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418 results for “prefrontal cortex”

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OpenNeuro48/100

Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations

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openCC0Jan 2020View details →
OpenNeuro48/100

Developmental change in prefrontal cortex recruitment supports the emergence of value-guided memory

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openCC0Jan 2021View details →
zenodo48/100

Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus

<p>This is supplementary data and source data for the manuscript,&nbsp;<em>"Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus"</em>.</p> <p><strong>Abstract</strong>: Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n=399, dorsolateral prefrontal cortex: n=377, and hippocampus: n=394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.</p>

opencc-by-4.0Dec 2023View details →
zenodo44/100

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, Charri&egrave;re 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.&nbsp;&nbsp; &nbsp;&#39;2018_LeMerre_Neuron.pdf&#39; - this is a pdf version of the online publication.<br> 2.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_data.mat&#39; - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_data.mat&#39; - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4.&nbsp;&nbsp; &nbsp;&#39;Opto_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5.&nbsp;&nbsp; &nbsp;&#39;Mus_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6.&nbsp;&nbsp; &nbsp;&#39;Learning_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7.&nbsp;&nbsp; &nbsp;&#39;Exposed_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&rsquo;.<br> 9.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap2.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&rsquo;; &rsquo;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 10.&nbsp;&nbsp; &nbsp;&#39;scatterplot_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39;.<br> 11.&nbsp;&nbsp; &nbsp;&#39;SEP_colormtrx.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39;; &#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39;.<br> 12.&nbsp;&nbsp; &nbsp;&#39;zscore_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 13.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Chronic_LFP_dataViewer.m&#39;.<br> 14.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Chronic_LFP_data.mat&#39;.<br> 15.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Silicon_Probe_dataViewer.m&#39;.<br> 16.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Silicon_Probe_data.mat&#39;.<br> 17.&nbsp;&nbsp; &nbsp;&#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18.&nbsp;&nbsp; &nbsp;&#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19.&nbsp;&nbsp; &nbsp;&#39;plot_fig2A_SEP_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20.&nbsp;&nbsp; &nbsp;&#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21.&nbsp;&nbsp; &nbsp;&#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22.&nbsp;&nbsp; &nbsp;&#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23.&nbsp;&nbsp; &nbsp;&#39;plot_fig3B_Amplitude_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Randomization.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26.&nbsp;&nbsp; &nbsp;&#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27.&nbsp;&nbsp; &nbsp;&#39;plot_fig4A_SEP_H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28.&nbsp;&nbsp; &nbsp;&#39;plot_fig4B_Amplitude_ H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31.&nbsp;&nbsp; &nbsp;&#39;plot_fig4D_Photoinhibitions.m&#39; - this is a Matlab code, which analyses the data in &#39;Opto_Inactivation_data.mat&#39;, and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32.&nbsp;&nbsp; &nbsp;&#39;plot_figS2D_Performance_DetectionTask_NeutralExposition.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33.&nbsp;&nbsp; &nbsp;&#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34.&nbsp;&nbsp; &nbsp;&#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35.&nbsp;&nbsp; &nbsp;&#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36.&nbsp;&nbsp; &nbsp;&#39;plot_figS4B_Pharmacological_Inactivations.m&#39; - this is a Matlab code, which analyses the data in &#39;Mus_Inactivation_data.mat&#39;, and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37.&nbsp;&nbsp; &nbsp;&#39;Load_LFP_Multisite_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Chronic_LFP_data.mat&#39;.<br> 38.&nbsp;&nbsp; &nbsp;&#39;Load_Silicon_Probe_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Silicon_Probe_data.mat&#39;.<br> 39.&nbsp;&nbsp; &nbsp;&#39;Load_Optogenetic_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Opto_Inactivation_data.mat&#39;.<br> 40.&nbsp;&nbsp; &nbsp;&#39;Load_Pharmacological_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Mus_Inactivation_data.mat&#39;.<br> 41.&nbsp;&nbsp; &nbsp;&#39;bonf_holm.m&#39; - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code &#39;plot_figS4B_Pharmacological_Inactivations.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42.&nbsp;&nbsp; &nbsp;&#39;boundedline.m&#39; - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&rsquo;; &#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43.&nbsp;&nbsp; &nbsp;&#39;inpaint_nans.m&#39; - this is a Matlab code, which is called in the Matlab code &#39;boundedline.m&#39;.<br> 44.&nbsp;&nbsp; &nbsp;&#39;PSTH_Simple.m&#39; - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.</p>

opencc-by-4.0Dec 2017View details →
zenodo44/100

Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS

<p>A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O).<br>Citation of the article related to this dataset:</p> <div> <div><strong>Guevara, E.</strong> (2024). <em>Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS</em> [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>

opencc-by-4.0Oct 2023View details →
zenodo40/100

Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex

<p>These datasets and codes were used for analysis in a manuscript titled &quot;Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex.&quot; For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

ASIC2 Deletion in Medial Prefrontal Cortex Enhances Social Dominance in Mice

<p>Social dominance is essential for maintaining a stable social society and has well-established positive and negative impacts on sociable animals, including humans. However, the regulatory mechanisms governing social dominance, as well as the crucial regulators and biomarkers involved, remain poorly understood. We discover that mice lacking acid-sensing ion channel 2 (ASIC2) exhibit a persistent higher social dominance ranking compared to their wild-type cagemates. Conversely, the overexpression of ASIC2 in the medial prefrontal cortex (mPFC) reverses the dominance hierarchy observed in ASIC2 knockout mice. ASIC2 deletion prolongs the inactivation time of ASICs, resulting in enhanced ASIC-dependent synaptic transmission and plasticity in the mPFC through the protein kinase A signaling pathway. Furthermore, ASIC2 exhibits distinct functional roles in excitatory and inhibitory neurons, thereby modulating the balance of neuronal activities underlying social dominance behaviors&mdash;a phenomenon suggestive of a cell-subtype-specific mechanism. Finally, this research establishes a foundational understanding of the mechanisms governing social dominance formation, offering potential insights for the management or prevention of social disorders, such as depression and anxiety.</p>

opencc-by-4.0Nov 2023View details →
zenodo40/100

Transcranial direct current stimulation (tDCS) over the left prefrontal cortex does not affect time-trial self-paced cycling performance: Evidence from oscillatory brain activity and power output.

<p>This research will shed new light into the bidirectional relationship between acute aerobic exercise, brain and cognition. This is based on the particular role of executive (cognitive) function during exercise. The rationale of our study is that stimulation of the prefrontal cortex that has been repeatedly associated with executive function, would facilitate or impair self-paced aerobic exercise. This would also affect cognitive performance immediately after exercise. We will use a modified flanker&rsquo;s task as a form of assessing executive function (see below for further details). The flanker&rsquo;s task implies two different stimuli, one congruent and one incongruent. Relative to &ldquo;congruent&rdquo; stimuli, these &ldquo;incongruent&rdquo; stimuli are usually accompanied by increased response times (RTs) and decreased accuracy. To stimulate the prefrontal cortex, we use transcranial direct-current stimulation (tDCS). tDCS is able to induce cortical changes by hyperpolarizing (anodal) or depolarizing (cathodal) neuron&rsquo;s&nbsp;resting membrane potential.<br> Therefore, the hypotheses of this research are:<br> 1) Anodal stimulation (relative to sham and cathodal stimulation) will improve self-paced aerobic exercise and, consequently it will also improve subsequent cognitive performance.<br> 2) Cathodal stimulation (relative to sham and anodal stimulation) will impair self-paced aerobic exercise and subsequent cognitive performance.<br> &nbsp;</p>

opencc-by-4.0May 2018View details →
zenodo40/100

Data and codes from: Oscillatory Codes for Memory and Perception in the Prefrontal Cortex

<p>These datasets and codes were used for analysis in a manuscript titled &quot;Oscillatory Codes for Memory and Perception in the Prefrontal Cortex.&quot; For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>

opencc-by-4.0Jul 2021View details →
dryad40/100

Data for: The cerebellum regulates fear extinction through thalamo-prefrontal cortex interactions in male mice

<p>Fear extinction is a form of inhibitory learning that suppresses the expression of aversive memories and plays a key role in the recovery of anxiety and trauma-related disorders. Here, using male mice, we identify a cerebello-thalamo-cortical pathway regulating fear extinction. The cerebellar fastigial nucleus (FN) projects to the lateral subregion of the mediodorsal thalamic nucleus (MD), which is reciprocally connected with the dorsomedial prefrontal cortex (dmPFC). The inhibition of FN inputs to MD in male mice impairs fear extinction in animals with high fear responses and increases the bursting of MD neurons, a firing pattern known to prevent extinction learning. Indeed, this MD bursting is followed by high levels of the dmPFC 4 Hz oscillations causally associated with fear responses during fear extinction, and the inhibition of FN-MD neurons increases the coherence of MD bursts and oscillations with dmPFC 4 Hz oscillations. Overall, these findings reveal a regulation of fear-related thalamo-cortical dynamics by the cerebellum and its contribution to fear extinction.</p>

opencc-zeroFeb 2023View details →
dryad40/100

Data for: The cerebellum regulates fear extinction through thalamo-prefrontal cortex interactions in male mice

Open the record for dataset details and reuse information.

publicFeb 2023View details →
OpenNeuro36/100

Layer-dependent activity in human prefrontal cortex during working memory

Open the record for dataset details and reuse information.

openJan 2019View details →
dryad36/100

Stable and dynamic representations of value in the prefrontal cortex

<p>This data set was compiled for the experiments detailed in <em>Enel, P., Wallis, J., &amp; Rich, E. (2019). Stable and dynamic representations of value in the prefrontal cortex. </em><i>Elife</i> <em>9 (2020): e54313 </em><span class="doi"><a class="doi__link" href="https://doi.org/10.7554/eLife.54313">10.7554/eLife.54313</a></span></p> <p>Optimal decision-making requires that stimulus-value associations are kept up to date by constantly comparing the expected value of a stimulus with its experienced outcome. To do this, value information must be held in mind when a stimulus and outcome are separated in time. However, little is known about the neural mechanisms of working memory (WM) for value. Contradicting theories have suggested WM requires either persistent or transient neuronal activity, with stable or dynamic representations respectively. To test these hypotheses, we recorded neuronal activity in the orbitofrontal and anterior cingulate cortex of two monkeys performing a valuation task. We found that features of all hypotheses were simultaneously present in prefrontal activity, and no single hypothesis was exclusively supported. Instead, mixed dynamics supported robust, time invariant value representations while also encoding the information in a temporally specific manner. We suggest that this hybrid coding is a critical mechanism supporting flexible cognitive abilities.</p>

opencc-zeroSep 2020View details →
zenodo36/100

Transcription start sites from capped small RNA-seq of rat nucleus accubmens and prefrontal cortex

<p>Small RNAs of &sim;15&ndash;60 nt were size selected by denaturing gel electrophoresis starting from total RNA extracted from 14 rat brain tissue dissections. For csRNA libraries, cap selection was followed by decapping, adapter ligation, and sequencing. For input libraries, 10% of small RNA input was used for decapping, adapter ligation, and sequencing. After library quality check by gel electrophoresis, the samples were sequenced using the Illumina NextSeq 500 platform using 75 cycles single end. Sequencing reads were aligned to the rat mRatBN7.2 genome assembly using STAR v2.5.3a aligner with default parameters. Transcriptional start regions were defined using HOMER&rsquo;s findPeaks tool.&nbsp;</p> <p>Duttke, S.H., Montilla-Perez, P., Chang, M.W., Li, H., Chen, H., Carrette, L.L.G., de Guglielmo, G., George, O., Palmer, A.A., Benner, C., et al. (2022). Glucocorticoid Receptor-Regulated Enhancers Play a Central Role in the Gene Regulatory Networks Underlying Drug Addiction. Front. Neurosci. 16, 858427.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

A Prefrontal Cortex Map based on Single Neuron Activity

<p>Intermediate results files (after preprocessing) to recreate the figures from:</p> <p><em><strong>A Prefrontal Cortex Map based on Single Neuron Activity, </strong></em>Pierre Le Merre*, Katharina Heining*<em>,&nbsp;Marina&nbsp;Slashcheva, Felix&nbsp;Jung,&nbsp;Eleni&nbsp;Moysiadou, Nicolas&nbsp;Guyon,&nbsp;Ram&nbsp;Yahya, Hyunsoo&nbsp;Park, Fredrik&nbsp;Wernstal, and </em><em>Marie&nbsp;</em><em>Carl&eacute;n.</em> Equal contribution.</p> <p>Article currently available at: https://www.biorxiv.org/content/10.1101/2024.11.06.622308v2</p> <p>The raw data can be found at: https://dandiarchive.org/dandiset/001260?pos=1</p> <p>Description: Brain-wide high-density extracellular recordings (Neuropixels) in head-fixed mice during distinct passive listening and auditory behavioral tasks. About half of the recordings are in the prefrontal cortex (PFC) and half from other brain regions (3 cortical and 10 subcortical). In PFC we include 11 subregions: secondary motor area (MOs), anterior cingulate area &ndash; dorsal and ventral part (ACAd, ACAv), prelimbic area (PL), infralimbic area (ILA), orbital area &ndash; medial, lateral, and ventrolateral part (ORBm, ORBvl, ORBl), agranular insular area &ndash; dorsal and ventral part (AId, AIv), and frontal pole (FRP).</p> <p>The code generating these intermediate files from the raw data can be found at: https://github.com/hejDMC/pfcmap</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Prefrontal Cortex Contribution in Transitive Inference

<p><span>Raw neural signals and event series were extracted, structured on a trial-by-trial basis, adapted for the analyses to be performed, and then stored as MATLAB files. The MATLAB files included in this repository contain behavioral and neural data structures and can be used by the analysis codes available at <span>https://doi.org/10.6084/m9.figshare.27951621.v1</span><br></span></p>

opencc-by-4.0Dec 2023View details →
dryad36/100

Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex

<p>This dataset contains iEEG neural recordings and behavior movement direction in a navigation task from human subjects undergoing inpatient monitoring for seizure localization. The experimental design, including an explanation of metrics of interest, is detailed in Chen et al (2018) <em>Current Biology</em> and Chen et al (2021) <em>Science Advances</em>. EXAMPLE electrode data from the vmPFC and EC ROIs are included. These data are used for grid-like modulation of theta power in vmPFC and EC.</p>

opencc-zeroNov 2021View details →
zenodo36/100

Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex (data & code)

<p><strong>Data and code for:</strong></p> <p>Ap&scaron;valka, D., Ferreira, C. S., Schmitz, T. W., Rowe, J. B., &amp; Anderson, M. C. (2022). Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex. <em>Nature Communications, </em> <strong>13, </strong>274<em>.</em> <a href="https://doi.org/10.1038/s41467-021-27926-w"> https://doi.org/10.1038/s41467-021-27926-w</a></p> <blockquote> <p>Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.</p> </blockquote>

openother-openNov 2021View details →
zenodo36/100

Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: Animal Behavior

<p>The excel Source DATA file contains the data described in Figures 2c, 2d, 2f, and 3b and Supplementary Figure 3b and 3c. The fiber photometry data described in Supplementary Figure 9 are found in the CSV files. The CSV file names reflect animal IDs.&nbsp;</p>

opencc-by-3.0-usDec 2021View details →
zenodo36/100

Data set of study The role of REM sleep in consolidating executive function: a prefrontal cortex perspective

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record