Deep independence network analysis of structural brain imaging: A simulation study

Eduardo Castro, Devon Hjelm, Sergey Plis, Laurent Dinh, Jessica Turner, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this paper is to further validate theoretically and empirically a nonlinear independent component analysis (ICA) algorithm implemented with a deep learning architecture. We first revisited its formulation to verify its consistency with the criterion of minimization of mutual information. Then, we applied the nonlinear independent component estimation algorithm (NICE) to synthetic 2D images that resemble structural magnetic resonance imaging (sMRI) data. This data was generated by mixing spatial components that represent axial slices of sMRI tissue concentration images. Next, we generated the images under linear and mildly nonlinear mixtures, being able to show that NICE matches ICA when the data is generated by using the conventional linear mixture and outperforms ICA for the nonlinear mixture of components. The obtained results are promising and suggest that NICE has potential to find richer brain networks if applied to real sMRI data, provided that small conditioning adjustments are performed along with this approach.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • NICE
  • Nonlinear ICA
  • deep learning
  • simulation
  • structural MRI

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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