Faculty Bibliography

PURPOSE/OBJECTIVE: METHODS:Like many other MRI simulators, ours discretizes magnetic fields in space. However, we extend the MR signal simulation at each grid point from the 0th-order approximation, which assumes piecewise constant fields, to a 1st-order approximation, which assumes piecewise linear fields. We solve the signal equation by analytically integrating over each grid cube, assuming linear field variations, and then summing over all cubes. We provide analytical integrals for several pulse sequences. RESULTS:The 1st-order approximation captures strongly varying fields and associated intravoxel dephasing more accurately, avoiding severe "ringing" artifacts present in the usual 0th-order simulations. This enables simulations on a much coarser grid, facilitating computational feasibility. CONCLUSION/CONCLUSIONS:The first-order simulator enables the evaluation of unconventional scanner designs with strongly varying magnetic fields.

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.

Astrocytes differentiate and mature during postnatal development, but the molecular mechanisms linking their maturation to neuronal function remain unclear. We investigated the role of Wnt/β-catenin signaling and its effector, the transcription factor TCF7L2, in postnatal astrocytes using single-nucleus RNA sequencing, imaging, morphometric analysis, microdialysis, and electrophysiology in Tcf7l2 conditional knockout (cKO) mice. Loss of Tcf7l2 caused widespread transcriptional dysregulation in astrocytes, particularly in genes related to amino acid and ion transport, as well as membrane potential regulation. These mice showed disrupted amino acid homeostasis, astrocyte swelling, and impaired extracellular potassium clearance in the somatosensory cortex. These astrocytic changes were accompanied by altered gene expression in cortical pyramidal neurons, reduced excitability, and a hyperpolarized resting membrane potential. Our results suggest that astrocytic TCF7L2 is crucial in coordinating ion and amino acid transport in adulthood, thereby contributing to maintaining extracellular homeostasis and supporting neuronal function. This study identifies TCF7L2 as a key regulator of astrocyte-mediated neurophysiological support and underscores the importance of its role in astrocyte maturation during postnatal development.

Despite extensive research on hemispheric asymmetries, the mechanisms regulating lateralized brain functions are incompletely understood. Growing evidence suggests that lateralized neural circuits are side-specifically controlled, in part, by neuropeptides acting as neuromodulators, paracrine factors, and neurohormones. This review highlights evidence supporting this concept in the contexts of lateralized pain processing in the amygdala, control of auditory signaling, lateralized interoceptive signaling, and side-specific endocrine regulation. Our focus is primarily on rodent studies, with supporting data from humans and nonmammalian species, including turtles and nematodes. Left-right side-specific control may be rooted in a bipartite, lateralized organization of neuropeptide systems. Neuropeptides with asymmetric actions may act locally within specific brain regions or be coordinated across the neuraxis. These findings converge on a model in which neuropeptides enable lateralized control through interconnected mechanisms spanning gene expression, neural circuits, and behavioral outcomes.

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925-0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.

Pyramidal cells (PCs) of hippocampal area CA2 exhibit increased excitability in temporal lobe epilepsy (TLE) and in mouse models of TLE. In epileptic mice, selective inhibition of CA2 PCs reduces chronic seizures. Here we asked if activating CA2 PCs increases seizures. Mice expressing Cre recombinase in CA2 PCs (Amigo2-Cre mice) were injected with the convulsant pilocarpine to induce a period of severe seizures (status epilepticus, SE), which leads to chronic seizures after 3-4 weeks (epilepsy). Epileptic mice were injected with a Cre-dependent adeno-associated virus (AAV) to express an excitatory designer receptor exclusively activated by designer drug (eDREADD; hM3Dq) in dorsal CA2 bilaterally and implanted with subdural EEG electrodes. After recovery, mice were recorded continuously using video and EEG for 6 weeks, 3 weeks with drinking water containing the eDREADD activator clozapine-N-oxide (CNO) and 3 weeks without CNO. CA2 activation with CNO caused a significant increase in seizure frequency and duration. Seizures occurred in clusters (many seizures per day over several consecutive days) and mice given water with CNO had a greater maximum number of seizures per day during a cluster compared to water without CNO. CNO had no significant effect in control mice. In naïve Amigo2-Cre mice expressing hM3Dq, pre-treatment with CNO before pilocarpine administration shortened the latency to SE and increased EEG power at the start of SE. Taken together with prior findings, the results suggest that CA2 is a control point for regulating seizures in the pilocarpine mouse model of TLE.

Monitoring intracellular calcium is central to understanding cell signaling across nearly all cell types and organisms. Fluorescent genetically encoded calcium indicators (GECIs) remain the standard tools for in vivo calcium imaging, but require intense excitation light, leading to photobleaching, background autofluorescence and phototoxicity. Bioluminescent GECIs, which generate light enzymatically, eliminate these artifacts but have been constrained by low dynamic range and suboptimal calcium affinities. Here we show that CaBLAM ('calcium bioluminescence activity monitor'), an engineered bioluminescent calcium indicator, achieves an order-of-magnitude improvement in signal contrast and a tunable affinity matched to physiological cytosolic calcium. CaBLAM enables single-cell and subcellular activity imaging at video frame rates in cultured neurons and sustained imaging over hours in awake, behaving animals. These capabilities establish CaBLAM as a robust and general alternative to fluorescent GECIs, extending calcium imaging to regimes where excitation light is undesirable or infeasible.

Transcranial AC stimulation (tACS) of the cerebellum can entrain spiking activity in the Purkinje cells (PCs) of the cerebellar cortex and, through their projections, the cells in the cerebellar nuclei (CN). In this paper, we investigated if the cells in the motor thalamus (Mthal) can also be modulated (i.e. spikes entrained) via the CN-Mthal projections in rodents. A total of 82 thalamic cells were found, presumably in the Mthal by their stereotaxic coordinates, that were modulated by tACS of the cerebellum. Out of the 346 cells isolated, the thalamic cells with shorter action potentials and regular firing patterns had a higher probability of modulation by cerebellar stimulation than the cells with wider action potentials. The modulation level had a tuning curve with a maximum around 100-200 Hz. Spike histograms over the stimulation cycle transitioned between unimodal and bimodal distributions depending on the frequency. Most cells had a unimodal distribution at low frequencies, a bimodal distribution for frequencies between 80-125 Hz, and then a unimodal one for frequencies above 150 Hz. In addition, tACS of the motor cortex (MC) was also tested in a subset of thalamic cells. Unlike cerebellar stimulation, modulation levels peaked at two distinct frequencies, presumably due to entrainment through multiple MC-Mthal pathways with different preferred frequencies. The results demonstrate the feasibility of modulating a deep brain structure such as the thalamus through multi-synaptic pathways by stimulation of the cerebellar cortex (and the motor cortex) using a non-invasive neuromodulation method.