Spatio-temporal models of mental processes from fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 57; no. 2; pp. 362 - 377
• Main Authors: Janoos, Firdaus, Machiraju, Raghu, Singh, Shantanu, Morocz, Istvan Ákos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2011; Elsevier Limited
• Subjects: Algorithms; Brain; Brain - physiology; Brain Mapping - methods; Brain-state; fMRI; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging; Markov; Mean field; Mental Processes - physiology; Metabolism; Methods; Models, Neurological; Multivariate; Multivariate Analysis; Semi-supervised; Spatio-temporal; Spatio-temporal; Mean field; fMRI; Markov; Semi-supervised; Brain-state; Multivariate
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.03.047

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. ► Multivariate spatio-temporal model of mental processes using state-space formalism. ► Semi-supervised method allowing for discovery of intrinsic patterns in data. ► Novel feature-space derived from an explicit formulation of functional similarity. ► Efficient estimation using expectation maximization with mean-field approximation. ► Comparative and quantitative single and multi-subject analysis results.