liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
A validation framework for probabilistic maps using Heschl's gyrus as a model.
Queen's University.
University of British Columbia.
Queen's University.
Queen's University.ORCID iD: 0000-0002-7810-1333
2010 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 50, no 2, 532-544 p.Article in journal (Refereed) Published
Abstract [en]

Probabilistic maps are useful in functional neuroimaging research for anatomical labeling and for data analysis. The degree to which a probability map can accurately estimate the location of a structure of interest in a new individual depends on many factors, including variability in the morphology of the structure of interest over subjects, the registration (normalization procedure and template) applied to align the brains among individuals for constructing a probability map, and the registration used to map a new subject's data set to the frame of the probabilistic map. Here, we take Heschl's gyrus (HG) as our structure of interest, and explore the impact of different registration methods on the accuracy with which a probabilistic map of HG can approximate HG in a new individual. We assess and compare the goodness of fit of probability maps generated using five different registration techniques, as well as evaluating the goodness of fit of a previously published probabilistic map of HG generated using affine registration (Penhune et al., 1996). The five registration techniques are: three groupwise registration techniques (implicit reference-based or IRG, DARTEL, and BSpline-based); a high-dimensional pairwise registration (HAMMER) as well as a segmentation-based registration (unified segmentation of SPM5). The accuracy of the resulting maps in labeling HG was assessed using evidence-based diagnostic measures within a leave-one-out cross-validation framework. Our results demonstrated the out performance of IRG and DARTEL compared to other registration techniques in terms of sensitivity, specificity and positive predictive value (PPV). All the techniques displayed relatively low sensitivity rates, despite high PPV, indicating that the generated probability maps provide accurate but conservative estimates of the location and extent of HG in new individuals.

Place, publisher, year, edition, pages
2010. Vol. 50, no 2, 532-544 p.
National Category
URN: urn:nbn:se:liu:diva-78204DOI: 10.1016/j.neuroimage.2009.12.074OAI: diva2:531708
Available from: 2012-06-07 Created: 2012-06-07 Last updated: 2015-04-16

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Johnsrude, Ingrid
In the same journal

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 12 hits
ReferencesLink to record
Permanent link

Direct link