Neuroinformatics in Functional Neuroimaging

Finn Årup Nielsen

AbstractThis Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database.

Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular ``known'' subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data.

Talairach foci from the BrainMap database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap database constituted among others: Entry errors, errors in the article and unusual terminology.

Statistical analysis and visualization have received much attention in neuroinformatics for functional neuroimaging and a large set of methods have been developed. Some of the most important analysis methods are reviewed with emphasis on cluster analysis, singular value decomposition, Molgedey-Schuster independent component analysis and linear models with FIR-filters. Furthermore, canonical ridge analysis is introduced as a mean for analysis of singular data. It can be viewed as a regularized canonical correlation analysis and in the limit of infinite regularization this is similar to a type of partial least squares. The model is also related to redundancy analysis, thus canonical ridge analysis subsumes different multivariate analyses and the solutions between them can be found by varying a continuous regularization parameter.

Scientific and information visualization methods are also reviewed with emphasis on VRML-based 3D visualization for functional neuroimaging results.
Keywordsneuroinformatics, functional neuroimaging, canonical correlation analysis, author cocitation
TypePh.D. thesis [Academic thesis]
Year2001    Month September
PublisherInformatics and Mathematical Modelling, Technical University of Denmark
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
IMM no.IMM-PHD-2001-87
NoteDue to copyright issues the PDF-file does not contain all articles that were included in the full thesis
Electronic version(s)[pdf] [ps.gz]
Publication link
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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