Data archive for: Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks

Authors: Hamed Haque, Sheng H. Wang, Felix Siebenhühner, Edwin M. Robertson, J. Matias Palva, Satu Palva

Year: 2025

Contact: Hamed Haque, hamed.haque@helsinki.fi; hamed.haque@glasgow.ac.uk

This data archive contains synchronization data of twenty healthy human participants performing a visual working memory (VWM) task while being scanned using concurrent electroencephalography (EEG) and magnetoencephalography (MEG). For each analysis, the relevant input and supporting files that allow replication of the workflow and results are included. Scripts used for these analysis can be found in GitHub (https://github.com/palvalab/vwm_synchronization).

In the present work we analyse large-scale synchronization networks in multiple frequency bands during a VWM task. We identified distinct inter-areal networks in the alpha and theta-bands, with the alpha-band synchronization network shown to be localized to feature-selective visual areas. We also identified subgraphs in the alpha-band that were shared across memory contents and thus formed the executive core of VWM.

Description of the Data and file structure

Summary of data

Data in this repository are structured in 22 different folders. The metadata folder contain various supporting files necessary for proper analysis and interpretation of the rest of the dataset. The plot_data folder contains various intermediary and output files that are necessary for performing each of the analysis. The rest of the 20 folders correspond to each of the 20 subjects of the study. They are structured in the format S0** with the last two digits corresponding to the unique identifier for each subject.

Usage notes

All files in the dataset included in the repository fall into one of the three file formats:

.csv A CSV (comma-separated values) file is a plain text file used to store tabular data, with each row representing values separated by a comma. CSV files can be opened with a standard text editor such as Notepad, Microsoft Excel, or imported into Python using the csv or pandas libraries with the csv.reader() or pandas.read_csv() functions respectively.

.xlsx These are zipped, XML-based excel files created by Microsoft Excel also used to store tabular data. XLSX files can be opened and edited using Microsoft Excel or imported into Python using the pandas.read_excel() function.

.tdms A Technical Data Management Streaming (TDMS) is a file format designed by National Instruments (NI) to store measurement data. Data are stored in a clear hierarchical structural organized by file, group, and channel. TDMS files can be opened directly using LabVIEW, in Microsoft Excel using the ‘TDM Excel Add-In for Microsoft Excel’ tool, or in Python using the nptdms library through the nptdms.TdmsFile function. Routines for reading TDMS files in Python are provided in the GitHub repository associated with this dataset. Below is example code for viewing TDMS files in python:

from nptdms import TdmsFile

tdms_file = TdmsFile.read("your_file.tdms")
data = tdms_file.as_dataframe() 

for group in tdms_file.groups():
    for channel in group.channels():
        print(f"{group.name}/{channel.name}: {channel.data}")

Metadata

The folder metadata contain various supporting files that typically span several different analysis.

__group_statistics_csv

The folders within ‘__group_statistics_csv’ contain synchronization matrices at the group level for different conditions. These are group level connectomes for the Pearson correlation between phase synchronization and behavioural performance. The folders are arranged in the format: 3x2_Feature_x_Obj EdgeD  Pearsonconditiontail. The condition refer to the feature and number of objects to be memorized during the VWM while tail refers the statistics performed at either the positive or negative tail.

Within each of these subfolders, the files are stored in the format: Phase-Phase 1-1 Original No-Surrogates cPLV Lag_1-0 Lag=1.000 Low = frequency hi = frequency parc2009_200AFS measure.csv. The frequency refers to the frequency in which the source-reconstructed MEEG data was filtered to before the synchronization was estimated. The measure refers to different ways of presenting the significant matrix: for each edge, ES is the effect size, p-value is the p-value, sign-stat is the r statistic value of the Pearson correlation test.

In each of these csv files, the adjacency matrices are in the shape 200 x 200 with the three time windows stacked vertically, resulting in a final shape of 600 x 200.

difference_masks

These contain the group level statistical mask for edges where the contrast difference between visual features was significant. There are two files, Alpha.csv and Theta.csv and they both contain 200 x 200 binary matrices. A 1 indicates a significant edge while a 0 indicates a non-significant edge.

Morphing operators

This folder contains, for each subject, the morphing matrix that can convert the 400-parcels into the 200 parcels of the split-Destrieux parcellation. The morphing operator is of the shape 400 x 200, with each column representing each parcel of the 200 parcel atlas and each row the contribution of each parcel of the 400 parcel atlas.

behavioral_df.csv

Provides the Hit Rates for each subject (S001 to S020), per condition (Shape, Color, or Spatial) and load (2 or 4).

cf_matrix_high_full.csv

The cross-frequency (CF) matrix used for computing inter-areal n:m synchronization, with n representing the low frequency and m the high frequency. Frequencies used as low frequencies (first column) and as high frequencies (2nd to 9th column) for CF ratios 1:2—1:9. All frequencies appear in the first column.

current_DEM.csv

Contains the Deny Edge Matrix (DEM) which is a binary 200 x 200 matrix. 1’s indicate the edge should be included for analysis while 0’s indicate that the edge should be excluded from group analysis. The DEM is meant to exclude poorly reconstructable parcel edges (based on parcel fidelity and cross-patch PLV) from group level analyses.

Functional_boundaries.csv

Label of each parcel indicating which functional visual subsystem it belongs to. The file contains a single column of length 200 with each row representing a parcel. The value corresponds to the identifier of specific visual subsystems.

Patch_Grouping_divisions.csv

Label of each parcel indicating which of the 7 Yeo subsystems it belongs to. Each of the 200 rows corresponds to a parcel with the first column indicating the Yeo subsystem the parcel belongs to.

Plot_data

The folder ‘plot_data’ contains the files specific to each of the main analyses performed in the study.

Figure_2

Contrast_betn_features: Contains the K values (fraction of significant edges) for each frequency for each contrast difference between visual features. Each file in this folder represents a contrast between two conditions, with the positive and negative tails in separate files. The first column is the frequency and the second column the K value.

Graph_strength: The two files in this folder, GS_Alpha.csv and GS_Theta.csv, contain the mean graph strength for retention of each visual feature. The first column has the visual feature (Shape, Color, Spatial) and the second column the mean graph strength, which is the average iPLV value of the significant inter-areal network. Each row represents a subject.

Wilcoxon: Same as ‘Contrast_betn_features’ but rather than presenting the K values for differences between conditions, contains the K values for the significant edges after a one-sample Wilcoxon signed-rank test for each visual feature. The ‘jackknife’ folder contains the same contrasts as the parent folder but presents the K values for each jackknife resample.

Figure_3

Single_condition: Contains the group-level connectomes for the Wilcoxon signed-rank test between alpha phase synchronization of the retention and baseline period. Each file contains a 200 x 200 adjacency matrix with the non-zero values being the average iPLV value when the edge is significant.

Subsystem_edges: The two files provide the mean strength of each susbsystem edge, separately for alpha and theta. This is provided in a four column tabular format with the last column providing the mean edge strength (average iPLV value) for the given subject, condition, and subsysyem edge.

Figure_4

6_8-05: Inside the subfolder ‘c_limit(0.06)’, each of the four files contain group-level connectomes in the theta-band. The AM 0, 1, 2, and 99 refer to the Shape-specific, Color-specific, Location-specific, and Shared networks, respectively. Each connectome is a 200 x 200 adjacency matrix.

11-25_13-06: Same as above but for the alpha-band

Figure_5

Correlation: Each file contains the K values for each frequency for the correlation between phase synchronization and behavioural performance. The naming of the files as well as the K values within each file follow the convention used in ‘Figure 2/Contrast_betn_features’.

Graph_strength: Each file provides the graph strength for each feature (Shape, Color, Spatial) and frequency band (alpha, theta). Within each file, each row represents a subject and the third column, iPLV, contains the mean iPLV for the significant network.

Figure_6

Amplitude: For each subject, and separately for the retention and baseline window, classification accuracy in decoding the memorized feature of each trial. Files are separate for each subject (S0**), load (2 or 4), and whether baseline (BL) window was used or not. In each file, the decoding accuracy for Early and Late is provided in separate columns, with decoding accuracy being the proportion of trials accurately classified.

Synchrony: Same as above but for phase synchronization.

Figure_7

PAC_python: Each file contains the K values for significant phase-amplitude coupling of the inter-areal networks in each of the visual features. K values for each n:m ratio (e.g. 1-2, 1-3) are provided in separate files. The naming of the files as well as the K values within each file follow the convention used in ‘Figure 2/Wilcoxon/jackknife’.

Subject folders (S0**)

Each of the twenty subject folders (from S001, S002, … S020) follow the same folder and file structure. There are three folders inside each S0** folder.

3x2_Feature_x_Obj: contains the connectomes of phase synchronization for each feature (Shape, Color, Location) and separated by load (load 2 or load 4).

3x2_Load_avg: contains the connectome of phase synchronization for each feature (Shape, Color, Location) averaged across the two loads.

3x2_Load_avg_PAC: contains the connectome of phase-amplitude coupling for each feature (Shape, Color, Location).

In each of the three folders, the connectome data at the subject level is provided in .tdms files. The filename structure for each TDMS file is as follows:

EdgeDAmplitude-Phase ratio Original No-Surrogates cPLV Lag_1-0 Lag=1.000 Low = low_frequency condition folder_name high_frequencyHz.tdms

ratio: for 3x2_Feature_x_Obj and 3x2_Load_avg, the ratio is always 1-1. In 3x2_Load_avg_PAC, since the connectomes reflect n:m synchronization, the ratio would be different based on the values of low_frequency and high_frequency

low_frequency and high_frequency: for 3x2_Feature_x_Obj and 3x2_Load_avg, low_frequency and high_frequency are the same while they are different for 3x2_Load_avg_PAC. This indicates the frequency in which the source reconstructed data was narrow-band filtered to before the synchronization estimates were obtained.

condition: this indicates the visual feature in which the synchronization was estimated from. The condition is either Shape_HIT, Color_HIT, or Spatial_HIT, each corresponding to a visual feature. For 3x2_Feature_x_Obj, the load is also provided, e.g. Shape2_HIT.

folder_name: this is the folder in which the file is located and is either 3x2_Feature_x_Obj, 3x2_Load_avg, or 3x2_Load_avg_PAC.

Within each TDMS file the subject-level connectome is provided for the specific condition and frequency band. The connectome for the baseline, early retention, and late retention are provided in the groups 0, 1, and 2, respectively. Each connectome is a 400 x 400 adjacency matrix with each edge containing the iPLV value.

Human subjects data

We have received explicit consent from the participants that de-identified data could be published in the public domain. To de-identify the data, all personal information that may be used to identify a participant has been removed. Each subject is only labelled via a numbered code (e.g. S001) and subject data only contains connectome data (400 x 400 matrices of complex valued numbers). Any physiological data that could be used to identify the subjects (e.g. raw EEG/MEG traces, MRIs etc.) are not included in the dataset.