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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Polymer Implantable Electrode Foundry: A shared resource for manufacturing polymer-based microelectrodes for neural interfaces

Large scale monitoring of neural activity at the single unit level can be achieved via electrophysiological recording using implanted microelectrodes. While neuroscience researchers have widely employed chronically implanted electrode-based interfaces for this purpose, a commonly encountered limitation is loss of highly resolved signals arising from immunological response over time. Next generation electrode-based interfaces improve longitudinal signal quality using the strategy of stabilizing the device-tissue interface with microelectrode arrays constructed from soft and flexible polymer materials. The limited availability of such polymer microelectrode arrays has restricted access to a small number of researchers able to build their own custom devices or who have developed specific collaborations with engineering researchers who can produce them. Here, a new technology resource model is introduced that seeks to widely increase access to polymer microelectrode arrays by the neuroscience research community. The Polymer Implantable Electrode (PIE) Foundry provides custom and standardized polymer microelectrode arrays as well as training and guidance on best-practices for implantation and chronic experiments.

neuroscience↗

bioRxiv2023

Preserving functional network structure under anesthesia in the marmoset monkey brain

Initiatives towards acquiring large-scale neuroimaging data in non-human primates promise improving translational neuroscience and cross-species comparisons. Crucial among these efforts is the need to expand sample sizes while reducing the impact of anesthesia on the functional properties of brain networks. Yet, the effects of anesthesia on non-human primate brain networks remains unclear. Here, we demonstrate that isoflurane anesthesia induces a variety of brain states in the marmoset brain with dramatically altered functional connectivity profiles. As an alternative, we recommend using a continuous infusion of the sedative medetomidine, supplemented with a low concentration of isoflurane. With this protocol, we observed robust visual activation during flickering light stimulation and identified resting-state networks similar to the awake state. In contrast, isoflurane alone led to a suppressed visual activation and the absence of awake-like network patterns. Comparing states using a graph-theoretical approach, we confirmed that the structure of functional networks is preserved under our proposed anesthesia protocol but is lost using isoflurane alone. We believe that the widespread adoption of this protocol will be a step towards advancing translational neuroscience initiatives in non-human primate neuroimaging. To promote the shared use of neuroimaging resources, we share our datasets on the Marmoset connectome project. HighlightsO_LIMedetomidine combined with isoflurane is an effective protocol for driving visual BOLD responses compared to isoflurane-only anesthesia. C_LIO_LIIndependent component analysis revealed similar resting-state networks for medetomidine-isoflurane anesthesia and awake states. C_LIO_LIThe structure of resting-state networks was maintained under medetomidine-isoflurane but lost under isoflurane-only anesthesia. C_LI

neuroscience↗

bioRxiv2023

Isolating neural signatures of conscious speech perception with a no-report sine-wave speechparadigm

Identifying neural correlates of conscious perception is a fundamental endeavor of cognitive neuroscience. Most studies so far have focused on visual awareness along with trial-by-trial reports of task relevant stimuli, which can confound neural measures of perceptual awareness with post-perceptual processing. Here, we used a three-phase sine-wave speech paradigm that dissociated between conscious speech perception and task relevance while recording EEG in humans of both sexes. Compared to tokens perceived as noise, physically identical sine-wave speech tokens that were perceived as speech elicited a left-lateralized, near-vertex negativity, which we interpret as a phonological version of a perceptual awareness negativity. This response appeared between 200 and 300 ms after token onset and was not present for frequency-flipped control tokens that were never perceived as speech. In contrast, the P3b elicited by task-irrelevant tokens did not significantly differ when the tokens were perceived as speech versus noise, and was only enhanced for tokens that were both perceived as speech and relevant to the task. Our results extend the findings from previous studies on visual awareness and speech perception, and suggest that correlates of conscious perception, across types of conscious content, are most likely to be found in mid-latency negative-going brain responses in content-specific sensory areas. Significance StatementHow patterns of brain activity give rise to conscious perception is a fundamental question of cognitive neuroscience. Here, we asked whether markers of conscious speech perception can be separated from task-related confounds. We combined sine-wave speech - a degraded speech signal that is heard as noise by naive individuals but can readily be heard as speech after minimal training - with a no-report paradigm that independently manipulated perception (speech versus non-speech) and task (relevant versus irrelevant). Using this paradigm, we were able to identify a marker of speech perception in mid-latency responses over left frontotemporal EEG channels that was independent of task. Our results demonstrate that the "perceptual awareness negativity" is present for a new type of perceptual content (speech).

neuroscience↗

bioRxiv2023

Spike synchrony as a measure of Gestalt structure

The function of spike synchrony has long been debated in the neuroscience community. Some researchers view synchrony as a byproduct of brain activity, while others argue that it serves as a mechanism for binding perceptual features. We, however, argue for an alternative view on the computational role of synchrony: it can serve as a mechanism for estimating the prior probability of incoming stimuli. In V1, this probability can be estimated by comparing the input stimulus with previously acquired visual experience. Such experience is encoded in horizontal intracortical connections, as they are subject to change, learn, and adapt to incoming visual stimuli throughout the course of life. Thus, the V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on the synchrony of neuronal firings in V1. In our work, we used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found significant differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation. Author SummaryNeurons in the brain fire synchronously in different contexts, in response to different stimuli. However, the function of synchrony is very debatable in the neuroscience community. We suggest that synchrony plays an important role in brain computations: it can estimate how well a network is familiar with the incoming stimulus. We tested our hypothesis on the simulation of neuronal activity in a model of primary visual cortex - a brain region which is processing simple visual features, such as lines of different angle orientation. The connections between neurons in this brain area store the acquired visual experience in the form of its statistics, such as edge cooccurrences. We built the connectivity in our model in accordance with principles of connectivity in primary visual cortex, assuming that it reflects natural images statistics. We tested our model on artificial stimuli, which represented more and less experienced visual features. Our results showed that spike synchrony is a global phenomenon which emerges across the large parts of the network, and it reflects the familiarity of the network with the stimulus. Thus, it can serve as a form of recognition memory and has a potential for usage in various applications which require estimation of input familiarity.

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bioRxiv2023

The multiscale topological organization of the functional brain network in adolescent PTSD

The experience of an extremely aversive event can produce enduring deleterious behavioral and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. In this work, we aim to study the whole-cortex functional organization of adolescents with PTSD without the a priori selection of specific regions of interest or functional networks. To do so, we built on the network neuroscience framework and specifically on multisubject community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), an increased coupling between regions belonging to unimodal (sensory) regions and a reduced coupling between transmodal (association) regions in the adolescent PTSD group. These results open up an intriguing possibility concerning an altered large-scale cortical organization in adolescent PTSD. Significance StatementThe understanding of brain responses following the experience of a traumatic event and the eventual apparition of posttraumatic stress disorder (PTSD) symptoms during youth remains an active topic of research in clinical neuroscience. We adapted a multisubject community detection algorithm to study the functional brain organization of PTSD from a whole cortex and multiscale topological perspective, thereby taking into account the complex (network) organization of the brain. PTSD patients and control subjects presented a different whole cortex functional organization that remained present across topological scales. PTSD patients showed a decreased interaction in transmodal cortices and, inversely, an enhanced interaction in unimodal cortices. This investigation highlights the importance of studying the brain from a complex and whole cortex perspective.

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bioRxiv2023

Perceptual Foundation and Extension to Phase Tagging for Rapid Invisible Frequency Tagging (RIFT)

Recent years have seen the emergence of a visual stimulation protocol called Rapid Invisible Frequency Tagging (RIFT) in cognitive neuroscience. In RIFT experiments, visual stimuli are presented at a rapidly and sinusoidally oscillating luminance, using high refresh rate projection equipment. Such stimuli result in strong steady-state responses in visual cortex, measurable extracranially using EEG or MEG. The high signal-to-noise ratio of these neural signals, combined with the alleged invisibility of the manipulation, make RIFT a potentially promising technique to study the neural basis of visual processing. In this study, we set out to resolve two fundamental, yet still outstanding, issues regarding RIFT; as well as to open up a new avenue for taking RIFT beyond frequency tagging per se. First, we provide robust evidence that RIFT is indeed subjectively undetectable, going beyond previous anecdotal reports. Second, we demonstrate that full-amplitude luminance or contrast manipulation offer the best tagging results. Third and finally, we demonstrate that, in addition to frequency tagging, phase tagging can reliably be used in RIFT studies, opening up new avenues for constructing RIFT experiments. Together, this provides a solid foundation for using RIFT in visual cognitive neuroscience.

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bioRxiv2024

Decoding of resting-state using task-based multivariate pattern analysis supports the Incentive-Sensitization Theory in nicotine use disorder

BackgroundThe Incentive-Sensitization Theory postulates that addiction is primarily driven by the sensitization of the brains reward system to addictive substances, such as nicotine. According to this theory, exposure to such substances leads to an increase in wanting, while liking the experience remains relatively unchanged. Although this candidate mechanism has been well substantiated through animal brain research, its translational validity for humans has only been partially demonstrated so far, with evidence from human neuroscience data being very limited. MethodsFrom fMRI data of N=31 individuals with Nicotine Use Disorder, we created multivoxel patterns capable of capturing wanting and liking-related dimensions from a smoking cue-reactivity task. Using these patterns, we then designed a novel resting-state reading method to evaluate how much wanting or liking still persist as a neural trace after watching the cues. ResultsWe found that the persistence of wanting-related brain patterns at rest increases with longer smoking history but this was not the case for liking-related patterns. Interestingly, such behavior has not been observed for non-temporal measures of smoking intensity. ConclusionThis study provides basic human neuroscience evidence that the dissociation between liking and wanting escalates over time, further substantiating the Incentive-Sensitization Theory, at least for Nicotine Use Disorder. These results suggest that treatment approaches could be personalized to account for the variability in individuals neural adaptation to addiction by considering how individuals differ in the extent to which their incentive salience system is sensitized.

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bioRxiv2024

AnNoBrainer, an Automated Annotation of Mouse Brain Images using Deep Learning

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that less rigorous, less time-consuming methods of histopathological assessment are employed by non-pathologists, especially for early discovery and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ([~]50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts histopathological assessment standards (>85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

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bioRxiv2024

Development of a Marmoset Apparatus for Automated Pulling (MarmoAAP) to Study Cooperative Behaviors

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

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bioRxiv2024

Neural Manifold Capacity Captures Representation Geometry, Correlations, and Task-Efficiency Across Species and Behaviors

From an eagle spotting a fish in shimmering water to a scientist extracting patterns from noisy data, many cognitive tasks require untangling overlapping signals. Neural circuits achieve this by transforming complex sensory inputs into distinct, separable representations that guide behavior. Data-visualization techniques convey the geometry of these transformations, and decoding approaches quantify performance efficiency. However, we lack a framework for linking these two key aspects. Here we address this gap by introducing a data-driven analysis framework, which we call Geometry Linked to Untangling Efficiency (GLUE) with manifold capacity theory, that links changes in the geometrical properties of neural activity patterns to representational untangling at the computational level. We applied GLUE to over seven neuroscience datasets--spanning multiple organisms, tasks, and recording techniques--and found that task-relevant representations untangle in many domains, including along the cortical hierarchy, through learning, and over the course of intrinsic neural dynamics. Furthermore, GLUE can characterize the underlying geometric mechanisms of representational untangling, and explain how it facilitates efficient and robust computation. Beyond neuroscience, GLUE provides a powerful framework for quantifying information organization in data-intensive fields such as structural genomics and interpretable AI, where analyzing high-dimensional representations remains a fundamental challenge.

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bioRxiv2024

TRPC6 is a mechanosensitive channel essential for ultrasound neuromodulation in mammalian brain

Ultrasound neuromodulation has become an innovative technology that enables non-invasive intervention in mammalian brain circuits with high spatiotemporal precision. Despite the expanding utility of ultrasound neuromodulation in the neuroscience research field and clinical applications, the molecular and cellular mechanisms by which ultrasound impacts neural activity in the brain are still largely unknown. Here, we report that transient receptor potential canonical 6 (TRPC6), a mechanosensitive non-selective cation channel, is essential for ultrasound neuromodulation of mammalian neurons in vitro and in vivo. We first demonstrated that ultrasound irradiation elicited rapid and robust Ca2+ transients mediated via extracellular Ca2+ influx in cultured mouse cortical and hippocampal neurons. Ultrasound-induced neuronal responses were massively diminished by blocking either the generation of action potential or synaptic transmission. Importantly, both pharmacological inhibition and genetic deficiency of TRPC6 almost completely abolished neuronal responses to ultrasound. Furthermore, we found that intracerebroventricular administration of a TRPC6 blocker significantly attenuated the population of neuronal firings in the cerebral cortex evoked by transcranial ultrasound irradiation in mice. Our findings indicate that TRPC6 is an indispensable molecule of ultrasound neuromodulation in the intact mammalian brains, providing fundamental understanding of biophysical molecular mechanisms of ultrasound neuromodulation as well as insight into its future feasibility in neuroscience and translational researches in humans.

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bioRxiv2024

Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org, converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed, however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure-function relationships. Availability and implementationOur method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers. Key pointsO_LIA plethora of neuronal morphologies is available in a point-and-diameter format, but there are no robust techniques capable of converting these morphologies into geometrically realistic models that can be used to conduct subcellular simulations. C_LIO_LIWe present a scalable method capable of synthesizing high fidelity watertight ultrastructural manifolds of complete neuronal models from their one-dimensional descriptions using the synaptic data obtained from the digitally reconstructed neuronal circuits of the Blue Brain Project. C_LIO_LIResulting manifold models comprise geometrically realistic somata and spine geometries, enabling accurate in silico experiments that can probe intricate structure-function relationships. C_LIO_LIOur method is extensible and can be seamlessly applied to other cellular structures such as astroglial morphologies and even large networks of cerebral vasculature. C_LI

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bioRxiv2024

Tools for Cre-mediated conditional deletion of floxed alleles from developing cerebellar Purkinje cells

The Cre-lox system is an indispensable tool in neuroscience research for targeting gene deletions to specific cellular populations. Here we assess the utility of several transgenic Cre lines, along with a viral approach, for targeting cerebellar Purkinje cells. Using a combination of a fluorescent reporter line (Ai14) to indicate Cre-mediated recombination and a floxed Dystroglycan line (Dag1flox) we show that reporter expression does not always align precisely with loss of protein. The commonly used Pcp2Cre line exhibits a gradual mosaic pattern of Cre recombination in Purkinje cells from P7-P14, while loss of Dag1 protein is not complete until P30. Ptf1aCre drives recombination in precursor cells that give rise to GABAergic neurons in the embryonic cerebellum, including Purkinje cells and molecular layer interneurons. However, due to its transient expression in precursors, Ptf1aCre results in stochastic loss of Dag1 protein in these neurons. NestinCre, which is often described as a "pan-neuronal" Cre line for the central nervous system, does not drive Cre-mediated recombination in Purkinje cells. We identify a Calb1Cre line that drives efficient and complete recombination in embryonic Purkinje cells, resulting in loss of Dag1 protein before the period of synaptogenesis. AAV8-mediated delivery of Cre at P0 results in gradual transduction of Purkinje cells during the second postnatal week, with loss of Dag1 protein not reaching appreciable levels until P35. These results characterize several tools for targeting conditional deletions in cerebellar Purkinje cells at different developmental stages and illustrate the importance of validating the loss of protein following recombination. Significance StatementThe development of Cre lines for targeting gene deletions to defined cellular populations has led to important discoveries in neuroscience. As with any tool, there are inherent limitations that must be carefully considered. Here we describe several Cre lines available for targeting of cerebellar Purkinje cells at various developmental stages. We use the combination of a Cre-dependent fluorescent reporter line and conditional deletion of the synaptic scaffolding molecule Dystroglycan as an example to highlight the potential disconnect between the presence of a fluorescent reporter and the loss of protein.

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bioRxiv2024

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research. Author summaryModel fitting is a widely used method in scientific research. It involves tuning the free parameters of a model until its output best matches the corresponding experimental data. Finding the optimal parameter combination can be a difficult task for more complex models with many unknown parameters, and a large variety of different approaches have been proposed to solve this problem. However, setting up a parameter search task and employing an efficient algorithm for its solution requires considerable technical expertise. We have developed a software framework that helps users solve this task, focusing on the domain of detailed models of single neurons. Our open-source software, called Neuroptimus, has a graphical interface that guides users through the steps of setting up a parameter optimization task, and allows them to select from more than twenty different algorithms to solve the problem. We have also compared the performance of these algorithms on a set of six parameter search tasks that are typical in neuroscience, and identified several algorithms that delivered consistently good performance. Finally, we designed and implemented a website that allows users to view and analyze our results and to add their own results to the database.

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bioRxiv2024

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience investigates the brains connectome, revealing that cognitive functions are underpinned by dynamic neural networks. This study investigates how distinct cognitive abilities--working memory and inhibition--are supported by unique brain network configurations, which are constructed by estimating whole-brain networks through mutual information. The study involved 195 participants who completed the Sternberg Item Recognition and Flanker tasks while undergoing EEG recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. Results indicate that working memory and inhibition are associated with different network attributes, with working memory relying on distributed networks and inhibition on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, as weak connections could potentially lead to a more stable and support networks of memory and inhibition. The findings indirectly support the Network Neuroscience Theory of Intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brains network dynamics. Author summaryThis study analyzes how working memory and inhibition correspond to distinct neural network patterns by constructing whole-brain networks via mutual information from EEG data of 195 subjects performing cognitive tasks. Findings reveal working memory is supported by distributed connections while inhibition depends on segregated ones. The research underscores the importance of both strong and weak neural connections in cognitive function and supports the notion that cognitive functions emerge from the brain network of distinct topology. Moreover, it highlights the need to account for individual and task-specific variations to fully grasp the diverse network dynamics influencing cognitive abilities.

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bioRxiv2024

Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT)

O_LITranscriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". C_LIO_LITo address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. C_LIO_LIAlthough Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/588301v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@d8a5beorg.highwire.dtl.DTLVardef@1697a1aorg.highwire.dtl.DTLVardef@1cf4cf8org.highwire.dtl.DTLVardef@c634dd_HPS_FORMAT_FIGEXP M_FIG C_FIG Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@b8ac07org.highwire.dtl.DTLVardef@2628f8org.highwire.dtl.DTLVardef@14bb803org.highwire.dtl.DTLVardef@1ddbfborg.highwire.dtl.DTLVardef@5567e8_HPS_FORMAT_FIGEXP M_TBL C_TBL

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bioRxiv2024

Beyond cortical geometry: brain dynamics shaped by long-range connections

A fundamental topological principle is that the container always shapes the content. In neuroscience, this translates into how the brain anatomy shapes brain dynamics. From neuroanatomy, the topology of the mammalian brain can be approximated by local connectivity, accurately described by an exponential distance rule (EDR). The compact, folded geometry of the cortex is shaped by this local connectivity and the geometric harmonic modes can reconstruct much of the functional dynamics. However, this ignores the fundamental role of the rare long-range cortical connections, crucial for improving information processing in the mammalian brain, but not captured by local cortical folding and geometry. Here we show the superiority of harmonic modes combining rare long-range connectivity with EDR (EDR+LR) in capturing functional dynamics (specifically long-range functional connectivity and task-evoked brain activity) compared to geometry and EDR representations. Importantly, the orchestration of dynamics is carried out by a more efficient manifold made up of a low number of fundamental EDR+LR modes. Our results show the importance of rare long-range connectivity for capturing the complexity of functional brain activity through a low-dimensional manifold shaped by fundamental EDR+LR modes. Significance StatementExplaining how structure of the brain gives rise to its emerging dynamics is a primary pursuit in neuroscience. We describe a fundamental anatomical constraint that emphasises the key role of rare long-range connections in explaining functional organisation of the brain in terms of spontaneous and task-evoked activity. Specifically, this constraint unifies brain geometry and local connectivity through the Exponential Distance Rule while considering the long-range exceptions to this local connectivity as derived from the structural connectome. In addition, when using this structural information, we show that the task-evoked brain activity is described by a low-dimensional manifold of several modes suggesting that less is more for the efficient information processing in the brain.

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bioRxiv2024

Three-Dimensional Molecular Atlas of Octopus Arm Neuroanatomy Highlights Spatial and Functional Complexity

Octopus arms, notable for their complex anatomy and remarkable flexibility, have sparked significant interest within the neuroscience community. However, there remains a dearth of knowledge about the molecular and functional identities of various cell types in the arms nervous system. To address this gap, we used hybridization chain reaction (HCR) to identify distinct neuronal types in the arms of the pygmy octopus, Octopus bocki, including putative dopaminergic, octopaminergic, serotonergic, GABAergic, glutamatergic, cholinergic, and peptidergic neurons. We obtained high-resolution multiplexed fluorescent images at 0.28x0.28x1.0 M voxel size from 10 arm base and arm tip cross sections (each 50 M thick) and created three-dimensional reconstructions of the axial ganglia, illustrating the spatial distribution of multiple neuronal populations. Our analysis unveiled anatomically distinct and molecularly diverse scattered neurons, while also highlighting multiple populations of dense small excitatory neurons that appear uniformly distributed throughout the cortical layer. Our data provide new insights into how different types of neurons may contribute to the ability of an octopus to interact with its environment and execute complex tasks. In addition, our findings establish a benchmark for future studies, allowing pioneering exploration of octopus arm molecular neuroanatomy, and offering exciting new avenues in invertebrate neuroscience research.

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