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Multiresolution Functional Connectivity Analysis Refines Functional Connectivity Networks in Individual Brains

Citation Author(s):
Alessio Medda, Shella Keilholz
Submitted by:
Jacob Billings
Last updated:
23 February 2016 - 1:44pm
Document Type:
Presentation Slides
Document Year:
2015
Event:
Presenters:
Jacob Billings
Categories:
 

Recent advances in functional connectivity (FC) analysis of functional magnetic resonance imaging (fMRI) data facilitate the characterization of the brain’s intrinsic functional networks (FC-fMRI). Because the fMRI signal does not provides a perfect representation of neuronal activity, the potential for FC-fMRI to identify functionally relevant networks critically depends upon separating overlapping signals from one another and from external noise. As a step in data preconditioning, researchers often band-pass filter fMRI signals to the range from 0.01 Hz to 0.1 Hz. However, coordinated network oscillations operate across multiple frequencies. Thus, it is not clear that the view of FC-fMRI networks within a single spectral range produces the fullest characterization of brain’s multiple and overlapping systems. The following study addresses this limitation by advancing a multiscale fractionation of FC-fMRI networks, as well methods for quantifying cross-spectral network similarity. These methods clearly and consistently represent group-level brains as composed of well-known functional networks.

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