Vincent, J. P. | bioRxiv Science

bioRxiv2025

A scalable, all-optical method for mapping synaptic connectivity with cell-type specificity

Single-cell transcriptomics has uncovered the vast heterogeneity of cell types that compose each region of the mammalian brain, but describing how such diverse types connect to form functional circuits has remained challenging. Current methods for measuring the presence and strength of synaptic connections principally rely on low-throughput whole-cell recording approaches. However, the development of optical tools for perturbing and observing neural activity--now notably including genetically encoded voltage indicators--presents an exciting opportunity to vastly increase the throughput of physiological connectivity mapping. Here, we combine massively parallel optical measurements of synaptic strength with a novel pipeline for thick-tissue spatial transcriptomics to assay synaptic connectivity motifs with high sensitivity, high throughput, and cell-type specificity. We apply this approach in the motor cortex to describe new cell-type-specific synaptic innervation patterns for long-range thalamic and contralateral input onto more than 1000 motor cortical neurons.

neuroscience↗

bioRxiv2023

Assessing cross-contamination in spike-sorted electrophysiology data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner. SIGNIFICANCE STATEMENTHigh-density silicon probes are widely used to record the activity of large populations of neurons while animals are engaged in complex behavior. In this approach, each electrode records spikes from many neurons, and "spike sorting" algorithms are used to group the spikes originating from each neuron together. This process is error-prone, however, and so the ability to assess spike sorting accuracy is essential for properly interpreting neural activity. The rate of inter-spike-interval (ISI) violations is commonly used to assess spike sorting accuracy, but the relationship between ISI violation rate and sorting accuracy is complex and poorly understood. Here, we describe this relationship in detail and provide guidelines for how to properly use ISI violation rate to assess spike sorting accuracy.

neuroscience↗