Non-separable spatiotemporal brain hemodynamics contain neural information

Felix Bießmann, Yusuke Murayama, Nikos K. Logothetis, Klaus Robert Müller, Frank C. Meinecke

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

The goal of many functional Magnetic Resonance Imaging (fMRI) studies is to infer neural activity from hemodynamic signals. Classical fMRI analysis approaches assume a canonical hemodynamic response function (HRF), which is identical in every voxel. Canonical HRFs imply space-time separability. Many studies explored the relevance of non-separable HRFs. These studies were focusing on the relationship between stimuli or electroencephalographic data and fMRI data. It is not clear from these studies whether non-separable spatiotemporal dynamics of fMRI signals contain neural information. This study provides direct empirical evidence that non-separable spatiotemporal deconvolutions of multivariate fMRI time series predict intracortical neural signals better than standard canonical HRF models. Our results demonstrate that there is more neural information in fMRI signals than detected by most analysis methods.

Original languageEnglish
Pages (from-to)140-147
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7263 LNAI
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventInternational Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011 - Sierra Nevada, Spain
Duration: 2011 Dec 162011 Dec 17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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