Flexible statistical modelling detects clinical functional magnetic resonance imaging activation in partially compliant subjects.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/165931
- author
- Waites, Anthony B ; Mannfolk, Peter LU ; Shaw, Marnie E ; Olsrud, Johan LU and Jackson, Graeme D
- organization
- publishing date
- 2007
- 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
- pmid:17275613
- 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
- 2016-04-01 11:46:54
- date last changed
- 2022-03-28 02:59:06
@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}},
keywords = {{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}},
doi = {{10.1016/j.mri.2006.09.044}},
volume = {{25}},
year = {{2007}},
}