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40 results for “Calcium imaging”
Calcium imaging of odor responses in the fruit fly mushroom body
<p><strong>Abstract</strong></p> <p>This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023.</p> <p><strong>Methods</strong></p> <p>Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013.</p> <p><strong>Animal preparation</strong></p> <p>Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.</p> <p> </p> <p><strong>Odor delivery </strong></p> <p>The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7). Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research). This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer.</p> <p><strong>Calcium imaging</strong></p> <p>Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane. Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition. Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession.</p> <p><strong>Imaging analysis</strong></p> <p>Data were analyzed using MATLAB and R (http://www.R-project.org). To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008). Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010). Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image. To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session. Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial. Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01.</p> <p><br> <strong>References</strong></p> <p>Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers. J Neurogenet 23:156 –172. </p> <p>Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107.</p> <p>Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785.</p> <p>Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461.</p> <p>Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory.</p> <p>Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv.</p> <p>Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260.</p> <p>Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881.</p> <p>Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746.</p> <p>Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370.</p> <p><strong>Usage notes</strong></p> <p>The files are all in csv format, and can be easily opened in R or Python or other programming languages.</p> <p>Please see the README.md file for directions on how to use the data.</p> <p>The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.</p>
CA3 axonal calcium imaging
<p>Memorizing locations that are harmful or dangerous is a key capability of all organisms, and requires an integration of affective and spatial information. In mammals, the dorsal hippocampus mainly processes spatial information, while the intermediate to ventral hippocampal divisions receive affective information via the amygdala. However, how spatial and aversive information is integrated is currently unknown.</p> <p>To address this question, we recorded the activity of hippocampal long-range CA3 axons at single axon resolution in mice forming an aversive spatial memory. We show that intermediate CA3 to dorsal CA3 (i-dCA3) projections rapidly overrepresent areas preceding the location of an aversive stimulus, due to a spatially selective addition of newly place-coding axons, followed by a spatially nonspecific stabilization. This sequence significantly improves the encoding of location by the i-dCA3 axon population.</p> <p>These results suggest that i-dCA3 axons transmit a precise, denoised and stable signal indicating imminent danger to dorsal hippocampus.</p>
Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
<p><strong>Abstract</strong></p> <p>Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (<em>test-GT</em>) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding <em>test-GT</em> samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.</p> <p> </p> <p><strong>Methods</strong></p> <p><strong><em>Manual Generation of test-GT</em></strong></p> <p>The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience.</p> <p><strong><em>Segmentation of Coronary Arteries using a Deep Learning Model</em></strong></p> <p>For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available:</p> <ul> <li><span>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater.</span> A sample belonging to the <em>test-GT</em> set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding <em>test-GT</em> sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned <em>test-GT</em> sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>
CaImAn: An open source tool for scalable Calcium Imaging data Analysis
<p>Advances in fluorescence microscopy enable monitoring larger brain areas <em>in-vivo</em> with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to preprocessing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data.</p> <p>To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets, that are contained in this open access repository. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.</p> <p>In order to reproduce the results of the paper or download the annotations and the raw movies, please refer to the readme.md at:</p> <p>https://github.com/flatironinstitute/CaImAn/blob/master/use_cases/eLife_scripts/README.md</p> <p> </p>
FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis calcium dataset
<p>The dataset was used in paper FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis named as 1MP. The dataset was only used to test FIOLA motion correction performance.<br> The dataset was collected for the paper Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice (link: https://pubmed.ncbi.nlm.nih.gov/24582958/) but never published before. Data was recorded in the left lobule of the cerebellum of an awake mouse using the calcium indicator GCaMP6f. GCaMP6f was selectively expressed in Purkinje cells via a combinatorial virus strategy (as explained in the paper).</p> <p>For other datasets used in paper FIOLA, check the original paper and sources they were published.</p> <p> </p> <p> </p>
Data from: A comprehensive suite for extracting neuron signals across multiple sessions in one-photon calcium imaging
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Analysis dataset from: Simultaneous dual-color calcium imaging in freely-behaving mice
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CA3 axonal calcium imaging
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TRPV1 calcium imaging assay data from testing of capsaicinoids
<p>irTRPV1 calcium imaging assay data collected in HTS format included legend to files, raw data, feature files with description, aligned data and final GraphPad file. Correspoinding paper's DOI will be added once generated.</p>
Mouse CA1 Calcium Imaging and Behavioural Dataset in 3x3 Geometric Morph Paradigm
<p>The following dataset was collected by Dr. J. Quinn Lee, Dr. Alexandra T. Keinath, and Erica Cianfarano in the laboratory of Dr. Mark P. Brandon. All methods and details are described in the original research article reporting these data published in <em>Neuron</em>: Lee, Keinath, Cianfarano, and Brandon (2025) Identifying representational structure in CA1 to benchmark theoretical models of cognitive mapping. Any use of the following dataset must cite the original publication in Neuron. The code base to reproduce all analyses and figures can be found at: <a href="https://github.com/jquinnlee/georepca1">https://github.com/jquinnlee/georepca1</a></p> <p dir="auto">The dataset (Python joblib files or MATLAB .mat files in the zipped "data" folder) are given names of animal IDs from the original study that can be downloaded from Zenodo and contain the following fields in each file:</p> <p dir="auto">SFPs: spatial footprints (also known as ROI) for every registered cell, centered over each cell. Shape - Dimx, dimy, number of SFPs (ROIs), number of days. If cell is not registered it will be nan along dimx and dimy for a given day.</p> <p dir="auto">blocked: location of blocked (occluded) partitions in 3x3 design of environment. Location of partitions are shown in paper, but are organized in the following way – [[0, 1, 2], [3, 4, 5], [6, 7, 8]]. If no partitions are blocked, value is -1.</p> <p dir="auto">centroids: centroid of spatial footprint. Shape – number of cells, x-y location, number of days.</p> <p dir="auto">envs: environment shape identified with string name</p> <p dir="auto">maps: three types of maps generated from the dataset. “sampling” is the occupancy of animal in each spatial bin, shape – xbins, ybins, number of days. “smoothed” is the event rate map smoothed with 2.5 cm gaussian kernel, shape – xbins, ybins, number of cells, number of days. “unsmoothed” is the same event rate map data without smoothing.</p> <p dir="auto">position: x-y position data for all days. List shape number of days, with shape on each day indicating x-y position in first dimension, and number of temporal bins / frames in second dimension.</p> <p dir="auto">trace: rise-extracted calcium traces, where “1” indicates a significant event. See paper for details on processing pipeline. If cell is not registered on given day, will appear as nan the same shape.</p> <p dir="auto">Precomputed results can also be downloaded in the zipped "results" folder to avoid recomputing main results from scratch using the Github code base linked above.</p>
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
<p>Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity.</p> <p>Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations.</p> <p>Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice.</p> <p>This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.</p>
Code used in exporting calcium imaging data from raw traces for Veit et al. 2022
<p>This contains scripts and functions used in extracting stimulus responses from raw calcium imaging traces stored in HDF5 format. Note the current version does not contain the HDF5 files with raw traces.</p>
Preliminary data and analysis from ultrafast calcium or voltage imaging recordings at 8-bits resolution using a Kinetix camera
<p><span>This dataset was obtained from brain slices of the mouse. Data are from transversal hippocampal slices from </span><span>30-40 postnatal days old C57Bl6 mice (of both genders), stained with the Ca<sup>2+</sup> indicator Fluo-4 AM; or from layer-5 pyramidal neurons loaded intracellularly either with the Ca<sup>2+</sup> indicator Oregon Green BAPTA-5N or with the voltage sensitive dye JPW1114. <span> </span>Details are in the Read_me file. This dataset cannot be used for publications without permission of the contact person.</span></p>
Research data of Calcium Imaging after electrical stimulation
<p>For intracellular calcium ions (Ca<sup>2+</sup>) mobilization analysis by Calcium Imaging, a human osteoblast-like cell line MG-63 (ATCC ®, CRL1427™) were used [1-3]. The MG-63 cell line has similar characteristics in terms of morphological behavior, adhesion and signaling properties as primary human osteoblasts [3]. Cells were cultured at 37 °C in a humidified atmosphere (5% CO<sub>2</sub>) in Dulbecco's modified eagle medium (DMEM; Gibco) containing 10% fetal calf serum (FCS; PAA Laboratories) and 1% antibiotic (gentamicin, Ratiopharm GmbH) [1-3]. Cells in the near-confluent state (70-80%) were used for the corresponding <em>in vitro</em> experiments. Therefore, cells were washed with PBS, trypsinized with 0.05% trypsin/0.02% EDTA (PAA) for 5 min and then treated with medium to stop the reaction. After centrifugation, 2x10<sup>6</sup> cells / 2ml were incubated shaking in complete medium at 37°C for a complete independent experiment. For the method, 2.5x10<sup>5</sup> cells were stained per electrical stimulation setup (non-stimulated, 1 V 7.9 Hz, 1 V 20 Hz, 5 V 7.9 Hz or 5 V 20 Hz). First, a wash step was performed with PBS, and after centrifugation, sedimented cells were stained with a membrane-permeable calcium indicator fluo-3/AM (Life Technologies Corporation; 5 μM) in slightly hypotonic 4-(2-hydroxyethyl)- 1-piperazineethanesulfonic acid (HEPES) buffer [1-3] shaking at 37°C. After incubation for 40 min, cells were centrifuged and then resuspended in 250 µl of isotonic HEPES. In a 12-well plate, 750 µl of isotonic HEPES was placed. Then the stained suspended cells were added, and immediately electrical stimulation by using IonOptix chamber was started under the LSM780 (Carl Zeiss). After 5 min of adhesion phase, the global calcium signal of the attaching cells was visualized using the inverted LSM 780 with a C Apochromat 40× water immersion objective. Fluo-3/AM dye was excited with the argon ion laser at 488 nm (emission at 515 nm). A full frame (512 x 512 pixels) at maximum pinhole aperture was evaluated using Zen2011 (black edition) software (Carl Zeiss) and "time series" mode. The first time series with electrical stimulation included 150 cycles each 2 s. After this time series (total electrical stimulation of 10 min), the electrical stimulation was stopped and a second time series without electrical stimulation was started. This second time series comprised 240 cycles every 2 s. After the 90th cycle of these series, fluo-3/AM stained cells were treated with 10 mM adenosine-triphosphate (ATP; SERVA). Mean fluorescence intensity of cells was evaluated using Zen2012 software (blue edition) and Mean ROI mode for defined areas of individual single cells [1-3].</p> <p>[1] Staehlke S, Koertge A, Nebe B (2015) Intracellular calcium dynamics dependent on defined microtopographical features of titanium. Biomaterials 46: 48–57.</p> <p>[2] Staehlke S, Rebl H, Finke B, Mueller P, Gruening M, Nebe JB (2018) Enhanced calcium ion mobilization in osteoblasts on amino group containing plasma polymer nanolayer. Cell Biosci 8: 22.</p> <p>[3] Staehlke S, Rebl H, Nebe B (2019) Phenotypic stability of the human MG-63 osteoblastic cell line at different passages. Cell Biol Int 43:22-32.</p>
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>
Calcium imaging data from: Functional organization of visual responses in the octopus optic lobe
<p>Cephalopods are highly visual animals with camera-type eyes, large brains, and a rich repertoire of visually guided behaviors. However, the cephalopod brain evolved independently from that of other highly visual species, such as vertebrates, and therefore the neural circuits that process sensory information are profoundly different. It is largely unknown how their powerful but unique visual system functions, since there have been no direct neural measurements of visual responses in the cephalopod brain. In this study, we used two-photon calcium imaging to record visually evoked responses in the primary visual processing center of the octopus central brain, the optic lobe, to determine how basic features of the visual scene are represented and organized. We found spatially localized receptive fields for light (ON) and dark (OFF) stimuli, which were retinotopically organized across the optic lobe, demonstrating a hallmark of visual system organization shared across many species. Examination of these responses revealed transformations of the visual representation across the layers of the optic lobe, including the emergence of the OFF pathway and increased size selectivity. We also identified asymmetries in the spatial processing of ON and OFF stimuli, which suggest unique circuit mechanisms for form processing that may have evolved to suit the specific demands of processing an underwater visual scene. This study provides insight into the neural processing and functional organization of the octopus visual system, highlighting both shared and unique aspects, and lays a foundation for future studies of the neural circuits that mediate visual processing and behavior in cephalopods.</p>
Multisite imaging of neural activity using a genetically encoded calcium sensor in the honey bee
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Calcium imaging data from: Functional organization of visual responses in the octopus optic lobe
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Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
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Honey bee antennal lobe calcium imaging
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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.