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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548484.0, MPI für biologische Kybernetik / Biologische Kybernetik
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity
Authors:Blaschko, M.; Shelton, J.; Bartels, A.
Editors:Bengio, Y.; Schuurmans, D.; Lafferty, J.; Williams, C.; Culotta, A.
Date of Publication (YYYY-MM-DD):2010-04
Title of Proceedings:Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009
Start Page:126
End Page:134
Physical Description:9
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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 discriminative
regression techniques in a semi-supervised setting. We evaluate this setting
empirically yielding three main results: (i) regression 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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6064
URL:http://nips.cc/Conferences/2009/
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