Modelling Spatiotemporal Variability of Brain Responses in Functional Ultrasound

The brain stands as the most powerful processor in the known universe. It generates a continuous stream of electrical and chemical signals that underpin every thought, sensation, and action. Our past efforts in decoding these signals have made it possible to diagnose and treat many neurological disorders, helped us gain a deeper understanding of cognitive processes and consciousness, and paved the way for brain-computer interfaces. To take another step forward in our long but rewarding journey of discovering the brain's complex organization, we rely on advances in imaging technologies and signal processing.

Functional ultrasound is a neuroimaging technique that has emerged in the last decade, and has gained remarkable attention since then. The popularity of this technique stems from its portability, high resolution and affordability. Functional ultrasound can detect subtle fluctuations in local blood dynamics, which serve as delayed indicators of the underlying changes in neuronal activity. The goal of this thesis is to develop novel signal models and processing algorithms that can reveal the spatial and temporal characteristics of hemodynamic activity induced by external stimuli using functional ultrasound.

Existing techniques that explore how the brain reacts in response to stimuli model the design variables using a linear time-invariant system with binarized input representations, marking when a stimulus is on or off. However, experimental evidence suggests that the brain reacts in a more intricate manner. While some regions exhibit consistent responses to repeated stimuli, others can show substantial variation even when exposed to the same stimulus seconds apart. In our in-vivo experiments, we particularly focus on key regions within the mouse visual processing pathway, which are analogous to those in the human brain. We track how visual information flows across these areas, and propose methods that can incorporate the spatiotemporal variability of brain responses when identifying evoked activity. Using these methods, we show that functional ultrasound can capture the dynamic nature of brain responses with high spatial and temporal resolution, and provide us with further insights into the functional organization of the brain. Future directions of this dissertation include multimodal processing of the functional ultrasound signal together with neuronal activity, aiming to enhance our understanding of neurovascular coupling.