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482 results for “reward”
Integration of sweet taste and metabolism determines carbohydrate reward-study 3
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Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD)
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Working Memory and Reward in Adults
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Reward biases spontaneous neural reactivation during sleep
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Differential contributions of ventral striatum subregions in the motivational and hedonic components of the affective response to reward
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Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Behavior Data
<p>Behavior assay data used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 2, all panels</li> <li>Figure 4, all panels</li> <li>Ext. Fig 3, panels a-c, e, f</li> <li>Ext. Fig 5, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>ddHTP.zip contains the control raw data in MATLAB files</li> <li>HTP.zip contains the experimental raw data in MATLAB files</li> <li>The various Behavior_summary_data.csv files contain the subject details and extracted analyses, as detailed in each title.</li> <li>ddHTP_other_DART_pilots.zip and the corresponding .csv file hold the raw and analyzed data for the ddHTP controls of ongoing experiments shown in Ext. Fig 5c. </li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Histology from Behavior Data Mice
<p>Histology images for the experimental +HTP behavior mice in the dataset 10.5281/zenodo.10903566. This histology was used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Ext. Fig 3, panel d</li> </ul> <p>Specifics of the data:</p> <ul> <li>HTP_Histo_Cohort_VX.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VX</li> <li>HTP_Histo_Cohort_VZ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VZ</li> <li>HTP_Histo_Cohort_VAJ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAJ</li> <li>HTP_Histo_Cohort_VAK.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAK</li> <li> Behavior_summary_data_HTP_Histology.csv contains the summarized fluorescence quantification per animal</li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Electrophysiology Data
<p>In vivo electrophysiological data, slice electrophysiological data, and HTP vs tyrosine hydroxylase immunohistochemistry data. These data were used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 1, panels c-f</li> <li>Ext. Fig 1, all panels</li> <li>Ext. Fig 2, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>Slice_Ephys .csv files contain the summarized in vitro electrophysiology data.</li> <li>HTP_Mouse_*.7z and ddHTP_Mouse_*.7z are zipped folders with the raw data, sorted data, and extracted cells for each recording, grouped by mouse the recordings were obtained from.</li> <li>HTP final_grouped spiking analysis_n39.mat is the MATLAB data file with all the extracted firing metrics for all 39 +HTP cells.</li> <li>ddHTP final_grouped spiking analysis_n18.mat is the MATLAB data file with all the extracted firing metrics for all 18 ddHTP cells.</li> <li> Ephys_Summary_Data .csv files contain the summarized in vivo electrophysiology data.</li> <li>VHist4_HTP and TH.7z contains the raw histological images, drawn ROIs, and ilastik cell counting performed to compare tyrosine hydroxylase and HTP expression.</li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Fiber Photometry Data
<p>Fiber photometry data. These data were used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 3, all panels</li> <li>Ext. Fig 4, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>FiberPho_Cohort_*.zip files contain all of the raw fiber photometry and behavior data for each mouse, grouped by cohort.</li> <li>FiberPho_Histo_Cohort_*.zip files contain the histological images and ROIs for each mouse, grouped by cohort. </li> <li>HTP_grouped_data.zip contains the grouped analysis fiber photometry and behavior MATLAB files for the experimental group.</li> <li>ddHTP_grouped_data.zip contains the grouped analysis fiber photometry and behavior MATLAB files for the control group.</li> <li>The various .csv files contain the summarized mouse, cohort, and analysis details and data.</li> </ul> <p> </p>
Original single session datasets from "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements."
<p>This archive contains the original single-session recording datasets associated with the paper "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements" by Allison E Hamilos, Giulia Spedicato, Ye Hong, Fangmiao Sun, Yulong Li, and John A Assad (https://doi.org/10.1101/2020.05.13.094904). Files can be loaded and collated with code from our GitHub repository to reproduce all analyses (https://www.github.com/harvardschoolofmouse).</p>
Data Set related to Synaptic inhibition in the lateral habenula shapes reward anticipation.
<p>The lateral habenula (LHb) supports learning processes enabling the prediction of upcoming rewards. While reward-related stimuli decrease the activity of LHb neurons, whether this anchors on synaptic inhibition to guide reward-driven behaviors remains poorly understood. Here, we combine in vivo two-photon calcium imaging with Pavlovian conditioning in mice and report that anticipatory licking emerges along with decreases in cue-evoked calcium signals in individual LHb neurons. In vivo multiunit recordings and pharmacology reveal that the cue-evoked reduction in LHb neuronal firing relies on GABA<sub>A</sub>-receptor activation. In parallel, we observe a postsynaptic potentiation of GABA<sub>A</sub>-receptor-mediated inhibition, but not excitation, onto LHb neurons together with the establishment of anticipatory licking. Finally, strengthening or weakening postsynaptic inhibition with optogenetics and GABA<sub>A</sub>-receptor manipulations enhances or reduces anticipatory licking, respectively. Hence, synaptic inhibition in the LHb shapes reward anticipation.</p>
Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"
<p>Data set for: Le Merre P, Esmaeili V, Charrière E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1. '2018_LeMerre_Neuron.pdf' - this is a pdf version of the online publication.<br> 2. 'Chronic_LFP_data.mat' - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3. 'Silicon_Probe_data.mat' - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4. 'Opto_Inactivation_data.mat' - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5. 'Mus_Inactivation_data.mat' - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6. 'Learning_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7. 'Exposed_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8. 'p_value_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig2B_Amplitude_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m'; 'plot_fig4A_SEP_H_vs_M.m’.<br> 9. 'p_value_colormap2.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m’; ’plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 10. 'scatterplot_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m'.<br> 11. 'SEP_colormtrx.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig1B_Sensory_Evoked_Potentials.m'; 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m'.<br> 12. 'zscore_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 13. 'Chronic_LFP_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Chronic_LFP_dataViewer.m'.<br> 14. 'Chronic_LFP_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Chronic_LFP_data.mat'.<br> 15. 'Silicon_Probe_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Silicon_Probe_dataViewer.m'.<br> 16. 'Silicon_Probe_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Silicon_Probe_data.mat'.<br> 17. 'plot_fig1B_Sensory_Evoked_Potentials.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18. 'plot_fig1C_Silicon_Probe_Hit_trials.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19. 'plot_fig2A_SEP_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20. 'plot_fig2B_Amplitude_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21. 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22. 'plot_fig3A_SEP_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23. 'plot_fig3B_Amplitude_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24. 'plot_fig3C_ROC_Trained_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25. 'plot_fig3C_ROC_Randomization.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26. 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27. 'plot_fig4A_SEP_H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28. 'plot_fig4B_Amplitude_ H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29. 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30. 'plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31. 'plot_fig4D_Photoinhibitions.m' - this is a Matlab code, which analyses the data in 'Opto_Inactivation_data.mat', and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32. 'plot_figS2D_Performance_DetectionTask_NeutralExposition.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33. 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34. 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35. 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36. 'plot_figS4B_Pharmacological_Inactivations.m' - this is a Matlab code, which analyses the data in 'Mus_Inactivation_data.mat', and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37. 'Load_LFP_Multisite_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Chronic_LFP_data.mat'.<br> 38. 'Load_Silicon_Probe_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Silicon_Probe_data.mat'.<br> 39. 'Load_Optogenetic_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Opto_Inactivation_data.mat'.<br> 40. 'Load_Pharmacological_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Mus_Inactivation_data.mat'.<br> 41. 'bonf_holm.m' - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code 'plot_figS4B_Pharmacological_Inactivations.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42. 'boundedline.m' - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m’; 'plot_fig3C_ROC_Trained_vs_Exposed.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4A_SEP_H_vs_M.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43. 'inpaint_nans.m' - this is a Matlab code, which is called in the Matlab code 'boundedline.m'.<br> 44. 'PSTH_Simple.m' - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.</p>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
Mu-opioid receptor-dependent changes in social reward across adolescence in mice
<p>Datasets used in publication "Mu-opioid receptor-dependent changes in social reward across adolescence in mice" (in preparation). The social conditioned place preference data were pre-processed before statistical analysis. Three diffrent datatables with the same dataset reflect the steps of the pre-processing:</p> <p>1. Animals that spent more than 70% of the pretest time in one of the contexts were excluded (datable "30_70%")</p> <p>2. Outliers were identified using Grubbs test, and removed (datatable "no_outlier")</p> <p>3. In cases where the final number of animals conditioned in each context was not equal (due to an unequal number of animals passing the 70% criterion or unequal number of animals in the litter), we pseudorandomly trimmed the larger group using a Python script (file number 10). The exception from completely random selection was introduced to preserve a mean 50% initial context preference during the pretest</p>
Dataset supplementing "Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11."
<p>These data supplement the publication</p> <p>Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11.</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>exp1_data.mat contains data of experiment 1</p> <p>exp2_data.mat contains data of experiment 2</p> <p>figure2_3.m and figure4_5.m exemplify usage of the data and reproduce the figures 2-5 of the aforementioned article.</p>
Supplementary material for: Testing the peak-end rule in bumblebees: lack of preference for a higher-reward sequence when the final reward is disappointing
<h3>ABSTRACT</h3> <p>The peak-end rule describes the tendency to evaluate experiences by their most intense and final moments, rather than considering the entire experience as a whole. While this cognitive bias is well-established in humans, studies on nonhuman animals are very limited. Bumblebees make foraging decisions largely based on past experiences, but whether peak-end effects influence their subsequent flower choices is still unknown. Here, we trained individual <em>Bombus terrestris</em> workers on two artificial flower types, blue and yellow, over 12 consecutive foraging bouts. One flower type offered a sequence of three high-quality rewards (25 μL drops of 50% w/w sucrose solution: “consistent” sequence), while the other provided the same sequence but ended with an additional, lower-quality reward (25 μL drop of 20% w/w sucrose solution: “poor end” sequence). We then tested the bees' flower type preference in a final binary choice. Bees showed a strong preference for blue flowers, both in their initial and overall visits. Across all visits during a 1-minute period, they also favoured flowers associated with the “consistent” sequence, though this preference was significant only when these flowers were yellow. Interestingly, despite offering more sucrose per foraging bout, bees did not favour the “poor end” sequence flower. This study is, to our knowledge, the first to investigate peak-end effects in an insect. How bees evaluate sequential rewards when foraging remains largely unexplored, yet could provide valuable insights into nectar distribution and plant-pollinator co-evolution.</p>
A robust method for the measurement of social reward in adult mice
<p>Dataset contains four files.</p> <p><strong>File 1. Harda_et_al._2022_all_data.xlsx</strong></p> <p>All data used in the publication. Results of the social conditioned place preference test perofmed on adult female laboratory mice (strain: C57BL/6).</p> <p><strong>File 2. Harda_et_al._2022_initial_pref_30_70%.xls</strong></p> <p>All data contained in File 1, except for animals that showed initial preference for any of the contexts exceeding 70%.</p> <p><strong>File 3. Harda_et_al._2022_initial_pref_30_70%_trimmed.xlsx</strong></p> <p>Data contained in File 2, randomly trimmed to the same number of animals for each social context. Trimming was performed separately for each experimental group. Data were trimmed by custom R script (File 4).</p> <p><strong>File 4. trimmer.R</strong></p> <p>R script used to randomly trimm data to the same number of animals for each social context.</p>
Data from: Neural interactions in the human frontal cortex dissociate reward and punishment learning
<p>How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to reward and punishment. The non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.</p>
Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"
<p>Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831<br><br></p> <p>There are 2 files in this upload:</p> <p>1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon.</p> <p>2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".</p>
ScienceDex guides
Understand access before you commit
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.