Identifying directed links in large scale functional networks: application to brain fMRI.
BACKGROUND
Biological experiments increasingly yield data representing large ensembles of interacting variables, making the application of advanced analytical tools a forbidding task. We present a method to extract networks of correlated activity, specifically from functional MRI data, such that: (a) network nodes represent voxels, and (b) the network links can be directed or undirected, representing temporal relationships between the nodes. The method provides a snapshot of the ongoing dynamics of the brain without sacrificing resolution, as the analysis is tractable even for very large numbers of voxels.


RESULTS
We find that, based on topological properties of the networks, the method provides enough information about the dynamics to discriminate between subtly different brain states. Moreover, the statistical regularities previously reported are qualitatively preserved, i.e. the resulting networks display scale-free and small-world topologies.


CONCLUSIONS
Our method expands previous approaches to render large scale functional networks, and creates the basis for an extensive and -due to the presence of mixtures of directed and undirected links- richer motif analysis of functional relationships.
Cecchi
Guillermo A
GA
Computational Biology Center, T,J, Watson IBM Research Center, Yorktwon Heights, New York, USA. gcecchi@us.ibm.com
Rao
A Ravishankar
AR
Centeno
Maria V
MV
Baliki
Marwan
M
Apkarian
A Vania
AV
Chialvo
Dante R
DR
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer