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Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

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Sofia-Eirini Kotti (TU Delft)
Aybüke Erol (TU Delft)
Borbála Hunyadi (TU Delft)

Abstract:
Functional ultrasound (fUS) is an emerging technique that images cerebral blood volume (CBV) changes with high sensitivity, without the use of contrast agents. In other words, fUS detects blood flow, or the number of moving red blood cells, in the voxels. These CBV changes are caused by the increased metabolic demand of active tissue and, thus, reflect neuronal activity in the corresponding brain area. The main advantages of this technique are that it can image the whole depth of the brain with high spatial (50-500um) and temporal resolution (10-100ms), and that it constitutes a potentially portable solution, as opposed to functional magnetic resonance imaging, the currently predominant modality in functional brain imaging. The fundamental challenge that comes with this technique is that it only provides an indirect measure of brain activity through the neurovascular coupling (NVC), which is the link between local neural activity and the resulting changes in the cerebral blood flow. This is a system with dynamic and non-linear characteristics which are only partially known. Moreover, besides the activity of interest, fUS records a mixture of other ongoing brain activity, physiological artifacts and noise. The goal of this research is estimate the brain’s hemodynamic response function (HRF), which is the mathematical representation of the NVC. The HRF is commonly described in literature using a linear time invariant system. In this work, we assume a nonlinear time invariant model that describes the relationship between the brain’s response to stimuli and the measured voxel time courses, and we use higher order kernels to characterize this model. Our results on fUS data obtained from mice reveal that including nonlinearities in the HRF achieves a significantly more precise modelling of the fUS signal compared to the linear system assumption under specific stimulus conditions.