Functional subdivision of group-ICA results of fMRI data collected during cinema viewing

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four di...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2012-07, Vol.7 (7), p.e42000-e42000
Main Authors: Pamilo, Siina, Malinen, Sanna, Hlushchuk, Yevhen, Seppä, Mika, Tikka, Pia, Hari, Riitta
Format: Article
Language:English
Subjects:
Adult
Attention
Biology
Brain
Brain - physiology
Brain mapping
Brain research
Cognition & reasoning
Decomposition
Drama
Emotions
Engineering
Functional magnetic resonance imaging
Humans
Independent component analysis
Laboratories
Magnetic resonance
Magnetic Resonance Imaging
Medicine
Networks
Neuroimaging
NMR
Nuclear magnetic resonance
Reproducibility of Results
Science
Sensorimotor system
Social and Behavioral Sciences
Studies
Trends
Viewing
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0042000