New Jersey Institute Of Technology

NIH Research Projects · FY 2026 · 2022-05

The cerebellum has been overlooked for its potential for neuromodulation for decades. Traditionally thought of as critical for motor coordination, anatomical, clinical and imaging evidence now indicate that the cerebellum also has central roles in cognition and emotion, and that cerebellar dysfunction impacts these functions. Consistent with these findings of cerebellar involvement in motor and non-motor functions, projections from the cerebellar nuclei (CN) target, via the thalamus, both motor and non-motor areas of the cortex and the basal ganglia. Thus, modulation of cerebellar outputs should be able to affect areas throughout the forebrain, and therefore has the potential to treat numerous disorders. Gap in Prior Research: Stimulation of the cerebellar cortex produced mixed results in clinical trials almost fifty years ago, which discouraged further attempts. Whereas stimulation of the CN, where the efferent axons from the cerebellar cortex converge, has recently been shown to have clinical benefits in patients and animal models, reviving interest in cerebellar stimulation as a therapeutic tool. However, direct stimulation of the CN requires surgical implantation of deep brain stimulation leads into the cerebellum. Research Opportunity: Focused Ultrasound (FUS) and Transcranial Electrical Stimulation (tES) are two non- invasive brain stimulation methods that have great potentials for clinical applications and provide ideal tools for cerebellar stimulation. FUS has the potential to stimulate the CN directly with its superior focusing and steering capabilities. tES would be the preferred method for stimulation of the cerebellar cortex due to its ease of application and the inexpensive equipment involved. Advances in the past two decades on functional imaging and anatomical mapping provide an improved understanding of the circuitry of the cerebellar cortex and its connections to the CN. Thus, we have novel tools and the knowledge base to develop effective protocols both for direct stimulation of the CN and indirect modulation of them via stimulation of the cerebellar cortex. Current Proposal: The overarching goal of this proposal is to develop effective modulation paradigms of cerebellar output both by direct stimulation of the CN using FUS and indirect CN modulation via electrical stimulation of the cerebellar cortex. Optimal stimulation parameters will be investigated for selective stimulation of neuronal subtypes in the cortex and the CN. Novel mechanisms of neuromodulation will also be investigated that can emerge from combined application of the two methods on the cerebellar circuits. The modulation paradigms developed should generalize to numerous motor and non-motor functions in which the cerebellum is involved.

NIH Research Projects · FY 2025 · 2022-01

PROJECT ABSTRACT The discovery of functional brain connectivity (FC) and functional networks (FNs) have propelled the neuroimaging field, particularly in functional magnetic resonance imaging (fMRI), which has experienced an exponential growth in recent years. FNs have allowed us to better understand extrinsic and intrinsic brain properties in various disease and healthy states, leading to better characterization of neuropsychiatric disorders. However, current fMRI analyses are constrained to the gray matter (GM) region of the brain and fMRI data from the white matter (WM) region are often discarded, which makes up approximately 50% of the brain by volume. Many brain disorders have been associated with WM deficiencies, since WM is critical for the transmission of information to the GM cortical areas. Despite findings of blood-oxygen-level-dependent (BOLD) signals in the WM, WM-FNs are yet to be fully characterized, and neither the mechanism by which WM-FNs may affect GM-FNs, nor how WM-FNs are associated with phenotypic traits are known. The long-term goal of this project is to better understand the effect of WM-FNs on normal cognitive functions of the human brain and apply fMRI data from various healthy and diseased populations for more reliable diagnostics and monitoring. The rationale for this study is based on our preliminary studies which investigated WM-FNs using the Human Connectome Project dataset. We found that WM-FNs are correlated with subregions of the corpus callosum, a critical WM region relaying information between the two cortical hemispheres. Furthermore, we determined an overlap between the WM-FNs and tracts from diffusion tensor imaging (DTI). In this study we will examine WM-FNs of the whole brain using resting fMRI data from two large independent cohorts. We hypothesize that the FN measures derived from WM will be similar to that of GM and the metrics can be used to reliably predict phenotypic traits. The hypothesis will be tested with the following specific aims: Aim1: To develop and evaluate the time-series, FC and FN characteristics of WM of the whole - brain; Aim 2: To investigate WM-phenotype associations and the predictability of phenotypes using WM-FNs; and Aim 3: To develop and disseminate a WM-FN toolbox. To the best of our knowledge, this study will be the first to examine the reliability and validity of WM-FNs in resting fMRI data, and its relation to brain function. The proposed work holds significant contribution since it will facilitate the use of WM-FN methods for the neuroimaging community, which currently lacks the necessary analytic tools to reliably characterize WM function. This study will provide a strong foundation f or future clinical use of both WM-FNs and GM-FNs, to understand brain function more comprehensively, in addition to facilitating the use of reliable and reproducible WM-FC methods.

NIH Research Projects · FY 2024 · 2021-09

Project Summary The broad long-term objective of the project concerns the development of novel machine learning methods and computational tools for modeling genomic data, driven by significant biological questions and experiments. Analyzing single-cell RNA-seq (scRNA-seq) data and spatial genomic data poses substantial computational and bioinformatics challenges. The specific aim of the project is to develop novel model-based deep learning methods that incorporate prior biological information to model scRNA-seq data and spatial genomic data. These challenges are motivated by the PI’s close collaborations with biomedical investigators. The proposed approaches are designed to integrate biological information to enhance both analytical performance and biological interpretability. These methods rely on a novel integration of biological insights and statistical techniques with deep learning to analyze the noisy, sparse, and over-dispersed scRNA-seq and spatial genomic data. This integration includes a zero- inflated negative binomial model, autoencoder, variational autoencoder, and deep embedding. The new methods can be applied to various essential analytical tasks for the analysis of scRNA- seq and spatial genomic data, leading to improved interpretability. They will facilitate effective analyses of the increasingly important scRNA-seq datasets and contribute to the ongoing studies with which the PI is currently collaborating, such as Paneth cell regulation, regeneration of human hair follicles, and melanoma. The project will develop practical and feasible computer programs to implement the proposed methods and evaluate their performance through real applications. The work outlined in this proposal will provide deep learning methods for modeling scRNA-seq data and studying complex phenotypes and biological systems, offering insights into each of the biological areas represented by the various datasets. All programs developed under this grant, along with detailed documentation, will be made available free of charge to interested researchers. Undergraduate researchers from diverse backgrounds will be recruited as an integral part of the project to implement the most critical aspects of the proposed aims. This research project aims to stimulate the interests of students, encouraging them to consider a career in the biomedical sciences.

NIH Research Projects · FY 2025 · 2014-04

Project Summary/Abstract: During the past 6 years, our study team investigated the neural mechanism of typically-occurring convergence insufficiency (TYP-CI), the most common binocular vision disorder in children and young adults (3.4% to 12.7%5–11) leading to 20 publications12–31 with 4 more in review and 6 in preparation. We conducted the only randomized clinical trial (RCT) integrating objective eye movement and fMRI outcome measures, achieving 100% planned enrollment and retention of 100 young adults.28 Our results localized the reduction in functional activity for TYP-CI compared to controls within the oculomotor vermis (OVM) and the cuneus. Functional activity in the OVM and cuneus was significantly correlated to convergence peak velocity providing the first mechanistic identification of these deficits that create significant burden to those afflicted. 23 Our longitudinal results discovered that the neural mechanistic change stimulated by office-based vergence /accommodative therapy (OBVAT) is an increase in the frontal eye field (FEF) and thalamus functional activity. Increased functional activity from the FEF and thalamus significantly correlates to convergence peak velocity. 23,32,33 Results are leading to personalized point-of-care therapies remediating the debilitating symptoms for TYP-CI patients. While our research and results of other RCTs show that OBVAT is the most effective treatment for remediating symptoms and improving vision function in both TYP-CI children 34–36 and adults, 37,38 none of these participants had a history of head injury, a pathology that has been linked to CI. Our research team has demonstrated that the prevalence of CI is higher (38% to 49%) in children 39,40 and adults 41,42 with persistent post-concussive symptoms (PPCS-CI), than in the non-concussed population. Currently, there is no validated treatment for PPCS-CI. This difference in prevalence, mode of onset (longstanding versus sudden onset), and severity of the condition has led to a debate about whether the diagnostic and management procedures effective for TYP-CI should be utilized for PPCS-CI, and strongly suggests that new research is needed to optimize PPCS-CI management. We are uniquely positioned to provide answers to these questions by building on our work establishing the neurofunctional mechanism of TYP-CI and OBVAT administered to TYP-CI. Such research is of great importance because PPCS-CI is associated with debilitating visual symptoms impacting the return to school/sports, 43–47 work, 48–51 or driving. 52 We have identified three significant gaps for the treatment of PPCS-CI that must be addressed to determine its most effective management. First, given the obvious differences in etiology, are there significant differences between TYP-CI and PPCS-CI related to objective eye movement measures (peak velocity, final amplitude, and repeatability) due to underlying neural mechanistic differences? Second, what is the underlying neural mechanism of OBVAT for PPCS-CI compared to TYP-CI? Third, how effective is OBVAT for PPCS-CI and is the dosage of administration different than TYP-CI? This renewal addresses these gaps in clinical science.

NIH Research Projects · FY 2026 · 2000-12

Inter-individual variability is a common observation in studying neural systems but is often ignored in favor of developing a general understanding of properties and responses that are the mean across individuals. This is because we accept that neural circuits should produce stable, robust, and consistent activity, characteristic for each behavioral context. All neural activity results from ionic currents that shape the outputs of individual neurons or produce synaptic interactions. However, within the same neuron type, these currents and the channels that underlie their expression show considerable variability across individuals. It remains unclear how neurons with variable levels of ionic currents and synaptic interactions could produce consistent circuit output activity with low inter-individual variability of output attributes. The inter-individual variability of ionic currents is not a static problem, as neuromodulators tune those currents, thereby providing flexibility to neural circuit activity and behavior. While neuromodulation itself can be a source of inter-individual variability, functional circuit output should not only be similar across individuals at some basic level but should also respond similarly to neuromodulators that shape it into different modes of operation, adaptive to different behavioral contexts. While the role neuromodulators play in providing flexibility of circuits in individuals is well studied, the role of neuromodulation in producing consistent functional circuit output across individuals is not. Multiple neuromodulators act on a neural circuit at the same time. Such comodulation is understood to enhance flexibility by increasing the number of possible circuit states. We propose that comodulation with excitatory modulators that have convergent cellular effects promotes consistency and robustness of circuit output. An increase in the number of circuit elements that are targeted results in more consistent overall circuit activation, and an overall increase in excitability pushes individuals closer to shared upper boundaries of neuron and synapse activation. We will test this hypothesis in the stomatogastric nervous system, which has been instrumental in establishing the general organizing principles of convergent and divergent comodulation and is an ideal testbed for understanding neural circuit dynamics. We will use electrophysiological methods to determine the effects of single and multiple neuromodulators on individual circuit components, circuit operation, and output activity. We will apply different sequences of increasing numbers of modulators, either with converging or diverging cellular and synaptic actions, and determine the effects on inter-individual variability and mean similarity of circuit output attributes. In parallel, we will use biophysical approaches, computational modeling and mathematical analysis to understand the underlying cellular and ionic mechanisms in circuit components that account for consistent circuit output. This work will produce a general framework to understand the role of comodulation in the production of robust and consistent circuit output.