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Session: Brain
Session starts: Friday 27 January, 14:00
Presentation starts: 15:00
Room: Room 558

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Ruben Wijnands (Delft University of Technology)
Justin Dauwels (Delft University of Technology)
Ines Serra (Erasmus Medical Center)
Pieter Kruizinga (Erasmus Medical Center)
Aleksandra Badura (Erasmus Medical Center)
Borbála Hunyadi (Delft University of Technology)

Abstract:
Functional ultrasound (fUS) is a novel large-scale brain imaging technique that measures hemodynamic responses as a time series of images. Thereby, fUS measures neural activity indirectly through the neurovascular coupling (NVC). Often, such a time series of images 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 that are here defined as coactivation patterns (CAPs) of neural populations at a certain time point. CAPs are inferred by first estimating the activity of neural populations that underlie the observed fUS signals through a deconvolution procedure using the non-negative least absolute shrinkage and selection operator (NNLASSO). Then, using expectation maximization (EM), recurring neural CAPs and their transition preferences are learned from the reconstructed activity of neural populations. Our results show that our model and corresponding 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.