neuPrint: An open access tool for EM connectomics

Frontiers in Neuroinformatics, Journal Year: 2022, Volume and Issue: 16

Published: July 20, 2022

Due to advances in electron microscopy and deep learning, it is now practical reconstruct a connectome, description of neurons the chemical synapses between them, for significant volumes neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled downloading data, performance was not serious problem. But new much larger upend these assumptions. These networks contain tens thousands millions connections, with yet pending, are interest large community non-specialists. Allowing other scientists make use this data needs more than publication-it requires tools that publicly available, easy use, efficiently handle data. We introduce neuPrint address analysis challenges. Neuprint contains two major components-a web interface programmer APIs. The designed allow any scientist worldwide, using only browser, quickly ask answer typical biological queries about connectome. APIs computer-savvy complex or higher volume queries. NeuPrint also provides features assessing reconstruction quality. Internally, organizes connectome as graph stored neo4j database. This gives high queries, access though public well documented query language Cypher, will extend future connectomics databases. Our experience an experiment open science. find fraction readers article proceed examine directly. In our case preprints worked exactly intended, inquiries PDF downloads starting immediately after pre-print publication, little affected formal publication later. From we deduce many interested suggesting data-only papers can appreciated release speed up propagation scientific results months. providing, keeping, available online imposes substantial additional costs research.

Language: Английский

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Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales

Current Biology, Journal Year: 2019, Volume and Issue: 30(1), P. 70 - 82.e4

Published: Dec. 19, 2019

Language: Английский

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A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing

Genome Research, Journal Year: 2021, Volume and Issue: 31(10), P. 1767 - 1780

Published: June 4, 2021

Single-cell genomics is rapidly advancing our knowledge of the diversity cell phenotypes, including both types and states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive atlas projects characterizing a wide range organisms tissues are currently underway. As result, it critical that transcriptional phenotypes discovered defined disseminated in consistent concise manner. Molecular biomarkers have historically played an important role biological research, from defining immune surface protein expression to diseases their molecular drivers. Here, we describe machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages nonlinear attributes random forest feature binary scoring approach discover minimal combinations optimally capture type identity represented complete scRNA-seq profiles. The genes selected provide barcode serves as useful tool for downstream investigation necessary sufficient characteristics semantic definition. use identify human brain middle temporal gyrus reveals importance signaling noncoding RNAs neuronal identity.

Language: Английский

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Scaling of sensory information in large neural populations shows signatures of information-limiting correlations

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Jan. 20, 2021

Abstract How is information distributed across large neuronal populations within a given brain area? Information may be roughly evenly populations, so that total scales linearly with the number of recorded neurons. Alternatively, neural code might highly redundant, meaning saturates. Here we investigate how sensory about direction moving visual stimulus hundreds simultaneously neurons in mouse primary cortex. We show sublinearly due to correlated noise these populations. compartmentalized correlations into information-limiting and nonlimiting components, then extrapolate predict grows even larger tens thousands encode 95% direction, much less than These findings suggest uses widely distributed, but nonetheless redundant supports recovering most from smaller subpopulations.

Language: Английский

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Finding new and better treatments for psychiatric disorders

Neuropsychopharmacology, Journal Year: 2023, Volume and Issue: 49(1), P. 3 - 9

Published: Aug. 15, 2023

Abstract In contrast to most fields of medicine, progress discover and develop new improved psychiatric drugs has been slow disappointing. The vast majority currently prescribed treat schizophrenia, mood anxiety disorders are arguably no more effective than the first generation introduced well over 50 years ago. With only a few exceptions current work via same fundamental mechanisms action as first-generation agents. Here we describe reasons for this outline number areas research that involve greater reliance on experimental therapeutics utilizing recent advances in neuroscience better understand disease biology. We exemplify potential impact these focus with several examples novel agents have emerged which support our optimism newer, tolerated agents, horizon. Together existing newer could offer markedly functional outcomes millions people still disabled by disorders.

Language: Английский

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Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Cell Reports, Journal Year: 2023, Volume and Issue: 42(6), P. 112574 - 112574

Published: June 1, 2023

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, dendritic synapse locations are constrained by experimental data. The includes long-range inputs from seven thalamic regions noradrenergic inputs. Connectivity depends on cell class depth at sublaminar resolution. accurately predicts in vivo layer- cell-type-specific responses (firing rates LFP) associated behavioral states (quiet wakefulness movement) manipulations (noradrenaline receptor blockade thalamus inactivation). generate mechanistic hypotheses underlying the observed activity analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate interpret M1 data sheds light multiscale dynamics several conditions behaviors.

Language: Английский

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Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects

Frontiers in Systems Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: Sept. 23, 2022

Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From advent wheel development artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation brain functions become trending topic both in academic and public debates improving physical mental The last years seen plethora suggestions for boosting biochemical, physical, behavioral strategies are being explored field enhancement. Despite expansion biochemical approaches, various known boost abilities diseased healthy individuals. Clinical applications neuroscience technologies offer alternatives pharmaceutical approaches devices diseases that been fatal, so far. Importantly, distinctive aspect these technologies, which shapes existing anticipated participation augmentations, is used compare contrast them. As preview next two decades progress augmentation, this article presents plausible estimation many virtues, demerits, applications. review also focuses ethical implications challenges linked modern neuroscientific technology. There times when it looks as if ethics discussions more concerned with hypothetical than factual. We conclude by providing recommendations potential future studies areas, taking into account advancements innovation enhancement, analyzing historical patterns, considering neuroethics looking at other related forecasts.

Language: Английский

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BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

Journal of Neuroscience, Journal Year: 2019, Volume and Issue: 39(42), P. 8275 - 8284

Published: Oct. 16, 2019

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance understanding healthy and diseased brain circuit function technological innovation. Core principles for this include validation dissemination myriad innovative technologies, tools, methods, resources emerging from BRAIN-funded research. Innovators, funding agencies, non-Federal partners are working together develop strategies making these products usable, available, accessible scientific community. Here, we describe several early supporting technologies. We aim invigorate a dialogue with neuroscience research community, interdisciplinary collaborators, trainees about existing future opportunities cultivating groundbreaking into mature, integrated, adaptable systems. Along accompanying Society Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools Resources Community," spotlight work investigator teams who progress toward providing services These tools access neural circuits at multiple levels analysis, subcellular composition brain-wide network connectivity, including following: integrated systems EM- florescence-based connectomics, advances in immunolabeling capabilities, recording analyzing functional connectivity. Investigators how they provide community will contribute achieving goals Initiative. Finally, addition celebrating contributions investigators, Mini-Symposium illustrate broader diversity investments cutting-edge technologies resources.

Language: Английский

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Raman spectroscopy and neuroscience: from fundamental understanding to disease diagnostics and imaging

The Analyst, Journal Year: 2020, Volume and Issue: 145(10), P. 3461 - 3480

Published: Jan. 1, 2020

In recent years, Raman spectroscopy-based methods have contributed significantly to the understanding of neurological structure, function, and disease.

Language: Английский

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Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

European Journal of Neuroscience, Journal Year: 2022, Volume and Issue: 55(7), P. 1724 - 1741

Published: March 18, 2022

Abstract Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for total neuronal populations species, number and definition each specific type are still intensively investigated. Hippocampome.org open‐source knowledge base with morphological, physiological molecular information 122 rodent hippocampal formation. While such framework identifies all known this system, their relative abundances remain largely unknown. This work quantitatively counts by literature mining numerical optimization. We report neurons identified main neurotransmitter (glutamate or GABA) axonal‐dendritic patterns throughout 26 subregions layers dentate gyrus, Ammon's horn, subiculum entorhinal cortex. produce sensitivity analysis reliable ranges summarize amounts across broad families defined biomarkers expression firing dynamics. Study density distributions indicates that dendritic‐targeting interneurons, but not other classes, independent volumes. All extracted values, experimental evidence related software code released on Hippocampome.org.

Language: Английский

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