EEG Experiment Scripting Tool for Novice Programmers

Accessible, portable, and affordable technology has made computing one of the main methodologies in brain and behavioral research. This development presents university neuroscience and psychology departments with a major problem: most of their students have no computer programming experience, and the time intensity of learning a computer programming language is a barrier that prevents them from practicing the computational concepts and algorithmic thinking increasingly at the core of research in these fields. This is the case in the University of Puget Sound (UPS) Electroencephalography (EEG) lab, where students researching how electrical activity in the brain responds to stimuli (e.g., images) are unable to program their own stimuli. These students consequently miss out on a fundamental aspect of their research, learning the methodology of organizing and manipulating their stimuli algorithmically. This problem is not unique to UPS, but rather a general and increasing trend across universities. In this research, I began developing a software tool that enables novice programmers to build EEG experiment stimuli scripts without first learning a programming language. User feedback suggests that the EEG scripting tool is already more intuitive and user-friendly than the software environment, MATLAB, that is used to code scripts in the UPS EEG lab. Future development of this tool will work to expand the types and orderings of stimuli, add additional output file types, and include an educational component by teaching users basic, relevant computer programming concepts

Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex

Human cortex transcriptomic studies have revealed a hierarchical organization of γ-aminobutyric acid-producing (GABAergic) neurons from subclasses to a high diversity of more granular types. Rapid GABAergic neuron viral genetic labeling plus Patch-seq (patch-clamp electrophysiology plus single-cell RNA sequencing) sampling in human brain slices was used to reliably target and analyze GABAergic neuron subclasses and individual transcriptomic types. This characterization elucidated transitions between PVALB and SST subclasses, revealed morphological heterogeneity within an abundant transcriptomic type, identified multiple spatially distinct types of the primate-specialized double bouquet cells (DBCs), and shed light on cellular differences between homologous mouse and human neocortical GABAergic neuron types. These results highlight the importance of multimodal phenotypic characterization for refinement of emerging transcriptomic cell type taxonomies and for understanding conserved and specialized cellular properties of human brain cell types.</p