The overarching goal of this project is to examine the neural mechanisms that underlie decision-making in humans. The process linking human cognition to behavior is decision-making: the key driver of human personality, fundamental for survival, and essential for our ability to learn and adapt. It is well established that humans, in general, make emotional based decisions. Likewise, it is known that the process of decision-making is non-stationary in the sense that it adapts to changing stimuli and outcomes; and that these brain regions change as a function of age and environment. Finally, patients with psychiatric disorders frequently have alterations in decision-making, which stem from dysfunction of neural circuits that produce different cognitive and emotional symptoms that thereby affect decisions. The reason we know so little about such disorders is because we really don’t know in detail enough about how these neural circuits work in healthy subjects or how disease affects decision-making circuitry.
Understanding human decision-making will require accessing several brain structures in cognitive and emotional networks during behavior and measuring their electrical activities at millisecond resolution. Typically, decision-making studies have been limited to a few case studies wherein subjects have lesions in a particular structure or where PET and fMRI are used to measure activity in healthy subjects. Both of these approaches have limitations. Lesions don’t provide actual neural data to ascertain the brain’s role during behavior, whereas PET and fMRI provide correlates of neural activity but suffer from poor temporal resolution. To observe electrical activity at sufficient resolution, investigators have measured neuronal decision-related signals in animals and human, but can only probe one or two structures simultaneously. However, the inability to simultaneously sample multiple regions significantly limits the types of questions that can be asked and animal studies suffer from direct human translatability.
The limitations of our knowledge stem from the complexity of the spatial and temporal resolution of the information needed to build and validate appropriate computational models. In our lab, and through collaborations with Dr. John Gale and Dr. Jorge Gonzalez-Martinez at the Cleveland Clinic, we are examining the neural mechanisms that underlie decision-making by directly recording the simultaneous neuronal activities of multiple-brain structures, including the cognitive, limbic, and hippocampal networks, using intracranial stereoelectroencephalography (SEEG) methodologies during a decision-making task in humans. Unlike other electrophysiological methods, SEEG provides a means to examine many cortical and subcortical brain structures simultaneously (up to 200 recording sites) at timescales (millisecond resolution) relevant to behavior. From these recordings, we are using sophisticated data analytics and network-based computational models to capture the spatial and temporal dynamics of decision-making. Finally, we plan to electrically stimulate specific nodes of the networks to change or disrupt subject decisions. These manipulations are essential in that they will allow us to validate our models and to examine the function of individual limbic nodes on human decision-making behavior.
This work is done in collaboration with Drs John Gale and Jorge Gonzalez-Martinez at the Cleveland Clinic.