Andrew M. Shelton

Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a framework adapted from multiomics data analysis that weights multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that this alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can transfer labels to unannotated recordings and confirm that inferred cell types exhibit properties consistent with ground truth. Crucially, we show that PhysMAP can also be used to iteratively detect batch effects which confound classification. Together, these results establish PhysMAP as a tool for studying multiple cell types simultaneously and gaining insight into neural circuit dynamics.

Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. To identify cell types, we developed PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that such alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can be used to infer multiple cell types simultaneously in unannotated datasets and show that the properties of inferred types are consistent with the known properties of these cell types. Finally, we provide a first-of-its-kind demonstration of how PhysMAP can help understand how multiple cell types interact to drive circuit dynamics. Collectively, these results demonstrate that multimodal representations from PhysMAP enable the study of multiple cell types simultaneously, thus providing insight into neural circuit dynamics.

The claustrum is thought to be one of the most highly interconnected forebrain structures, but its organizing principles have yet to be fully explored at the level of single neurons. Here, we investigated the identity, connectivity, and activity of identified claustrum neurons in Mus musculus to understand how the structure’s unique convergence of input and divergence of output support binding information streams. We found that neurons in the claustrum communicate with each other across efferent projection-defined modules which were differentially innervated by sensory and frontal cortical areas. Individual claustrum neurons were responsive to inputs from more than one cortical region in a cell-type and projection-specific manner, particularly between areas of frontal cortex. In vivo imaging of claustrum axons revealed responses to both unimodal and multimodal sensory stimuli. Finally, chronic claustrum silencing specifically reduced animals’ sensitivity to multimodal stimuli. These findings support the view that the claustrum is a fundamentally integrative structure, consolidating information from around the cortex and redistributing it following local computations.

Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for an opto-tagged visual cortex dataset and brain region classification for the public International Brain Laboratory brain-wide map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings. The project page and code are available at https://ibl-nemo.github.io/.

The claustrum is thought to be one of the most highly interconnected forebrain structures but its organizing principles have yet to be fully explored at the level of single neurons. Here, we investigated the identity, connectivity, and activity of identified claustrum neurons to understand how the structure’s unique convergence of input and divergence of output support binding information streams. We found that neurons in the claustrum communicate with each other across efferent projection-defined modules which were differentially innervated by sensory and frontal cortical areas. Individual claustrum neurons were responsive to inputs from more than one cortical region in a cell-type and projection-specific manner, particularly between areas of frontal cortex. In vivo imaging of claustrum axons revealed responses to both unimodal and multimodal sensory stimuli. Finally, chronic claustrum silencing specifically reduced animals’ sensitivity to multimodal stimuli. These findings support the view that the claustrum is a fundamentally integrative structure, consolidating information from around the cortex and redistributing it following local computations.

Accurate anatomical characterizations are necessary to investigate neural circuitry on a fine scale, but for the rodent claustrum complex (CLCX), this has yet to be fully accomplished. The CLCX is generally considered to comprise two major subdivisions, the claustrum (CL) and the dorsal endopiriform nucleus (DEn), but regional boundaries to these areas are debated. To address this, we conducted a multifaceted analysis of fiber‐ and cytoarchitecture, genetic marker expression, and connectivity using mice of both sexes, to create a comprehensive guide for identifying and delineating borders to CLCX, including an online reference atlas. Our data indicated four distinct subregions within CLCX, subdividing both CL and DEn into two. Additionally, we conducted brain‐wide tracing of inputs to CLCX using a transgenic mouse line. Immunohistochemical staining against myelin basic protein (MBP), parvalbumin (PV), and calbindin (CB) revealed intricate fiber‐architectural patterns enabling precise delineations of CLCX and its subregions. Myelinated fibers were abundant dorsally in CL but absent ventrally, whereas PV expressing fibers occupied the entire CL. CB staining revealed a central gap within CL, also visible anterior to the striatum. TheNr2f2, Npsr1, andCplx3genes expressed specifically within different subregions of the CLCX, andRprmhelped delineate the CL‐insular border. Furthermore, cells in CL projecting to the retrosplenial cortex were located within the myelin sparse area. By combining own experimental data with digitally available datasets of gene expression and input connectivity, we could demonstrate that the proposed delineation scheme allows anchoring of datasets from different origins to a common reference framework.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

Accurate anatomical characterizations are necessary to investigate neural circuitry on a fine scale, but for the rodent claustrum complex (CC) this has yet to be fully accomplished. The CC is generally considered to comprise two major subdivisions, the claustrum (CL) and the dorsal endopiriform nucleus (DEn), but regional boundaries to these areas are highly debated. To address this, we conducted a multifaceted analysis of fiber- and cyto-architecture, genetic marker expression, and connectivity using mice of both sexes, to create a comprehensive guide for identifying and delineating borders to the CC. We identified four distinct subregions within the CC, subdividing both the CL and the DEn into two. Additionally, we conducted brain-wide tracing of inputs to the entire CC using a transgenic mouse line. Immunohistochemical staining against myelin basic protein (MBP), parvalbumin (PV), and calbindin (CB) revealed intricate fiber-architectural patterns enabling precise delineations of the CC and its subregions. Myelinated fibers were abundant in dorsal parts of the CL but absent in ventral parts, while parvalbumin labelled fibers occupied the entire CL. Calbindin staining revealed a central gap within the CL, which was also visible at levels anterior to the striatum. Furthermore, cells in the CL projecting to the retrosplenial-cortex were located within the myelin sparse area. By combining our own experimental data with digitally available datasets of gene expression and input connectivity, we could demonstrate that the proposed delineation scheme allows anchoring of datasets from different origins to a common reference framework. Significance statement: Mice are a highly tractable model for studying the claustrum complex (CC). However, without a consensus on how to delineate the CC in rodents, comparing results between studies is challenging. It is therefore important to expand our anatomical knowledge of the CC, to match the level of detail needed to study its functional properties. Using multiple strategies for identifying claustral borders, we created a comprehensive guide to delineate the CC and its subregions. This anatomical framework will allow researchers to anchor future experimental data into a common reference space. We demonstrated the power of this new structural framework by combining our own experimental data with digitally available data on gene expression and input connectivity of the CC.

The claustrum is known for its extensive connectivity with many other forebrain regions, but its elongated shape and deep location have made further study difficult. We have sought to understand when mouse claustrum neurons are born, where they are located in developing brains and when they develop their widespread connections to cortex. We established that a well-characterised parvalbumin-plexus, which identifies the claustrum in adults, is only present from postnatal day (P)21. A myeloarchitectonic outline of the claustrum can be derived from a triangular fibre arrangement from P15. A dense patch of Nurr1+ cells is present at its core, and is already evident at birth. BrdU-birthdating of forebrain progenitors reveals that the majority of claustrum neurons are born during a narrow time window centred on embryonic day (E)12.5, which is later than the adjacent subplate and endopiriform nucleus. Retrograde tracing revealed that claustrum projections to anterior cingulate (ACA) and retrosplenial cortex (RSP) follow distinct developmental trajectories. Claustrum-ACA connectivity matures rapidly, and reaches adult-like innervation density by P10, whereas claustrum-RSP innervation emerges later over a protracted time window. This work establishes the timeline of claustrum development, and provides a framework for understanding how the claustrum is built and develops its unique connectivity.

The claustrum is thought to be one of the most highly interconnected forebrain structures but its organizing principles have yet to be fully explored at the level of single neurons. Here, we investigated the identity, connectivity, and activity of identified claustrum neurons to understand how the structure's unique convergence of input and divergence of output support binding information streams. We found that neurons in the claustrum communicate with each other across efferent projection-defined modules which were differentially innervated by sensory and frontal cortical areas. Individual claustrum neurons were responsive to inputs from more than one cortical region in a cell-type and projection-specific manner, particularly between areas of frontal cortex. In vivo imaging of claustrum axons revealed responses to both unimodal and multimodal sensory stimuli. Finally, chronic claustrum silencing specifically reduced animals' sensitivity to multimodal stimuli. These findings support the view that the claustrum is a fundamentally integrative structure, consolidating information from around the cortex and redistributing it following local computations.