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Neuroimage. 2015 Apr 1;109:217-31. doi: 10.1016/j.neuroimage.2015.01.013. Epub 2015 Jan 15.

Large-scale probabilistic functional modes from resting state fMRI.

Author information

  • 1Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK; Oxford Centre for Human Brain Activity (OHBA), Oxford, UK; Life Sciences Interface Doctoral Training Centre (LSI-DTC), Oxford, UK. Electronic address: samuel.harrison@ndcn.ox.ac.uk.
  • 2Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK; Oxford Centre for Human Brain Activity (OHBA), Oxford, UK.
  • 3Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK.
  • 4Department of Anatomy and Neurobiology, Washington University, Medical School, St. Louis, MO, USA.
  • 5Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands.

Abstract

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is 'at rest'. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

KEYWORDS:

Bayesian modelling; Functional parcellation; ICA; Resting state fMRI; Subject variability

PMID:
25598050
PMCID:
PMC4349633
DOI:
10.1016/j.neuroimage.2015.01.013
[PubMed - indexed for MEDLINE]
Free PMC Article
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