Faculty Bibliography

Beyond the vast array of functional roles attributed to serotonin (5-HT) in the brain, changes in 5-HT levels have been shown to accompany changes in behavioral states, including WAKE, NREM, and REM sleep. Whether 5-HT dynamics at shorter time scales can be seen to delineate substates within these larger brain states remains an open question. Here, we performed simultaneous recordings of extracellular 5-HT using a recently developed G-Protein-Coupled Receptor-Activation-Based 5-HT sensor (GRAB5-HT3.0) and local field potential in the hippocampal CA1 of mice, which revealed the presence of prominent ultraslow (<0.05 Hz) 5-HT oscillations both during NREM and WAKE states. Interestingly, the phase of these ultraslow 5-HT oscillations was found to distinguish substates both within and across larger behavioral states. Hippocampal ripples occurred preferentially on the falling phase of ultraslow 5-HT oscillations during both NREM and WAKE, with higher power ripples concentrating near the peak specifically during NREM. By contrast, hippocampal-cortical coherence was strongest, and microarousals and intracranial EMG peaks were most prevalent during the rising phase in both wake and NREM. Overall, ultraslow 5-HT oscillations delineate substates within the larger behavioral states of NREM and WAKE, thus potentially temporally segregating internal memory consolidation processes from arousal-related functions.

Decreased brain levels of coenzyme Q₁₀ (CoQ₁₀), an endogenously synthesized lipophilic antioxidant^1,2, underpin encephalopathy in primary CoQ₁₀ deficiencies^3,4 and are associated with common neurodegenerative diseases and the ageing process^5,6. CoQ₁₀ supplementation does not increase CoQ₁₀ pools in the brain or in other tissues. The recent discovery of the mammalian CoQ₁₀ headgroup synthesis pathway, in which 4-hydroxyphenylpyruvate dioxygenase-like protein (HPDL) makes 4-hydroxymandelate (4-HMA) to synthesize the CoQ₁₀ headgroup precursor 4-hydroxybenzoate (4-HB)⁷, offers an opportunity to pharmacologically restore CoQ₁₀ synthesis and mechanistically treat CoQ₁₀ deficiencies. To test whether 4-HMA or 4-HB supplementation promotes CoQ₁₀ headgroup synthesis in vivo, here we administered 4-HMA and 4-HB to Hpdl^-/- mice, which model an ultra-rare, lethal mitochondrial encephalopathy in humans. Both 4-HMA and 4-HB were incorporated into CoQ₉ and CoQ₁₀ in the brains of Hpdl^-/- mice. Oral treatment of Hpdl^-/- pups with 4-HMA or 4-HB enabled 90-100% of Hpdl^-/- mice to live to adulthood. Furthermore, 4-HB treatment stabilized and improved the neurological symptoms of a patient with progressive spasticity due to biallelic HPDL variants. Our work shows that 4-HMA and 4-HB can modify the course of mitochondrial encephalopathy driven by HPDL variants and demonstrates that CoQ₁₀ headgroup intermediates can restore CoQ₁₀ synthesis in vivo.

Sharp wave-ripples (SPW-Rs) are critical to hippocampal function, and the same is true of gonadal steroids, but the interactions are unclear. We find that surgical removal of the gonads greatly reduces SPW rates in both sexes. Ripples are greatly reduced also. Testosterone treatment rescues SPW and ripple rates in males, and 17β-estradiol restores SPW rates in females. We also find that male SPW rates are higher than females but have less power. Furthermore, in intact females, SPW rates fluctuate with the stage of the ovarian cycle. These data demonstrate that hippocampal SPWs are significantly affected by gonadal removal, testosterone, and 17β-estradiol. In addition, there are sex differences. The data are consistent with past demonstrations that testosterone and 17β-estradiol play central roles in hippocampus and significantly expand the views of hormone action and SPW-Rs.

Precision-targeted ultrasonic neuromodulation offers immense potential for studying brain function and treating neurological diseases. Yet, its application has been limited by challenges in achieving precise spatio-temporal control and monitoring of ultrasound effects on brain circuits. Here we show that transcranial ultrasound elicits direct and highly focal responses, which can be dynamically steered at spatio-temporal scales relevant for neural function. Furthermore, holographic transcranial ultrasound stimulation allows direct control of the stimulated volume and actively modulates local and mid-range network projections, effectively lowering the activation threshold by an order of magnitude. To better understand this previously unexplored excitability regime not fully explained by the conventional pressure-frequency dyad, we developed a dual modelling framework, where both an empirical and a mechanistic model were constructed to capture the intricacies of holographic transcranial ultrasound stimulation. These models achieve qualitative agreement with our experimental results, suggesting that these findings are predominantly driven by putative network interactions. Our results bring insight on the complex interaction mechanisms of ultrasound with neural tissue and highlight its potential for the noninvasive interfacing of distributed brain networks.

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.

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.

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.

OBJECTIVE:To validate the carotid web (CW) risk stratification assessment described in previous works within a larger cohort of patients with symptomatic and incidentally found asymptomatic CWs. METHODS:A retrospective analysis of our institution's electronic medical records identified all patients with a diagnosis of CW from 2017 to 2024. We included symptomatic patients and those with asymptomatic CWs, that is, incidentally found webs without history of stroke or transient ischemic attack. Patient charts were reviewed for demographics, imaging, comorbidities, and a diagnosis of stroke after diagnosis of asymptomatic CW. All angles were measured as described in previous work on a sagittal reconstruction of neck CT angiography in which the common carotid artery (CCA), external carotid artery, and internal carotid artery (ICA) were well visualized, together with the CW itself. Principal component analysis and logistic regression were performed to evaluate the association between high-risk angles and stroke risk.  RESULTS: Twenty-six symptomatic and 26 asymptomatic patients were identified. Of note, the number of patients with hypertension, hyperlipidemia, and smoking history was 17 (65.0%), 16 (62.0%), and 8 (31.0%) for symptomatic patients and 18 (69.0%), 17 (65.0%), and 15 (58.0%) for asymptomatic patients. All angular measurements showed statistically significant associations with stroke status. The CCA-web-pouch angle showed the strongest association (p=2.07×10⁻⁴), followed by the CCA-pouch-tip angle (p=3.23×10⁻⁴), ICA-web-pouch angle (p=0.004), and ICA-pouch-tip angle (p=0.005). Each additional high-risk angle increased the odds of stroke by 9.47-fold (p<0.0001). The associated probability of stroke increased from 6.3% with no high-risk angles to 39.1% with one high-risk angle and further to 85.9% with two high-risk angles. The model demonstrated high sensitivity, correctly identifying 84.6% of positive cases, and high specificity, correctly identifying 88.5% of negative cases. The F1 score was 0.863, indicating good overall model performance.  CONCLUSION: Given this successful stratification of CWs into high- and low-risk groups, the utilization of geometric CW parameters may play a role in improving patient selection for intervention in the setting of incidentally diagnosed CW. .

Animals need daily intakes of three macronutrients: sugar, protein, and fat. Under fasted conditions, however, animals prioritize sugar as a primary source of energy. They must detect ingested sugar-specifically D-glucose-and quickly report its presence to the brain. Hypothalamic neurons that can respond to the caloric content in the gut regardless of the identity of macronutrient have been identified, but until now, the existence of neurons that can encode the specific macronutrients remained unknown. We found that a subset of corticotropin-releasing factor (CRF)-expressing neurons in the hypothalamic paraventricular nucleus (CRF^PVN) respond specifically to D-glucose in the gut, separately from other macronutrients or sugars. CRF^PVN neuronal activity is essential for fasted mice to develop a preference for D-glucose. These responses of CRF^PVN neurons to intestinal D-glucose require a specific spinal gut-brain pathway including the dorsal lateral parabrachial nuclei. These findings reveal the neural circuit that encodes the identity of D-glucose.