Modeling dynamic functional connectivity using a wishart mixture model

• Published in: 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) pp. 1 - 4
• Main Authors: Nielsen, Søren F. V., Madsen, Kristoffer H., Schmidt, Mikkel N., Mørup, Morten
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2017
• Subjects: Bayes methods; Brain modeling; Data models; Hidden Markov models; Predictive models; Signal to noise ratio
• DOI: 10.1109/PRNI.2017.7981505

Dynamic functional connectivity (dFC) has recently become a popular way of tracking the temporal evolution of the brains functional integration. However, there does not seem to be a consensus on how to choose the complexity, i.e. number of brain states, and the time-scale of the dynamics, i.e. the window length. In this work we use the Wishart Mixture Model (WMM) as a probabilistic model for dFC based on variational inference. The framework admits arbitrary window lengths and number of dynamic components and includes the static one-component model as a special case. We exploit that the WMM framework provides model selection by quantifying models generalization to new data. We use this to quantify the number of states within a prespecified window length. We further propose a heuristic procedure for choosing the window length based on contrasting for each window length the predictive performance of dFC models to their static counterparts and choosing the window length having largest difference as most favorable for characterizing dFC. On synthetic data we find that generalizability is influenced by window length and signal-tonoise ratio. Too long windows cause dynamic states to be mixed together whereas short windows are more unstable and influenced by noise and we find that our heuristic correctly identifies an adequate level of complexity. On single subject resting state fMRI data we find that dynamic models generally outperform static models and using the proposed heuristic points to a windowlength of around 30 seconds provides largest difference between the predictive likelihood of static and dynamic FC.