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  1. The Virtual Brain
  2. TVB-763

Implement a new analyzer: spatial ICA

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    • Type: Task
    • Status: Closed
    • Priority: Major
    • Resolution: Won't Fix
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: Analyzers
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      Description

      Spatial ICA is a widely used technique to analyze MRI data (volumetric time series).
      MRI data give information on brain activation patterns with a decent spatial resolution
      and the activation maps obtained via sICA could help to identify parts of the brain that
      are related to specific tasks.

      -----------------------------------------------------------------------------------------------------------------------------
      Spatial ICA should be general enough to work with:

      • TimeSeriesRegion,
      • TimeSeriesSurface and
      • TimeSeriesVolume datatypes.

      Input dimensions:
      ..........................

      T: total number of time points
      L: total number of voxels (spatial unit: could be regions or vertices)

      • The spatial three-dimensional data structure is rearranged to a one-dimensional vector where the index will run from 1 to L, where L is the total number of voxels.
      • The input will contain T vectors of length L.

      Issues:
      ...........
      The performance of ICA algorithms degrades when the number of sources increases.
      In the case of sICA, we will have to compute at most T sources. Performance will
      degrade as a function of time-points.
      We can suggest to temporally subsample the signal.

      NOTE: In BOLD signals, the components are ordered as a function of time. Thus sICA could be performed in sliding windows.

      Objectives:
      ................

      • extract non-stimulus related components or,
      • decompose stimulus related components into various independent subcomponents.

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              Assignee:
              paula.sanz-leon Paula Sanz Leon
              Reporter:
              paula.sanz-leon Paula Sanz Leon
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