A feature selection method based on a neighborhood approach for contending with functional and anatomical variability in fMRI group analysis of cognitive states-Reference-Cited by-同舟云学术

A feature selection method based on a neighborhood approach for contending with functional and anatomical variability in fMRI group analysis of cognitive states

Published:2017-06-29 Issue:3 Volume:21 Page:661-677
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Juárez-Castillo Efrén¹,Acosta-Mesa Héctor Gabriel¹,Fernandez-Ruiz Juan²,Cruz-Ramirez Nicandro¹

Affiliation:

1. Centro de Investigación en Inteligencia Artificial, Universidad Veracruzana, Xalapa, Veracruz, México

2. Departamento de Fisiología, Facultad de Medicina, Universidad Nacional Autónoma de México, México

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference34 articles.

1. Capturing inter-subject variability with group independent component analysis of fmri data: A simulation study;Allen;Neuroimage,2012

2. Face recognition in human extrastriate cortex;Allison;Journal of Neurophysiology,1994

3. Multimodal image coregistration and partitioning – a unified framework;Ashburner;Neuroimage,1997

4. Evaluating functional localizers: The case of the ffa;Berman;NeuroImage,2010

5. Patterns of activity in the categorical representations of objects;Carlson;Journal of Cognitive Neuroscience,2003

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