<P>MELODIC 3.0 uses Independent Component Analysis to decompose a
to decompose a single or multiple 4D data sets into different spatial and temporal components. For ICA group analysis, MELODIC uses either Tensorial Independent Component Analysis (TICA), where data is decomposed into
single or multiple 4D data sets into different spatial and temporal
spatial maps, time courses and subject/session modes or a simpler temporal concatenation approach. MELODIC can pick out different
components. For ICA group analysis, MELODIC uses either Tensorial
activation and artefactual components without any explicit time series model being specified.
Independent Component Analysis (TICA, where data is decomposed into
spatial maps, time courses and subject/session modes) or a simpler
temporal concatenation approach. MELODIC can pick out different
activation and artefactual components without any explicit time series
model being specified.
<P>For more detail on MELODIC and an updated journal reference, see
<P>For more detail on MELODIC and an updated journal reference, see
the <A
the <A
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@@ -34,8 +38,8 @@ quote the journal reference listed there.
ALT="Example GUI view">
ALT="Example GUI view">
<p>To call the MELODIC GUI, either type <b>Melodic</b> in a terminal
<p>To call the MELODIC GUI, either type <b>Melodic</b> in a terminal
(type <b>Melodic_gui</b> on Mac or Windows), or
(type <b>Melodic_gui</b> on Mac), or run <b>fsl</b> and press
run <b>fsl</b> and press the <b>MELODIC</b> button.
the <b>MELODIC</b> button.
<p>Before calling the GUI, you need to prepare each session's
<p>Before calling the GUI, you need to prepare each session's
data as a 4D NIFTI or Analyze format image; there are utilities in
data as a 4D NIFTI or Analyze format image; there are utilities in
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@@ -79,8 +83,11 @@ files in any MELODIC directories instead.
<p>First, set the filename of the 4D input image
<p>First, set the filename of the 4D input image
(e.g. <b>/users/sibelius/origfunc.nii.gz</b>) by pressing <b>Select 4D
(e.g. <b>/users/sibelius/origfunc.nii.gz</b>) by pressing <b>Select 4D
data</b>. You can select multiple files if you want MELODIC to perform a group analysis or if you want to run separate ICAs.
data</b>. You can select multiple files if you want MELODIC to perform
Results for each input file will be saved in separate .ica directories, the name of which is based on the input data's filename (unless you enter an <b>Output directory</b> name).
a group analysis or if you want to run separate ICAs with the same
setup. Results for each input file will be saved in separate .ica
directories, the name of which is based on the input data's filename
(unless you enter an <b>Output directory</b> name).
<p><b>Delete volumes</b> controls the number of initial FMRI volumes
<p><b>Delete volumes</b> controls the number of initial FMRI volumes
<p>The Stats section lets you control some of the options for the decomposition. The default setting will most probably already be set to what you would want most of the time.
<p>The Stats section lets you control some of the options for the decomposition. The default setting will most probably already be set to what you would want most of the time.
<p> By default, MELODIC will variance-normalise
<p> By default, MELODIC will variance-normalise timecourses.
<p> By default, Melodic will automatically estimate the number of
<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>
components from the data - you can switch this option off and then can specify the number of components explicitly.<p>
<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>.
<LI><b>Single-session ICA:</b> 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 sum of 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.
<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 amounts 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).
<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>
<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>).
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 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>
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>
</UL>
</UL>
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<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.
<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 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.
<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>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>.
<p>Estimated components typically fall into 2 classes: components which describe effects common to all or most subjects/sessions, and components which describe effects only contained in a small number of subjects/sessions. 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>.
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being twice as bad as false-negatives you should change this value
being twice as bad as false-negatives you should change this value
to 0.66...
to 0.66...
<p> You can select the image used for the generation of the spatial maps.
<p> You can select the background image used for the generation of the
spatial map overlay images.
<p> If you select the <b>Output full stats folder</b> option, MELODIC will save thresholded maps and probability maps in a <code>/stats</code> subdirectory within its output folder.
<p> If you select the <b>Output full stats folder</b> option, MELODIC will save thresholded maps and probability maps in a <code>/stats</code> subdirectory within its output folder.
<p>You can specify a temporal design matrix and in the case of a group analysis also a session/subject design matrix as well as corresponding contrast matrices. If these matrices are set in the GUI, MELODIC will perform a post-hoc regression analysis on estimated time courses and session/subject modes. This can be a helpful tool in order to identify whether or not a given component is task related. The matrices themselves can be created easily using the <ahref=../feat5/programs.html><b>Glm</b></a> GUI.
<p>You can specify a temporal design matrix (and in the case
of a group analysis also, a session/subject design matrix) as well as corresponding contrast matrices. If these matrices are set in the GUI, MELODIC will perform a post-hoc regression analysis on estimated time courses and session/subject modes. This can be a helpful tool in order to identify whether or not a given component is task related. The matrices themselves can be created easily using the <ahref=../feat5/programs.html><b>Glm</b></a> GUI.
<aname="buttons"></a>
<aname="buttons"></a>
<hr><H3>Bottom Row of Buttons</H3>
<hr><H3>Bottom Row of Buttons</H3>
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<p>In the case of TICA or simple time series concatenation the time course plotted is the rank-1 approximation to all the different time courses that correspond to the given spatial map within the population.
<p>In the case of TICA or simple time series concatenation the time course plotted is the rank-1 approximation to all the different time courses that correspond to the given spatial map within the population.
<p>If a temporal design was specified in the <ahref="#poststats"target="_top">Post-Stats</a> section then the time series plot will also contain a plot of the total model fit. In addition, a simple GLM table will describe the fit in detail, providing information of the regression parameter estimates (PEs). Furthermore, MELODIC will perform a simple F-test on the estimated time course and the total model fit. For taskrelated components the model fit will explain large amounts of the variation contained in the estimated time couse. In addition, if a contrast matrix was specified, the table will also contain Z-statistics and p-values for all the contrasts.
<p>If a temporal design was specified in the <ahref="#poststats"target="_top">Post-Stats</a> section then the time series plot will also contain a plot of the total model fit. In addition, a simple GLM table will describe the fit in detail, providing information of the regression parameter estimates (PEs). Furthermore, MELODIC will perform a simple F-test on the estimated time course and the total model fit. For task-related components the model fit will explain large amounts of the variation contained in the estimated time couse. In addition, if a contrast matrix was specified, the table will also contain Z-statistics and p-values for all the contrasts.
If a group analysis was carried out then the report page will also include information on the distribution of the effect size across the population. A simple plot and a boxplot show the relative effect size across the different sessions/subjects. If a design matrix was specified in the GUI setup then MELODIC will also include a GLM regression fit table.
If a group analysis was carried out then the report page will also include information on the distribution of the effect size across the population. A simple plot and a boxplot show the relative effect size across the different sessions/subjects. If a design matrix was specified in the GUI setup then MELODIC will also include a GLM regression fit table.
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remove.
remove.
<LI> In a terminal, run the MELODIC denoising, using the
<LI> In a terminal, run the MELODIC denoising, using the
where you should replace the comma-separated list of component numbers with the list that you previously recorded when viewing the MELODIC report.<br>
where you should replace the comma-separated list of component numbers with the list that you previously recorded when viewing the MELODIC report.<br>
</UL>
</UL>
The output file <code> denoised_data.nii.gz</code> then contains the filtered and denoised data set which can be used e.g. within FEAT. When running FEAT on this data make sure that the analysis is set to <code>Stats + Post-stats </code> as you do not want to run the other filtering steps (smoothing etc.) again on this data.
The output file <code> denoised_data.nii.gz</code> then contains the filtered and denoised data set which can be used e.g. within FEAT. When running FEAT on this data make sure that the analysis is set to <code>Stats + Post-stats </code> as you do not want to run the other filtering steps (smoothing etc.) again on this data.
