GLM-Regularized Low-Rank Factorization For Extracting Functional Response From Swept-3D Functional Ultrasound

Functional ultrasound (fUS) is an emerging neuroimaging modality that indirectly measures neural activity by detecting fluctuations in local blood dynamics. fUS acquisitions typically rely on the use of a 1D array transducer, which records hemodynamic activity in a single plane. A new technique named swept-3D fUS imaging obtains a full 3D volume of the brain by continuously moving a 1D array back-and-forth over the volume of interest. The standard procedure in fUS imaging involves filtering and averaging a number of ultrasound frames obtained at a single location to compute power-Doppler images, yet, in case of swept-3D fUS, the location of the recorded slice shifts at each time instant due to probe motion. In this work, we aim at discovering task-relevant components from 3D fUS data while taking into account the spatiotemporal differences in adjacent slices. We propose an alternating optimization scheme with general liner model-based regularization, and validate our method on swept-3D fUS data by identifying active regions and time traces within the mouse brain during a visual experiment.