Faculty Bibliography

Ultra low-field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio of ULF-MRI often necessitates reductions in spatial resolution and, combined with the field-dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF-MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high-field (HF) MRI brain volumes, and test-retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF-MRIs when combining orthogonal imaging directions for T₂-weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.

In nearly all mammalian species, newborn pups are weak and vulnerable, relying heavily on care and protection from parents for survival. Thus, developmentally hardwired neural circuits are in place to ensure the timely expression of parental behaviors. Furthermore, several neurochemical systems, including estrogen, oxytocin, and dopamine, facilitate the emergence and expression of parental behaviors. However, stress can adversely affect these systems, impairing parental behaviors. In this review, we will summarize our current knowledge regarding the impact of stress on pup-directed behavior circuits that lead to infant neglect, abuse, and, in extreme cases, killing. We will discuss various stressors that influence parental behaviors at different life stages and how stress induces changes in the neurochemical systems that support parental care, ultimately leading to its poor performance.

From fertilization onwards, the cells of the human body acquire variations in their DNA sequence, known as somatic mutations. These postzygotic mutations arise from intrinsic errors in DNA replication and repair, as well as from exposure to mutagens. Somatic mutations have been implicated in some diseases, but a fundamental understanding of the frequency, type and patterns of mutations across healthy human tissues has been limited. This is primarily due to the small proportion of cells harbouring specific somatic variants within an individual, making them more challenging to detect than inherited variants. Here we describe the Somatic Mosaicism across Human Tissues Network, which aims to create a reference catalogue of somatic mutations and their clonal patterns across 19 different tissue sites from 150 non-diseased donors and develop new technologies and computational tools to detect somatic mutations and assess their phenotypic consequences, including clonal expansions. This strategy enables a comprehensive examination of the mutational landscape across the human body, and provides a comparison baseline for somatic mutation in diseases. This will lead to a deep understanding of somatic mutations and clonal expansions across the lifespan, as well as their roles in health, in ageing and, by comparison, in diseases.

Around 40% of the US population and 1 in 6 individuals worldwide have obesity, with the incidence surging globally^1,2. Various dietary interventions, including carbohydrate, fat and, more recently, amino acid restriction, have been explored to combat this epidemic^3-6. Here we investigated the impact of removing individual amino acids on the weight profiles of mice. We show that conditional cysteine restriction resulted in the most substantial weight loss when compared to essential amino acid restriction, amounting to 30% within 1 week, which was readily reversed. We found that cysteine deficiency activated the integrated stress response and oxidative stress response, which amplify each other, leading to the induction of GDF15 and FGF21, partly explaining the phenotype^7-9. Notably, we observed lower levels of tissue coenzyme A (CoA), which has been considered to be extremely stable¹⁰, resulting in reduced mitochondrial functionality and metabolic rewiring. This results in energetically inefficient anaerobic glycolysis and defective tricarboxylic acid cycle, with sustained urinary excretion of pyruvate, orotate, citrate, α-ketoglutarate, nitrogen-rich compounds and amino acids. In summary, our investigation reveals that cysteine restriction, by depleting GSH and CoA, exerts a maximal impact on weight loss, metabolism and stress signalling compared with other amino acid restrictions. These findings suggest strategies for addressing a range of metabolic diseases and the growing obesity crisis.

Social behaviors, including parental care, mating, and fighting, all depend on the hormonal milieu of an organism. Decades of work highlighted estrogen as a key hormonal controller of social behaviors, exerting its influence primarily through binding to estrogen receptor alpha (ERα). Recent technological advances in chemogenetics, optogenetics, gene editing, and transgenic model organisms have allowed for a detailed understanding of the neuronal subpopulations and circuits for estrogen action across Esr1-expressing interconnected brain regions. Focusing on rodent studies, in this review we examine classical and contemporary research demonstrating the multifaceted role of estrogen and ERα in regulating social behaviors in a sex-specific and context-dependent manner. We highlight gaps in knowledge, particularly a missing link in the molecular cascade that allows estrogen to exert such a diverse behavioral repertoire through the coordination of gene expression changes. Understanding the molecular and cellular basis of ERα's action in social behaviors provides insights into the broader mechanisms of hormone-driven behavior modulation across the lifespan.

Astrocytes, the bushy, star-shaped glial cells of the brain and spinal cord, support the proper development and function of many cells in the central nervous system. In response to disease or injury they transform, adopting varied morphologies, molecular signatures, and functions-this state of transformation is known as reactivity. For over a century, the reactivity of astrocytes has been recognized, but it is the recent surge in technological innovation that has shed light on the diverse nature of this reactivity. It is this developing understanding of the heterogeneity of reactive astrocytes across disease-specific contexts and a spatiotemporal gradient that now excites the astrocyte field. In this review, we discuss the current understanding of reactive astrocyte heterogeneity, highlight the biological implications of this heterogeneity, and propose future approaches to aid in fully understanding the heterogeneity of reactive astrocytes.

INTRODUCTION/BACKGROUND:Underlying causes of Alzheimer's disease (AD) remain unknown, making it imperative to identify molecular mechanisms driving the pathobiology of AD onset and progression. METHODS:Laser capture microdissection was used to isolate layer III pyramidal neurons from post mortem human prefrontal cortex (Brodmann area 9). Single population RNA sequencing was conducted using tissue from subjects with no cognitive impairment (NCI), mild cognitive impairment (MCI), and AD. Differentially expressed genes (DEGs) were compared across groups. RESULTS:DEGs increased from prodromal (MCI vs. NCI) to progression (AD vs. MCI) to frank AD (AD vs. NCI). The majority of DEGs and pathways shared between prodromal and progression exhibited a change in the direction of dysregulation unlike pathways between progression and frank AD. DISCUSSION/CONCLUSIONS:Candidate genes and pathways were identified that demarcate early-stage AD onset from AD progression, providing a roadmap to study cortical cellular vulnerability and key targets for intervention at early stages of AD. HIGHLIGHTS/CONCLUSIONS:Pyramidal neuron differentially expressed genes (DEGs) are directionally divergent between prodromal, progression, and frank Alzheimer's disease (AD). Pyramidal neuron DEGs are directionally convergent between progression and frank AD. Dysfunctional bioenergetic pathways increased dysregulation as the AD spectrum progressed. Immune response pathways were more dysregulated in frank AD than prodromal stages. DEGs, = biological pathways, and interactomes demarcate specific stages across the AD spectrum.

OBJECTIVES/OBJECTIVE:Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies. MATERIALS AND METHODS/METHODS:This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases. RESULTS:On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts. CONCLUSIONS:Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.

Alston's singing mice (Scotinomys teguina) are highly vocal Central American rodents that produce structured "songs" (duration: 5-10 s),¹