Current Research Directions


Dr. David Giovannucci:

Work in the Giovannucci lab uses a variety of electrophysiological, optical and biochemical methods to define the cellular and molecular mechanisms that underlie the cytosolic calcium dynamics that control salt, peptide and protein secretion.  The exocytotic secretion of neuropeptide has profound consequences for neuronal function, cardiovascular homeostasis and GI tract regulation in health and disease. This work has significance for human health concerns such as GI cancer, hypertension and dry mouth. The lab also leads a multidisciplinary/multi-PI project advancing the use of biomarkers in saliva that correlate with neural stress, brain trauma and human performance. The Giovannucci lab has been continually supported by government, military, intramural or private grant funding.

Dr. Robert Smith

The Cognitive Disorders Research Laboratory (CDRL) is focused on asking and answering the largest possible questions in translational neuroscience.  We combine proteomic, kinomic, and mechanistic approaches to study severe neuropsychiatric illnesses including schizophrenia, depression and autism spectrum disorders.  Our recent work includes establishing bioenergetic dysfunction as a consequence of developing a brain with broken synapses.  In a ground breaking series of experiments, we started with observations from postmortem brain samples, identified drugs using a novel bioinformatics work flow, and reversed cognitive defects in an animal model of cognitive dysfunction with a repurposed FDA approved drug.

Dr. Sinead O'Donovan

My research focus is on gaining a greater understanding of pathophysiological mechanisms underlying severe mental illnesses like schizophrenia and major depression.  Taking a reverse-translational approach, this work utilizes postmortem tissue from subjects with neuropsychiatric disorders and applies a range of molecular and "omic" methods to elucidate disease associated changes in human brain. 

Dr. John Wall:

The structural complexity of the adult human cerebral cortex is a subject of frequent astonishment.  Equally astounding is the issue of how this complex structure is continuously maintained from week-week in an individual adult person.  Cortical structure can change due to, e.g. learning and injury; however, from aging work on healthy adult groups it is currently thought that during week-week baseline living mature cortical structure is statically maintained. This static view of cortical maintenance had, surprisingly, never been directly tested in an individual person.  Our human brain imaging group has recently used an unconventional longitudinal N-of-1 MRI design to study cortical structural maintenance by reiteratively sampling cortical thickness at regular week intervals over several months in an individual person. The results suggest cortical thickness undergoes continuously reversing incremental and decremental fluctuations over week and multi-week intervals. 

This different view of cortical maintenance has provocative implications.  One possibility is that ongoing thickness maintenance fluctuations reflect homeostatic maintenance of brain structure which, in turn, is related to broader systemic homeostatic maintenance of the body.  Given the current view of brain maintenance, where brain structure is statically maintained over short intervals, there has been no reason to consider this possibility. We are exploring this issue by continuous tracking of cortical maintenance fluctuations and concurrent variations in systemic metabolic and other factors associated with body maintenance, to test for relationships between maintenance of the body and brain structure at an individual person level.  This work has potential applications for understanding individual specific brain/body maintenance interactions that are of interest for developing N-of-1 precision medicine thinking.

Dr. Ipe Ninan:

Our studies over the last 10 years have been focused on how environmental, genetic and developmental factors influence the fear circuitry. We have demonstrated the synaptic mechanisms underlying the effect of a common single nucleotide polymorphism in the BDNF gene, the BDNF Val66Met, on cognitive and anxiety-related behaviors. Consistently, our studies have also demonstrated the key molecular mechanisms by which BDNF or the lack thereof modulates synapses. Another major interest of my lab has been understanding how fear expression is altered during adolescence. Our research has revealed a diminished plasticity and an altered somatostatin interneuron-mediated inhibition in the adolescent medial prefrontal cortex, a brain region critical for fear extinction. Despite the progress in our understanding of the synaptic and circuit mechanisms involved in fear regulation, little is known about the mechanisms underlying the resistance to extinguishing fear memory. This forced us to think beyond the classical fear circuitry. Consistent with the recently suggested role of the medial habenula-interpeduncular nucleas pathway in aversive behaviors, we have uncovered previously unknown synaptic plasticity mechanism in the medial habenula-interpeduncular nucleus pathway as well as their role in fear memory. Given that the medial habenula-interpeduncular nucleus pathway in upstream of the median raphe nucleus, which regulates fear memory expression, we are interested in understanding the role of the medial habenula-interpeduncular nucleus-median raphe pathway in fear regulation. Apart from the traditional electrophysiological and fear beavior paradigms, we employ optogenetic and pharmacogentic apporoached to elucidate the involvement of this pathway in fear regulation. 

Dr. Ram Shukla

My research focuses on understanding the similarities and diversities associated with psychiatric disorders. With my expertise both in computational and experimental biology I seek to develop a unified theory explaining the molecular cause and neuronal microcircuit changes associated with psychiatric disorders, experimentally proof those theories in animal model, and translate my findings to a therapeutic outcome. I work with both self-generated and publicly available transcriptomics data sets. However, besides the low cost of sequencing and publicly available repository of transcriptomic data there are various limitation in understanding psychiatric disorders. My research interests as described below briefly are focused on developing tools and model to overcome these limitations and accomplish the overarching goal of a unified theory of psychiatric disorders.

Reference disease model: To understand the psychiatric disorders at the molecular and neuronal-circuit level, its heterogeneity poses a significant limitation. To overcome this, I study them in association with a comorbid and penetrant phenotype for instance age and epilepsy. Here, the penetrant phenotype establishes a rigorous reference for global transcriptomic change and similarities to or deviation from this reference helps to fill the gaps in understanding changes in the psychiatric disorder.

Single cell transcriptomics: Given the heterogeneous nature of brain and diversity in neuronal cell types, much of disease complexity may be associated with the neuronal cells forming the canonical microcircuit. The majority of publicly available transcriptomic data sets for psychiatric disorders which I am currently using were conducted in tissue homogenate, and it's not feasible to repeat all prior studies. I use machine learning to harness the recent upsurge in cell-specific data and integrate it with the homogenate data to gather new and important previously missed information regarding gene expression alterations within specific cell populations.

Network biology: The causal mechanism underlying the psychiatric disorders may reveal effective prevention and treatment but are poorly understood. I employ Bayesian network analyses to translate complex biological events into mathematically defined graphs which can be used to model causal relationships. In collaboration with statistician and machine learning expert I am also working on novel methods to determine causality.

Drug discovery: To translate my findings into a therapeutic outcome I am developing drug discovery and repositioning approaches based on causality. Most of the theories explaining psychotic disorders are based on unintended discoveries of medications that have been more likely to cure the disorder. Of example, the use of iproniazid and imipramine contributed to the monoamine hypothesis of depression, fluoxetine led to the discovery of serotonin in relation to depression, ketamine, contributed to the glutamate hypothesis of depression and lithium, highlighted the role of neuronal cell homeostasis in bipolar disorder. Drug discovery, along with its translational role, will also open windows for novel mechanistic theories explaining different psychiatric disorders.

Students Project:

Gene family-based ontology: Utilizing the HGNC and text mining-based approach we aim to get rid of redundancy observed in present ontological approaches. - Hunter Eby

 RNA editing: Something missing over the pass several years of RNAseq based transcriptomic research! Utilizing the publicly available RNAseq dataset we are developing tools to study changes in RNA editing sites in various psychiatric disorders. - Ali Imami


Last Updated: 6/27/22