Microglia, the resident immune cells in the brain, have multiple functions including synaptic pruning to preserve resources, phagocytosis of apoptotic cells, and isolation and removal of foreign material. Depending on local environmental stimuli, microglia can change their shape between multiple states including highly branched, branched, or ameboid. To better understand microglia responses to changes in the brain environment, I investigated morphological shape features that include changes in area, circularity, and aspect ratio among other important features. I specifically focused on the microglial response to oxygen-glucose deprivation (OGD). Oxygen-glucose deprivation is a condition where the brain fails to receive the necessary oxygen and nutrients for growth and maintenance, resulting in higher levels of stress and cytotoxicity. Investigating the effects of OGD on microglia is part of a larger effort - developing a fluorescent imaging pipeline called microFIBER. Our goal for microFIBER is to create an unbiased, detailed, and replicable analysis pipeline for the robust characterization of microglia morphology. Images are from a previous investigation into effects of OGD on neonatal rat brains in the Nance Lab. We used SciKit-Image along with other Python packages to segment, label, and quantify the geometry of fluorescent-labeled microglia cells in the images. SciKit-Image’s module RegionProps was used to quantify shape features by drawing certain properties over the objects and then measuring those drawings. I then analyzed the response of microglia in non-treated, 1.5-hour OGD exposure, and 3-hour OGD exposure via data analysis in Python and Excel. I further divided these treatment groups into regional comparisons of the cortex, hippocampus, and thalamus. Results from statistical analysis supported differences between treatment groups and brain region, including statistically relevant differences in microglial circularity, area, and axes lengths. Differences in shape features could be used in the future as markers for diseased or distressed conditions for medical diagnosis.

Prediction of response to preoperative or ‘neoadjuvant’ chemotherapy (NAC) can help guide treatment strategies for patients with triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer. Breast magnetic resonance imaging (MRI) can provide noninvasive measurements of the microenvironment across a tumor volume. We hypothesize that pre-treatment measurements from dynamic contrast-enhanced (DCE-) MRI reflecting tumor perfusion and vascular function are predictive of NAC response for TNBC patients. Women with TNBC who underwent pre-treatment MRI and NAC at our institution (2005-2019) were retrospectively identified. DCE-MRI was acquired at 2, 5, and 8 minutes after contrast injection. From DCE-MRI, whole tumor contrast kinetics measures including functional tumor volume (FTV), percent enhancement (PE) at 2 mins post-contrast and signal enhancement ratio (SER) were calculated, and hotspot measures of peak PE and peak SER (representing the highest mean PE and SER, respectively, for 3?3 voxel subregions) were determined. Imaging measurements were compared between those with complete pathologic response (pCR; no residual cancer present in the breast at surgery) and non-pCR patients with a two-tailed Student’s t-Test (p<0.05 considered significant). 95 women (median age: 49, range: 30-79 years) with TNBC were evaluated, of which 29 (31%) achieved pCR. FTV was significantly higher in non-pCR patients (21.1±28.1 cc) compared to pCR patients (8.6±11.3 cc, p<0.01). Peak SER was also higher in non-pCR patients (1.8±0.3) compared to pCR patients (1.7±0.3), trending toward significance (p=0.06). No significant differences between groups were observed in peak PE measures. Patients with lower pre-treatment tumor FTV and peak SER on DCE-MRI were more likely to achieve pCR after standard NAC. These findings indicate that baseline DCE-MRI measurements may help predict response and assist in optimizing treatment plans for TNBC patients, such as selecting more aggressive regimens incorporating immune checkpoint inhibitors or other novel agents in predicted non-responders.

Women with dense breasts have increased amounts of fibroglandular tissue (FGT) and are at higher risk of developing breast cancer. Quantitative measurement of FGT from magnetic resonance imaging (MRI) could provide more robust measurement of density, supplanting conventional qualitative radiologist assessments. Current quantitative methods involve manual selection of a signal intensity threshold, which can be time consuming and subjective. Fuzzy c-means (FCM) clustering is an automated approach to tissue segmentation, offering a reproducible process for quantifying FGT volume. The aim of this study is to evaluate the efficacy of the FCM clustering in identifying FGT compared to manual thresholding. Women (N=10) who underwent screening breast MRIs at our institution were evaluated in this preliminary study. Fat-suppressed T₁-weighted pre-contrast images acquired as part of their clinical breast MRI exams were used for FGT segmentation. Prior to segmentation, I cropped the images to include only the breast. FGT was then segmented two ways, 1) manually, using a signal intensity threshold that I chose and adjusted and 2) automatically, using existing lab software for FCM clustering. The Sørensen-Dice similarity coefficient was calculated between the manual and automatic segmentations for each patient to determine the degree of overlap. The concordance correlation coefficient (CCC) was calculated between automatic and manual segmentation volumes across the whole data set. Across the 10 patients, an average (± standard deviation) Dice coefficient of 0.81±0.04 was observed, indicating good spatial agreement between the manual and automatic segmentations. The CCC between the FGT volume from manual and automated segmentation was 0.89, demonstrating high correlation in volume estimates between the two methods. Fuzzy c-means clustering was determined to be an effective and efficient method of FGT segmentation in breast MRI data. Future work will evaluate the application of this technique in assessment of background parenchymal enhancement, a clinical marker of cancer risk.

Although the prevalence of cannabis use among pregnant individuals in the US has increased dramatically over the past decade, limited research is available on the impacts of prenatal cannabis exposure (PCE) on infant development. The main psychoactive compound in cannabis, tetrahydrocannabinol (THC), has been shown to cross the placenta during pregnancy, suggesting potential impact on fetal development. Previous studies yield contradictory findings, yet many link prenatal cannabis use to postnatal outcomes such as impaired motor development. However, many of these studies were conducted before recreational cannabis use was legalized in many states, and often failed to control for known teratogens such as tobacco and alcohol. To address this gap, pregnant individuals from the greater Seattle area who used cannabis frequently (3-5 days/week) during the first trimester (PCE; n=37) or did not use any (control; n=35) were enrolled into this observational study. Use of cannabis, medications, and other drugs were tracked in real-time via weekly surveys throughout pregnancy. After birth, we assessed infants between 6-9 months of age using the Early Motor Questionnaire (EMQ). Birth information including weight, length, head circumference, and Apgar scores (1/5min) were also obtained for each infant after birth. I will investigate group differences on the EMQ and sample characteristics using independent samples T-tests. I hypothesize that infants with PCE will have poorer motor development compared to control infants. I also hypothesize that infants with PCE will have reduced birth weight, head circumference, and length. Due to increasingly widespread cannabis use, research on the impacts of PCE while controlling for demographic factors and known teratogens remains essential to support pregnant individuals in making informed decisions. Additional research on the relationship between PCE dosage and frequency on infant brain development will further aid in providing this necessary and comprehensive guidance.

The glymphatic system is the waste clearance system in the central nervous system, and it is most active during sleep. This system clears toxic solutes from the brain and include amyloid beta peptides, tau aggregates amongst others. Accumulation of these proteins is the hallmark of many degenerative diseases, including Alzheimer's disease (AD). Therefore, there is high interest in studying the glymphatic system and its role in the pathogenesis of AD. Typically, intrathecal contrast injections are used to study glymphatic flow. However, it is impractical to use this method for studies since they are invasive and need multiple MRIs over almost 24 hours. In this study, the effectiveness of intra-voxel incoherent motion (IVIM) is explored, which is a non-invasive diffusion MRI method that only takes minutes to complete. This procedure measures the velocity and distance of molecules at slow motion over long distances (D*) and fast motion over short distances (D). We hypothesize that glymphatic flow is associated with D*. The glymphatic clearance mechanism is sleep active and even acute sleep restriction or deprivation is known to cause transient impairments in its flow. To determine whether D* is sensitive to glymphatic flow, we compare measurements in subjects with a full night of sleep and in those with sleep deprivation. We expect that D* will be reduced after a night of sleep deprivation compared to that after a full night’s sleep.