Human behavior is complex, involving several cognitive processes such as decision-making, memory, attention, and social cognition that seamlessly and dynamically interplay.
Neurally, these processes depend on a wide network of densely interconnected brain regions that communicate at synaptic timescales. To understand human cognition, we need to record activity in these brain regions and map it onto observable behavior. This is a particularly challenging and fascinating problem in human beings, who are capable of sophisticated reasoning, abstraction, and complex social behavior. In addition, it is challenging to record human brain activity with sufficient biological detail, anatomical precision, and temporal resolution.
We use a powerful and novel combination of behavioral tasks, computational models, and invasive recordings in clinical neurosurgical patients, providing unique access to high-resolution neural activity. Our goal is twofold: to understand how activity in the human brain generates behavior and to develop novel treatments for neuropsychiatric conditions.
Everyday behavior relies on interactions among multiple cognitive systems (memory, learning, planning, attention) to generate adaptive actions. To act, we need to estimate potential courses of action, remember earlier experiences, evaluate uncertainty, attend to relevant information, and learn from the outcomes of our actions. This requires interplay between the brain areas that support these different processes, which are organized in distinct yet overlapping circuits. Using reinforcement learning models, we characterize behavioral strategies and map them onto the underlying neural activity to understand the interplay between brain circuits and cognitive processes.
We examine the relationship between neural activity across frequency bands and brain regions (orbitofrontal, lateral prefrontal, cingulate cortices, etc.) and overt choice behavior using a combination of iEEG recordings and neuroeconomic probes of decision-making.
We examine neural activity – both population and single-neuron – in multiple brain regions during behavior using a combination of iEEG recordings, neuroeconomic probes of decision-making, and reinforcement-learning-based computational modeling.
Brain regions are specialized, but are functionally organized in circuits and networks through dense interconnections. As a consequence, generating behavior requires the coordinated activity of multiple brain areas. During choice, decision information is highly distributed. We seek to understand the contribution of distributed and localized brain activity to these processes, and to build decoding models that can predict behavior from neural activity alone. In the future, we hope to generalize these models to be able to build individualized models that predict the mapping of brain activity to brain states with high accuracy.
We examine the relationship between neural activity across frequency bands and brain regions (orbitofrontal, lateral prefrontal, cingulate cortices, etc.) and overt choice behavior using a combination of iEEG recordings and neuroeconomic probes of decision-making.
We use linear dynamical systems to characterize time-varying neural dynamics across a variety of brain regions, both prefrontal (orbitofrontal, lateral prefrontal, cingulate cortices) and subcortical (striatum, hippocampus, amygdala) and construct patient-individualized decoding models that can predict trial-by-trial behavior with high accuracy.
Our current approaches for the treatment of depression and bipolar disorder are insufficient - there is a significant proportion of patients who don’t improve after therapy or pharmaceutical treatment. For these patients with depression, a potential treatment avenue involves neurosurgical approaches in which affected brain areas are directly stimulated using a chronically implanted electrode. For patients with bipolar disorder, targeted neuromodulatory treatments have the potential to address unpredictable transitions in mood states, yet the brain circuitry contributing to these shifts remains unclear. Because these disorders are highly prevalent in intractable epilepsy patients undergoing invasive electrophysiological monitoring, we use this opportunity to study differences in their behavior and brain. We hope to combine the insights derived from these studies with invasive neurostimulation and neuromodulation approaches to develop new patient-tailored therapeutic strategies.
We examine the relationship between neural activity across frequency bands and brain regions (orbitofrontal, lateral prefrontal, cingulate cortices, etc.) and overt choice behavior using a combination of iEEG recordings and neuroeconomic probes of decision-making.
In this project, we combine distributed iEEG recordings and reinforcement learning models of decision-making to study reward and mood processing across multiple brain areas. This approach allows for identification of the brain areas with aberrant activity during reward processing and uses an algorithmic targeting strategy to develop new stimulation therapeutic approaches. Additionally, we record data from patients undergoing sEEG, which is used to identify the origin of hard-to-treat seizures in the brain – potentially providing new targets for neuromodulation.
In addition to the individual activation of specialized brain areas, behavior depends on the action of neuromodulator systems that regulate brain activity globally. The action of these neuromodulators, such as dopamine and serotonin, is essential for correct brain function in decision-making, motor control and mood. However, studying them directly in the human brain at the temporal resolution needed to study behavior is difficult.
We examine the relationship between neural activity across frequency bands and brain regions (orbitofrontal, lateral prefrontal, cingulate cortices, etc.) and overt choice behavior using a combination of iEEG recordings and neuroeconomic probes of decision-making.
In this project, we use custom-made carbon fiber electrodes during surgical deep-brain stimulation interventions to carry out fast (10 times per second) estimation of neuromodulator concentrations in deep brain areas. In this way, we study the subsecond dynamics of dopamine and serotonin during behavior.
