HuNeuD - The Human Neuron Discovery Program

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Grant synopsis: The Human Neuron Discovery (HuNeuD) program seeks to discover the properties of human neurons and circuits. HuNeuD has grown out of my initial Discovery Grant cultivated by HQP, and key collaborations. HuNeuD's long term goal is to determine the morphological, electrophysiological, transcriptomic, and connectivity of human neurons that define their `cell-type'. Advanced physical and computational tools, and a multi-scale approach, will link cell-types across different spatial scales of human electrophysiological recordings - for the first time activity of specific cell-types will be related to human brain processing. General background: With a rapidly expanding `toolset' for cell-typing, and mapping connectivity of microcircuits, it is of great interest to characterize human cell-types and the microcircuits they comprise. Our pursuits will compliment international efforts to create an atlas of human cell-types (ie. Allen Institute Cell Types Program) using consistent technical protocols for tissue processing, and handling, while pursuing our unique research objectives. Research plan | Human cell-type characterizations: Building from our whole-cell experience, further cell-type characterization will employ single-cell RNA sequencing (scRNAseq). scRNAseq extracts the transcriptomic profile (genetic fingerprint) of the recorded neuron in which morphology and electrophysiology have been established. The cell-type characterization will be performed in model slice systems (one of which we have created) to induce oscillations - the signature of collective neuronal dynamics. A phase-metric will quantify how and when a cell-type is active relative to ongoing oscillations, to anchor cell-type activity across recording scales. Scaling up, multi-electrode array (MEA) recordings, provide extracellular recordings of local field potentials (LFPs) across all cortical laminae. MEAs also record spiking of single neurons - aka single unit activity (SUA). Phase-metrics will be obtained for the SUA. Human in-vivo experiments: Translating our technical experience of recording SUA in humans, high-density laminar probes will record SUA and LFPs (analogous to the MEA) from across cortical layers during behavioral tasks that modulate oscillations. SUA will be anchored back to cell-types by computing phase-metrics. Cell-type connectivity: One approach to extract connectivity from time series data is statistical mechanics. The Ising model captures some aspects of collective neuronal dynamics, but is limited as it binarizes signals as -1 or +1. The Pott's model, a generalization of the Ising model, can have any number of states. We expect the Potts model to be a general frame work for both discrete (spiking), and continuous (LFP) time series data. Multi-scale mathematical models: Computational modelling will be a complementary approach to anchor cell-types to cortical oscillations. Such models can help conceptually bridge between spatial scales.