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bioRxiv2023

Microsaccades track location-based object rehearsal in visual working memory

Besides controlling eye movements, the brains oculomotor system has been implicated in the control of covert spatial attention and the rehearsal of spatial information in working memory. We investigated whether the oculomotor system also contributes to rehearsing visual objects in working memory when object location is never asked about. To address this, we tracked the incidental use of locations for mnemonic rehearsal via directional biases in microsaccades while participants maintained two visual objects (coloured oriented gratings) in working memory. By varying the stimulus configuration (horizontal, diagonal, and vertical) at encoding, we could quantify whether microsaccades were more aligned with the configurational axis of the memory contents, as opposed to the orthogonal axis. Experiment 1 revealed that microsaccades continued to be biased along the axis of the memory content several seconds into the working-memory delay. In Experiment 2, we confirmed that this directional microsaccade bias was specific to memory demands, ruling out lingering effects from passive and attentive encoding of the same visual objects in the same configurations. Thus, by studying microsaccade directions, we uncover oculomotor-driven rehearsal of visual objects in working memory through their associated locations. SIGNIFICANCE STATEMENTHow humans rehearse information in working memory is a foundational question in psychology and neuroscience. To provide insight into the cognitive and neural bases of working-memory rehearsal, we turned to microsaccades - small eye-movements produced by the brains oculomotor system. We reveal how microsaccades track the locations of visual objects during memory rehearsal, even when object locations are never asked about. This brings three advances. From a psychology standpoint, it demonstrates how memory rehearsal automatically engages object locations. From a neuroscience standpoint, it demonstrates how such location-based rehearsal relies on brain circuitry that also controls our eyes. Finally, from a practical standpoint, it demonstrates how microsaccades can be utilised to track the properties of working-memory rehearsal across space and time.

neuroscience↗

bioRxiv2023

End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging

Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models which can explain neural decoding for intended behaviors are crucial to identify essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data. Our convolutional neural network (CNN)-based end-to-end decoder combined with recurrent neural network (RNN) classifies the behavioral states with high accuracy and robustness to individual differences on temporal scales of sub-seconds. Using the CNN-RNN decoder, we identify that the forelimb and hindlimb areas in the somatosensory cortex significantly contribute to behavioral classification. Our findings imply that the end-to-end approach has the potential to be an interpretable deep learning method with unbiased visualization of critical brain regions. Author SummaryDeep learning is used in neuroscience, and it has become possible to classify and predict behavior from massive data of neural signals from animals, including humans. However, little is known about how deep learning discriminates the features of neural signals. In this study, we perform behavioral classification from calcium imaging data of the mouse cortex and investigate brain regions important for the classification. By the end-to-end approach, an unbiased method without data pre-processing, we clarify that information on the somatosensory areas in the cortex is important for distinguishing between resting and moving states in mice. This study will contribute to the development of interpretable deep-learning technology.

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bioRxiv2023

Resting State Brain Connectivity analysis from EEG and FNIRS signals

Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both shortand longrange organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.

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bioRxiv2023

Site effects depth denoising and signal enhancement using dual-projection based ICA model

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realize the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based denoising methods are the two primary methods used to denoise scanner/site-related effects. Unfortunately, there are challenges with both ICA-based and GLM-based denoising methods to remove site effects completely when the signals of interest and scanner/site-related noises are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove the scanner/site-related effects more completely. This method can separate the signal effects correlated with noise variables from the identified noise effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a traveling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the proposed GLM- and ICA-based denoising methods and our DP-based ICA denoising method. Results show that DP-based ICA

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bioRxiv2023

Human-level reinforcement learning performance of recurrent neural networks is linked to hyperperseveration, not directed exploration

A key feature of animal and human decision-making is to balance the exploration of unknown options for information gain (directed exploration) versus selecting known options for immediate reward (exploitation), which is often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently gained traction in both human and systems neuroscience work on reinforcement learning, due to their ability to show meta-learning of task domains. Here we comprehensively compared the performance of a range of RNN architectures as well as human learners on restless four-armed bandit problems. The best-performing architecture (LSTM network with computation noise) exhibited human-level performance. Computational modeling of behavior first revealed that both human and RNN behavioral data contain signatures of higher-order perseveration, i.e., perseveration beyond the last trial, but this effect was more pronounced in RNNs. In contrast, human learners, but not RNNs, exhibited a positive effect of uncertainty on choice probability (directed exploration). RNN hidden unit dynamics revealed that exploratory choices were associated with a disruption of choice predictive signals during states of low state value, resembling a win-stay-loose-shift strategy, and resonating with previous single unit recording findings in monkey prefrontal cortex. Our results highlight both similarities and differences between exploration behavior as it emerges in meta-learning RNNs, and computational mechanisms identified in cognitive and systems neuroscience work.

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bioRxiv2023

Sex similarities and dopaminergic differences in interval timing

Rodent behavioral studies have largely focused on male animals, which has limited the generalizability and conclusions of neuroscience research. Working with humans and rodents, we studied sex effects during interval timing that requires participants to estimate an interval of several seconds by making motor responses. Interval timing requires attention to the passage of time and working memory for temporal rules. We found no differences between human females and males in interval timing response times (timing accuracy) or the coefficient of variance of response times (timing precision). Consistent with prior work, we also found no differences between female and male rodents in timing accuracy or precision. In female rodents, there was no difference in interval timing between estrus and diestrus cycle stages. Because dopamine powerfully affects interval timing, we also examined sex differences with drugs targeting dopaminergic receptors. In both female and male rodents, interval timing was delayed after administration of sulpiride (D2-receptor antagonist), quinpirole (D2-receptor agonist), and SCH-23390 (D1-receptor antagonist). By contrast, after administration of SKF-81297 (D1-receptor agonist), interval timing shifted earlier only in male rodents. These data illuminate sex similarities and differences in interval timing. Our results have relevance for rodent models of both cognitive function and brain disease by increasing represenation in behavioral neuroscience.

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bioRxiv2023

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

Functional magnetic resonance imaging (fMRI) is a methodological cornerstone of neuroscience. Most studies measure blood-oxygen-level-dependent (BOLD) signal using echo-planar imaging (EPI), Cartesian sampling, and image reconstruction with a one-to-one correspondence between the number of acquired volumes and reconstructed images. However, EPI schemes are subject to trade-offs between spatial and temporal resolutions. We make strides in overcoming these limitations by measuring BOLD with a gradient recalled echo (GRE) with a 3D radial-spiral phyllotaxis trajectory at a high sampling rate (28.49ms) on standard 3T field strength. The framework enables the reconstruction of 3D signal time courses with whole-brain coverage at simultaneously higher nominal spatial (1mm3) and temporal (up to 250ms) resolutions, as compared to optimized EPI schemes. Additionally, we apply motion correction directly to the k-space raw data, enabling flexible motion-corrected reconstructions; the desired temporal resolution to observe hemodynamic responses can be chosen after scanning. By showing activation in the calcarine sulcus of 20 participants completing an ON-OFF visual paradigm, we demonstrate the reliability of our method for applications in cognitive neuroscience research.

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bioRxiv2023

Chronotate: An open-source tool for manual timestamping and quantification of animal behavior

A core necessity to behavioral neuroscience research is the ability to accurately measure performance on behavioral assays, such as the novel object location and novel object recognition tasks. These tasks are widely used in neuroscience research and measure a rodents instinct for investigating novel features as a proxy to test their memory of a previous experience. Automated tools for scoring behavioral videos can be cost prohibitive and often have difficulty distinguishing between active investigation of an object and simply being in close proximity to an object. As such, many experimenters continue to rely on hand scoring interactions using stopwatches which makes it difficult to review scoring after-the-fact and results in the loss of temporal information. Here, we introduce Chronotate, a free, open-source tool to aid in manually scoring novel object behavior videos. The software consists of an interactive video player with keyboard integration for marking timestamps of behavioral events during video playback, making it simple to quickly score and review bouts of rodent-object interaction. In addition, Chronotate outputs detailed interaction bout data, allowing for nuanced behavioral performance analyses. Using this detailed temporal information, we demonstrate that novel object location performance peaks within the first 3 seconds of interaction time and preference for the novel location becomes reduced across the test session. Thus, Chronotate can be used to determine the temporal structure of interactions on this task and can provide new insight into the memory processes that drive this behavior. Chronotate is available for download at: https://github.com/ShumanLab/Chronotate.

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bioRxiv2023

conn2res: A toolbox for connectome-based reservoir computing

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses nonlinear dynamics of high-dimensional dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary architectures and arbitrary dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from simple spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

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bioRxiv2023

Directed networks and resting-state effective brain connectivity with state-space reconstruction using reservoir computing causality

Causality theory is a complex field involving philosophy, mathematics, and computer science. It relies on the temporal precedence of cause over a consequence or unidirectional propagation of changes. Despite these simple principles, normative modeling of causal relationships is conceptually and computationally challenging. Model-free approaches provide insights into large, complex, and chaotic networks, but suffer from false positive and false negative inferences caused by meaningless statistical and temporal correlations. Machine learning advancements have extended these data-driven methods to nonlinear systems, yet inherited similar drawbacks as linear approaches. Interestingly, newer proposals within this model-free paradigm reverse the temporal precedence using the internal structure of the driven variable to recover information from the driving one. Efficient machine learning models combined with these state space reconstruction methods automate part of the process, potentially reducing inductive biases during training and inference. However, their translation into neuroscience, especially neuroimaging, is limited due to complex interpretations and a lack of systematic analyses of the results. Here, we exploited these methods combining them with normative analyses to reconstruct chaotic relationships and networks emerging from neuroimaging data. We validated the proposed scores with a chaotic yet solved system and rebuilt brain networks both in synthetic and real scenarios. We compared our method and heuristics with well-established alternatives providing a comprehensive and transparent benchmark. We obtained higher accuracies and reduced false inferences compared to Granger causality in tasks with known ground truth. When tested to unravel directed influences in brain networks meaningful predictions were found to exist between nodes from the default mode network. The presented framework explores reservoir computing for causality detection, offering a conceptual detour from traditional premises and has the potential to provide theoretical guidance opening perspectives for studying cognition and neuropathologies. Author summaryIn sciences, reliable methods to distinguish causes from consequences are crucial. Despite some progress, researchers are often unsatisfied with the current understanding of causality modeling and its predictions. In neuroscience, causality detection requires imposing world models or assessing statistical utility to predict future values. These approaches, known as model-based and model-free, have advantages and drawbacks. A recent model-free approach augmented with artificial networks tries to autonomously explore the internal structure of the system, (i.e, the state space), to identify directed predictions from consequences to causes but not the other way around. This has not been extensively studied in large networks nor in the human brain, and systematic attempts to reveal its capabilities and inferences are lacking. Here, the proposal is expanded to large systems and further validated in chaotic systems, challenging neuronal simulations, and networks derived from real brain activity. Although the manuscript does not claim true causality, it presents new ideas in the context of current trends in data-driven causality theory. Directed networks encoding causality are hypothesized to contain more information than correlation-based relationships. Hence, despite its evident difficulties, causality detection methods can hold the key to new and more precise discoveries in brain health and disease.

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bioRxiv2023

Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses

Antibody-based imaging techniques rely on reagents whose performance may be application-specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique. Significance StatementArray tomography (AT) is a powerful volume microscopy technique for high-dimensional analysis of complex protein populations in cells and organelles, including synapses. AT involves the use of ultrathin serial sections embedded in resin and subjected to multiple rounds of immunofluorescence antibody (Ab) labeling and imaging. AT relies on antibody-based detection of proteins but because commercial antibodies are typically validated for other applications they often fail for AT. To identify antibodies with high probability of success in AT we developed a novel screening strategy and used this to create a comprehensive database of AT-validated antibodies for neuroscience.

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bioRxiv2023

Learning better with Dale's Law: A Spectral Perspective

Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dales Law, which implies that neurons must be excitatory (E) or inhibitory (I). Dales Law is generally absent from RNNs because simply partitioning a standard networks units into E and I populations impairs learning. However, here we extend a recent feedforward bio-inspired EI network architecture, named Dales ANNs, to recurrent networks, and demonstrate that good performance is possible while respecting Dales Law. This begs the question: What makes some forms of EI network learn poorly and others learn well? And, why does the simple approach of incorporating Dales Law impair learning? Historically the answer was thought to be the sign constraints on EI network parameters, and this was a motivation behind Dales ANNs. However, here we show the spectral properties of the recurrent weight matrix at initialisation are more impactful on network performance than sign constraints. We find that simple EI partitioning results in a singular value distribution that is multimodal and dispersed, whereas standard RNNs have an unimodal, more clustered singular value distribution, as do recurrent Dales ANNs. We also show that the spectral properties and performance of partitioned EI networks are worse for small networks with fewer I units, and we present normalised SVD entropy as a measure of spectrum pathology that correlates with performance. Overall, this work sheds light on a long-standing mystery in neuroscience-inspired AI and computational neuroscience, paving the way for greater alignment between neural networks and biology.

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bioRxiv2023

Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales--from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

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bioRxiv2023

Generating realistic neurophysiological time series with denoising diffusion probabilistic models

In recent years, deep generative models have had a profound impact in engineering and sciences, revolutionizing domains such as image and audio generation, as well as advancing our ability to model scientific data. In particular, Denoising Diffusion Probabilistic Models (DDPMs) have been shown to accurately model time series as complex high-dimensional probability distributions. Experimental and clinical neuroscience also stand to benefit from this progress, since accurate modeling of neurophysiological time series, such as electroencephalography (EEG), electrocorticography (ECoG), and local field potential (LFP) recordings, and their synthetic generation can enable or improve a variety of neuroscientific applications. Here, we present a method for modeling multi-channel and densely sampled neurophysiological recordings using DDPMs, which can be flexibly applied to different recording modalities and experimental configurations. First, we show that DDPMs can generate realistic synthetic data for a variety of datasets including different recording techniques (LFP, ECoG, EEG) and species (rat, macaque, human). DDPM-generated time series accurately capture single- and multi-channel statistics such as frequency spectra and phase-amplitude coupling, as well as fine-grained and dataset-specific features such as sharp wave-ripples. In addition, synthetic time series can be generated based on additional information like experimental conditions or brain states. We demonstrate the utility and flexibility of DDPMs in several neuroscience-specific analyses, such as brain-state classification and imputation of missing channels to improve neural decoding. In summary, DDPMs can serve as accurate generative models of neurophysiological recordings, and have a broad utility in the probabilistic generation of synthetic time series for neuroscientific applications.

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bioRxiv2023

Neuronal Population Statistics, Computations, and Mechanisms of a Feedforward Convolutional Neural Network that Learns to Covertly Attend

Covert visual attention allows the brain to select different regions of the visual world without eye movements. Predictive cues of a target location orient covert attention and improve perceptual performance. In most computational models, researchers explicitly incorporate an attentional mechanism that alters processing at the attended location (gain, noise reduction, divisive normalization, biased competition, Bayesian priors). Here, we assess the emergent neuronal mechanisms of Convolutional Neural Networks (CNNs) that exhibit behavioral signatures of covert attention, despite lacking a built-in attention mechanism. We use neuroscience-inspired approaches to analyze 1.8M units of CNNs trained on the cueing paradigm. Consistent with neurophysiology, we show early layers with retinotopic neurons separately tuned to the target or cue, and later layers with neurons with joint tuning and increased cue influence on target responses. CNN computational stages mirror a Bayesian ideal observer (BIO), but with more gradual transitions. The cue influences the target sensitivity through four mechanisms. A BIO-like cue-weighted location summation, and three mechanisms absent in the BIO: an opponency across locations, a summation/opponency location combination, and interaction with the thresholding Rectified Linear Unit (ReLU). Re-analyses of mices superior colliculus neuronal activity during a cueing task show CNN-predicted but previously unreported cue-inhibitory, location- summation, and location-opponent cells in addition to the commonly reported cue (attention) excitatory cells. The novel single-unit CNN analysis approach establishes a likely system-wide characterization mediating covert attention and a framework to identify new neuron types and emergent computational mechanisms contributing to perceptual behavior. Significance StatementCues predictive of a target location orient covert attention and improve perceptual performance. Studies have focused on how attention influences neural activity, but how cues activate attention and how entire neuronal populations result in behavioral signatures of attention is not understood. Using neuroscience-inspired approaches to characterize properties of 1.8M CNN neurons, we predict new neuron types and emergent mechanisms. A re-analysis of mices superior colliculus activity shows new neuron types predicted by the CNN. The findings might explain how organisms, from primates and crows to insects, show behavioral signatures of covert attention using a variety of cue-target-location neuronal properties. The new CNN single-neuron analysis can guide neurophysiological studies to identify potential new neuron types and their computational contributions to perception.

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bioRxiv2023

AngoraPy: A Python Toolkit For Modelling Anthropomorphic Goal-Driven Sensorimotor Systems

AO_SCPLOWBSTRACTC_SCPLOWGoal-driven deep learning is increasingly used to supplement classical modeling approaches in computational neuroscience. The strength of deep neural networks lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing. Whereas goal-driven modeling is becoming increasingly common in perception neuroscience, its application to sensorimotor control is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. To mitigate this hurdle, we introduce AngoraPy, a modeling library that provides researchers with the tools to train complex recurrent convolutional neural networks that model sensorimotor systems.

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bioRxiv2023

Long-term human organotypic brain slice cultures: a detailed protocol to provide a comprehensive framework for single-neuron and neuronal network investigations

BackgroundThe investigation of the human brain at cellular and microcircuit level remains challenging due to the fragile viability of neuronal tissue, inter- and intra-variability of the samples and limited availability of human brain material. New methodHere, we present an optimized work-up to use resected tissue from brain surgeries for live cell experiments in vitro. Comparison with existing methodsWe provide a reworked, detailed protocol of the production, culturing and viral transduction of human organotypic brain slice cultures for research purposes. ResultsWe highlight the critical pitfalls of the culturing process of the human brain tissue and present results on viral expression, single-cell Patch-Clamp recordings, as well as multi-electrode array recordings over a prolonged period of time. Additionally, our statistics show that brain tissue from patients of any age and morbidity can be used for organotypic brain slice cultures if carefully selected. ConclusionsOrganotypic brain slice cultures are of great value for basic neuroscience and disease modeling over a time course of three weeks. HighlightsO_LILong-term human organotypic brain slice cultures are viable for 2-3 weeks and provide a framework for basic neuroscience and disease modeling C_LIO_LIWe provide a reworked, detailed protocol for organotypic human brain slice culture production and maintenance C_LIO_LIWe show results of long-term culturing of human organotypic brain slice cultures in terms of viral transduction, whole-cell Patch-Clamp recordings and multi-electrode array recordings C_LIO_LIStatistics of 16 surgeries show the correlation between the overall success rates of efficient culturing and viral transduction with the patient age and morbidity C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/561508v1_fig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@17c985aorg.highwire.dtl.DTLVardef@f651fdorg.highwire.dtl.DTLVardef@180d32eorg.highwire.dtl.DTLVardef@a896b5_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1, Graphical abstract.C_FLOATNO Schematic overview of preparation, maintenance and experimental workup of human organotypic brain slice cultures. After surgical resection, human brain tissue is prepared, sliced and cultured at air-liquid-interface between human cerebrospinal fluid and defined incubator atmosphere, allowing for week-long viability. This enables extensive experimental workup such as viral transduction, single cell and multi-electrode array electrophysiological recordings. C_FIG

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bioRxiv2023

From connectome to effectome: learning the causal interaction map of the fly brain

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons--thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

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