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  When structure affects function: The need for partial volume effect correction in functional and resting state magnetic resonance imaging studies

Dukart, J., & Bertolino, A. (2014). When structure affects function: The need for partial volume effect correction in functional and resting state magnetic resonance imaging studies. PLoS One, 9(12): e114227. doi:10.1371/journal.pone.0114227.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0025-1A43-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7C56-2
Genre: Journal Article

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Dukart, Jürgen1, 2, Author              
Bertolino, Alessandro1, 3, Author
Affiliations:
1Pharma Research and Early Development, F. Hoffmann-La Roche, Basel, Switzerland, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Italy, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2014-07-212014-11-052014-12-02
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0114227
PMID: 25460595
PMC: PMC4252146
Other: eCollection 2014
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 9 (12) Sequence Number: e114227 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850