DOI: 10.5061/dryad.cnp5hqcj2
title: Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording

description: Associating unfamiliar stimuli with appropriate behavior through
 experience is crucial for survival. While task-relevant information has
 been found to be distributed across multiple brain regions, how regional
 nodes in this distributed network reorganize their functional interactions
 throughout learning remains to be elucidated. Here, we performed chronic,
 large-scale single-unit recording across 10 cortical and subcortical
 regions using ultra-flexible microelectrode arrays in mice performing a
 visual decision-making task and tracked mesoscale functional network
 dynamics throughout learning. Task learning reshaped interregional
 functional connectivity, leading to the emergence of a subnetwork
 involving visual and frontal regions during the acquisition of correct
 No-Go responses. This reorganization was accompanied by a more widespread
 representation of visual stimulus across regions, and a region’s network
 rank strongly predicted its peak timing of visual information encoding.
description: Behavioral experiments were controlled using custom Matlab
 (MathWorks) scripts together with digital I/O devices (Arduino Uno R3,
 Arduino) for trial events and triggers. Neural signals were amplified and
 filtered with the SpikeGadgets 1024-channel recording system
 (SpikeGadgets). Task-related behavioral events were converted to TTL
 pulses and simultaneously recorded by the SpikeGadgets system to ensure
 synchronization with neural data. Data analyses were conducted using
 custom Matlab scripts.
description: # Data from: Dynamics of mesoscale brain network during decision-making
 learning revealed by chronic, large-scale single-unit recording Dataset
 DOI: [10.5061/dryad.cnp5hqcj2](10.5061/dryad.cnp5hqcj2) ## Description of
 the data and file structure Dataset: SpkAndBehav SubFolder name: Early
 (early training stage data of learning group); Expert(expert training
 stage data of learning group); Fruitless(fruitless learning group) Each
 mat file in the 3 folders contains behavior data and spike time data from
 one session of one mouse performing the visual go/no task, and can be
 opened in MATLAB. Variables: log.res_vector: stimulus and outcome of each
 trial. ``` sign of the number means correct/wrong. + is correct, - is
 wrong. number is visual stimulus identity. 1 is the go stimulus, 2 is the
 no go stimulus. 1 = hit , 2 = correct rejection; -1 = miss; -2 = false
 alarm. ``` log.rsp_vector: response latency of each trial, relative to
 stimulus onset. log.lick_time_vector: time of detected licks in each
 trial, relative to stimulus onset. spk_time_in_trial: contains spike time
 data from all the units in this session, aligned to stimulus on set.
 spk_time_in_trial{K}{N}: spike time of unit K, in trial N. unit_tag: brain
 region identity of each unit.
 \============================================================= Dataset:
 OptoManipulationBehav SubFolder name: OFC stim (OFC stimulus period
 manipulation); OFC res(OFC response period manipulation); V2M stim (V2M
 stimulus period manipulation); V2M res(V2M response period manipulation);
 Ctrl stim (control group stimulus period manipulation); Ctrl res(control
 group response period manipulation); Each mat file in the 6 folders
 contains behavior data from one session of one mice performing the visual
 go/no task, and can be opened in MATLAB. Variables: log.res_vector:
 stimulus and outcome of each trial. ``` sign of the number means
 correct/wrong. + is correct, - is wrong. number is visual stimulus
 identity. 1 is the go stimulus, 2 is the no go stimulus. 1 = hit , 2 =
 correct rejection; -1 = miss; -2 = false alarm. ``` log.rsp_vector:
 response latency of each trial, relative to stimulus onset.
 log.lick_time_vector: time of detected licks in each trial, relative to
 stimulus onset.
 \============================================================= m files:
 ana_GNGS3_TSPEDyn_Sig02_TS_Shared.m: the main analysis used in the paper.
 A scipt written specifically for the shared data in the paper“Dynamics of
 mesoscale brain network during decision-making learning revealed by
 chronic, large-scale single-unit recording”. It requires shared data which
 contains "log",
 "spk_time_in_trial",'unit_tag' It also requires the
 function "TSPE.m" and "parfor_progress.m" to run.
 Input parameters: input_fn: filename to read. t1: time before stimulus, in
 seconds. should be a negative value. t2: stimulus period length，in
 seconds. should be a positive value. t3: response window length, in
 seconds. should be a positive value. t2 + t3 - t1 should not exceed 3.8.
 eff_TSPE_window: in ms. the time window that algorithm considers
 cross-correlation to be effective. period_window: in ms. in each window
 the regional connection matrix "connection_between_region" is
 calculated. core_num: should be set according to number of cpu in local
 machine. If a very large number is set, MATLAB would return an error and
 tell you the approriate number. Output parameters: The script will
 automatically save a file，named accoring to input file. the variable
 "C_reg_collection_full" contains the desired data.
 "C_reg_collection_full" is a n x t cell, n is trial number, t is
 time window number. In the cells corresponding to hit trials and cr trials
 there will be a 10 x 10 matrix containing the raw regional connection
 strength of brain network, in the sequence indicated by
 "r_name": for example, data in the 2nd row, 3rd conlumn indicate
 functional output from V2M to V2L. Similarly,
 “C_reg_collection_outputsum_ranked_full” ,
 “C_reg_collection_inputsum_ranked_full” and
 "C_reg_collection_ranked_among_all_full" contains rankized data
 from "C_reg_collection_full" .(see paper for more details) Trial
 identity can be retrieved from behav_log.res_vector and effective_range. 1
 is for hit, 2 is for cr, -1 is for miss, -2 is for fa. Command to repeat
 analysis in the paper:
 ana_GNGS3_TSPEDyn_TS_Shared('C57#xxx-dxxx_SpkAndBehav-Shared.mat',-0.4,0.8,2,20,200,xx)