Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs

• Published in: IEEE transactions on medical imaging Vol. 37; no. 1; pp. 230 - 240
• Main Authors: Bolton, Thomas A. W., Tarun, Anjali, Sterpenich, Virginie, Schwartz, Sophie, Van De Ville, Dimitri
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-01-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l ₁ regularisation; Adult; Algorithms; Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Computer Simulation; Couplings; Dynamic functional connectivity; Functional magnetic resonance imaging; Hidden Markov models; Humans; Image Processing, Computer-Assisted - methods; innovation-driven co-activation patterns; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Markov Chains; Neuroimaging; Pipelines; sparse coupled hidden Markov model; Technological innovation; total activation; Variation; Young Adult
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2017.2755369

Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.