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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Harrison SJ,Woolrich MW,Robinson EC,Glasser MF,Beckmann CF,Jenkinson M,Smith SMdoi
10.1016/j.neuroimage.2015.01.013subject
Has Abstractpub_date
2015-04-01 00:00:00pages
217-31eissn
1053-8119issn
1095-9572pii
S1053-8119(15)00020-8journal_volume
109pub_type
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