characteriseMELODIC-FslWiki
Research Overview
Single-session ICA: This will perform standard 2D ICA on each of the input files. The input data will each be represented as a 2D time x space matrix. MELODIC then de-composes each matrix separately into pairs of time courses and spatial maps. The original data is assumed to be the
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 () 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 melodic_mix 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. Multi-session Tensor-ICA: This will perform a 3D Tensor-ICA decomposition of the data. All individual data sets will be represented as a single time x space x sessions/subjects block of data. Tensor-ICA will decompose this block of data into triplets of time courses, spatial maps and session/
subject modes, which - for each component - characterise the signal variation across the temporal, spatial and subject/session domain.
CategoryFunctional CategoryMELODIC
MELODIC (17:02:49 16-07-2013由MarkJenkinson编辑)

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