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DST (Dynamic Synchronization Toolbox): A MATLAB Implementation of the Dynamic Phase-Locking Pipeline from Stimulus Transformation into Motor Action: Dynamic Graph Analysis Reveals a Posterior-to- Anterior Shift in Brain Network Communication of Older Subjects Cover

DST (Dynamic Synchronization Toolbox): A MATLAB Implementation of the Dynamic Phase-Locking Pipeline from Stimulus Transformation into Motor Action: Dynamic Graph Analysis Reveals a Posterior-to- Anterior Shift in Brain Network Communication of Older Subjects

By: Nils Rosjat and  Silvia Daun  
Open Access
|Aug 2022

Figures & Tables

Figure 1

Schematic drawing of the three software parts: connectivity, statistics and graph measures.

Table 1

Options for rPLV calculation in step 1.

OPTIONTYPEDESCRIPTION
electrodesinteger listsubset of electrode indices of interest
freqsinteger listfrequency range for time-frequency decomposition
baselineinteger(begin, end) of time-interval for relative baseline
multiple_condsbooleansingle (false) or multiple (true) conditions
switch_handsbooleanenables mapping of electrodes to other hemisphere
channels_newinteger listnew order of electrodes for mapped condition
channels_oldinteger listold order of electrodes for mapped condition
contrastbooleanenabling contrasting conditions
contrast_condsinteger listindices of two conditions to contrast
avg_freqsinteger listfrequencies of interest for averaging
Figure 2

Flowchart depicting the logical sequence of the dynamic graph calculations.

Table 2

Options for statistical testing and dynamic graph construction in step 2.

OPTIONTYPEDESCRIPTION
pidstringmultiple comparisons ‘original’, ‘individual’, ‘uncorr’
pID_fixdoublefixed p-value for corrected stats
p_fixdoublefixed p-value for uncorrected stats
q_FDRdoubleq-value for FDR-correction
compstringtype of comparison ‘baseline’ or ‘zero’
test_interval_startintegerstart of testinterval in ms
test_interval_endintegerend of testinterval in ms
baseline_startintegerstart of baseline in ms
baseline_endintegerend of baseline in ms
taskintegerdefinition of task by id (contrast appears last)
contrastbooleanenabling contrasting conditions
timeinteger listsampling timepoints
sampling_rateintegersampling rate of the data
rplv:relative phase-locking value for each dataset in a cell {num, subjects, 1}, each cell stores the rPLV with dimensions [time, channel, channel, conditions].
trials:number of trials in each experimental condition and subject.
rplv_mean:group average of rPLV in [time, channel, channel, conditions].
sig_ti_FDR:significant timepoints after statistics and multiple comparisons as a cell {channel, channel}.
xa:list of significant intervals [intervals, 3] with information about start timepoint, stop timepoint and the amount of timepoints to the next interval for each channel pair stored in a cell {channel, channel}.
length:list of length of significant intervals for each channel pair stored in a cell {channel, channel}.
Agg:aggregated graph showing the frequency of all connections over the whole interval in a matrix [channel, channel].
bet:temporal betweenness centrality in a matrix [timepoint, channel].
hub:temporal hub nodes, i.e. nodes with highest betweenness centrality, in a matrix [timepoint, 2].
clusters:clusters assignment for each channel and timepoint [channel, timepoints].
node_flex:node flexibility for each channel stored in a matrix [2, channel].
deg:each channels node degree over time in a matrix [timepoint, channel].
DOI: https://doi.org/10.5334/jors.394 | Journal eISSN: 2049-9647
Language: English
Submitted on: Sep 8, 2021
Accepted on: Jul 14, 2022
Published on: Aug 1, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2022 Nils Rosjat, Silvia Daun, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.