The National Institute of Mental Health has awarded a $3.4 million grant to Thilo Womelsdorf, associate professor of psychology, biomedical engineering and computer science, to study the underlying mechanisms of action for a new generation of potential drugs that may improve cognition and motivation and enhance brain network activity affected in schizophrenia.
Womelsdorf’s team is developing augmented reality computer games that test how drugs enhance attention, memory and motivation of subjects. Motivation will be measured as subjects’ willingness to put forth effort to achieve goals and by how subjects overcome frustration from negative feedback. Reduced cognitive and motivational abilities are hallmarks in patients diagnosed with schizophrenia.
Schizophrenia is a chronic, debilitating psychiatric disorder that affects approximately 1 percent of the global population. Historically, it has been tremendously difficult to treat because of the complexity of its symptoms. While FDA-approved antipsychotic drugs mitigate delusional behaviors and hallucinations known as “positive symptoms,” they provide little to no relief for the severe cognitive impairments and “negative symptoms,” which include lack of motivation and social withdrawal.
A central aim of the newly funded research is to understand the working mechanisms of a powerful new generation of psychoactive drugs being developed at the Warren Center for Neuroscience Drug Discovery. Womelsdorf teamed up with his colleague Carrie K. Jones, director of development for the WCNDD and associate professor of pharmacology, to seek to understand how improved drug compounds developed at the WCNDD can be modeled for clinical efficacy. A key hypothesis is that these drugs can become therapeutics that improve the communication in specialized brain networks and thereby improve cognition and motivation. Womelsdorf’s lab has been at the forefront of deciphering the mechanisms of neural communication and of studying the effects of rapidly synchronizing neural circuits during cognition.
“This research will allow us to understand how allosteric modulating drugs affect communication of those brain areas that support cognition, motivation and mood,” said Womelsdorf, who also is a faculty affiliate of the Vanderbilt Brain Institute. “These NIH funds will be instrumental to our work in clarifying the efficacy of drug concentrations in improving the functioning of brain cells and how they contribute to efficient neural network communication supporting complex functions.”
Over the past decade, researchers at the WCNDD have developed highly selective positive allosteric modulators for subtypes of the cholinergic system—a group of organized nerve cells that regulate brain functions that are disrupted in schizophrenia, including arousal, cognition and motivation, by modulating neural activity—as novel therapeutic strategies for improving cognitive deficits and negative symptoms caused by schizophrenia and other central nervous system disorders.
“An important emphasis of this research is to clarify how the action of these M1 and M4 PAMs support not only cognitive functions, but also how they enhance motivation and emotion,” Womelsdorf said. “This is an important goal because existing drugs supporting cognitive functioning in individuals with schizophrenia often have no positive influence on emotional disturbances such as depressive mood and lack of energy.”
“We are excited at the WCNDD to have the opportunity to collaborate with Womelsdorf and his research team to investigate the impact of our M1 and M4 PAM mechanisms,” Jones said. Data from this collaborative study promises to provide insights for the successful translation of these novel therapeutic mechanisms into future efficacy trials in schizophrenia.
Other researchers on the project include Janusz Pawliszyn, a world-renowned pioneer of neurochemical sampling techniques from the University of Waterloo, and Kari Hoffman, associate professor of psychology at Vanderbilt. Hoffman is expert in automatized recognition of complex behaviors from video material using advanced machine learning tools, which will be applied to understanding changes in behavioral regulation during drug actions.