Data Science & Informatics

Human brainstem serves many plays critical roles in health and disease. Unfortunately, it has been vastly under-studied because of its physical inaccessibility in animal models, and its low contrast-to-noise ratio (CNR) for functional magnetic resonance imaging (fMRI) in human studies. At conventional fMRI field strengths, CNR is an order-of-magnitude lower in brainstem than in cerebral cortex. Recently, ultra-high-field (UHF) scanners are becoming more-and-more available for fMRI.

Deep Neural Network Approaches for Closed- Loop Deep Brain Stimulation Using Cortical and Subcortical Sensing Principal Investigators: R. Mark Richardson, MD, PhD, Department ofNeurologicaJ Surgery and Robert S. Turner, PhD, Department ofNeurobiology, University ofPittsburgh; Wolf-Julian Neumann, MD, and Andrea A. Kiihn, MD, Department ofNeurology, Charite-Universitatsmedizin Berlin. Co-Investigators: Benjamin Blankertz, PhD, Department of Computer Science, Technische Universitat Berlin; Tom Mitchell, PhD, Machine Learning Department, Carnegie Mellon University.

Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved.

The first major goal of this work is to learn how certain brain regions (olfactory system, hippocampus, and cerebellum) learn very complex stimuli that employ a combinatorial code to identify stimuli as points in a high-dimensional space. For example, the simple fruit fly olfactory system uses the firing rates of 50 different types of odorant receptors to identify each odor by placing it at a point in a SO-dimensional space.

When we speak, listeners hear us and understand us we speak correctly. But we also hear ourselves, and this auditory feedback affects our ongoing speech: delaying it causes dysfluency; perturbing its pitch or formants induces compensation. Yet we can also speak intelligibly even when we can't hear ourselves. For this reason, most models of speech motor control suppose that during speaking auditory processing is only engaged when auditory feedback is available. In this grant, we propose to investigate a computational model of speaking that represents a major departure from this.

Mental health diseases such as depression are a major burden on society and new treatment options are strongly needed. One strategic goal of NIMH is to develop novel therapies based on discoveries in neuroscience. TMS is a non-invasive neuromodulation technique that can directly interfere with brain activity. TMS is FDA-approved for depression treatment, however, shows mixed efficacy.

Throughout the brain, specialized systems carry out different but complementary functions, sometimes independently but often in cooperation. However, we do not understand how their activity is dynamically coordinated, and dysregulation of this is associated with many mental health conditions. Neuronal oscillations, which are detectable in local field potentials (LFPs) at various frequencies, are a promising target for this coordination.

Humans and animals often make decisions under uncertainty, whereby each decision affects not only the immediate reward gain but also longer-term information gain. While important advances have been made in understanding human learning and decision-making, there is still a lack of understanding of the different motivational factors that come into play when the behavioral context confers systematically varying amounts of reward and information gain.

The overall goal of this project is to develop a reinforcement learning (RL) theory of motivation, understood here as motivational salience, and to test the conclusions of this theory using experimental observations obtained in the ventral pallidum (VP). Animals' actions depend on the shifting values of internal demands determined by physiological or behavioral conditions, such as thirst, hunger, addiction, specific nutrient deficiency, etc.

Despite substantial progress characterizing neural responses, it is particularly challenging to determine causal interactions within recurrently connected circuits due to the confounding influence of the interconnections. This proposed project pioneers a nascent field of closed-loop computational neuroscience that enables real-time feedback stimulation during experiments to decouple recurrently connected elements and make stronger causal inferences about their interactions.