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  Augmentation of fMRI Data Analysis using Resting State Activity and Semi-supervised Canonical Correlation Analysis

Shelton, J., Blaschko, M., & Bartels, A. (2010). Augmentation of fMRI Data Analysis using Resting State Activity and Semi-supervised Canonical Correlation Analysis. Poster presented at NIPS 2010 Women in Machine Learning Workshop (WiML 2010), Whistler, BC, Canada.

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 Creators:
Shelton, JA1, 2, Author           
Blaschko, MB1, 2, Author           
Bartels, A2, 3, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: Resting state activity is brain activation that arises in the absence of any task, and is usually measured
in awake subjects during prolonged fMRI scanning sessions where the only instruction given is to
close the eyes and do nothing. It has been recognized in recent years that resting state activity is
implicated in a wide variety of brain function. While certain networks of brain areas have different
levels of activation at rest and during a task, there is nevertheless significant similarity between
activations in the two cases. This suggests that recordings of resting state activity can be used as
a source of unlabeled data to augment kernel canonical correlation analysis (KCCA) in a semisupervised
setting. We evaluate this setting empirically yielding three main results: (i) KCCA tends
to be improved by the use of Laplacian regularization even when no additional unlabeled data are
available, (ii) resting state data seem to have a similar marginal distribution to that recorded during
the execution of a visual processing task implying largely similar types of activation, and (iii) this
source of information can be broadly exploited to improve the robustness of empirical inference in
fMRI studies, an inherently data poor domain.

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 Dates: 2010-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 7044
 Degree: -

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Title: NIPS 2010 Women in Machine Learning Workshop (WiML 2010)
Place of Event: Whistler, BC, Canada
Start-/End Date: 2010-12-05

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