Biology subjects

Economo, M. N.

Publications and source records attributed to Economo, M. N..

16 recordsLinked to original sources

bioRxiv2026

BiXformer: A Bidirectional Cross Attention Transformer for Disentangling Inter-Regional Neural Dynamics

Advances in high-throughput neural recording technologies enable simultaneous measurement of activity across multiple brain regions in behaving animals, producing datasets of unprecedented scale and richness. Interpreting these data remains challenging due to the bidirectional and temporally offset nature of inter-regional communication, where feedforward and feedback signals are superimposed within neural populations. We introduce BiXformer, a bidirectional cross-attention transformer that disentangles these interactions by decomposing inter-regional communication into causal and acausal streams using directionally masked attention. By enforcing temporal constraints within attention heads, BiXformer recovers low-dimensional, directed latent dynamics and estimates communication delays without relying on linearity or stationarity assumptions. We validate the model on synthetic datasets with known ground-truth delays, demonstrating accurate recovery of both latent structure and inter-regional timing. Applied to simultaneous neural-behavioral recordings and multi-region neural recordings during a movement task, BiXformer reveals interpretable, temporally structured components consistent with the coexistence of sensory feedback and motor-related signals. These results establish BiXformer as a flexible framework for uncovering dynamic, directed communication in complex neural circuits.

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bioRxiv2026

Distinct cell-type contributions and network topography of theta-nested gamma oscillations in the medial entorhinal cortex

Theta-nested gamma oscillations in the medial entorhinal cortex (mEC) are essential for spatial coding and memory, but the underlying cellular mechanisms remain unclear. We combined optogenetics, whole-cell electrophysiology, intracellular voltage imaging, and local field potential (LFP) recordings in acute slices from CaMKII-ChR2 mice to investigate how excitation and inhibition shape theta-gamma coupling in layer II/III mEC. During theta-frequency stimulation, fast-spiking interneurons received strong gamma-frequency excitation and fired rhythmic bursts, whereas stellate and pyramidal neurons fired more sparsely and were dominated by gamma-frequency inhibition. This sparse firing could support the selective firing of grid cells. Excitatory post-synaptic currents in interneurons preceded inhibitory currents and LFP gamma by [~]3 ms, supporting a pyramidal-interneuron network gamma (PING) mechanism. Pyramidal neurons fired on the descending phase of the gamma cycle, whereas stellate cells and fast-spiking interneurons fired before and after the trough, respectively. Intracellular voltage imaging revealed network gamma synchronization among excitatory neurons at a population level, with topographic clustering of subthreshold membrane potentials, but not spike timing, while individual neurons often skipped gamma cycles. These findings identify the dominant role of reciprocal E-I interactions in generating theta-nested gamma oscillations and highlight distinct cell-type contributions to the temporal dynamics of the mEC. Further, a biophysically realistic computational model predicted gamma cycle skipping in stellate cells and burst firing in fast-spiking interneurons during PING. Our experimental and computational results provide mechanistic insight into how the intrinsic properties of mEC cell types generate oscillatory activity in a manner that could support grid cell function and spatial computation. Significance StatementTheta-nested gamma oscillations in the medial entorhinal cortex (mEC) are essential for spatial navigation and memory, yet the underlying circuit mechanisms remain incompletely understood. Using optogenetics, voltage imaging, electrophysiology, and biophysically realistic computational modeling, we show that reciprocal interactions between excitatory neurons and fast-spiking interneurons generate robust gamma oscillations through a pyramidal-interneuron network gamma (PING) mechanism. We reveal cell-type specific differences in gamma phase-locking and demonstrate that principal neurons synchronize at the population-level across large laminar distances (up to 800 {micro}m). Further, the voltage activity, but not spike timing, of principal neurons exhibited topographic clustering. These findings clarify how local circuits in mEC generate temporal dynamics critical for grid cell coding and further constrain computational models of spatial representation.

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bioRxiv2026

Dynamic engagement of the motor cortex in controlling movement

Neural circuits do not contribute equally or continuously to behavior. In mice, the motor cortex can be essential or dispensable for movement in different contexts, but how it is dynamically recruited as behavioral demands evolve remains unclear. Here, we demonstrate that motor cortical involvement in movement exhibits rapid, discrete state transitions even during movements that otherwise appear continuous. While robust and reproducible across animals, the timing and presence of these state transitions are highly sensitive to task structure. We find that motor cortical engagement is sustained under conditions of sensorimotor uncertainty and dissipates rapidly when sensorimotor contingencies are resolved and actions and outcomes thus become predictable. These findings reveal a previously unrecognized layer of fast-timescale flexibility in the neural control of movement and offer a conceptual framework for understanding how cortical circuits dynamically govern behavior as demands evolve.

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bioRxiv2025

Mapping neural subspace dynamics onto the structure of the mouse descending motor system

The motor cortex supports various cognitive and motor functions. To prevent interference between these processes, the associated neural dynamics may be organized into orthogonal subspaces. In this subspace model, activity in a movement-null subspace encodes internal processes, while activity in a movement-potent subspace relates to movements. The biological implementation of this model - how activity in different subspaces map onto neural circuits - remains unclear. Particularly, it is unknown whether different cell types, with specific connectivity patterns, preferentially contribute to specific subspaces. Here, we test whether the cell type that directly links motor cortex to motor centers in the medulla and spinal cord - lower layer 5b extratelencephalic neurons (L-ETN) - preferentially encodes activity contained in the movement-potent subspace. We performed cell-type-specific recordings in the motor cortex while mice performed a delayed-response licking task, and decomposed population activity into movement-null and movement-potent subspaces. We find that L-ETN activity spans both movement-null and movement-potent subspaces in a manner that could not be distinguished from the broader motor cortex population. Notably, downstream medullary circuits retain a subset of the movement-null dynamics contained in motor cortex, with select movement-null signals specifically filtered out. These results indicate that a distinct cortical output alone does not mediate the cancelation of null dynamics; movement-null computations are instead distributed across the descending motor hierarchy.

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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.

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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.

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bioRxiv2023

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the mixed selectivity often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.

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bioRxiv2023

Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy

Voltage imaging with cellular specificity has been made possible by the tremendous advances in genetically encoded voltage indicators (GEVIs). However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines signal-to-noise ratio (SNR) and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating, while also maximizing signal detection efficiency. The resulting benefits in SNR and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different GEVI classes.

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bioRxiv2022

Imaging through Windansee electrode arrays reveals a small fraction of local neurons following surface MUA

Prior studies have shown that neuronal spikes can be recorded with microelectrode arrays placed on the cortical surface. However, the etiology of these spikes remains unclear. Because the top cortical layer (layer 1) contains very few neuronal cell bodies, it has been proposed that these spikes originate from neurons with cell bodies in layer 2. To address this question, we combined two-photon calcium imaging with electrophysiological recordings from the cortical surface in awake mice using chronically implanted PEDOT:PSS electrode arrays on transparent parylene C substrate. Our electrode arrays (termed Windansee) were integrated with cortical windows offering see-through optical access while also providing measurements of local field potentials (LFP) and multiunit activity (MUA) from the cortical surface. To enable longitudinal data acquisition, we have developed a mechanical solution for installation, connectorization, and protection of Windansee devices aiming for an unhindered access for high numerical aperture microscope objectives and a lifetime of several months while worn by a mouse. Contrary to the common notion, our measurements revealed that only a small fraction of layer 2 neurons from the sampled pool (~13%) faithfully followed MUA recorded from the surface above the imaging field-of-view. Surprised by this result, we turned to computational modeling for an alternative explanation of the MUA signal. Using realistic modeling of neurons with back-propagating dendritic properties, we computed the extracellular action potential at the cortical surface due to firing of local cortical neurons and compared the result to that due to axonal inputs to layer 1. Assuming the literature values for the cell/axon density and firing rates, our modeling results show that surface MUA due to axonal inputs is over an order of magnitude larger than that due to firing of layer 2 pyramidal neurons. Thus, a combination of surface MUA recordings with two-photon calcium imaging can provide complementary information about the input to a cortical column and the local circuit response. Cortical layer I plays an important role in integration of a broad range of cortico-cortical, thalamocortical and neuromodulatory inputs. Therefore, detecting their activity as MUA while combining electrode recording with two-photon imaging using optically transparent surface electrode arrays would facilitate studies of the input/output relationship in cortical circuits, inform computational circuit models, and improve the accuracy of the next generation brain-machine interfaces.

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bioRxiv2021

High-speed, multi-Z confocal microscopy for voltage imaging in densely labeled neuronal populations

Genetically encoded voltage indicators (GEVIs) hold great promise for monitoring neuronal population activity, but GEVI imaging in dense neuronal populations remains difficult due to a lack of contrast and/or speed. To address this challenge, we developed a novel confocal microscope that allows simultaneous multiplane imaging with high-contrast at near-kHz rates. This approach enables high signal-to-noise ratio voltage imaging in densely labeled populations and minimizes optical crosstalk during concurrent optogenetic photostimulation.

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bioRxiv2021

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator

The ability to optically image cellular transmembrane voltage at millisecond-timescale resolution can offer unprecedented insight into the function of living brains in behaving animals. The chemigenetic voltage indicator Voltron is bright and photostable, making it a favorable choice for long in vivo imaging of neuronal populations at cellular resolution. Improving the voltage sensitivity of Voltron would allow better detection of spiking and subthreshold voltage signals. We performed site saturation mutagenesis at 40 positions in Voltron and screened for increased {Delta}F/F0 in response to action potentials (APs) in neurons. Using a fully automated patch-clamp system, we discovered a Voltron variant (Voltron.A122D) that increased the sensitivity to a single AP by 65% compared to Voltron. This variant (named Voltron2) also exhibited approximately 3-fold higher sensitivity in response to sub-threshold membrane potential changes. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, with lower baseline fluorescence. Introducing the same A122D substitution to other Ace2 opsin-based voltage sensors similarly increased their sensitivity. We show that Voltron2 enables improved sensitivity voltage imaging in mice, zebrafish and fruit flies. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

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bioRxiv2020

Accurate localization of linear probe electrodes across multiple brains

Recently developed silicon probes have large numbers of recording electrodes on long linear shanks. Specifically, Neuropixels probes have 960 recording electrodes distributed over 9.6 mm shanks. Because of their length, Neuropixels probe recordings in rodents naturally span multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals have to be aligned to each other and to a standardized brain coordinate system. Here we report a workflow for accurate localization of individual electrodes in standardized coordinates and aligned across individual brains. This workflow relies on imaging brains with fluorescent probe tracks and warping 3-dimensional image stacks to standardized brain atlases. Electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed ground-truth experiments, in which motor cortex outputs are labelled with ChR2 and a fluorescence protein. Recording from brain regions targeted by these outputs reveals better than 100 m accuracy for electrode localization.

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bioRxiv2019

Attractor dynamics gate cortical information flow during decision-making

Decisions about future actions are held in memory until enacted, making them vulnerable to distractors. The neural mechanisms controlling decision robustness to distractors remain unknown. We trained mice to report optogenetic stimulation of somatosensory cortex, with a delay separating sensation and action. Distracting stimuli influenced behavior less when delivered later during delay - demonstrating temporal gating of sensory information flow. Gating occurred even though distractor-evoked activity percolated through the cortex without attenuation. Instead, choice-related dynamics in frontal cortex became progressively robust to distractors as time passed. Reverse-engineering of neural networks trained to reproduce frontal-cortex activity revealed that chosen actions were stabilized via attractor dynamics, which gated out distracting stimuli. Our results reveal a dynamic gating mechanism that operates by controlling the degree of commitment to a chosen course of action. One Sentence SummaryMechanisms controlling state-dependent communication between brain regions allow for robust action-selection.

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bioRxiv2019

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

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bioRxiv2018

Inhalation frequency controls reformatting of mitral/tufted cell odor representations in the olfactory bulb

In mammals olfactory sensation depends on inhalation, which controls activation of sensory neurons and temporal patterning of central activity. Odor representations by mitral and tufted (MT) cells, the main output from the olfactory bulb (OB), reflect sensory input as well as excitation and inhibition from OB circuits, which may change as sniff frequency increases. To test the impact of sampling frequency on MT cell odor responses, we obtained whole-cell recordings from MT cells in anesthetized male and female mice while varying inhalation frequency via tracheotomy, allowing comparison of inhalation-linked responses across cells. We characterized frequency effects on MT cell responses during inhalation of air and odorants using inhalation pulses and also playback of sniffing recorded from awake mice. Inhalation-linked changes in membrane potential were well-predicted across frequency from linear convolution of 1 Hz responses and, as frequency increased, near-identical temporal responses could emerge from depolarizing, hyperpolarizing or multiphasic MT responses. However, net excitation was not well predicted from 1 Hz responses and varied substantially across MT cells, with some cells increasing and others decreasing in spike rate. As a result, sustained odorant sampling at higher frequencies led to increasing decorrelation of the MT cell population response pattern over time. Bulk activation of sensory inputs by optogenetic stimulation affected MT cells more uniformly across frequency, suggesting that frequency-dependent decorrelation emerges from odor-specific patterns of activity in the OB network. These results suggest that sampling behavior alone can reformat early sensory representations, possibly to optimize sensory perception during repeated sampling.\n\nSignificance statementOlfactory sensation in mammals depends on inhalation, which increases in frequency during active sampling of olfactory stimuli. We asked how inhalation frequency can shape the neural coding of odor information by recording from projection neurons of the olfactory bulb while artificially varying odor sampling frequency in the anesthetized mouse. We found that sampling an odor at higher frequencies led to diverse changes in net responsiveness, as measured by action potential output, that were not predicted from low-frequency responses. These changes led to a reorganization of the pattern of neural activity evoked by a given odorant that occurred preferentially during sustained, high-frequency inhalation. These results point to a novel mechanism for modulating early sensory representations solely as a function of sampling behavior.

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bioRxiv2017

Shared and distinct transcriptomic cell types across neocortical areas

Neocortex contains a multitude of cell types segregated into layers and functionally distinct regions. To investigate the diversity of cell types across the mouse neocortex, we analyzed 12,714 cells from the primary visual cortex (VISp), and 9,035 cells from the anterior lateral motor cortex (ALM) by deep single-cell RNA-sequencing (scRNA-seq), identifying 116 transcriptomic cell types. These two regions represent distant poles of the neocortex and perform distinct functions. We define 50 inhibitory transcriptomic cell types, all of which are shared across both cortical regions. In contrast, 49 of 52 excitatory transcriptomic types were found in either VISp or ALM, with only three present in both. By combining single cell RNA-seq and retrograde labeling, we demonstrate correspondence between excitatory transcriptomic types and their region-specific long-range target specificity. This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex.

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