Large-scale Probabilistic Functional Modes from resting state fMRI
Publication year
2015Source
NeuroImage, (2015), pp. 217-231ISSN
Publication type
Article / Letter to editor
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Organization
PI Group Statistical Imaging Neuroscience
Journal title
NeuroImage
Page start
p. 217
Page end
p. 231
Subject
220 Statistical Imaging NeuroscienceAbstract
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.
This item appears in the following Collection(s)
- Academic publications [238441]
- Donders Centre for Cognitive Neuroimaging [3824]
- Electronic publications [122518]
- Open Access publications [97513]
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