October 20, 2020

Statelets: high dimensional predominant shapes in dynamic functional network connectivity

Dynamic functional network connectivity (dFNC) analysis has been attracting interest over the past years by elucidating crucial details of brain activation patterns in severe neurological or psychiatric disorders. Consequently, a considerable amount of work has been conducted to analyze dFNC and leverage it for an improved understanding of neuro-dynamics and cognition. However, state-of-art methods mostly focus on fixed patterns of connectivity that reoccur. Such approaches do not capture the more transient feature space of the brain’s neural system and its dynamic properties. Likewise, the dynamical system approaches to model the system continuously and do not well capture unmodelled heterogeneity across subjects and time. Here, we seek an improved understanding of these connectivity states and the mechanism through which their dynamics vary across individuals. Relying on the concept of shapelets to model the temporal dynamics of functional associations between intrinsic brain networks, we develop the ‘statelet’ – a high dimensional state-shape representation of dynamics. We handle scale differences using the earth mover distance as our similarity metric and utilizing kernel density estimation to build a probability density profile for each shapelet. Unlike clustering the time series, we approximate short, variable-length connectivity patterns using the probability density profile of the shapelets from lower dimensions. We apply the proposed approach to study patients with schizophrenia (SZ) exhibit and identify reduced modularity increased statelet recurrence in their brain network organization relative to healthy controls (HC). An analysis of the connections’ consistency across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.

 bioRxiv Subject Collection: Neuroscience

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