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  Adaptive thresholding for reliable topological inference in single subject fMRI analysis

Gorgolewski, K. J., Storkey, A. J., Bastin, M. E., & Pernet, C. R. (2012). Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Frontiers in Human Neuroscience, 6: 245. doi:10.3389/fnhum.2012.00245.

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 Creators:
Gorgolewski, Krzysztof J.1, 2, Author              
Storkey, Amos J.1, Author
Bastin, Mark E.2, 3, Author
Pernet, Cyril R.2, Author
Affiliations:
1School of Informatics, University of Edinburgh, United Kingdom, ou_persistent22              
2Brain Research Imaging Centre, University of Edinburgh, United Kingdom, ou_persistent22              
3School of Healthcare Science, University of Edinburgh, United Kingdom, ou_persistent22              

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Free keywords: Mixture models; Random field theory; False negative errors; Spatial accuracy; Reliability
 Abstract: Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates. Similarly, simulations show that adaptive thresholding performs better than fixed thresholding in terms of over and underestimation of the true activation border (i.e., higher spatial accuracy). Finally, through simulations and a motor test–retest study on 10 volunteer subjects, we show that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined offering an automatic yet flexible way to threshold single subject fMRI maps.

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Language(s): eng - English
 Dates: 2012-03-122012-08-062012-08-25
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: PMC: 3427544
PMID: 22936908
DOI: 10.3389/fnhum.2012.00245
Other: eCollection 2012
 Degree: -

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Title: Frontiers in Human Neuroscience
  Abbreviation : Front Hum Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 6 Sequence Number: 245 Start / End Page: - Identifier: ISSN: 1662-5161
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5161