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Abstract:
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We will present a recently developed framework, connICA, a data-driven approach based on independent component analyses that decomposes individual connectivity patterns of the human brain (both structural and functional) into independent connectivity traits or patterns.
We will show, as an overview, how this approach may be used in three different ways. First, to identify independent connectivity traits and link them to cognitive processes and to clinical conditions. Second, we will show another application of this framework, where individual connectivity patterns may be improved or refined from a group-level analysis, in the sense of increasing the individual fingerprint of the dataset. Third, we will show the potential of this approach when studying dynamical functional connectivity while individuals are at rest and while performing specific tasks.
Finally, other potential uses and interpretations of connICA will be discussed.
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