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Functional connectivity hubs of the mouse brain.

Authors :
Liska, Adam
Galbusera, Alberto
Schwarz, Adam J.
Gozzi, Alessandro
Source :
NeuroImage. Jul2015, Vol. 115, p281-291. 11p.
Publication Year :
2015

Abstract

Recent advances in functional connectivity methods have made it possible to identify brain hubs — a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10538119
Volume :
115
Database :
Academic Search Index
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
103136930
Full Text :
https://doi.org/10.1016/j.neuroimage.2015.04.033