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Neuroimage. 2013 Nov 15;82:616-633. doi: 10.1016/j.neuroimage.2013.05.108. Epub 2013 Jun 2.

Resting state network estimation in individual subjects.

Author information

1
Department of Biomedical Engineering, Washington University.
2
Department of Neurology and Neurosurgery, Washington University.
3
Department of Neuroscience and Imaging, University of Chieti, "G. d'Annunzio," Italy.
4
Department of Radiology, Washington University.
5
Department of Anatomy and Neurobiology, Washington University.
#
Contributed equally

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.

KEYWORDS:

Brain mapping; Functional connectivity; Multilayer perceptron; Resting state network; Supervised classifier; fMRI

PMID:
23735260
PMCID:
PMC3909699
DOI:
10.1016/j.neuroimage.2013.05.108
[Indexed for MEDLINE]
Free PMC Article

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