<p> By default, Melodic will automatically estimate the number of
components from the data - you can switch this option off and then can specify the number of components explicitly.<p>
<p> You can now select the type of analysis. MELODIC currently offers three options
<UL>
<LI><b>Single-session ICA:</b> This will perform standard 2D ICA runs on each of the input files. Data will be represented as a time x space matrix and be de-composed into a pairs of time courses and spatial maps.
<LI><b>Multi-session temporal concatenation:</b> This will perform a 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). For each component the final mixing matrix <code>melodic_mix</code> contains the temporal response of all different data concatenated into a single column vector. The final reported time course will be the best rank-1 approximation to these different responses.
<LI><b>Multi-session Tensor-ICA:</b> This will perform a 3D Tensor-ICA decomposition of the data. Data will be represented as a time x space x sessions block of data and be de-composed into triplets of time courses, spatial maps and session/subject modes. For more details on the decomposition see the technical report <ahref="http://www.fmrib.ox.ac.uk/analysis/techrep/"> TR04CB1 </a>.
</UL>
<p> You can now select the type of analysis. MELODIC currently offers three options:
<p>
<tableborder=0>
<TR><TDwidth=50%>
<UL>
<LI><b>Single-session ICA:</b> This will perform standard 2D ICA runs on each of the input files. The input data sets 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 sum over outer products of time courses and spatial maps. All the different time courses (one per component) will be saved in the <i>mixing matrix</i><code>melodic_mix</code> and all the spatial maps (one per component) will be saved in the 4D file <code>melodic_IC</code>.
<p>When using separate analyses, MELODIC will attempt to find components which are relevant and non-Gaussian relative to the residual fixed-effects within session/subject variation. It is recommended to use this option in order to check for session-specific effects (such as MR artefacts). You will need to use this option if you want to perform MELODIC denoising using <ahref="#regfilt">fsl_regfilt</a>. When using single-session ICA the component are ordered in order of decreasing amount of uniquely explained variance.
<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).
<p>
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.
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>
<LI><b>Multi-session Tensor-ICA:</b> 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 de-compose 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.
<p>It is recommended to use this approach for data where the stimulus paradigm is consistent between session/subjects. Tensor-ICA assumes that the temporal response pattern is the same across the population and provides a single decomposition for all original data sets. MELODIC will attempt to find components which are highly non-Gaussian relative to the full mixed-effects variance of the residuals.
<p>Estimated components typically fall into 2 classes: components which describe effect common to all or most data sets and components which describe effects only contained in a small number of data sets. The former will have a non-zero estimated effect size while the latter will have an effect size around 0 for most subjects/sessions and only few high non-zero values. These different types of components can be identified easily by looking at the boxplots provided. When using Tensor-ICA the components are ordered in order of decreasing amount of median response amplitude. For details on the decomposition see the technical report <ahref="http://www.fmrib.ox.ac.uk/analysis/techrep/"> TR04CB1 </a>.
