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213 results for “neural activity”
Multisite imaging of neural activity using a genetically encoded calcium sensor in the honey bee
<p><span>Understanding of the neural bases for complex behaviors in Hymenoptera insect species has been limited by a lack of tools that allow measuring neuronal activity simultaneously in different brain regions. </span><span>Here, we developed the first pan-neuronal genetic driver in a Hymenopteran model organism, the honey bee, and expressed the calcium indicator GCaMP6f under the control of the honey bee <em>synapsin</em> promoter. We show that GCaMP6f is widely expressed in the honey bee brain, allowing it to record neural activity from multiple brain regions. </span><span>To assess the power of this tool, we focused on the olfactory system, recording simultaneous responses from the antennal lobe, and from the more poorly investigated lateral horn and mushroom body calyces. Neural responses to 16 distinct odorants demonstrate that odorant quality (chemical structure) and quantity are faithfully encoded in the honey bee antennal lobe. In contrast, odor coding in the lateral horn departs from this simple physico-chemical coding, supporting the role of this structure in coding the biological value of odorants. We further demonstrate robust neural responses to several bee pheromone odorants, key drivers of social behavior, in the lateral horn. Combined, these brain recordings represent the first use of a neurogenetic tool for recording large-scale neural activity in a eusocial insect, and will be of utility in assessing the neural underpinnings of olfactory and other sensory modalities and of social behaviors and cognitive abilities.</span></p>
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
<p>Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation</p> <p>Megan E. Young, Camille Spencer-Salmon, Clayton Mosher, Sarita Tamang, Kanaka Rajan, and Peter H. Rudebeck</p> <p>This dataset contains peripheral physiology (heart rate) and single neuron activity data from the paper entitled “Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation” by Young, Spencer-Salmon and colleagues.</p> <p>The study investigated how neurons in macaque subcallosal anterior cingulate cortex, basolateral amygdala, and rostromedial striatum encoded anticipated reward during Pavlovian and instrumental tasks.</p> <p>Heart rate data were pre-processed using methods described in the paper and were downsampled to 50 Hz for analysis. Neural activity data were pre-processed using steps as described in the paper.</p> <p>The dataset is saved as .MAT files.</p> <p>Files included:</p> <p>‘Pavlovian_task_neurons.mat’ – single neuron data from the Pavlovian task. Each of the 656 rows represents a single neuron and its associated information.</p> <p>‘Instrumental_task_neurons.mat’ – single neuron data from the instrumental task. Each of the 425 rows represents a single neuron and its associated information.</p> <p>“heart_rate.mat” – heart rate data from monkeys D and H.</p> <p><br> File structure and information:</p> <p>Pavlovian_task_neurons.mat</p> <p>Structure “Pavlovian_task_neurons”<br> - “Pavlovian_task_neurons.unit_name” – neuron specific identifier<br> - “Pavlovian_task_neurons.monkeynumber” – subject specific #<br> - “Pavlovian_task_neurons.monkeyname” – subject specific name<br> - “Pavlovian_task_neurons.date” – date on which data were recorded<br> - “Pavlovian_task_neurons.session” – session identifier from date (a-d)<br> - “Pavlovian_task_neurons.channel” – recording channel data recorded from<br> - “Pavlovian_task_neurons.wavemark” – waveform number (a-e)<br> - “Pavlovian_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “Pavlovian_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “Pavlovian_task_neurons.condition” – condition # from Monkey Logic for each of the trials (1 by n trials)<br> - “Pavlovian_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “Pavlovian_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)<br> - “Pavlovian_task_neurons.stimID” – stimulus shown on that trial (1 = neutral, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).</p> <p><br> Instrumental_task_neurons.mat</p> <p>Structure “Instrumental_task_neurons”<br> - “instrumental_task_neurons.unit_name” – neuron specific identifier<br> - “instrumental_task_neurons.monkeynumber” – subject specific #<br> - “instrumental_task_neurons.monkeyname” – subject specific name<br> - “instrumental_task_neurons.date” – date on which data were recorded<br> - “instrumental_task_neurons.session” – session identifier from date (a-d)<br> - “instrumental_task_neurons.channel” – recording channel data recorded from<br> - “instrumental_task_neurons.wavemark” – waveform number (a-e)<br> - “instrumental_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “instrumental_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “instrumental_task_neurons.condition” – condition 7-18 from Monkey Logic for each of the trials (1 by n trials). CNDs 7,8,13,14= CS+ juice vs CS+ water; CNDs 9,10,15,16= CS+ juice vs CS-; CNDs 11,12,17,18= CS+ water vs CS-.<br> - “instrumental_task_neurons.choice” – outcome associated with chosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.unchosen” - outcome associated with unchosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.chosenside” – side of the screen chosen (left [0] or right [1]) (1 by n trials)<br> - “instrumental_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “instrumental_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)</p> <p>heart_rate.mat</p> <p>Structures – “monkey_d_hr” – monkey D heart rate data<br> – “monkey_h_hr” – monkey H heart rate data</p> <p>Structure of monkey_d/h_hr<br> - “monkey_d/h_hr.trial_type” – trial type presented (1 = neutral, 2 = unsignaled, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).<br> - “monkey_d/h_hr.trials” – number of trials in each session by trial type<br> - “monkey_d/h_hr.session – percent change in heart rate for each trial from -200 ms to 3500ms after stimulus onset. Column 1 = session; Column 2 = trial type; Column 3 = trial number; Columns 4 – 3703 = percent change in heart rate.</p> <p> </p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Comparison of Active vs Passive Neural Mobilizations Effects in Improving Burning Pain, Muscular Strength, and Range of Motion in Patients With Diabetic Neuropathy
ClinicalTrials.gov study NCT07141992. IPD Sharing: NO. Countries: 1. Publications: 1.
An fMRI Study of Opioid-related Changes in Neural Activity
ClinicalTrials.gov study NCT02818036. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Efficacy of Combining Movement Representation Techniques to Active Neural Mobilization in the Mechanosensitivity of the Median Nerve in Asymptomatic Volunteers.
ClinicalTrials.gov study NCT07154017. IPD Sharing: YES. Countries: 1. Publications: 1.
Opioid Modulation and Neural Reward Activation in Healthy Adults
ClinicalTrials.gov study NCT04854551. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Impact of Exercise on Subthalamic Nucleus Neural Activity in Parkinson's Disease
ClinicalTrials.gov study NCT05905302. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: The transcription factor Pou3f1 promotes neural fate commitment via activation of neural lineage genes and inhibition of external signaling pathways
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Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
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Multisite imaging of neural activity using a genetically encoded calcium sensor in the honey bee
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The neural activity of auditory conscious perception
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Alarm cues and alarmed conspecifics: Neural activity during social learning from different cues in Trinidadian guppies
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Data from: Precise control of neural activity using dynamically optimized electrical stimulation
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Data from: Periaqueductal gray activates antipredatory neural responses in the amygdala of foraging rats
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Type-I nNOS neurons orchestrate cortical neural activity and vasomotion
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Mouse behavior and neural activities of dopamine and D1- and D2- neurons in the tail of the striatum under threat-reward conflict and dopamine action onto the striatal neurons
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Data from: Low-dimensional spatio-temporal dynamics underlie cortex-wide neural activity
<p>Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (~75%) could be explained by a limited set of ~14 'motifs' of neural activity. Each motif captured a unique spatio-temporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.</p>
Neural activity during a simple reaching task in macaques is counter to gating and rebound in basal ganglia-thalamic communication
<div class="page"> <div class="layoutArea"> <div class="column"> <p>Task-related activity in the ventral thalamus, a major target of basal ganglia output, is often assumed to be permitted or triggered by changes in basal ganglia activity through gating- or rebound-like mechanisms. To test those hypotheses, we sampled single-unit activity from connected basal ganglia output and thalamic nuclei (globus pallidus-internus, GPi, and ventrolateral-anterior nucleus, VLa) in monkeys performing a reaching task. Rate increases were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously-recorded GPi-VLa pairs rarely showed short-timescale spike-to-spike correlations or slow across-trials covariations and both were equally positive and negative. Finally, spontaneous GPi bursts and pauses were both followed by small, slow reductions in VLa rate. These results appear incompatible with standard gating and rebound models. Still, gating or rebound may be possible in other physiological situations: Simulations show how GPi-VLa communication can scale with GPi synchrony and GPi-to-VLa convergence, illuminating how synchrony of basal ganglia output during motor learning or in pathological conditions may render this pathway effective. Thus, in the healthy state, basal ganglia-thalamic communication during learned movement is more subtle than expected, with changes in firing rates possibly being dominated by a common external source.</p> </div> </div> </div>
Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"
<p>Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"</p> <p>Nature Communications; 2024 Nov 24;15(1):10185. doi: 10.1038/s41467-024-54742-9.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.