For organisms to exhibit spatial awareness, they must detect the origin of sensory stimuli and process this information accurately. One potential mechanism underlying this ability is neuronal time delays, where differences in neural activation timing encode spatial relationships. To investigate this, we employed Drosophila melanogaster larvae as our model organism, due to their relatively simple and accessible nervous system, ease of imaging, and extensive genetic toolkit for studying dynamic neuronal activity. Larvae expressing GCaMP8f, a high-sensitivity calcium indicator, under the GAL4/UAS gene expression system were used to record real-time neuronal activity in immobilized larvae in response to mechanosensory stimuli. A 500 Hz, 70-decibel tone was presented from varying spatial orientations to assess directionally-dependent neural activation patterns or timing discrepancies. Our analysis focuses on detecting latency shifts in the neural circuit, aiming to shed light on how temporal differences in sensory processing contribute to spatial perception. Results revealed changes in neuronal activity patterns and timing, offering insights into how larvae use temporal neural encoding to process spatial information.

Astrocytes are glial cells found in the central nervous system (CNS) that have a variety of functions in health and disease, notably in wound repair. When stroke or traumatic injury occurs in the CNS, astrocytes proliferate and form a protective border around the lesion, in a process called proliferative astrocyte reactivity. Surrounding the CNS is the meninges, a protective layer with a high degree of immune surveillance. Astrocytes tile the CNS, including in the outermost brain region next to the meninges where they are referred to as parameningeal astrocytes. Increasing evidence suggests that the border formed by parameningeal astrocytes are involved in microbial defense in a manner similar to astrocytes that form borders around lesions. However, these parameningeal astrocytes have not been studied in detail. We aim to identify molecular characteristics of these parameningeal astrocytes using immunohistochemical staining. We focus on looking for molecules involved in microbial defense and regulation of inflammation, including looking at transcriptional regulators.

Mild traumatic brain injury (mTBI) affects over 42 million people globally each year, yet it is difficult to diagnose with standard neuroimaging, complicating treatment. Heart rate variability (HRV), a non-invasive biomarker of autonomic nervous system function, has shown promise in capturing the physiological impact of mTBI and tracking recovery. This study investigates whether HRV, measured through wearable technology (Oura Ring), can predict autonomic responses to noxious stress induced by transcranial magnetic stimulation (TMS). Participants wore the Oura Ring to collect continuous HRV data at home and underwent TMS treatment. HRV was analyzed during baseline (90-day average), pre-TMS (days prior to stimulation), and during TMS sessions. Statistical analyses examine correlations between resting HRV and in-lab responses to this noxious stimulation. Data collection and analysis are ongoing. This study aims to determine whether real-world HRV metrics can serve as predictive markers of autonomic resilience under stress. The significance of this work lies in its potential to integrate wearable HRV monitoring into clinical practice, offering scalable, objective tools for tracking recovery, tailoring interventions, and improving treatment outcomes for individuals with mTBI.

Cognitive flexibility, an individual's ability to adapt learned behaviors to fit new contexts, is crucial for survival. The cerebellum's role in such higher order cognition is not well understood. Outputs from Cerebellar Nuclei (CN) are known to project to several forebrain regions, including the thalamus, prefrontal cortex, implicating the role of cerebellar outputs in cognition. Cerebellar Purkinje cells (PCs) regulate CN output by tonically inhibiting it. We are investigating the cerebellar contributions to cognitive flexibility by pairing a reversal learning paradigm with the chemogenetic suppression of PCs to modulate CN outputs. Mice undergo a 2-cue training paradigm where they learn to associate a reward with only one of the two odor-cues. During reversal, these mice learn to associate the reward to the second odor instead, a behavior requiring cognitive flexibility. To understand the role of CN outputs to the forebrain in cognitive flexibility, we inhibit PC activity during this learning paradigm to measure the cognitive effects. Electrophysiological readings of PC and CN firing activity during reversal events to identify what neural signals the cerebellum provides in support of cognitive flexibility, improving our fundamental understanding of underlying mechanisms.

Traditional psychiatric diagnostic frameworks categorize mental disorders into discrete conditions despite variability in symptom presentation and high rates of comorbidity. A dimensional approach viewing psychopathology as continuous rather than categorical may offer a more nuanced understanding of psychiatric illness. To assess the neurological validity of this framework, this study uses electroencephalography (EEG) and Machine Learning (ML) techniques to evaluate the Global Interaction Measure (GIM) as a neural biomarker of psychopathology. EEG, Working Memory (WM), and symptomatology data were obtained from the Multi-Level Assays of Working Memory and Psychopathology dataset. GIM values were calculated across 4 canonical frequency bands (alpha, beta, gamma, and theta) from EEG recorded at rest and during 3 WM tasks. A Support Vector Regression (SVR) ML model was trained to predict psychiatric symptom severity, WM scores, and psychopathology factors using the GIM values as features. Model performance was evaluated using Pearson correlation coefficients (r-values), with significance tested against a null distribution generated from an SVR model trained on shuffled outcome data. The model achieved statistically significant predictions across 6 outcomes, suggesting a meaningful relationship between the GIM and neural correlates of psychopathology. These results provide support for the dimensional framework and highlight the GIM’s potential as a functional connectivity measure relevant to transdiagnostic psychopathology.