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2 results on '"fMRI encoding models"'

1. Multilayer Scattering Image Analysis Fits fMRI Activity in Visual Areas.

Author

Eickenberg, Michael, Gramfort, Alexandre, and Thirion, Bertrand

Abstract

The scattering transform is a hierarchical signal transformation that has been designed to be robust to signal deformations. It can be used to compute representations with invariance or tolerance to any transformation group, such as translations, rotations or scaling. In image analysis, going beyond edge detection, its second layer captures higher order features, providing a fine-grain dissection of the signal. Here we use the output coefficients to fit blood oxygen level dependent (BOLD) signal in visual areas using functional magnetic resonance imaging. Significant improvement in the prediction accuracy is shown when using the second layer in addition to the first, suggesting biological relevance of the features extracted in layer two or linear combinations thereof. [ABSTRACT FROM PUBLISHER]

Published
2012
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2. Multilayer Scattering Image Analysis Fits fMRI Activity in Visual Areas

Author

Michael Eickenberg, Alexandre Gramfort, Bertrand Thirion, Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, Feature extraction, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, fMRI encoding models, 01 natural sciences, Signal, Edge detection, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Computer vision, scattering transform, visual cortex, 0101 mathematics, Layer (object-oriented design), Linear combination, Scaling, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], Blood-oxygen-level dependent, Scattering, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, [ SCCO.NEUR ] Cognitive science/Neuroscience, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract

International audience; The scattering transform is a hierarchical signal transformation that has been designed to be robust to signal deformations. It can be used to compute representations with invariance or tolerance to any transformation group, such as translations, rotations or scaling. In image analysis, going beyond edge detection, its second layer captures higher order features, providing a fine-grain dissection of the signal. Here we use the output coefficients to fit blood oxygen level dependent (BOLD) signal in visual areas using functional magnetic resonance imag- ing. Significant improvement in the prediction accuracy is shown when using the second layer in addition to the first, suggesting biological relevance of the features extracted in layer two or linear combinations thereof.

Published
2012

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