Predictive assessment of models for dynamic functional connectivity

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2018-05, Vol.171, p.116-134
• Main Authors: Nielsen, Søren F.V., Schmidt, Mikkel N., Madsen, Kristoffer H., Mørup, Morten
• Format: Article
• Language: English
• Subjects: Brain - physiology; Brain architecture; Brain mapping; Connectome - methods; Dynamic functional connectivity; EEG; Electroencephalography - methods; Functional magnetic resonance imaging; Hidden Markov models; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Markov analysis; Medical imaging; Models, Neurological; Neural networks; Neural Pathways - physiology; Neuroimaging; Pattern recognition; Predictive likelihood; Time series
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.12.084

In neuroimaging, it has become evident that models of dynamic functional connectivity (dFC), which characterize how intrinsic brain organization changes over time, can provide a more detailed representation of brain function than traditional static analyses. Many dFC models in the literature represent functional brain networks as a meta-stable process with a discrete number of states; however, there is a lack of consensus on how to perform model selection and learn the number of states, as well as a lack of understanding of how different modeling assumptions influence the estimated state dynamics. To address these issues, we consider a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data. Examining several prominent models of dFC (in their probabilistic formulations) we demonstrate our framework on synthetic data, and apply it on two real-world examples: a face recognition EEG experiment and resting-state fMRI. Our results evidence that both EEG and fMRI are better characterized using dynamic modeling approaches than by their static counterparts, but we also demonstrate that one must be cautious when interpreting dFC because parameter settings and modeling assumptions, such as window lengths and emission models, can have a large impact on the estimated states and consequently on the interpretation of the brain dynamics. •Probabilistic models of dynamic functional connectivity can be assessed through prediction.•Prediction demonstrates support for multiple states in functional neuroimaging data.•The number of states and their characteristics is strongly influenced by the choice of model.•The interpretation of dynamic functional connectivity should always take model specification into account.