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  Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity

Blaschko, M., Shelton, J., & Bartels, A. (2010). Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 126-134). Red Hook, NY, USA: Curran.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C0C0-8 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-93C8-6
Genre: Konferenzbeitrag

Urheber

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 Urheber:
Blaschko, M, Autor           
Shelton, J1, 2, Autor           
Bartels, A2, 3, Autor           
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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Schlagwörter: -
 Zusammenfassung: 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.

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Sprache(n):
 Datum: 2010-04
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 6064
 Art des Abschluß: -

Veranstaltung

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Titel: 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2009-12-07 - 2009-12-10

Entscheidung

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Quelle 1

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Titel: Advances in Neural Information Processing Systems 22
Genre der Quelle: Konferenzband
 Urheber:
Bengio, Y, Herausgeber
Schuurmans, D, Herausgeber
Lafferty, J, Herausgeber
Williams, C, Herausgeber
Culotta, A, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 126 - 134 Identifikator: ISBN: 978-1-615-67911-9