Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. 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 model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on 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 the mouse brain’s 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.

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The original version of this article was revised to update the Figure 8 (panel B) image. The correct image should have a yellow shape as presented below. (Figure presented.). The original article has been corrected.

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Power Doppler (PD) imaging has become a staple in high frame rate ultrasound imaging due to its ability to image small vessels and slow-moving flows, such as in the case of imaging blood flow in the brain. Alternatively, color Doppler (CD) can be used to determine the one-dimensional directional information of the blood scatterers. This can help determine if the flow is arterial or venous, or distinguish between adjacent flows that have an opposite direction. Current methods for estimating 2D blood velocity vectors rely mostly on trigonometric solutions using synthetic apertures or, large plane-wave angles in transmission and sub-apertures in receive to be able to resolve the 2D vector. Relative to PD or CD techniques, these methods are more computationally expensive and have not been successfully used to image blood flow direction within micrometer sized vasculature. In this paper, we propose to use the orientations of the vessels derived from a directional spatial filter in combination with the CD signal to enhance the PD images with directional information. This approach was tested on simulated data as well as on a 2D image containing brain vasculature of a mouse.@en