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PLoS One. 2014 Dec 2;9(12):e114227. doi: 10.1371/journal.pone.0114227. eCollection 2014.

When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies.

Author information

1
F. Hoffmann-La Roche, pRED, Pharma Research and Early Development, NORD DTA, Grenzacherstrasse 124, 4070 Basel, Switzerland; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
2
F. Hoffmann-La Roche, pRED, Pharma Research and Early Development, NORD DTA, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Bari, Italy.

Abstract

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality--an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

PMID:
25460595
PMCID:
PMC4252146
DOI:
10.1371/journal.pone.0114227
[Indexed for MEDLINE]
Free PMC Article

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