Biology subjects

Harris, J. A.

Publications and source records attributed to Harris, J. A..

At least 19 recordsLinked to original sources

bioRxiv2026

FLEX: A heparin-binding fusion partner engineered from fibroblast growth factor 1 to enhance protein expression, solubility and purity

Expression of aggregation-prone, unstable, or cytotoxic recombinant proteins remains a major bottleneck in both academic and industrial research. Although solubility-enhancing affinity tags can improve expression, they often compromise purification stringency, increase construct size, or require additional downstream processing. Here we report FLEX, a compact 15.5-kDa dual-function fusion tag engineered from human fibroblast growth factor-1 (FGF1) that integrates intrinsic protein-stabilising properties with high-affinity heparin binding. Structure-guided computational redesign of the FGF1 scaffold reduced exposed hydrophobic residues, removed flexible protease-susceptible regions, and expanded the electropositive surface while preserving the canonical heparin-binding interface. FLEX exhibits markedly improved thermal and chemical stability relative to wild-type FGF1 together with enhanced heparin affinity, enabling high-stringency washing and improved purity in a single affinity step. We demonstrate the broad utility of FLEX by expressing and purifying a panel of challenging proteins in Escherichia coli, including cytotoxic Pseudomonas aeruginosa virulence factors that are difficult to obtain in active form. Unexpectedly, FLEX also performed robustly in mammalian expression systems, where transiently expressed FLEX-tagged proteins were recovered at higher yield and purity than with gold standard Myc and Strep tags, including difficult targets such as Tribbles 3 (TRIB3). These findings establish FLEX as a versatile affinity-and-stabilisation tag that improves expression and purification across diverse systems, providing a practical new tool for structural, biochemical, and translational studies of otherwise intractable proteins.

biochemistry↗

bioRxiv2025

Topical Formulation of Repurposed FDA-Approved Compounds Inhibits Pseudomonas aeruginosa ExoU and Improves Corneal Infection Outcomes

Microbial keratitis, infection of the cornea, cause by ExotoxinU (ExoU) strains of Pseudomonas aeruginosa have a poor clinical outcome and response to antimicrobials. ExoU, a phospholipase, is secreted directly into host cells, causing their lysis. A screen of 3,034 FDA-approved compounds identified zinc pyrithione (Zp), bismuth subcitrate (Bis), and polymyxin B (Pol) as lead inhibitors of ExoU selectively inhibiting it without affecting human PLA2s or bacterial viability. The compounds have distinct inhibitory mechanisms: Zp disrupted ExoU oligomerization and protein stability; Bis impaired phosphatidylinositol 4,5-bisphosphate-dependent membrane association; Pol directly inhibited catalysis via its lipid-peptide architecture. Compound combinations enhanced ExoU inhibition in vitro to nanomolar concentrations. In mammalian cells, Bis and Pol promoted lysosomal trafficking and degradation of ExoU. Using a high-throughput microscopy platform, we screened 53 P. aeruginosa keratitis isolates, confirming broad efficacy of these inhibitors in exoU strains. Therapeutic efficacy was evaluated in ex vivo porcine corneas, Galleria mellonella, and in vivo mouse keratitis models. Topical delivery of ExoU inhibitors, particularly in combination, significantly reduced corneal opacity, ulceration, and stromal damage in porcine corneas without affecting bacterial load. In Galleria, compound combinations significantly maintained larval health, improved larval survival and delayed mortality. In a mouse eye infection model, combinatorial treatment reduced disease severity and preserved tissue viability without altering bacterial burden. These findings validate ExoU as a druggable virulence factor and support the repurposing of these compounds as an anti-virulence strategy for the treatment of P. aeruginosa infections in humans and veterinary medicine.

microbiology↗

bioRxiv2025

Inducing TRIB2 Targeted Protein Degradation to Reverse Chemoresistance in Acute Myeloid Leukemia

The myeloid oncogene TRIB2 is a key driver of acute myeloid leukaemia (AML) pathogenesis, promoting chemoresistance and blocking differentiation through ubiquitin-mediated degradation of the C/EBP transcription factor. Despite its stable and sometimes elevated expression across AML subtypes, TRIB2 remains a clinically-untargeted vulnerability. Here, we present a comprehensive investigation into TRIB2 degradation mechanisms using multimodal approaches, including CRISPR knockout, mutational protein stability, small molecule TRIB2 engagement and evaluation of a novel targeted protein degrader (TRIB2-PROTAC). We identify Afatinib, a multi-ERBB covalent inhibitor, as a rapid inducer of TRIB2 degradation, triggering AML cell death via an ERBB-independent pathway. Importantly, TRIB2 degradation synergized with cytarabine, the frontline AML chemotherapy, amplifying therapeutic efficacy. Mapping of TRIB2 ubiquitination sites revealed Lys-63 as critical for its own proteolytic turnover, and a Lys to Arg degradation-resistant mutant (KallR) conferred enhanced chemoresistance and increased leukaemic engraftment in vivo. CRISPR-mediated TRIB2 knockout validated an essential role in AML cell survival. Consistently, the novel TRIB2-PROTAC (compound 5K) achieved robust TRIB2 degradation and AML cell killing at low micromolar concentrations. These findings establish TRIB2 as a compelling therapeutic target in AML and demonstrate that leveraging the ubiquitin-proteasome system to degrade TRIB2 offers a promising strategy to overcome chemoresistance. This work provides strong preclinical rationale for the development of TRIB2-targeting therapies in AML.

cancer biology↗

bioRxiv2024

Brain Structural Organization Revealed by Unbiased Cell-Type Distribution Clustering

Cell-type composition across brain regions is a critical structural factor shaping both local and long-range brain circuits. Here, we employed single-cell resolution imaging of the mouse brain, combined with computational analyses, to map the distribution of 30 cell classes and types defined by gene marker expression in Cre recombinase-based genetic mouse models. This approach generated a comprehensive atlas of cell type-specific densities across the male and female brain, revealing (1) surprisingly broad sex differences in cells tagged by developmental cell-type markers, (2) shared cell type composition signatures among functionally related brain structures, and (3) close associations not only between specific cell types but also discrete cell type densities and anatomical regions and subregions. In summary, despite the relatively broad cell type classification enabled by the Cre mouse models, our findings highlight intricate relationships between brain cell type distribution and anatomical organization, associating distinct local cell densities with region-specific brain functions.

neuroscience↗

bioRxiv2024

Information, certainty, and learning.

More than four decades ago, Gibbon and Balsam (1981) showed that the acquisition of Pavlovian conditioning in pigeons is directly related to the informativeness of the conditioning stimulus (CS) about the unconditioned stimulus (US), where informativeness is defined as the ratio of the US-US interval (C) to the CS-US interval (T). However, the evidence for this relationship in other species has been equivocal. Here, we describe an experiment that measured the acquisition of appetitive Pavlovian conditioning in 14 groups of rats trained with different C/T ratios (ranging from 1.5 to 300) to establish how learning is related to informativeness. We show that the number of trials required for rats to start responding to the CS is determined by the C/T ratio, and the specific scalar relationship between the rate of learning and informativeness is similar to that previously obtained with pigeons. We also found that the response rate after extended conditioning is strongly related to T, with the terminal CS response rate being a scalar function of the CS reinforcement rate (1/T). Moreover, this same scalar relationship extended to the rats overall response rates (during the CS and inter-trial interval) which was directly proportional to the overall rate of reinforcement in the context (1/C). The findings establish that animals encode rates of reinforcement, and that conditioning is directly related to how much information the CS provides about the US. The consistency of these observations across species, captured by a simple regression function, suggests a universal model of conditioning. More than a century of laboratory-based research has been devoted to investigating how animals learn about simple relationships between events, such as learning to respond to a conditioned stimulus (CS) that is followed by an unconditioned stimulus (US) or learning to perform a specific action that is reinforced by a rewarding US. Much of that research has focussed on identifying what properties about the CS-US or response-US relationship are most important for learning. There is widespread consensus about the importance of three particular properties. One is the temporal contiguity between the events: conditioning emerges sooner when the US follows the CS or response closely in time. Another is the spacing of the learning trials: conditioning takes fewer trials when there is a long time-interval between each CS-US or response-US pairing. The third property is the contingency between the events: conditioning is more successful when the US occurs reliably in the presence of the CS or response, and does not occur in their absence.

animal behavior and cognition↗

bioRxiv2023

Endogenous pathology in tauopathy mice progresses via brain networks

Neurodegenerative tauopathies are hypothesized to propagate via brain networks. This is uncertain because we have lacked precise network resolution of pathology. We therefore developed whole-brain staining methods with anti-p-tau nanobodies and imaged in 3D PS19 tauopathy mice, which have pan-neuronal expression of full-length human tau containing the P301S mutation. We analyzed patterns of p-tau deposition across established brain networks at multiple ages, testing the relationship between structural connectivity and patterns of progressive pathology. We identified core regions with early tau deposition, and used network propagation modeling to determine the link between tau pathology and connectivity strength. We discovered a bias towards retrograde network-based propagation of tau. This novel approach establishes a fundamental role for brain networks in tau propagation, with implications for human disease. One-Sentence SummaryNovel whole brain imaging of p-tau deposition reveals retrograde-dominant network propagation in a tauopathy mouse model.

neuroscience↗

bioRxiv2023

Modeling the cell-type specific mesoscale murine connectome with anterograde tracing experiments

The Allen Mouse Brain Connectivity Atlas (MCA) consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class specific whole brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method which we previously used to fill in spatial gaps to also fill in a gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type and structure specific connectivities. We also show that the wild type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model. AUTHOR SUMMARYLarge-scale studies have described the connections between areas in multiple mammalian models in ever expanding detail. Standard connectivity studies focus on the connection strength between areas. However, when describing functions at a local circuit level, there is an increasing focus on cell types. We have recently described the importance of connection types in the cortico-thalamic system, which allows an unsupervised discovery of its hierarchical organization. In this study we focus on adding a dimension of connection type for a brain-wide mesoscopic connectivity model. Even with our relatively massive dataset, the data in the cell type direction for connectivity is quite sparse, and we had to develop methods to more reliably extrapolate in such directions, and to estimate when such extrapolations are impossible. This allows us to fill in such a connection type specific inter-areal connectivity matrix to the extent our data allows. While analyzing this complex connectivity, we observed that it can be described via a small set of factors. While not complete, this connectivity matrix represents a a categorical and quantitative improvement in mouse mesoscale connectivity models.

neuroscience↗

bioRxiv2022

Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden.

The relationship between brainwide functional decline and accumulation of pathological protein aggregates in Alzheimers disease (AD) is complex and not well understood. A set of highly interconnected cortical regions known as the default mode network (DMN) exhibits selective vulnerability to both functional decline and amyloid beta (A{beta}) plaques in early AD. One possibility is that early A{beta} accumulation in the DMN drives vulnerability. However, it is unknown whether there is something intrinsic to neuronal projections within the DMN that biases these circuits towards dysfunction. Here we directly test this hypothesis using long-term recordings of the spiking activity of ensembles of single units in freely behaving mice characterized by global cortical and hippocampal A{beta} burden (APP/PS1). Specifically, we track the interactions of a population of neurons within a DMN region and two additional populations that comprise monosynaptic targets, one within and the other outside the DMN. In addition, we record single neurons in hippocampus and examine interactions between the in-DMN and out-DMN cortical circuits triggered on hippocampal sharp-wave ripples, stereotyped hippocampal events that contribute to memory consolidation in the cortex. We examine the statistics of local activity as well as inter-regional communication in a region, genotype, and brain-state dependent manner. Our data reveal dysfunction restricted to the in-DMN projecting circuit. In contrast, communication along neuronal projections that originate in the DMN but target an out-DMN population is equivalent in APP/PS1 and WT mice. Circuit dysfunction is most evident throughout sleep, and particularly disrupted within sharp-wave ripples. Summarily, our results indicate that, even in the face of transgene overexpression and widespread A{beta}, there is distinct intrinsic and selective vulnerability. This vulnerability to amyloidosis is circuit-specific and conditioned on target, and neither source nor amyloid burden. These data raise the possibility that neuronal function in the DMN is not universally vulnerable; DMN subnetworks whose interactions involve targets outside the DMN may be resilient to A{beta}.

neuroscience↗

bioRxiv2022

GPU-Accelerated All-atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber

Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH condition; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here we report the first GPU-accelerated implementation, parameterization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd. cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pKas of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearsons correlation coefficient for the pKa shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pKas and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pKa upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pKa downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD offers proper pH control to improve the accuracies of MD simulations and enables mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.

biophysics↗

bioRxiv2022

Regional and cell type-specific afferent and efferent projections of the mouse claustrum

The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. However, its regional anatomy and cell-type-specific connections in the mouse remain largely undetermined. Here, we accurately delineated the boundary of the mouse CLA and quantitatively investigated its inputs and outputs brain-wide using anterograde and retrograde viral tracing and fully reconstructed single claustral principal neurons. At a population level, the CLA reciprocally connects with all isocortical modules. It also receives inputs from at least 35 subcortical structures but sends projections back to only a few of them. We found that cell types projecting to the CLA are differentiated by cortical areas and layers. We classified single CLA principal neurons into at least 9 cell types that innervate the diverse sets of functionally linked cortical targets. Axons of interneurons within the CLA arborize along almost its entire anteroposterior extent. Together, this detailed wiring diagram of the cell-type-specific connections of the mouse CLA lays a foundation for studying its functions.

neuroscience↗

bioRxiv2021

Selective G protein signaling driven by Substance P-Neurokinin Receptor structural dynamics

The neuropeptide Substance P (SP) is important in pain and inflammation. SP activates the neurokinin-1 receptor (NK1R) to signal via Gqand Gs proteins. Neurokinin A also activates NK1R, but leads to selective Gq signaling. How two stimuli yield distinct G-protein signaling at the same G-protein-coupled-receptor remains unclear. We determined cryo-EM structures of active NK1R bound to SP or the Gq-biased peptide SP6-11. Peptide interactions deep within NK1R are critical for receptor activation. Conversely, interactions between SP and NK1R extracellular loops are required for potent Gs signaling but not Gq signaling. Molecular dynamics simulations showed that these superficial contacts restrict SP flexibility deep in the NK1R pocket. SP6-11, which lacks these interactions, is dynamic while bound to NK1R. Structural dynamics of NK1R agonists therefore depend on interactions with the receptor extracellular loops and regulate G-protein signaling selectivity. Similar interactions between other neuropeptides and their cognate receptors may tune intracellular signaling.

biochemistry↗

bioRxiv2021

Fibrillar Aβ causes profound microglial metabolic perturbations in a novel APP knock-in mouse model

Microglial dysfunction is believed to play a pathogenic role in Alzheimers disease (AD). Here, we characterize the amyloid-{beta} related pathology and microglial responses in an engineered APP knock-in mouse model of familial AD. This model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered glial responses and neurodegeneration. Leveraging multi-omics approaches, we found lipid accumulation and an exacerbated disease-associated transcriptomic response in methoxy-X04-positive, phagocytic microglia. Together, these findings highlight the potential of this novel, open-access mouse model to investigate AD pathogenesis and demonstrate that fibrillar A{beta} triggers lipid dysregulation and immuno-metabolic perturbations in phagocytic microglia. HighlightsO_LINovel open-access APP KI mouse model shows salient AD pathological features C_LIO_LIDeep phenotyping of sorted microglia reveals profound lipidomic perturbations in line with Alois Alzheimers original descriptions of glial adipose inclusions C_LIO_LIImmunometabolic perturbations are exacerbated in microglia accumulating fibrillar A{beta} C_LI

neuroscience↗

bioRxiv2020

A generative modeling approach for interpreting population-level variability in brain structure

Understanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. To do this, we start by training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25 micron resolution. To then tap into the learned factors and validate the models expressiveness, we developed a novel bi-directional technique to interpret the latent space-by making structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space to provide insights into the representations of brain structure formed in deep neural networks.

neuroscience↗

bioRxiv2020

Regional, layer, and cell-class specific connectivity of the mouse default mode network

The evolutionarily conserved default mode network (DMN) is characterized by temporally correlated activity between brain regions during resting states. The DMN has emerged as a selectively vulnerable network in multiple disorders, so understanding its anatomical composition will provide fundamental insight into how its function is impacted by disease. Reproducible rodent analogs of the human DMN offer an opportunity to investigate the underlying brain regions and structural connectivity (SC) with high spatial and cell type resolution. Here, we performed systematic analyses using mouse resting state functional magnetic resonance imaging to identify the DMN and whole brain axonal tracing data, co-registered to the 3D Allen Mouse Common Coordinate Framework reference atlas. We identified the specific, predominantly cortical, brain regions comprising the mouse DMN and report preferential SC between these regions. Next, at the cell class level, we report that cortical layer (L) 2/3 neurons in DMN regions project almost exclusively to other DMN regions, whereas L5 neurons project to targets both in and out of the DMN. We then test the hypothesis that in- and out-DMN projection patterns originate from distinct L5 neuron sub-classes using an intersectional viral tracing strategy to label all the axons from neurons defined by a single target. In the ventral retrosplenial cortex, a core DMN region, we found two L5 projection types related to the DMN and mapped them to unique transcriptomically-defined cell types. Together, our results provide a multi-scale description of the anatomical correlates of the mouse DMN.

neuroscience↗

bioRxiv2020

Network structure of the mouse brain connectome with voxel resolution

Fine-grained descriptions of brain connectivity are fundamental for understanding how neural information is processed and relayed across spatial scales. Prior investigations of the mouse brain connectome have employed discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to the organization of the mammalian connectome. Here we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We show that integrative hub regions can be directionally segregated into neural sinks and sources, defining a hierarchical axis. We describe a set of structural communities that spatially reconstitute previously described fMRI networks of the mouse brain, and document that neuromodulatory nuclei are strategically wired as critical orchestrators of inter-modular and network communicability. Notably, like in primates, the directed mouse connectome is organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis. These structural features can be related to patterns of intralaminar connectivity and to the spatial topography of dynamic fMRI brain states, respectively. Together, our results reveal a high-resolution structural scaffold linking mesoscale connectome topography to its macroscale functional organization, and create opportunities for identifying targets of interventions to modulate brain function in a physiologically-accessible species.

neuroscience↗

bioRxiv2020

Towards domain-general predictive coding: Expected TMS excites the motor system less effectively than unexpected stimulation

The brains response to sensory input is modulated by prediction. For example, sounds that are produced by ones own actions, or those that are strongly predicted by environmental cues, are perceived as less salient and elicit an attenuated N1 component in the auditory evoked potential. Here we examined whether the neural response to direct stimulation of the brain is attenuated by prediction in a similar manner. Transcranial magnetic stimulation (TMS) applied over primary motor cortex can be used to gauge the excitability of the motor system. Motor-evoked potentials (MEPs), elicited by TMS and measured in peripheral muscles, are larger when actions are being prepared and smaller when actions are voluntarily suppressed. We tested whether the amplitude of MEPs was attenuated under circumstances where the TMS pulse can be reliably predicted, even though control of the relevant motor effector was never required. Self-initiation of the TMS pulse and reliable cuing of the TMS pulse both attenuated MEP amplitudes, compared to MEPs generated programmatically in an unpredictable manner. These results suggest that predictive coding may be governed by domain-general mechanisms responsible for all forms predictive learning.

neuroscience↗

bioRxiv2019

Distinct transcriptomic cell types and neural circuits of the subiculum and prosubiculum along the dorsal-ventral axis

Subicular region plays important roles in spatial processing and many cognitive functions and these were mainly attributed to subiculum (Sub) rather than prosubiculum (PS). Using single-cell RNA-sequencing (scRNA-seq) technique we have identified up to 27 distinct transcriptomic clusters/cell types, which were registered to anatomical sub-domains in Sub and PS. Based on reliable molecular markers derived from transcriptomic clustering and in situ hybridization data, the precise boundaries of Sub and PS have been consistently defined along the dorsoventral (DV) axis. Using these borders to evaluate Cre-line specificity and tracer injections, we have found bona fide Sub projections topographically to structures important for spatial processing and navigation. In contrast, PS along DV axis sends its outputs to widespread brain regions crucial for motivation, emotion, reward, stress, anxiety and fear. Brain-wide cell-type specific projections of Sub and PS have also been revealed using specific Cre-lines. These results reveal two molecularly and anatomically distinct circuits centered in Sub and PS, respectively, providing a consistent explanation to historical data and a clearer foundation for future functional studies. HighlightsO_LI27 transcriptomic cell types identified in and spatially registered to "subicular" regions. C_LIO_LIAnatomic borders of "subicular" regions reliably determined along dorsal-ventral axis. C_LIO_LIDistinct cell types and circuits of full-length subiculum (Sub) and prosubiculum (PS). C_LIO_LIBrain-wide cell-type specific projections of Sub and PS revealed with specific Cre-lines. C_LI In BriefDing et al. show that mouse subiculum and prosubiculum are two distinct regions with differential transcriptomic cell types, subtypes, neural circuits and functional correlation. The former has obvious topographic projections to its main targets while the latter exhibits widespread projections to many subcortical regions associated with reward, emotion, stress and motivation.

neuroscience↗

bioRxiv2019

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.

neuroscience↗