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Title: A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day
The mouse brain’s activity changes drastically over a day despite being generated from the same neurons and physical connectivity. To better understand this, we develop an experimental-computational pipeline to determine which neurons and networks are most active at different times of the day. We genetically mark active neurons of freely behaving mice at four times of the day with a c-Fos activity-dependent TRAP2 system. Neurons are imaged and digitized in 3D, and their molecular properties are inferred from the latest brain spatial transcriptomic dataset. We then develop a new computational method to analyze the network formed by the identified active neurons. Applying this pipeline, we observe region and layer-specific activation of neurons in the cortex, especially activation of layer five neurons at the end of the dark (wake) period. We also observe a shift in the balance of excitatory (glutamatergic) neurons versus inhibitory (GABAergic) neurons across the whole brain, especially in the thalamus. Moreover, as the dark (wake) period progresses, the network formed by the active neurons becomes less modular, and the hubs switch from subcortical regions, such as the posterior hypothalamic nucleus, to cortical regions in the default mode network. Taken together, we present a pipeline to understand which neurons and networks may be most activated in the mouse brain during an experimental protocol, and use this pipeline to understand how brain activity changes over the course of a day. more »« less
Dura-Bernal, Salvador; Suter, Benjamin A; Gleeson, Padraig; Cantarelli, Matteo; Quintana, Adrian; Rodriguez, Facundo; Kedziora, David J; Chadderdon, George L; Kerr, Cliff C; Neymotin, Samuel A; et al
(, eLife)
null
(Ed.)
The approximately 100 billion neurons in our brain are responsible for everything we do and experience. Experiments aimed at discovering how these cells encode and process information generate vast amounts of data. These data span multiple scales, from interactions between individual molecules to coordinated waves of electrical activity that spread across the entire brain surface. To understand how the brain works, we must combine and make sense of these diverse types of information. Computational modeling provides one way of doing this. Using equations, we can calculate the chemical and electrical changes that take place in neurons. We can then build models of neurons and neural circuits that reproduce the patterns of activity seen in experiments. Exploring these models can provide insights into how the brain itself works. Several software tools are available to simulate neural circuits, but none provide an easy way of incorporating data that span different scales, from molecules to cells to networks. Moreover, most of the models require familiarity with computer programming. Dura-Bernal et al. have now developed a new software tool called NetPyNE, which allows users without programming expertise to build sophisticated models of brain circuits. It features a user-friendly interface for defining the properties of the model at molecular, cellular and circuit scales. It also provides an easy and automated method to identify the properties of the model that enable it to reproduce experimental data. Finally, NetPyNE makes it possible to run the model on supercomputers and offers a variety of ways to visualize and analyze the resulting output. Users can save the model and output in standardized formats, making them accessible to as many people as possible. Researchers in labs across the world have used NetPyNE to study different brain regions, phenomena and diseases. The software also features in courses that introduce students to neurobiology and computational modeling. NetPyNE can help to interpret isolated experimental findings, and also makes it easier to explore interactions between brain activity at different scales. This will enable researchers to decipher how the brain encodes and processes information, and ultimately could make it easier to understand and treat brain disorders.
Simoes_de_Souza, Fabio M; Williamson, Ryan; McCullough, Connor; Teel, Alec; Futia, Gregory; Ma, Ming; True, Aaron; Crimaldi, John P; Gibson, Emily; Restrepo, Diego
(, Journal of Visualized Experiments)
Mice navigate an odor plume with a complex spatiotemporal structure in the dark to find the source of odorants. This article describes a protocol to monitor behavior and record Ca2+ transients in dorsal CA1 stratum pyramidale neurons in hippocampus (dCA1) in mice navigating an odor plume in a 50 cm x 50 cm x 25 cm odor arena. An epifluorescence miniscope focused through a GRIN lens imaged Ca2+ transients in dCA1 neurons expressing the calcium sensor GCaMP6f in Thy1-GCaMP6f mice. The paper describes the behavioral protocol to train the mice to perform this odor plume navigation task in an automated odor arena. The methods include a step-by-step procedure for the surgery for GRIN lens implantation and baseplate placement for imaging GCaMP6f in CA1. The article provides information on real-time tracking of the mouse position to automate the start of the trials and delivery of a sugar water reward. In addition, the protocol includes information on using of an interface board to synchronize metadata describing the automation of the odor navigation task and frame times for the miniscope and a digital camera tracking mouse position. Moreover, the methods delineate the pipeline used to process GCaMP6f fluorescence movies by motion correction using NorMCorre followed by identification of regions of interest with EXTRACT. Finally, the paper describes an artificial neural network approach to decode spatial paths from CA1 neural ensemble activity to predict mouse navigation of the odor plume.
Winding, Michael; Pedigo, Benjamin D.; Barnes, Christopher L.; Patsolic, Heather G.; Park, Youngser; Kazimiers, Tom; Fushiki, Akira; Andrade, Ingrid V.; Khandelwal, Avinash; Valdes-Aleman, Javier; et al
(, Science)
INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.
Abstract Traumatic brain injury (TBI) affects neural function at the local injury site and also at distant, connected brain areas. However, the real‐time neural dynamics in response to injury and subsequent effects on sensory processing and behaviour are not fully resolved, especially across a range of spatial scales. We used in vivo calcium imaging in awake, head‐restrained male and female mice to measure large‐scale and cellular resolution neuronal activation, respectively, in response to a mild/moderate TBI induced by focal controlled cortical impact (CCI) injury of the motor cortex (M1). Widefield imaging revealed an immediate CCI‐induced activation at the injury site, followed by a massive slow wave of calcium signal activation that travelled across the majority of the dorsal cortex within approximately 30 s. Correspondingly, two‐photon calcium imaging in the primary somatosensory cortex (S1) found strong activation of neuropil and neuronal populations during the CCI‐induced travelling wave. A depression of calcium signals followed the wave, during which we observed the atypical activity of a sparse population of S1 neurons. Longitudinal imaging in the hours and days after CCI revealed increases in the area of whisker‐evoked sensory maps at early time points, in parallel to decreases in cortical functional connectivity and behavioural measures. Neural and behavioural changes mostly recovered over hours to days in our M1‐TBI model, with a more lasting decrease in the number of active S1 neurons. Our results in unanaesthetized mice describe novel spatial and temporal neural adaptations that occur at cortical sites remote to a focal brain injury.
The precise development of neuronal morphologies is crucial to the establishment of synaptic circuits and, ultimately, proper brain function. Signaling by the axon guidance cue semaphorin 3A (Sema3A) and its receptor complex of neuropilin-1 and plexin-A4 has multifunctional outcomes in neuronal morphogenesis. Downstream activation of the RhoGEF FARP2 through interaction with the lysine-arginine-lysine motif of plexin-A4 and consequent activation of the small GTPase Rac1 promotes dendrite arborization, but this pathway is dispensable for axon repulsion. Here, we investigated the interplay of small GTPase signaling mechanisms underlying Sema3A-mediated dendritic elaboration in mouse layer V cortical neurons in vitro and in vivo. Sema3A promoted the binding of the small GTPase Rnd1 to the amino acid motif leucine-valine-serine (LVS) in the cytoplasmic domain of plexin-A4. Rnd1 inhibited the activity of the small GTPase RhoA and the kinase ROCK, thus supporting the activity of the GTPase Rac1, which permitted the growth and branching of dendrites. Overexpression of a dominant-negative RhoA, a constitutively active Rac1, or the pharmacological inhibition of ROCK activity rescued defects in dendritic elaboration in neurons expressing a plexin-A4 mutant lacking the LVS motif. Our findings provide insights into the previously unappreciated balancing act between Rho and Rac signaling downstream of specific motifs in plexin-A4 to mediate Sema3A-dependent dendritic elaboration in mammalian cortical neuron development.
Sun, Guanhua, Mano, Tomoyuki, Shi, Shoi, Li, Alvin, Ode, Koji L, Rosi-Andersen, Alex, Pedron, Erica, Brown, Steven A, Ueda, Hiroki R, Kompotis, Konstantinos, and Forger, Daniel B. A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day. Retrieved from https://par.nsf.gov/biblio/10649710. PLOS Biology 23.11 Web. doi:10.1371/journal.pbio.3003472.
Sun, Guanhua, Mano, Tomoyuki, Shi, Shoi, Li, Alvin, Ode, Koji L, Rosi-Andersen, Alex, Pedron, Erica, Brown, Steven A, Ueda, Hiroki R, Kompotis, Konstantinos, & Forger, Daniel B. A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day. PLOS Biology, 23 (11). Retrieved from https://par.nsf.gov/biblio/10649710. https://doi.org/10.1371/journal.pbio.3003472
Sun, Guanhua, Mano, Tomoyuki, Shi, Shoi, Li, Alvin, Ode, Koji L, Rosi-Andersen, Alex, Pedron, Erica, Brown, Steven A, Ueda, Hiroki R, Kompotis, Konstantinos, and Forger, Daniel B.
"A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day". PLOS Biology 23 (11). Country unknown/Code not available: PLOS Biology. https://doi.org/10.1371/journal.pbio.3003472.https://par.nsf.gov/biblio/10649710.
@article{osti_10649710,
place = {Country unknown/Code not available},
title = {A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day},
url = {https://par.nsf.gov/biblio/10649710},
DOI = {10.1371/journal.pbio.3003472},
abstractNote = {The mouse brain’s activity changes drastically over a day despite being generated from the same neurons and physical connectivity. To better understand this, we develop an experimental-computational pipeline to determine which neurons and networks are most active at different times of the day. We genetically mark active neurons of freely behaving mice at four times of the day with a c-Fos activity-dependent TRAP2 system. Neurons are imaged and digitized in 3D, and their molecular properties are inferred from the latest brain spatial transcriptomic dataset. We then develop a new computational method to analyze the network formed by the identified active neurons. Applying this pipeline, we observe region and layer-specific activation of neurons in the cortex, especially activation of layer five neurons at the end of the dark (wake) period. We also observe a shift in the balance of excitatory (glutamatergic) neurons versus inhibitory (GABAergic) neurons across the whole brain, especially in the thalamus. Moreover, as the dark (wake) period progresses, the network formed by the active neurons becomes less modular, and the hubs switch from subcortical regions, such as the posterior hypothalamic nucleus, to cortical regions in the default mode network. Taken together, we present a pipeline to understand which neurons and networks may be most activated in the mouse brain during an experimental protocol, and use this pipeline to understand how brain activity changes over the course of a day.},
journal = {PLOS Biology},
volume = {23},
number = {11},
publisher = {PLOS Biology},
author = {Sun, Guanhua and Mano, Tomoyuki and Shi, Shoi and Li, Alvin and Ode, Koji L and Rosi-Andersen, Alex and Pedron, Erica and Brown, Steven A and Ueda, Hiroki R and Kompotis, Konstantinos and Forger, Daniel B},
editor = {Hilgetag, Claus C}
}
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