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Flexible statistical modelling detects clinical functional magnetic resonance imaging activation in partially compliant subjects.

Waites, Anthony B; Mannfolk, Peter LU ; Shaw, Marnie E; Olsrud, Johan LU and Jackson, Graeme D (2007) In Magnetic Resonance Imaging 25(2). p.188-196
Abstract
Clinical functional magnetic resonance imaging (MRI) occasionally fails to detect significant activation, often due to variability in task performance. The present study seeks to test whether a more flexible statistical analysis can better detect activation, by accounting for variance associated with variable compliance to the task over time. Experimental results and simulated data both confirm that even at 80% compliance to the task, such a flexible model outperforms standard statistical analysis when assessed using the extent of activation (experimental data), goodness of fit (experimental data), and area under the operator characteristic curve (simulated data). Furthermore, retrospective examination of 14 clinical fMRI examinations... (More)
Clinical functional magnetic resonance imaging (MRI) occasionally fails to detect significant activation, often due to variability in task performance. The present study seeks to test whether a more flexible statistical analysis can better detect activation, by accounting for variance associated with variable compliance to the task over time. Experimental results and simulated data both confirm that even at 80% compliance to the task, such a flexible model outperforms standard statistical analysis when assessed using the extent of activation (experimental data), goodness of fit (experimental data), and area under the operator characteristic curve (simulated data). Furthermore, retrospective examination of 14 clinical fMRI examinations reveals that in patients where the standard statistical approach yields activation, there is a measurable gain in model performance in adopting the flexible statistical model, with little or no penalty in lost sensitivity. This indicates that a flexible model should be considered, particularly for clinical patients who may have difficulty complying fully with the study task. (c) 2007 Elsevier Inc. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
goodness of fit, statistically modelling, block design paradigm, fMRI
in
Magnetic Resonance Imaging
volume
25
issue
2
pages
188 - 196
publisher
Elsevier
external identifiers
  • wos:000244261300006
  • scopus:33846474270
ISSN
1873-5894
DOI
10.1016/j.mri.2006.09.044
language
English
LU publication?
yes
id
b7885f63-ed89-4849-bc52-1fe922f2e599 (old id 165931)
alternative location
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=17275613&dopt=Abstract
date added to LUP
2007-07-24 11:52:51
date last changed
2017-01-01 04:32:13
@article{b7885f63-ed89-4849-bc52-1fe922f2e599,
  abstract     = {Clinical functional magnetic resonance imaging (MRI) occasionally fails to detect significant activation, often due to variability in task performance. The present study seeks to test whether a more flexible statistical analysis can better detect activation, by accounting for variance associated with variable compliance to the task over time. Experimental results and simulated data both confirm that even at 80% compliance to the task, such a flexible model outperforms standard statistical analysis when assessed using the extent of activation (experimental data), goodness of fit (experimental data), and area under the operator characteristic curve (simulated data). Furthermore, retrospective examination of 14 clinical fMRI examinations reveals that in patients where the standard statistical approach yields activation, there is a measurable gain in model performance in adopting the flexible statistical model, with little or no penalty in lost sensitivity. This indicates that a flexible model should be considered, particularly for clinical patients who may have difficulty complying fully with the study task. (c) 2007 Elsevier Inc. All rights reserved.},
  author       = {Waites, Anthony B and Mannfolk, Peter and Shaw, Marnie E and Olsrud, Johan and Jackson, Graeme D},
  issn         = {1873-5894},
  keyword      = {goodness of fit,statistically modelling,block design paradigm,fMRI},
  language     = {eng},
  number       = {2},
  pages        = {188--196},
  publisher    = {Elsevier},
  series       = {Magnetic Resonance Imaging},
  title        = {Flexible statistical modelling detects clinical functional magnetic resonance imaging activation in partially compliant subjects.},
  url          = {http://dx.doi.org/10.1016/j.mri.2006.09.044},
  volume       = {25},
  year         = {2007},
}