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Data and code for: Ferro, D., van Kempen, J., Boyd, M., Panzeri, S. and Thiele, A., 2021. Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
Data contain laminar local field potentials in V1 and V4.
Code runs spectral power, coherence and Conditional Granger Causality (cGC) based on Mutual Information for interactions between and within V1-V4 laminar depths.
DOI
https://doi.org/10.1073/pnas.2022097118

Demetrio Ferro a3aac43bfe Upload files to 'Code' преди 3 години
Code a3aac43bfe Upload files to 'Code' преди 3 години
Data 1d504cfd48 Upload files to 'Data' преди 3 години
LICENSE 7f02438db9 Update 'LICENSE' преди 3 години
README.md a4a98dda6e Update 'README.md' преди 3 години
datacite.yml c0bd4f9601 Update 'datacite.yml' преди 3 години

README.md

V1-V4-LFPs-and-Visual-Attention

This repositoty contains electrophysiologcal Data symultaneously recorded in V1 and V4 during a selective visual attention task, and code to access the data, retrieve metadata and run the analysis described in detail in:

Ferro D, van Kempen J, Boyd M, Panzeri S, Thiele A. PNAS 2021. Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. doi:10.1073/pnas.2022097118

And previously previewed in preprint form:

Ferro D, van Kempen J, Boyd M, Panzeri S, Thiele A. BioRxiv 2020. Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. doi:10.1101/2020.06.09.142190.

The folder Data contains LFP data sampled at frequency of 1017.375 Hz, digitalized at 32 bits. The data can be accessed through the inspection of included metadata at different levels: session (also occasionally coded as 'PEN'), trials (each with its specific randomized set of stimuli color, stimuli color order, task-related delays etc.), laminar compartment (Supra-, Granular, Infra-granular), attentional condition (RF, OUT1, OUT2; OUTrp in the case where trials for OUT are pooled by a random subset of equal size from OUT1 and OUT2), and task-related time windows (composed of 1024 time points respectively aligned as: PreStimulus, PostStimulus, PostCue, PreFirstDim, PreSecondDim, PreThirdDim).

The folder Code contains the main functions developed to perform tha analyses and the results presented in the paper.

  1. The script 'getSubjectInfos()' accepts as input 'monkey 1' or 'monkey 2' and returns a data structure containing information about each recording session detailed by the subfields:

    sInfos=getSubjectInfos(sName)
    │   % sName valid inputs {'monkey 1', 'monkey 2'}; *REQUIRED FIELD
    │
    ├─── subjectName % the subject name;
    ├─── penIDs      % the ID of the sessions analysed;
    ├─── grcIDs %(not used) ID of task-conditions generating file;
    ├─── ctxIDs %(not used) ID of neural-related data setup;
    └─── penInfos    % a session-by-session data structure with session infos;
        │
        ├─── dateTime        % string indicating the date and time of recording;
        ├─── V1Contacts      % structure containing information about V1 recording contacts;
        │       ├─── Depths             % channel depth priorly aligned to earliest CSD sink (see Methods);
        │       ├─── Channels           % 1:16 or 17:32, label of the channel on the recording probe;
        │       ├─── ChRejected         % (very rare) channels rejected due to exper. failure / probe drift;
        │       ├─── ChSNRLowEq3        % channels with MUAe SNR <= 3 (see Methods);
        │       ├─── Quality            % 'OK','BAD', general comment about data quality by visual inspection;
        │       ├─── LaminarAlignments  % channel depth priorly aligned to earliest CSD sink binned at 0.15 mm;
        │       ├─── LaminarLayers      % 'TD'=too deep, 'I'=Infrag., 'G'=Gran.,  'S'=Suprag., 'TS'=too superf.;
        │       └─── SelectedChs        % channels selected to be analysed
        │
        ├─── V4Contacts
        │       ├─── Depths             % channel depth priorly aligned to earliest CSD sink (see Methods);
        │       ├─── Channels           % 1:16 or 17:32, ID of the channel used by experimenters; 
        │       ├─── ChRejected         % (very rare) channels rejected due to exper. failure / probe drift;
        │       ├─── ChSNRLowEq3        % channels with MUAe SNR <= 3 (see Methods);
        │       ├─── Quality            % 'OK','BAD', general comment about data quality by visual inspection;
        │       ├─── LaminarAlignments  % channel depth priorly aligned to earliest CSD sink binned at 0.15 mm;
        │       ├─── LaminarLayers      % 'TD'=too deep, 'I'=Infrag., 'G'=Gran.,  'S'=Suprag., 'TS'=too superf.;
        │       └─── SelectedChs        % channels selected to be analysed
        │
        ├─── penNum          % ID of the session analysed (redundant, added for consistency checks);
        ├─── grcNum          % (not used) # of task-conditions generating file;
        ├─── selectedTimeWinsFilePaths
        │       ├─── V1Contacts.LFPs    % filepath of V1 LFP data when automatically loaded (change if needed);
        │       ├─── V1Contacts.MUAs    % filepath of V1 MUA data when automatically loaded (deprecated);
        │       ├─── V4Contacts.LFPs    % filepath of V4 LFP data when automatically loaded (change if needed);
        │       └─── V4Contacts.MUAs    % filepath of V4 MUA data when automatically loaded (deprecated);
        │
        └─── grcColorOrder   % char triplet with stimuli presentation order (e.g. 'RBG');
    
    
    
  2. The script 'getSubjectData()' accepts as input 'monkey 1' or 'monkey 2' and returns a data structure containing information about each recording session detailed by the subfiels.

    
    sData=getSubjectData(sName,sVisualArea,sPenNumber,sDataType,sSelectedTimeWins)
    │   % sName             % {'monkey 1', 'monkey 2'}; *REQUIRED FIELD
    │   % sVisualArea       % {'V1', 'V4'}                               *REQUIRED FIELD
    │   % sPenNumber        % {double, 'GOOD', 'ALL'}  'ALL'  is default
    │   % sDataType         % {'LFPs', 'MUAEs', 'ALL'} 'LFPs' is default ('MUAEs' is deprecated)
    │   % sSelectedTimeWins % {'PreStim','PostStim','Stationary','PostCue','PeFirstDim','PreSecondDim','PreThirdDim','ALL'} MUTUALLY EXCLUSIVE, 'ALL' is default
    │
    ├─── subjectName % the subject name;
    ├─── penIDs      % the ID of the sessions analysed;
    ├─── penInfos    % a session-by-session data structure with session infos (see getSubjectInfos());
    └─── LfpStruct   % a session-by-session data structure with LFP data;
          │
          ├─── Sorted                               % string with date and time of recording;
          │       ├─── Labels                       % task-related metadata information;
          │       │       ├─── Dimming              % 1:3 stimulus at first, second or third dimming is attended;
          │       │       ├─── Outcome              % trial-by-trial behavioral outcome (0=successful/rewarded);
          │       │       ├─── GratCondition        % 1:36 unique task-related configuration (stimuli color x stimuli order x dimming order)
          │       │       ├─── selectedNlxEvents    % (deprecated) Neuralynx ID of events selected;
          │       │       ├─── SupraChsLamAlignsBi  % Channel index of selected Supra laminar alignments 
          │       │       ├─── GranrChsLamAlignsBi  % Channel index of selected Granr laminar alignments
          │       │       └─── InfraChsLamAlignsBi  % Channel index of selected Infra laminar alignments
          │       │      
          │       ├─── Delays                       % trial-by-trial dimming delays;
          │       │       ├─── Stationary           % stationary time window delay;
          │       │       ├─── CueOnset             % cue onset pseudo-random delay;
          │       │       ├─── FirstDim             % first dim pseudo-random delay;
          │       │       ├─── SecondDim            % second dim pseudo-random delay;
          │       │       └─── ThirdDim             % third dim pseudo-random delay;
          │       │      
          │       ├─── Full.Supra               
          │       │      ├─── RF             
          │       │      │     ├─── PreStimDataBi         % (chs x trials x 1024 timepoints) Pre-stimulus (≅ -1, 0 s before stimulus onset) LFPs bipolar derived
          │       │      │     ├─── PostStimDataBi        % (chs x trials x 1024 timepoints) Post-stimulus (≅ 0, 1 s after stimulus onset) LFPs bipolar derived
          │       │      │     ├─── PostStimDataBiZSc     % (chs x trials x 1024 timepoints) Post-stimulus (≅ 0, 1 s after stimulus onset) LFPs bipolar derived and Z-scored to Prestim (0 mean, 1 std)
          │       │      │     ├─── StationaryDataBi      % (chs x trials x 1024 timepoints) Stationary (≅ .2, 1.2 s after stimulus onset) LFPs bipolar derived 
          │       │      │     ├─── StationaryDataBiZSc   % (chs x trials x 1024 timepoints) Post-stimulus (≅ .2, 1.2 s after stimulus onset) LFPs bipolar derived and Z-scored to Prestim (0 mean, 1 std) 
          │       │      │     ├─── PostCueDataBi         % (chs x trials x 1024 timepoints) Post-cue (≅ 0, 1 s after cue onset) LFP data bipolar derived 
          │       │      │     ├─── PostCueDataBiZSc      % (chs x trials x 1024 timepoints) Post-cue (≅ 0, 1 s after cue onset) LFP data bipolar derived and Z-scored to Prestim (0 mean, 1 std)
          │       │      │     ├─── PreFirstDimDataBi     % (chs x trials x 1024 timepoints) Pre-firstdim (≅ -1, 0 s before first dim) LFPs bipolar derived 
          │       │      │     ├─── PreFirstDimDataBiZSc  % (chs x trials x 1024 timepoints) Pre-firstdim (≅ -1, 0 s before first dim) LFPs bipolar derived and Z-scored to Prestim (0 mean, 1 std)
          │       │      │     ├─── PreSecondDimDataBi    % (chs x trials x 1024 timepoints) Pre-seconddim (≅ -1, 0 s before second dim) LFPs bipolar derived 
          │       │      │     ├─── PreSecondDimDataBiZSc % (chs x trials x 1024 timepoints) Pre-seconddim (≅ -1, 0 s before second dim) LFPs bipolar derived and Z-scored to Prestim (0 mean, 1 std)
          │       │      │     ├─── PreThirdDimDataBi     % (chs x trials x 1024 timepoints) Pre-thirddim (≅ -1, 0 s before third dim) LFPs bipolar derived 
          │       │      │     ├─── PreThirdDimDataBiZSc  % (chs x trials x 1024 timepoints) Pre-thirddim (≅ -1, 0 s before third dim) LFPs bipolar derived and Z-scored to Prestim (0 mean, 1 std)
          │       │      │     ├─── gratCondFirstDim      % trial-by-trial first dimming RF task configuration
          │       │      │     ├─── gratCondSecondDim     % trial-by-trial second dimming RF task configuration
          │       │      │     └─── gratCondThirdDim      % trial-by-trial third dimming RF task configuration  
          │       │      │
          │       │      ├─── OUT1          
          │       │      │     ├─── PreStimDataBi         % (chs x trials x 1024 timepoints) Pre-stimulus (approx. -1000, 0 ms before stimulus onset) LFP data bipolar derived
          │       │      │     ├─── ...
          │       │      │     └─── gratCondThirdDim      % trial-by-trial third dimming OUT1 task configuration
          │       │      │
          │       │      └─── OUT2          
          │       │            ├─── PreStimDataBi         % (chs x trials x 1024 timepoints) Pre-stimulus (approx. -1000, 0 ms before stimulus onset) LFP data bipolar derived
          │       │            ├─── ...
          │       │            └─── gratCondThirdDim      % trial-by-trial third dimming OUT2 task configuration
          │       │      
          │       ├─── Full.Granr   % (see Full.Supra)     
          │       │      ├─── RF  
          │       │      ├─── ...      
          │       │      └─── OUT2        
          │       │      
          │       └─── Full.Infra   % (see Full.Supra)  
          │              ├─── RF      
          │              ├─── ...          
          │              └─── OUT2        
          │
          └─── penNum      % ID of the session analysed (redundant, added for consistency checks);
    
    
    
  3. The script 'process_Spectral_Power_V1V4_AllTimeWins()' performs spectral power analysis for each subject in V1 or in V4 and reports results organised session-by-session in the data structure 'PmtStruct' for all task-related time windows (depends on: 'support_tools.zip', 'support_routines.zip' included in the folder, please unzip in the local folder before running this script);

  4. The script 'process_Spectral_Coherence_V1V4_PreFirstDim()' performs spectral coherence analysis for each subject within V1, within V4, between V1-V4 depths, in the task time window before first stimulus dimming (though varying time window is very handy) and reports results organised session-by-session in the data structures: 'sCohV1Struct', 'sCohV4Struct' and 'SCohV1V4Struct' (depends on: 'support_tools.zip', 'support_routines.zip' included in the folder, please unzip in the local folder before running this script);

  5. The script 'process_cGC_within_V1V4_MultiMethods_PreFirstDim()' performs cGC analysis and plots results at the local level of columns V1 and V4 respectively, in the task time window before first stimulus dimming (though varying time window is very handy). Results are organised session-by-session in the data structure 'sGCsStruct' (depends on: 'support_tools.zip', 'support_routines.zip' included in the folder, please unzip in the local folder before running this script);

  6. The script 'process_cGC_between_V1V4_MultiMethods_PreFirstDim()' performs cGC analysis and plots results at the inter-areal level between V1 and V4, in the task time window before first stimulus dimming (though varying time window is very handy). Results are organised session-by-session in the data structure 'sGCsStructV1V4' (depends on: 'support_tools.zip', 'support_routines.zip' included in the folder, please unzip in the local folder before running this script);

datacite.yml
Title Data and code in "Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention"
Authors Ferro,Demetrio;Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Rovereto, Italy; Center for Brain and Cognition, and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain;ORCID:0000-0001-2345-6789
van Kempen,Jochem;Biosciences Institute, Newcastle University, NE1 7RU, Newcastle upon Tyne, United Kingdom;ORCID:0000-0002-0211-9545
Boyd,Michael;Biosciences Institute, Newcastle University, NE1 7RU, Newcastle upon Tyne, United Kingdom
Panzeri,Stefano;Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy;ORCID:0000-0003-1700-8909
Thiele,Alexander;Biosciences Institute, Newcastle University, NE1 7RU, Newcastle upon Tyne, United Kingdom;ORCID:0000-0003-4894-0213
Description Electrophysiological data recorded symultaneously in V1 and V4 and set of routines developed to map cortical information exchange within laminae and its modulation by selective visual attention.
License Creative Commons Attribution-NonCommercial-ShareAlike-4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/)
References Ferro, D., van Kempen, J., Boyd, M., Panzeri, S. and Thiele, A., bioRxiv 2020. Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. https://doi.org/10.1101/2020.06.09.142190 [https://doi.org/10.1101/2020.06.09.142190] (IsSupplementTo)
Ferro, D., van Kempen, J., Boyd, M., Panzeri, S. and Thiele, A., PNAS 2021. Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. https://doi.org/10.1073/pnas.2022097118 [https://doi.org/10.1073/pnas.2022097118] (IsSupplementTo)
Funding Funded by the Wellcome Trust [093104] (JvK, MB, AT), MRC [MR/P013031/1] (JvK, AT), NIH Brain Initiative [R01 NS108410] and [U19 NS107464U19] (SP) and Simons Foundation SFARI Explorer grant [602849] (SP).
Keywords Neuroscience
Visual Attention
Laminar circuitry
Resource Type Dataset