Large-scale Probabilistic Functional Modes from resting state fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 109; pp. 217 - 231
• Main Authors: Harrison, Samuel J., Woolrich, Mark W., Robinson, Emma C., Glasser, Matthew F., Beckmann, Christian F., Jenkinson, Mark, Smith, Stephen M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2015; Elsevier Limited; Academic Press
• Subjects: Bayesian analysis; Bayesian modelling; Biomedical research; Brain; Brain - physiology; Brain research; Computer Simulation; Connectome - methods; Fluctuation; Functional parcellation; Human; Humans; ICA; Image Processing, Computer-Assisted; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical research; Neurosciences; Probabilistic methods; Probability; Probability theory; Response functions; Resting state fMRI; Signal Processing, Computer-Assisted; Subject variability; Temporal logic; Resting state fMRI; Subject variability; Functional parcellation; Bayesian modelling; ICA
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.01.013

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. •We introduce a probabilistic model for modes in resting state fMRI.•Our hierarchical model captures subject variability and haemodynamic effects.•We illustrate its performance on simulated data and rfMRI data from 200 subjects.•We demonstrate the ability of our method to infer spatio-temporally interacting modes.