The conditional knockout (KO) of Ndufs4 in only GABAergic interneurons leads to a severe epilepsy phenotype, suggesting GABAergic interneurons drive the severe and often fatal epilepsy phenotype commonly reported in Leigh Syndrome (LS) patients. Dysfunctions or loss of parvalbumin (PV) interneurons, a subtype of GABAergic interneurons, have been shown to play a key role in the mechanisms of various forms of epilepsy both in human and animal models. The present study aims to target PV interneurons. We hypothesized that KO of Ndufs4 in PV interneurons will cause dysfunctions or loss of PV neurons leading to epilepsy in our cell-specific model of LS. Experimental mice models with Ndufs4^flx/flx/PVCre^flx/+ genotype for the mutants, and Ndufs4^flx/flx/PVCre^+/+ genotype for the controls were used. For imaging experiments, Ndufs4^flx/flx/Ai14^flx/+/PVCre^flx/+ were used for mutants and Ndufs4^+/+/Ai14^flx/+/PVCre^flx/+ were used for controls. Seizure susceptibility was assessed by recording occurrence, frequency and duration of seizures and epileptiform events. Mice susceptibility to provoked seizures was examined by the pentylenetetrazol (PTZ) challenge. Assessment of cell loss was tested in imaging studies. Ai14-labeled PV interneurons in key areas associated with epilepsy were counted between the two groups. Finally, to assess motor dysfunctions comorbid to epilepsy, I tracked the movement of mice of both genotypes. Our results showed PV mutants had an increase in the frequency of spontaneous myoclonic seizures and interictal spikes on electroencephalograms (EEGs). There was no difference in seizure susceptibility to PTZ seizures between mutants and controls, nor any major impairments in locomotor activity or anxiety like behavior in PV mutants. Finally, no cell loss changes in PV mutants were detected. In conclusion, PV mutants display a mild seizure phenotype with no cognitive or motor abnormalities, suggesting targeted Ndufs4 KO in PV interneurons drives a small portion of the severe epilepsy phenotype observed in LS.

Essential Tremor (ET) is the most common neurological movement disorder, impacting approximately 1% of the global population. Symptoms of the disorder are characterized by involuntary rhythmic motions of affected body parts and range greatly in tremor severity. As the disease progresses, pharmacological treatments often fail, requiring implantation of a deep brain stimulation (DBS) device to suppress symptoms. Effective treatment requires exhaustive physician tuning of stimulation parameters, which include numerous clinician visits for patients. One way that clinicians assess symptoms is to ask patients to perform behavioral tasks such as drawing spirals, which indicate non ideal stimulation through tremor patterns in the drawing. The goal of this study was to explore the feasibility and options for remote collection of symptom assessments, as well as to explore methods for detecting and characterizing tremor based on spiral features. Consented patients performed spiral drawing tasks multiple times a day on a personal digital assistant such as a smartphone with the collected data securely stored on the cloud. To ensure that access to a mobile device wouldn’t disqualify participants, we developed a device loaning process used in addition to an application developed by Runelabs as supporting infrastructure to collect data. The results of our research showed that we could not only collect data remotely over extended periods of time, but also replicated HOG (Histogram of Oriented Gradients) based classification algorithms on existing datasets to distinguish healthy vs tremor spirals with up to 94.5% accuracy. Previous lab-published results on the same dataset demonstrated a 98.3% accuracy using Principal Component Analysis, which illustrates the strength of our lab's prior work, though the higher efficiency of HOG classification is promising for larger datasets. This demonstrates our methods’ potential to allow for larger patient cohorts and possible integration with other inertial sensor data into a tremor classification model for future studies.

The older population (≥65 years old), projected to double by 2030, is the fastest growing age group and represents 20% of the US population. Normal aging of the brain results in a nearly 30% reduction of microvasculature in the cerebral cortex with significant drops in capillary density, vascular responses to metabolic demand, and reduced angiogenic capabilities signifying the “sensitive” nature of the aging brain to ischemic injuries. The aged spinal cord is also vulnerable to injuries; in recent years, spinal cord injuries (SCI) from ground falls are among the most common trauma suffered by older patients. Unfortunately, we know almost nothing about how spinal cord microvasculature, hemodynamics, and inflammation changes with age. The present study aims to address this knowledge gap. Our group has recently developed a novel intravital ultrafast contrast enhanced ultrasound (CEUS) imaging to visualize blood flow within the microvasculature with unparalleled temporal (30,000 frames per second) and spatial (down to 50 micron) resolution. Unlike other imaging modalities limited to only a few hundred microns deep from the surface of the tissue (e.g., laser speckle and two photon imaging), ultrafast CEUS imaging allows us to examine intraparenchymal microvascular structure and blood flow hemodynamics within the entire depth of the spinal cord tissue in real-time. We applied this innovative technique to study intraspinal microvasculature anatomy and function during normal aging, and age-related microvascular vulnerabilities after traumatic SCI (tSCI). We also examined differences in inflammatory markers from aging using basic histological and microscopy techniques. Because previous studies have detected microvascular density differences between male and female rodent and human models, we have also measured microvascular changes during normal aging in both sexes. Results from this study will ultimately be foundational to understanding sex and age-related alterations in both the static and dynamic microvascular function of the spinal cord.