Recent advances in neuroimaging technology have significantly contributed to a better understanding of human brain organization, and the development and application of more efficient clinical programs. However, the limitations and tradeoffs inherent to the existing techniques, prevent them from providing large-scale imaging of neural activity with high spatiotemporal resolution, deep penetration, and specificity in awake and behaving participants.

Recently, functional ultrasound imaging (fUSI) was introduced as a revolutionary technology that provides a unique combination of spatial coverage, unprecedented spatiotemporal resolution (~100 μm and ~100 ms) and compatibility with freely moving animals. While fUSI is a hemodynamic technique, its superior spatiotemporal performance and single-trial sensitivity offer a substantially closer connection to the underlying neuronal signals than achievable with other hemodynamic methods such as fMRI. In addition, the relative simplicity and portability of ultrasound have allowed fUSI to be performed in awake and behaving animals, providing minimally invasive neural imaging in species ranging from mice to humans. In vivo fUSI was first reported in 2011 by imaging cerebral blood volume (CBV) changes in the micro-vascularization of the rat brain during whisker stimulation. Since then, this technique has been applied to brain activity imaging during olfactory stimuli, resting state connectivity, behavioral tasks on freely moving rats, non-human primates (NHPs) and other animals. However, one of the great advantages of fUSI is the ability to detect hemodynamic changes of only 2% without averaging over multiple trials. The ability to rely on the accuracy of a single-trial is necessary if one intended on using functional ultrasound signal to detect moment-to-moment variations of the blood flow.

By taking advantage of the excellent sensitivity of fUSI, our team performed single-trial motor experiments in awake and behaving non-human primates (NHPs). We recorded from outside the dura and above the posterior parietal cortex (PPC), while animals performed memory-delayed reach and eye (saccade) movements. We then used fUSI signal from the delay period before movement to decode the animal’s intended direction and effector. We showed for the first time that fUSI is capable of capturing the preparatory motor activity in NHPs that precedes movement responses – a prerequisite to brain-machine interfaces (BMIs), a key application that could benefit from this technology. These results are a critical step in the development of neuro-recording and brain interface tools that are less invasive, high resolution, and scalable.

Recently, we took the next major leap in fUSI by extending this technology to study the pathophysiology of neuropsychiatric diseases in pre-clinical (pharmaco-fUSI, mouse model of schizophrenia) and clinical trials (i.e., patients with traumatic brain injury, chronic back pain and others) and to develop modern neuromodulation strategies. Overall, fUSI provides researchers with truly revolutionary capabilities to study the central nervous system in a wide range of species, opening new avenues to understanding basic mechanisms of neuropsychiatric diseases and developing new treatments.

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Sponsored by the UCR Data Science Center, the purpose of the Data Science Seminar is to foster collaborations between "core" Data Science faculty (from CSE/ECE/Stat Departments) and faculty/visitors from other sciences that face Data Science problems in their research. These informal gatherings are open to interested faculty and graduate students. Each meeting will start with a talk describing research problems and then a discussion will follow for questions, open problems, ideas for possible collaborations etc.

Dr. Vasileios Christopoulos