<LI><b>Multi-session temporal concatenation:</b> This will perform a single 2D ICA run on the concatenated data matrix (obtained by stacking all 2D data matrices of every single data set on top of each other).
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It is recommended to use this approach in cases where one is looking for common spatial patterns but can not assume that the associated temporal response is consistent between sessions/subjects. Examples include activation studies where the design was randomised between sessions or the analysis of data acquired without stimulation (<i>resting-state FMRI</i>).
<p>This approach does not assume that the temporal response pattern is the same across the population, though the final web report will contain the first Eigenvector of all different temporal responses as a summary time course. Access to all time courses is available: the time series plot is linked to a text file (<code>tXX.txt</code>) which contains the first Eigenvector, the best model fit in caase a time series design was specified and all different subject/session-specific time courses as columns.
<p>This approach does not assume that the temporal response pattern is the same across the population, though the final web report will contain the first Eigenvector of all different temporal responses as a summary time course. Access to all time courses is available: the time series plot is linked to a text file (<code>tXX.txt</code>) which contains the first Eigenvector, the best model fit in case a time series design was specified and all different subject/session-specific time courses as columns.
For each component the final mixing matrix <code>melodic_mix</code> contains the temporal response of all different data sets concatenated into a single column vector. The final reported time course will be the best rank-1 approximation to these different responses. <BR>
