Modeling and Inference of Sparse Neural Dynamic Functional Connectivity Networks Underlying Functional Ultrasound Data

Abstract

Functional ultrasound (fUS) is a novel neuroimaging technique that measures brain hemodynamics through a time series of Doppler images. The measured spatiotemporal hemodynamic changes reflect changes in neural activity through the neurovascular coupling (NVC). Often, such image time series is used to analyze dynamic functional connectivity (dFC) by directly computing a connectivity metric between the measured hemodynamic signals, ignoring the functional connectomics of underlying neural populations. This work proposes a novel fUS signal model, consisting of a hidden Markov model (HMM) cascaded with a convolutive model, that captures how fUS signals arise from a generative perspective while incorporating high-level biological functioning of neural populations. Consequently, the developed model enables inference of functional connectivity networks, being co-activation patterns (CAPs) of neural populations. Our results show that our methods can identify biologically plausible networks of functional connectivity. Furthermore, this method captures a difference in brain dynamics between wild-type and ${Shank2}^{-/-}$ mouse mutants.