Accelerating the continuous community sharing of digital neuromorphology data.

The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 h to 2 min. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.

Accelerating the continuous community sharing of digital neuromorphology data.

The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 hours to 2 minutes. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Automatic identification of scientific publications describing digital reconstructions of neural morphology.

The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects.

Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus.

Hippocampal area CA3 performs the critical auto-associative function underlying pattern completion in episodic memory. Without external inputs, the electrical activity of this neural circuit reflects the spontaneous spiking interplay among glutamatergic pyramidal neurons and GABAergic interneurons. However, the network mechanisms underlying these resting-state firing patterns are poorly understood. Leveraging the Hippocampome.org knowledge base, we developed a data-driven, large-scale spiking neural network (SNN) model of mouse CA3 with 8 neuron types, 90,000 neurons, 51 neuron-type specific connections, and 250,000,000 synapses. We instantiated the SNN in the CARLsim4 multi-GPU simulation environment using the Izhikevich and Tsodyks-Markram formalisms for neuronal and synaptic dynamics, respectively. We analyzed the resultant population activity upon transient activation. The SNN settled into stable oscillations with a biologically plausible grand-average firing frequency, which was robust relative to a wide range of transient activation. The diverse firing patterns of individual neuron types were consistent with existing knowledge of cell type-specific activity in vivo. Altered network structures that lacked neuron- or connection-type specificity were neither stable nor robust, highlighting the importance of neuron type circuitry. Additionally, external inputs reflecting dentate mossy fibers shifted the observed rhythms to the gamma band. We freely released the CARLsim4-Hippocampome framework on GitHub to test hippocampal hypotheses. Our SNN may be useful to investigate the circuit mechanisms underlying the computational functions of CA3. Moreover, our approach can be scaled to the whole hippocampal formation, which may contribute to elucidating how the unique neuronal architecture of this system subserves its crucial cognitive roles.

Automatic identification of scientific publications describing digital reconstructions of neural morphology.

Motivation: The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. Results: The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects. Availability: https://github.com/Joindbre/TextRelevancy. Contact: ascoli@gmu.edu. Supplementary information: Supplementary information, tool installation, and API usage are available at https://docs.joindbre.com.

Impaired rate-dependent depression of the H-reflex in type-2 diabetes, prediabetes, overweight and obesity: A cross-sectional study.

Type-2 diabetes is a chronic metabolic disorder characterized by hyperglycemia, resulting from deficits in insulin secretion or insulin resistance. According to the International Diabetes Federation, approximately 463 million people suffered from this condition in 2019, with a rapidly increasing impact in low-and middle-income countries. Obesity is a well-known risk factor for diabetes, and current data project a continuous increase in diabetes prevalence worldwide in obese individuals. Among the common complications, diabetic peripheral neuropathy (DPN) causes sensory symptoms, including pain that contributes to foot ulceration, and if not controlled, limb amputation may occur. The diagnosis of DPN is a clinical problem. Rate-dependent depression (RDD) of the Hoffmann reflex in the lower limbs has been proposed as a test to determine the presence of neuropathic pain in subjects with type-1 and type-2 diabetes. Recently, impaired RDD has been described in obese and diabetic rodent models. In this study, we characterized the RDD by evaluating the H-reflex at 0.2, 1, 2, 5, and 10 Hz in 39 patients with type-2 Diabetes mellitus (T2DM) and 42 controls without diabetes, subsequently classified as overweight/obese and prediabetic. A significant decrease in the RDD of the H-reflex was found in T2DM subjects at 1, 2, 5, and 10 Hz (P < .001) stimulation frequencies compared to controls, but not at 0.2 Hz (P = .48). A major finding of this study is that impaired RDD was also found in 11/25 overweight and obese subjects in at least 2 stimulation frequencies, being 10 of those classified in prediabetic levels according to their HbA1C values. The RDD of the H-reflex could be used as a quantitative and sensitive tool to study T2DM subpopulations with peripheral neuropathy. RDD could be used as a screening tool in combination with clinical tests to diagnose DPN and evaluate the progression of this condition.

A Method for Estimating the Potential Synaptic Connections Between Axons and Dendrites From 2D Neuronal Images.

Computational neuroscience aims to model, reproduce, and predict network dynamics for different neuronal ensembles by distilling knowledge derived from electrophysiological and morphological evidence. However, analyses and simulations often remain critically limited by the sparsity of direct experimental constraints on essential parameters, such as electron microscopy and electrophysiology pair/multiple recording evidence of connectivity statistics. Notably, available data are particularly scarce regarding quantitative information on synaptic connections among identified neuronal types. Here, we present a user-friendly data-driven pipeline to estimate connection probabilities, number of contacts per connected pair, and distances from the pre- and postsynaptic somas along the axonal and dendritic paths from commonly available two-dimensional tracings and other broadly accessible measurements. The described procedure does not require any computational background and is accessible to all neuroscientists. This protocol therefore fills the important gap from neuronal morphology to circuit organization and can be applied to many different neural systems, brain regions, animal species, and data sources. Graphic abstract: The processing protocol from 2D reconstructions to quantitated synaptic connections.

Comprehensive Estimates of Potential Synaptic Connections in Local Circuits of the Rodent Hippocampal Formation by Axonal-Dendritic Overlap.

A quantitative description of the hippocampal formation synaptic architecture is essential for understanding the neural mechanisms of episodic memory. Yet the existing knowledge of connectivity statistics between different neuron types in the rodent hippocampus only captures a mere 5% of this circuitry. We present a systematic pipeline to produce first-approximation estimates for most of the missing information. Leveraging the www.Hippocampome.org knowledge base, we derive local connection parameters between distinct pairs of morphologically identified neuron types based on their axonal-dendritic overlap within every layer and subregion of the hippocampal formation. Specifically, we adapt modern image analysis technology to determine the parcel-specific neurite lengths of every neuron type from representative morphologic reconstructions obtained from either sex. We then compute the average number of synapses per neuron pair using relevant anatomic volumes from the mouse brain atlas and ultrastructurally established interaction distances. Hence, we estimate connection probabilities and number of contacts for >1900 neuron type pairs, increasing the available quantitative assessments more than 11-fold. Connectivity statistics thus remain unknown for only a minority of potential synapses in the hippocampal formation, including those involving long-range (23%) or perisomatic (6%) connections and neuron types without morphologic tracings (7%). The described approach also yields approximate measurements of synaptic distances from the soma along the dendritic and axonal paths, which may affect signal attenuation and delay. Overall, this dataset fills a substantial gap in quantitatively describing hippocampal circuits and provides useful model specifications for biologically realistic neural network simulations, until further direct experimental data become available.SIGNIFICANCE STATEMENT The hippocampal formation is a crucial functional substrate for episodic memory and spatial representation. Characterizing the complex neuron type circuit of this brain region is thus important to understand the cellular mechanisms of learning and navigation. Here we present the first numerical estimates of connection probabilities, numbers of contacts per connected pair, and synaptic distances from the soma along the axonal and dendritic paths, for more than 1900 distinct neuron type pairs throughout the dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. This comprehensive dataset, publicly released online at www.Hippocampome.org, constitutes an unprecedented quantification of the majority of the local synaptic circuit for a prominent mammalian neural system and provides an essential foundation for data-driven, anatomically realistic neural network models.

TrkB-mediated activation of the phosphatidylinositol-3-kinase/Akt cascade reduces the damage inflicted by oxygen-glucose deprivation in area CA3 of the rat hippocampus.

The selective vulnerability of hippocampal area CA1 to ischemia-induced injury is a well-known phenomenon. However, the cellular mechanisms that confer resistance to area CA3 against ischemic damage remain elusive. Here, we show that oxygen-glucose deprivation-reperfusion (OGD-RP), an in vitro model that mimic the pathological conditions of the ischemic stroke, increases the phosphorylation level of tropomyosin receptor kinase B (TrkB) in area CA3. Slices preincubated with brain-derived neurotrophic factor (BDNF) or 7,8-dihydroxyflavone (7,8-DHF) exhibited reduced depression of the electrical activity triggered by OGD-RP. Consistently, blockade of TrkB suppressed the resistance of area CA3 to OGD-RP. The protective effect of TrkB activation was limited to area CA3, as OGD-RP caused permanent suppression of CA1 responses. At the cellular level, TrkB activation leads to phosphorylation of the accessory proteins SHC and Gab as well as the serine/threonine kinase Akt, members of the phosphoinositide 3-kinase/Akt (PI-3-K/Akt) pathway, a cascade involved in cell survival. Hence, acute slices pretreated with the Akt antagonist MK2206 in combination with BDNF lost the capability to resist the damage inflicted with OGD-RP. Consistently, with these results, CA3 pyramidal cells exhibited reduced propidium iodide uptake and caspase-3 activity in slices pretreated with BDNF and exposed to OGD-RP. We propose that PI-3-K/Akt downstream activation mediated by TrkB represents an endogenous mechanism responsible for the resistance of area CA3 to ischemic damage.

Aging-related impairments of hippocampal mossy fibers synapses on CA3 pyramidal cells.

The network interaction between the dentate gyrus and area CA3 of the hippocampus is responsible for pattern separation, a process that underlies the formation of new memories, and which is naturally diminished in the aged brain. At the cellular level, aging is accompanied by a progression of biochemical modifications that ultimately affects its ability to generate and consolidate long-term potentiation. Although the synapse between dentate gyrus via the mossy fibers (MFs) onto CA3 neurons has been subject of extensive studies, the question of how aging affects the MF-CA3 synapse is still unsolved. Extracellular and whole-cell recordings from acute hippocampal slices of aged Wistar rats (34 ± 2 months old) show that aging is accompanied by a reduction in the interneuron-mediated inhibitory mechanisms of area CA3. Several MF-mediated forms of short-term plasticity, MF long-term potentiation and at least one of the critical signaling cascades necessary for potentiation are also compromised in the aged brain. An analysis of the spontaneous glutamatergic and gamma-aminobutyric acid-mediated currents on CA3 cells reveal a dramatic alteration in amplitude and frequency of the nonevoked events. CA3 cells also exhibited increased intrinsic excitability. Together, these results demonstrate that aging is accompanied by a decrease in the GABAergic inhibition, reduced expression of short- and long-term forms of synaptic plasticity, and increased intrinsic excitability.

Chronic administration of the neurotrophic agent cerebrolysin ameliorates the behavioral and morphological changes induced by neonatal ventral hippocampus lesion in a rat model of schizophrenia.

Neonatal ventral hippocampal lesion (nVHL) in rats has been widely used as a neurodevelopmental model to mimic schizophrenia-like behaviors. Recently, we reported that nVHLs result in dendritic retraction and spine loss in prefrontal cortex (PFC) pyramidal neurons and medium spiny neurons of the nucleus accumbens (NAcc). Cerebrolysin (Cbl), a neurotrophic peptide mixture, has been reported to ameliorate the synaptic and dendritic pathology in models of aging and neurodevelopmental disorder such as Rett syndrome. This study sought to determine whether Cbl was capable of reducing behavioral and neuronal alterations in nVHL rats. The behavioral analysis included locomotor activity induced by novel environment and amphetamine, social interaction, and sensoriomotor gating. The morphological evaluation included dendritic analysis by using the Golgi-Cox procedure and stereology to quantify the total cell number in PFC and NAcc. Behavioral data show a reduction in the hyperresponsiveness to novel environment- and amphetamine-induced locomotion, with an increase in the total time spent in social interactions and in prepulse inhibition in Cbl-treated nVHL rats. In addition, neuropathological analysis of the limbic regions also showed amelioration of dendritic retraction and spine loss in Cbl-treated nVHL rats. Cbl treatment also ameliorated dendritic pathology and neuronal loss in the PFC and NAcc in nVHL rats. This study demonstrates that Cbl promotes behavioral improvements and recovery of dendritic neuronal damage in postpubertal nVHL rats and suggests that Cbl may have neurotrophic effects in this neurodevelopmental model of schizophrenia. These findings support the possibility that Cbl has beneficial effects in the management of schizophrenia symptoms.