Faculty Bibliography

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection.

Macroglia (astrocytes and oligodendrocytes) are required for normal development and function of the central nervous system, yet many questions remain about their emergence during the development of the brain and spinal cord. Here we used single-cell/single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) to analyze over 298,000 cells and nuclei during macroglia differentiation from mouse embryonic and human-induced pluripotent stem cells. We computationally identify candidate genes involved in the fate specification of glia in both species and report heterogeneous expression of astrocyte surface markers across differentiating cells. We then used our transcriptomic data to optimize a previous mouse astrocyte differentiation protocol, decreasing the overall protocol length and complexity. Finally, we used multi-omic, dual single-nuclei (sn)RNA-seq/snATAC-seq analysis to uncover potential genomic regulatory sites mediating glial differentiation. These datasets will enable future optimization of glial differentiation protocols and provide insight into human glial differentiation.

PURPOSE:To evaluate the feasibility and impact of offering genetic testing and counseling to patients with Parkinson's disease (PD), with the potential to enroll in gene-targeted clinical trials and improve clinical care. METHODS:A multicenter, exploratory pilot study at 7 academic hospital sites in the United States tracked enrollment and randomized participants to receive results and genetic counseling at local sites or by genetic counselors, remotely. Follow-up surveys measured participant/provider satisfaction, knowledge, and psychological impact. RESULTS:From September 5, 2019 to January 4, 2021, 620 participants were enrolled and 387 completed outcome surveys. There were no significant differences in outcomes between local and remote sites, with both arms reporting high knowledge and satisfaction scores (>80%). Notably, 16% of those tested had reportable PD gene variants (pathogenic/likely pathogenic/risk allele). CONCLUSION:Local clinicians, as well as genetic counselors, with educational support as needed, can effectively return genetic results for PD as we observed favorable outcome measures in both groups. Increasing access to PD genetic testing and counseling is urgent; this can inform future efforts to integrate genetic testing and counseling into clinical care for all those with PD.

In March 2022, the first ISMRM Workshop on Low-Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low-field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low-field MRI within traditional radiology practices, other point-of-care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low-field portable systems that are purpose-built for clinical decision-making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

BACKGROUND:Developing genomic resources for a diverse range of species is an important step towards understanding the mechanisms underlying complex traits. Specifically, organisms that exhibit unique and accessible phenotypes-of-interest allow researchers to address questions that may be ill-suited to traditional model organisms. We sequenced the genome and transcriptome of Alston's singing mouse (Scotinomys teguina), an emerging model for social cognition and vocal communication. In addition to producing advertisement songs used for mate attraction and male-male competition, these rodents are diurnal, live at high-altitudes, and are obligate insectivores, providing opportunities to explore diverse physiological, ecological, and evolutionary questions. RESULTS:Using PromethION, Illumina, and PacBio sequencing, we produced an annotated genome and transcriptome, which were validated using gene expression and functional enrichment analyses. To assess the usefulness of our assemblies, we performed single nuclei sequencing on cells of the orofacial motor cortex, a brain region implicated in song coordination, identifying 12 cell types. CONCLUSIONS:These resources will provide the opportunity to identify the molecular basis of complex traits in singing mice as well as to contribute data that can be used for large-scale comparative analyses.

Locomotion requires precise control of the strength and speed of muscle contraction and is achieved by recruiting functionally distinct subtypes of motor neurons (MNs). MNs are essential to movement and differentially susceptible in disease, but little is known about how MNs acquire functional subtype-specific features during development. Using single-cell RNA profiling in embryonic and larval zebrafish, we identify novel and conserved molecular signatures for MN functional subtypes and identify genes expressed in both early post-mitotic and mature MNs. Assessing MN development in genetic mutants, we define a molecular program essential for MN functional subtype specification. Two evolutionarily conserved transcription factors, Prdm16 and Mecom, are both functional subtype-specific determinants integral for fast MN development. Loss of prdm16 or mecom causes fast MNs to develop transcriptional profiles and innervation similar to slow MNs. These results reveal the molecular diversity of vertebrate axial MNs and demonstrate that functional subtypes are specified through intrinsic transcriptional codes.

The cardinal classes are a useful simplification of cortical interneuron diversity, but such broad subgroupings gloss over the molecular, morphological, and circuit specificity of interneuron subtypes, most notably among the somatostatin interneuron class. Although there is evidence that this diversity is functionally relevant, the circuit implications of this diversity are unknown. To address this knowledge gap, we designed a series of genetic strategies to target the breadth of somatostatin interneuron subtypes and found that each subtype possesses a unique laminar organization and stereotyped axonal projection pattern. Using these strategies, we examined the afferent and efferent connectivity of three subtypes (two Martinotti and one non-Martinotti) and demonstrated that they possess selective connectivity with intratelecephalic or pyramidal tract neurons. Even when two subtypes targeted the same pyramidal cell type, their synaptic targeting proved selective for particular dendritic compartments. We thus provide evidence that subtypes of somatostatin interneurons form cell-type-specific cortical circuits.

Cortical GABAergic interneurons (INs) represent a diverse population of mainly locally projecting cells that provide specialized forms of inhibition to pyramidal neurons and other INs. Most recent work on INs has focused on subtypes distinguished by expression of Parvalbumin (PV), Somatostatin (SST), or Vasoactive Intestinal Peptide (VIP). However, a fourth group that includes neurogliaform cells (NGFCs) has been less well characterized due to a lack of genetic tools. Here, we show that these INs can be accessed experimentally using intersectional genetics with the gene Id2. We find that outside of layer 1 (L1), the majority of Id2 INs are NGFCs that express high levels of neuropeptide Y (NPY) and exhibit a late-spiking firing pattern, with extensive local connectivity. While much sparser, non-NGFC Id2 INs had more variable properties, with most cells corresponding to a diverse group of INs that strongly expresses the neuropeptide CCK. In vivo, using silicon probe recordings, we observed several distinguishing aspects of NGFC activity, including a strong rebound in activity immediately following the cortical down state during NREM sleep. Our study provides insights into IN diversity and NGFC distribution and properties, and outlines an intersectional genetics approach for further study of this underappreciated group of INs.

PURPOSE/OBJECTIVE:To explore whether a machine-learning algorithm could accurately perform the initial screening of medical school applications. METHOD/METHODS:Using application data and faculty screening outcomes from the 2013 to 2017 application cycles (n = 14,555 applications), the authors created a virtual faculty screener algorithm. A retrospective validation using 2,910 applications from the 2013 to 2017 cycles and a prospective validation using 2,715 applications during the 2018 application cycle were performed. To test the validated algorithm, a randomized trial was performed in the 2019 cycle, with 1,827 eligible applications being reviewed by faculty and 1,873 by algorithm. RESULTS:The retrospective validation yielded area under the receiver operating characteristic (AUROC) values of 0.83, 0.64, and 0.83 and area under the precision-recall curve (AUPRC) values of 0.61, 0.54, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The prospective validation yielded AUROC values of 0.83, 0.62, and 0.82 and AUPRC values of 0.66, 0.47, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The randomized trial found no significant differences in overall interview recommendation rates according to faculty or algorithm and among female or underrepresented in medicine applicants. In underrepresented in medicine applicants, there were no significant differences in the rates at which the admissions committee offered an interview (70 of 71 in the faculty reviewer arm and 61 of 65 in the algorithm arm; P = .14). No difference in the rate of the committee agreeing with the recommended interview was found among female applicants (224 of 229 in the faculty reviewer arm and 220 of 227 in the algorithm arm; P = .55). CONCLUSIONS:The virtual faculty screener algorithm successfully replicated faculty screening of medical school applications and may aid in the consistent and reliable review of medical school applicants.

External rewards such as food and money are potent modifiers of behaviour^1,2. Pioneering studies established that these salient sensory stimuli briefly interrupt the tonic discharge of neurons that produce the neuromodulators dopamine (DA) and acetylcholine (ACh): midbrain DA neurons (DANs) fire a burst of action potentials that broadly elevates DA in the striatum^3,4 at the same time that striatal cholinergic interneurons (CINs) produce a characteristic pause in firing^5,6. These phasic responses are thought to create unique, temporally limited conditions that motivate action and promote learning^7-11. However, the dynamics of DA and ACh outside explicitly rewarded situations remain poorly understood. Here we show that extracellular DA and ACh levels fluctuate spontaneously and periodically at a frequency of approximately 2 Hz in the dorsal striatum of mice and maintain the same temporal relationship relative to one another as that evoked by reward. We show that this neuromodulatory coordination does not arise from direct interactions between DA and ACh within the striatum. Instead, we provide evidence that periodic fluctuations in striatal DA are inherited from midbrain DANs, while striatal ACh transients are driven by glutamatergic inputs, which act to locally synchronize the spiking of CINs. Together, our findings show that striatal neuromodulatory dynamics are autonomously organized by distributed extra-striatal afferents. The dominance of intrinsic rhythms in DA and ACh offers new insights for explaining how reward-associated neural dynamics emerge and how the brain motivates action and promotes learning from within.