Speaker: Josefina Correa (Brown Lab)

Title: Bayesian Hierarchical Autoregressive Parametric Estimation with Application to the Analysis of Multilevel  Electroencephalogram Signals

Abstract: Estimating the spectral content of electroencephalogram signals is a common approach to characterizing how the brain responds to external stimuli. In clinical studies, electroencephalogram signals are collected over multiple subjects and their spectra are computed using either Fourier-based or parametric approaches. A common analysis entails comparing the spectra of a wide-sense stationary data window after the stimulus onset to the spectra before. However, conventional approaches to analyzing these data do not account for between-subject variability, and could in turn provide inaccurate inferences. This work develops a Bayesian hierarchical auto-regressive modeling framework to estimate subject-level and population-level spectra. Our formulation provides a principled approach for constructing cohort-level estimates, which can be used to assess the extent to which a new subject is consistent with a cohort-level response. We validate our framework in simulation and apply it to the analysis of electroencephalogram signals from ten healthy volunteers undergoing propofol-mediated anesthesia. 

Speaker: Raleigh Linville (Heiman Lab)  

Title: Cross-species cellular atlas of the striatum defines cell type-specific and regional disease vulnerabilities.

Abstract: The striatum integrates dopamine and glutamate signals to regulate decision-making, movement, and reward. Despite its importance, the molecular diversity of its constituent neurons is not fully understood. Using single-nucleus RNA sequencing across 85 human samples spanning both dorsal and ventral regions of the striatum, we identify 14 neuronal subtypes with distinct molecular signatures and spatial organization. Rare subpopulations of striatal neurons and spatial gene expression gradients along the dorsolateral-ventromedial axis show notable differences between human and rodent that suggest species-divergent connectivity, disease mechanisms, and pharmacological targets. By integrating our data with genome-wide association studies, we identify novel sites of human-enriched opioid receptor expression, implicate the ventral striatum in chronic antipsychotic action, and propose a molecular mechanism for the dorsal striatum’s heightened vulnerability in Huntington’s disease. Our findings lay the foundation for understanding how specific striatal neurons contribute to both normal brain function and neurological disorders.