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Found 1 result for 11835605
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. 2002 Mar;15(3):146-56.
doi: 10.1002/hbm.10012.

Modeling of activation data in the BrainMap database: detection of outliers

Finn Arup Nielsen  1 , Lars Kai Hansen
Affiliations

Modeling of activation data in the BrainMap database: detection of outliers

Finn Arup Nielsen et al. Hum Brain Mapp. 2002 Mar.

Abstract

We describe a system for meta-analytical modeling of activation foci from functional neuroimaging studies. Our main vehicle is a set of density models in Talairach space capturing the distribution of activation foci in sets of experiments labeled by lobar anatomy. One important use of such density models is identification of novelty, i.e., low probability database events. We rank the novelty of the outliers and investigate the cause for 21 of the most novel, finding several outliers that are entry and transcription errors or infrequent or non-conforming terminology. We briefly discuss the use of atlases for outlier detection.

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Figures

Figure 1
Figure 1
The 1,231 classes sorted according to frequency. The most occurring words are “gyrus,” “cortex,” and “frontal.” The most occurring phrase is “frontal gyrus” as the 12th most frequent class. There are approximately 600 classes with more than two examples.
Figure 2
Figure 2
Probability density estimate of the “cerebellum” class in Talairach space in a Corner Cube Environment. The wireframe‐model is the first stage probability density estimation where all the locations are included and the polygon model is the second stage probability density estimate where the 5% most extreme are excluded. Note that two isolated “blobs” created by isolated, outlying locations were eliminated going from the first to the second level density. This figure as well as Figures 4 and 5 are made with the Brede Matlab toolbox available at http://hendrix.imm.dtu.dk/software/brede.
Figure 3
Figure 3
An automatically generated list of those locations estimated to have the highest novelty. “Paper,” “Exp.,” and “Loc.” correspond to the identifiers used in the BrainMap database. X, y, z, and “Lobar anatomy” are the associated fields in the database with the coordinates in centimeter and the “loglikehood” is our novelty measure. The “Full text” column indicates whenever it is possible to extract a link from the Entrez‐PubMed to the electronic full text of the articles.
Figure 4
Figure 4
Surface of the cerebellum from the Talairach Atlas with the “cerebellum” locations. The inferior part of the cerebellum is not in the atlas, thus not in the visualization. The contour shadows are the convex hull of the digitized Talairach cerebellum.
Figure 5
Figure 5
The ICBM cerebellum with all the cerebellum locations from BrainMap. The locations have been transformed by the inverse operation of Brett's [1999] nonlinear transformation (see text).
See this image and copyright information in PMC

References

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