Michael J. Frank - US grants

How humans regulate their behaviors is a fundamental question. With funding from the National Science Foundation, Dr. Michael Frank of Brown University is investigating interactions between different brain regions involved in how people monitor, learn, and control their actions. This research project focuses on how humans accomplish restrained control over behavior when confronted with difficult decisions. Theoretical models and empirical results suggest that the prefrontal cortex and the basal ganglia interact during these motivated behaviors, and that the neurochemical dopamine plays a central role in both the prefrontal cortex and basal ganglia brain regions. Using electroencephalography (EEG), the investigators are measuring participants' brain waves associated with prefrontal cortex activity, while they perform computerized cognitive tasks that assess learning and decision making in difficult circumstances. The brain wave activity is expected to predict participants' cognitive performance on these tasks. Critically, this brain-behavior relationship is predicted to differ as a function of genetic variants in dopamine function in both the prefrontal cortex and basal ganglia. In another experiment, researchers are directly manipulating dopamine pharmacologically in order to determine how these brain and behavior relationships are causally altered by dopamine levels. In all of their studies, the investigators use detailed mathematical models guided by contemporary theory to isolate specific brain-behavior relationships. It is theorized that both genetic variants and pharmacological manipulations affect the way that the brain monitors, learns, and controls actions. The brain wave measures are allowing the research team to define how neurochemicals modulate the processes of large neural systems.

This research has the goal to substantially advance our understanding of how humans are able to regulate their behaviors as a function of motivation and cognitive control. Scientists widely appreciate that there are large individual differences in these types of motivated behaviors, but only recently have they begun to understand some of the factors governing these differences. By combining multiple research approaches, this project is posed to reveal the ways in which genetic and neurochemical factors alter activity in brain areas that are critically involved in such behaviors. The project also has the potential to identify mechanisms that disrupt brain circuitry and lead to disorders in motivated behavior and cognitive control, including addiction and obsessive compulsive disorder among others.

This research project investigates the nature of interactions between different brain regions involved in how people learn and control their actions. Theoretical models and empirical data suggest that the prefrontal cortex (PFC) and basal ganglia (BG) interact in these types of motivated behavior, and that the neurochemical dopamine plays a central role in both of these brain regions. However, it has been difficult to isolate the separable contributions of these different brain systems to learning. Dr. Frank and colleagues will investigate how PFC augments human reinforcement learning by leveraging its well-studied roles in working memory, cognitive control, abstraction, and rule representation. This work has potential to substantially advance our understanding of how humans are able to regulate their behaviors as a function of motivation and cognitive control. Learning impairments are prevalent in many psychiatric disorders. While in some cases, such as Parkinson's disease, the mechanisms involved are relatively well understood, in others, such as schizophrenia, the underlying deficits are poorly characterized. It is crucial to properly isolate different components contributing to learning, so as to appropriately relate them to separable mechanisms giving rise to those deficits. If learning is considered as a unitary system, learning deficits that arise due to impairments in one process and brain system may be mistakenly attributed to the other system and lead to erroneous conclusions. Following the same approach will shed light on the actual cause of learning impairments. As such, this research has the potential to identify mechanisms that explain how disruption of such brain circuitry leads to disorders in motivated behavior, cognitive control, and impulsivity. Similarly, developmental learning disabilities may involve a deficit in abstraction and generalization. The aim is to better understand the underlying mechanisms and computations needed for such functions. Drs. Frank and Collins will also provide mentoring on computational and data analytic methods for under-represented women in the STEM disciplines.

Computerized experimental tasks will manipulate factors thought to depend on PFC function, including working memory load and the degree to which the discovery of coherent rules can be used to speed learning. Dr. Frank and colleagues use mathematical modeling to separately estimate the contributions of PFC function from that of basic BG processes. Using electroencephalography (EEG), the investigators will measure human participants' brain waves associated with PFC activity while they perform these tasks. Brain wave activity is expected to predict cognitive performance on these tasks. Critically, this brain-behavior relationship is expected to differ as a function of genetic variants that reflect differences in dopamine function in PFC and BG. Another study will directly manipulate dopamine (pharmacologically) in order to determine how these brain and behavior relationships are causally altered by dopamine levels. In all of these studies the investigators use detailed mathematical models to isolate specific brain-behavior relationships guided by contemporary theory. It is expected that genetic variants and pharmacological manipulations will affect the way that the brain learns from decision outcomes and controls actions.

Our training program for Interactionist Cognitive Neuroscience (ICoN) seeks to provide student-focused, interdisciplinary training in computational cognitive neuroscience that integrates data from multiple scales and levels of analysis. Transformative gains in understanding the human brain and mental health require integration across multiple levels of analysis. Recent historic advances in genetics and cellular biology are paving the way for understanding fundamentals of neural function. At the other end of the spectrum, methods for imaging and stimulating human brains non-invasively have led to revolutionary advances in discovering the macro-scale organization supporting perception, motivation, and cognition. Now, a major effort at the `systems' level between these two scales is beginning to uncover the activity, connectivity, and computations of neural circuits. The advent of this systems-level progress holds the promise of linking core circuit computations to emergent human behavior and leading to detailed, transdiagnostic models of mental illness. However, as we recently argued (Badre, Frank and Moore, 2015 Neuron), fulfilling this promise requires making direct links between circuit-level computation and the emergent function of the human system. We believe that integrating systems- and human neuroscience in this way demands a systematic approach built on two key strategies. First, formal computational models must be used to provide principled links between levels of analysis; and, second, complementary methods must be applied, and in the ideal case parallel human and non-human studies conducted in coordination. Achieving these aims requires a new generation of scientists that can take full advantage of multiple techniques and data sources, and who are deeply versed in computational theory. Traditional neuroscience training relies on an apprenticeship model that limits students to a single lab and level of inquiry. Thus, a specialized training program is required to specifically equip neuroscientists for this `Interactionist' approach. ICoN will provide this training emphasizing the two tenets: I. Computation is key to translating between levels. Students must be rigorously quantitatively trained in formal theory. A close corollary is that they must be fluent in the advanced analysis methods necessary for cross-level integration (e.g., machine learning). II. Next-generation scholars must have expertise at multiple levels. Students must be trained to use and integrate multiple methods and data sources. Further, they must have the skills (and courage) to pursue ideas to their next most logical step, to be question driven and not technique limited. Students will be trained to conduct integrative research projects across domains such as human cognitive neuroscience, systems neuroscience, and computational neuroscience.

The goal of understanding psychiatric disorders and advancing psychiatric treatments requires basic knowledge of not only what environmental, genetic and epigenetic factors underlie function and dysfunction, but also how these factors alter the circuit-level computations that are the proximal neural events to behavior. The advent of research in this area holds the promise of linking core computations of neural circuits to complex human behavior, with the ultimate goal of developing comprehensive, multilevel transdiagnostic models of neuropsychiatric disorders. Consequently, the emerging field of computational psychiatry is central to the NIMH mission. Despite its importance, there are very few opportunities to pursue training in this area. Consequently, the proposed training program seeks to take recent PhDs, with strong backgrounds in fields such as neuroscience, engineering, applied math, and computer science, and provide them with the tools to make important contributions to the nascent field of computational psychiatry. The proposed Training Program in Computational Psychiatry (TPCP) will take place at Brown University where there is a critical mass of basic researchers on the main campus and clinical researchers in the Department of Psychiatry and Human Behavior to conduct such a training program. We propose enrolling six fellows (3 per year) in the TPCP with the goal of training, more efficiently and effectively, nonclinical research fellows capable of collaborating with clinical researchers to advance knowledge of psychiatric disorders and treatments. The program embraces an apprenticeship model in which fellows work with a primary research trainer in a computational field and a secondary research mentor in clinical psychiatry. In this apprenticeship model, the trainer works closely with the fellow and a secondary clinical psychiatry mentor, who is conducting research in areas such as neuroimaging, neurostimulation, and digital phenotyping. These research areas are especially conducive to addressing important issues in computational psychiatry, whether they be model/theory-driven or data-driven. The proposed didactic program will include both core seminars and an individualized curriculum including fellow-selected courses in neuroscience, computer science, engineering, applied mathematics, or psychiatric disorders. All fellows attend core seminars on grant writing, responsible conduct of research, and rigor and reproducibility. The short-term final product is an NIH grant application on a computational psychiatry topic. The long-term goal is to produce a new cohort of academics who can conduct research in computational psychiatry and train the next generation of graduate students in this emerging field of inquiry.