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Session: Ultrasound
Session starts: Thursday 26 January, 10:30
Presentation starts: 10:30
Room: Room 559

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Aybüke Erol ()
Pieter Kruizinga ()
Borbála Hunyadi ()

Abstract:
Functional ultrasound (fUS) is a relatively new neuroimaging modality that indirectly measures brain activity. Particularly, fUS detects subtle fluctuations in blood volume induced by the local variations in neuronal activity through the phenomenon of neurovascular coupling. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we propose a deconvolution method for multivariate fUS time-series that reveals both the region-specific HRFs, and the underlying source signals that induce the hemodynamic responses. To that end, we model the fUS time-series as convolutive mixtures and apply block-term decomposition (BTD) on the tensor of lagged fUS autocorrelation matrices based on two assumptions: (1) HRFs are parametrizable, and (2) source signals are uncorrelated. In order to avoid falling into local minima while optimizing the non-convex cost function of BTD, we repeat BTD multiple times with random initializations, and apply hierarchical clustering to determine a stable final solution for the deconvolution [1]. We test our approach on simulations and two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within three crucial regions of the mouse brain’s image-forming pathway; namely the lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources. [1] A. Erol, C. Soloukey, B. Generowicz, N. Van Dorp, S. Koekkoek, P. Kruizinga and B. Hunyadi, “Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition,” arXiv: 2204.03711, May 2022.