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77 results for “neural circuits”
Data for: A neural circuit for wind-guided olfactory navigation
<p>To navigate towards a food source, animals must frequently combine odor cues that tell them what sources are useful with wind direction cues that tell them where the source can be found. Where and how these two cues are integrated to support navigation is unclear. Here we identify a pathway to the Drosophila fan-shaped body (FB) that encodes attractive odor and promotes upwind navigation. We show that neurons throughout this pathway encode odor, but not wind direction. Using connectomics, we identify FB local neurons called h∆C that receive input from this odor pathway and a previously described wind pathway. We show that h∆C neurons exhibit odor-gated, wind direction-tuned activity, that sparse activation of h∆C neurons promotes navigation in a reproducible direction, and that h∆C activity is required for persistent upwind orientation during odor. Based on connectome data, we develop a computational model showing how h∆C activity can promote navigation towards a goal such as an upwind odor source. Our results suggest that odor and wind cues are processed by separate pathways and integrated within the FB to support goal-directed navigation.</p>
Neural preprocessed data associated to "Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human"
<p>This dataset is composed of electrophysiology data from a tetraplegic human participant implanted with three 96 channel Utah arrays (Blackrock) in the supramarginal gyrus (SMG), ventral premotor cortex (PMV) and somatosensory cortex. The dataset includes preprocessed (spike sorted) firing rate data for 96 recorded channels, as described in Wandelt et al (2022), "Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human" published in Neuron (<a href="https://doi.org/10.1016/j.neuron.2022.03.009">10.1016/j.neuron.2022.03.009</a>).</p> <p>To run the code associated with the processed data, download it here https://zenodo.org/record/6330179.</p>
Dataset for "Gap junctions desynchronize a neural circuit to stabilize insect flight"
<p><strong>Data guide</strong></p> <p>For each figure, we provide one data folder. Each folder contains one overview Excel file that provides all source data that is shown in the respective figure. For some figure panels additional subfolders with original data files are provided. Please note that the data for figures 3A-J (except F and G) have been uploaded together with the analyses and simulation code and are accessible under the doi: 10.5281/zenodo.7740678</p> <p> </p> <p><strong>Data Figure 1: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 1.xlsx</li> <li>Folder Panel A: <ul> <li>Recordings shown in A and G with sorted spikes: figure 1a_original recording.smr</li> <li>Data matrix of waveforms shown in A as text file: figure 1a_waveforms_200818_561.5-566.5sec.txt</li> </ul> </li> <li>Folder Panels B+F: all original recordings with sorted spikes that have been analyzed for panels B and F: <ul> <li>171106_m1.SMR</li> <li>181029_m1.smr</li> <li>190522_m1.SMR</li> <li>200305_m1.smr</li> <li>200818_m1.smr</li> <li>200824_m1.smr</li> <li>200827_m1.SMR</li> <li>201005_m1.smr</li> </ul> </li> <li>Folder Panel C: all original recordings as .smr (Spike2) files that went into the analyses shown in figure 1c. <ul> <li>100 recordings in folders “Fly 1 to Fly 100”</li> <li>1 recording in folder “single animal”</li> <li>the waveforms of these 101 animals are also as text in file Figure 1.xlsx</li> </ul> </li> <li>Folder Panel D <ul> <li>The original data file of the recording shown in figure 1D as axon file: 20128002_OUT1_firing.abf</li> <li>The selected waveforms shown in figure 1D are also as text in file Figure 1.xlsx</li> </ul> </li> <li>All recordings that underlie the analysis in figure 1E are as number matrices in the file Figure 1.xlsx</li> <li>The spike events shown in figure 1G are as number matrices in the file Figure 1.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 2: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 2.xlsx</li> <li>The number matrices for all traces shown in figure 2A are as number matrices in the file Figure 2.xlsx</li> <li>Panel B: all number matrices of the event times that underlie the analyses shown in figure 2B: <ul> <li>Folder “control”: .txt of event times of the 7 control animals</li> <li>Folder “ShakB-RNAi”: .txt of event times of the 7 ShakB-RNAi-kd animals</li> <li>Folder “ShakB-oe”: .txt of event times of the 7 ShakB-oe (overexpression) animals</li> </ul> </li> <li>Panel C original confocal image stack for image data shown in figure 2C: <ul> <li>original Leica file: figure 2c_image stack.lif</li> <li>Folder “tiff” with same image stack saved as tiff</li> </ul> </li> </ul> <p> </p> <ul> <li>Panel D: The number matrices underlying the waveforms of the traces shown in Figure 2D are contained in the file Figure 2.xlsx</li> <li>Panel E: The number matrices underlying the waveforms of the traces shown in Figure 2E are contained in the file Figure 2.xlsx</li> <li>Panel F: The number matrices of all pre- and postsynaptic voltage areas of paired MN recordings and the analyses thereof for Figure 2F (left) are contained in the file Figure 2.xlsx. The number matrices of all pre- and postsynaptic voltage amplitudes of paired MN recordings and the analyses thereof for Figure 2F (right) are contained in the file Figure 2.xlsx</li> <li>Panel G: The number matrices underlying the waveforms of the traces shown in Figure 2G are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2H are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2I are contained in the file Figure 2.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 3: </strong>(all data for Fig. 3 except f and g are at doi: 10.5281/zenodo.7740678)</p> <ul> <li>Overview Excel file with all source data for Figures 3G and F: Figure 3.xlsx</li> <li>Panel F: The number matrices for all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel F: The number matrices for the waveforms of all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel G: The number matrices for the waveforms of all traces shown in figure 3G are contained in the file Figure 3.xlsx <ul> <li>original data file from which the traces in figure 3G (top) were taken: control MN4_3_0s-87s.SMR</li> <li>original data file from which the traces in figure 3G (bottom) were taken: UAS shab81 2022_10_07_0s-87s.SMR</li> </ul> </li> </ul> <p> </p> <p><strong>Data Figure 4: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 4.xlsx</li> <li>Panel A: The number matrices for all event signals and all waveform traces shown in figure 4A are as number matrices in the file Figure 4.xlsx</li> <li>Figure 4B (top): <ul> <li>The number matrices for the waveforms (voltage recording and fit) shown in Figure 4b (top) are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms of the voltage recordings that were fitted to obtain the averages shown in Figure 4B top are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (top) are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4B (middle): <ul> <li>The number matrices for the waveforms (calcium imaging and fit) shown in Figure 4b (middle) are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (middle) are contained in the file Figure 4.xlsx</li> <li>The original imaging data for this analysis is contained in movie S4</li> </ul> </li> <li>Figure 4B (bottom): <ul> <li>The number matrices for the waveforms (wingbeat frequency from events and fit) shown in Figure 4b (bottom) are contained in the file Figure 4.xlsx</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4C: <ul> <li>The number matrices for the waveform shown in Figure 4C are contained in the file Figure 4.xlsx</li> <li>The original calcium imaging movie as .avi file: DLM6_Imaging.avi</li> <li>The original calcium imaging movie as tiff stack: file DLM6_imaging_tiff</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4E: <ul> <li>The number matrices for the waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms that resulted in the average waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The original data files for analyses in Figure 4E are contained as text files in the folders ShakB_oe (7 text files) and control (7 text files)</li> </ul> </li> </ul>
Conditioning Neural Circuits to Improve Upper Extremity Function
ClinicalTrials.gov study NCT02611375. IPD Sharing: NO. Countries: 1. Publications: 13.
Deep TMS of Neural Circuits Associated With Stimulant Use Disorder
ClinicalTrials.gov study NCT06578429. IPD Sharing: YES. Countries: 1. Publications: 4.
RTMS Targets Neural Circuits for Smoking Cessation
ClinicalTrials.gov study NCT04903028. IPD Sharing: NO. Countries: 1. Publications: 7.
Assessing the Impact of Deep TMS Neuromodulation on Neural Circuits Associated With Alcohol Use Disorder
ClinicalTrials.gov study NCT06949423. IPD Sharing: YES. Countries: 1. Publications: 5.
Data for: A neural circuit for wind-guided olfactory navigation
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Functional architecture of neural circuits for leg proprioception in Drosophila
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Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit
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A neural circuit for flexible control of persistent behavioral states
<p>To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales. It remains unclear how neural circuits generate persistent behavioral states while maintaining flexibility to switch states when the context changes. Here, we elucidate the architecture of a circuit controlling the choice between roaming and dwelling foraging states in C. elegans. Through ensemble-level calcium imaging in freely-moving animals, we identify stable, circuit-wide activity patterns corresponding to each state. Mutual inhibition between two neuromodulatory systems underlies the persistence and mutual exclusivity of the opposing network states. We identify a sensory processing neuron that transmits information about food odors to both the roaming and dwelling circuits, biasing the animal towards different states in different contexts. These findings reveal a circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.</p>
Epidermal Growth Factor signaling promotes sleep through a combined series and parallel neural circuit
<p>Sleep requires sleep-active neurons that depolarize to inhibit wake circuits. Sleep-active neurons are under the control of homeostatic mechanisms that determine sleep need. However, little is known about the molecular and circuit mechanisms that translate sleep need into the depolarization of sleep-active neurons. During many stages and conditions in <i>C. elegans</i>, sleep requires a sleep-active neuron called RIS. Here, we defined the transcriptome of RIS to discover that genes of the Epidermal Growth Factor Receptor (EGFR) signaling pathway are expressed in RIS. With cellular stress, EGFR directly activates RIS. Activation of EGFR signaling in the ALA neuron has previously been suggested to promote sleep independently of RIS. Unexpectedly, we found that ALA activation promotes RIS depolarization. Our results suggest that ALA is a drowsiness neuron with two separable functions. (1) It inhibits specific behaviors such as feeding independently of RIS, (2) and it activates RIS. Whereas ALA plays a strong role in surviving cellular stress, surprisingly, RIS does not. In summary, EGFR signaling can depolarize RIS by an indirect mechanism through activation of the ALA neuron that acts upstream of the sleep-active RIS neuron as well as through a direct mechanism using EGFR signaling in RIS. ALA-dependent drowsiness rather than RIS-dependent sleep bouts appears to be important for increasing survival following cellular stress, suggesting that different types of behavioral inhibition play different roles in restoring health.</p>
Neural Circuits in Women With Abuse and Posttraumatic Stress Disorder
ClinicalTrials.gov study NCT01681849. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neurofeedback and Neural Plasticity of Self-Processing and Affect Regulation Circuits in Suicide Attempting Adolescents
ClinicalTrials.gov study NCT06183580. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A neural circuit for flexible control of persistent behavioral states
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Data from: Inferring neural circuit properties from optogenetic stimulation
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Epidermal Growth Factor signaling promotes sleep through a combined series and parallel neural circuit
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Zebrafish foxc1a is required for appendage specific neural circuit development
GEO Series GSE64125. Danio rerio. 8 samples. Type: Expression profiling by high throughput sequencing.
Glioblastoma remodeling of neural circuits in the human brain decreases survival
GEO Series GSE223065. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Astrocyte-derived Interleukin-33 promotes microglial synapse engulfment and neural circuit development I
GEO Series GSE109352. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.