Abstract：
:In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional task-fMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Alnæs D,Kaufmann T,Richard G,Duff EP,Sneve MH,Endestad T,Nordvik JE,Andreassen OA,Smith SM,Westlye LTdoi
10.1016/j.neuroimage.2015.01.026subject
Has Abstractpub_date
2015-04-01 00:00:00pages
260-72eissn
1053-8119issn
1095-9572pii
S1053-8119(15)00040-3journal_volume
109pub_type
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