[Comp-neuro] New temporal multiple kernel learning method for characterizing relating SC to connectivity dynamics and Brain state switching
dipanjan at cbcs.ac.in
Tue Oct 2 16:55:45 CEST 2018
It is my pleasure to share with you a very recent methods and data
driven modelling paper from our lab on how Resting State Dynamics Meets
Temporal Multiple Kernel Learning (tMKL) Model to explore SC-dFC-FC
tripartite relationship published in Neuroimage.
The proposed model uses spectral graph theory techniques to partitions
aspects of the whole-brain dynamics essentially into two parts: (i)
dynamics through identification of latent transient states, and (ii)
linking them to the underlying structural geometry. These two aspects
are captured using a novel blend of unique methods. The proposed
solution does not make strong assumptions about the underlying data and
is generally applicable to resting or task data for learning
subject-specific state transitions and for successfully characterizing
SC-dFC-FC relationship through a unifying framework.
MATLAB code for the proposed method can be downloaded from:
Any comments/questions/suggestions on the method/code are of course welcome.
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