Abstract：
:Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Hacker CD,Laumann TO,Szrama NP,Baldassarre A,Snyder AZ,Leuthardt EC,Corbetta Mdoi
10.1016/j.neuroimage.2013.05.108subject
Has Abstractpub_date
2013-11-15 00:00:00pages
616-633eissn
1053-8119issn
1095-9572journal_volume
82pub_type
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