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FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization

  • Marta Nuñez-GarciaEmail author
  • Sonja Simpraga
  • Maria Angeles Jurado
  • Maite Garolera
  • Roser Pueyo
  • Laura Igual
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Resting state fMRI is a powerful method of functional brain imaging, which can reveal information of functional connectivity between regions during rest. In this paper, we present a novel method, called Functional-Anatomical Discriminative Regions (FADR), for selecting a discriminative subset of functional-anatomical regions of the brain in order to characterize functional connectivity abnormalities in mental disorders. FADR integrates Independent Component Analysis with a sparse feature selection strategy, namely Elastic Net, in a supervised framework to extract a new sparse representation. In particular, ICA is used for obtaining group Resting State Networks and functional information is extracted from the subject-specific spatial maps. Anatomical information is incorporated to localize the discriminative regions. Thus, functional-anatomical information is combined in the new descriptor, which characterizes areas of different networks and carries discriminative power. Experimental results on the public database ADHD-200 validate the method being able to automatically extract discriminative areas and extending results from previous studies. The classification ability is evaluated showing that our method performs better than the average of the teams in the ADHD-200 Global Competition while giving relevant information about the disease by selecting the most discriminative regions at the same time.

Keywords

Resting-state fMRI Independent Component Analysis Elastic Net Feature selection Classification 

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Marta Nuñez-Garcia
    • 1
    • 2
    Email author
  • Sonja Simpraga
    • 1
  • Maria Angeles Jurado
    • 3
  • Maite Garolera
    • 4
  • Roser Pueyo
    • 3
  • Laura Igual
    • 1
  1. 1.Department of Applied Mathematics and AnalysisUniversity of BarcelonaBarcelonaSpain
  2. 2.Physense, DTICUniversitat Pompeu FabraBarcelonaSpain
  3. 3.Department of Psychiatry and Clinical Psicobiology, IR3CUniversity of BarcelonaBarcelonaSpain
  4. 4.Neuropsychology Unit,Clinical Research of Brain, Cognition and BehaviorConsorci Sanitari de TerrassaBarcelonaSpain

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