the instruction cue consistent with the
 hypothesis that information external to the recorded population
 temporarily alters the structure of the network at this moment.
description: <strong>We thank Jacob
 Reimer, Zach Haga, and Dawn Paulsen for collecting the data used in this
 work.</strong>
 <strong>Data collection and
 reaching task</strong> We used previously published datasets from two
 macaques, Monkey Rs and Monkey Rj, performing an instructed center-out
 reaching task (Hatsopoulos et
 al., 2004; O’Leary & Hatsopoulos, 2006). Subjects were trained to hold a cursor on a
 center target presented on a video screen using a 2D arm exoskeleton 
 (KINARM, Kingston, Ontario). One of eight radially positioned peripheral
 targets was then presented and served as an Instruction cue during which
 time the subjects were required to keep holding the cursor on the center
 target. After a 1 second delay period, the peripheral target began
 blinking (Go cue) instructing the subjects to move the cursor to the
 peripheral target (Figure 1A). Trial start was 0.5 s before the
 instruction cue appeared, and trial termination was 0.5 s after the
 peripheral target was acquired. Trial inclusion depended upon target
 acquisition falling within 1.5s following movement onset. We also only
 included correct trials. Movement onset is defined as the time when the
 hand velocity reached 5% of the peak velocity of the movement after the Go
 cue. Neural data were recorded from 96-channel Utah
 arrays implanted in the arm/hand area of primary motor cortex  (M1) on the
 precentral gyrus. Spike waveform snippets sampled at 30 kHz were extracted
 using a user-defined threshold (Cerebus BlackRock Microsystems, Salt Lake
 City, UT) and were sorted into individual units using Offline Sorter
 (Plexon, Dallas, TX). The surgical and behavioral procedures involved in
 this study were approved by the University of Chicago Institutional Animal
 Care and Use Committee.
description: This is the dataset used in: Dynamic Structure Of Motor Cortical
 Neuron Co-Activity Carries Behaviorally Relevant
 Information<br>https://doi.org/10.1101/2022.05.18.492501 <br><br>Find associated software here: <br>https://github.com/hatsopoulos-lab/macaque-dynamic_functional_networks.git<br><br>Find data structure and example functions (including how to construct Functional Networks) under:<br>https://github.com/hatsopoulos-lab/macaque-dynamic_functional_networks/center-out/run_data_demo.ipynb<br><br>The files are in .pkl format.<br>You can load it using: data = loadPickle(filepath)<br><br>'data' is structured in the following way:<br><br>data -&gt; (list)<br>|<br>--- data[direction] -&gt; (dict) corresponding to targets spatially located around a center target<br>                keys:['DirectionIndex', 'DirectionDegrees', 'numTrials', 'instructionTimes', 'goTimes', 'startMv', 'endMv', 'numCh', 'binwin', 'StartTimes', 'trialData'])<br>    |<br>    --- ['trialData'] -&gt; (list) list of trials within that direction<br>        |<br>        --- ['trialData'][trial] -&gt; (dict)<br>                           keys: ['trialNum', 'trialStart', 'trialInstruction', 'trialGo', 'trialEnd', <br>                                'trialXPos', 'trialXTime', 'trialYPos', 'trialYTime', 'unitData', <br>                                'trialNumUnits', 'trialSpiketimes', 'trialBinnedSpikes']<br>            |<br>            --- ['unitData'] -&gt; list of single units<br>                |<br>                --- ['unitData'][unit] -&gt; (dict)<br>                                  keys: ['chanNum', 'unitInChan', 'unitNum', 'spiketimes']