Complex-valued analysis and visualization of fMRI data for event-related and block-design paradigms

Pedro A. Rodriguez, Tulay Adali, Vince D. Calhoun

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

Abstract

Independent Component Analysis (ICA) has been noted to be promising for the study of functional magnetic resonance imaging (fMRI) data also in its native complex-valued form. In this paper, we demonstrate the first successful application of group ICA to complex-valued fMRI data of an event-related paradigm. We show that networks associated with event-related responses as well as intrinsic fluctuations of hemodymamic activity can be extracted for data collected during an auditory oddball paradigm. The intrinsic networks are of particular interest due to their potential to study cognitive function and mental illness, including schizophrenia. More importantly, we show that analysis of fMRI data in its complex form can increase the sensitivity and specificity in the detection of activated brain regions both for event-related and block design paradigms when compared to magnitude-only applications. In addition, we introduce a novel fMRI phase-based visualization (FPV) technique to identify activated voxels such that the complex nature of the data is fully taken into account.

Original languageEnglish (US)
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs
StatePublished - Dec 12 2012
Externally publishedYes
Event2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain
Duration: Sep 23 2012Sep 26 2012

Publication series

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

Other

Other2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
CountrySpain
CitySantander
Period9/23/129/26/12

Keywords

  • Complex-valued fMRI
  • Event-related
  • Group ICA
  • Phase
  • Visualization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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