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167 results for “rett”

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zenodo48/100

Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"

<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id. The columns are&nbsp;[&#39;T&#39;, &#39;F1&#39;, &#39;N1&#39;, &#39;F2&#39;, &#39;N2&#39;] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape&nbsp; images x 2 that hold&nbsp;x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in (&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;, &#39;Neutral_2&#39;).</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern &gt; 0.3.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id and treatment (&#39;NA&#39;). The columns contain [&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;, &#39;Neutral_2&#39;] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of [&#39;Neutral&#39;, &#39;Fear&#39;, &#39;Fear_2&#39;]. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id and treatment (&#39;NA&#39;=not applicable since no DREADD used). The columns contain [&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;, &#39;Neutral_2&#39;] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three&nbsp;numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three&nbsp;arrays stored under the variable names &#39;U&#39;,&nbsp;&#39;sigma&#39; and &#39;img_shape&#39;. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, [&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;,&nbsp; &#39;Neutral_2&#39;] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored&nbsp;in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs).&nbsp;</strong></p> <p>The dictionary is keyed on&nbsp; [&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;,&nbsp; &#39;Neutral_2&#39;] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J&nbsp;of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on&nbsp;[&#39;boundaries&#39;, &#39;annuli&#39;] contexts and each value is a 179 element list of&nbsp;arrays of boundary line coordinates or annulus point coordinates one&nbsp; per ROI&nbsp;detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three&nbsp;numpy arrays representing the basis images for mouse N019&nbsp;of genotype wild-type.</strong></p> <p>This dict has three&nbsp;arrays stored under the variable names &#39;U&#39;,&nbsp;&#39;sigma&#39; and &#39;img_shape&#39;. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C&nbsp;of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019&nbsp;of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204&nbsp;source images, height=516&nbsp;pixels and width=698&nbsp;pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of&nbsp;WT and RTT mice.</strong></p> <p>This dictionary is keyed on [&#39;wt&#39;, &#39;mecp2_pos&#39;, &#39;mecp2_neg&#39;] representing whether the pyramidal cell was recorded from a wild-type mouse (&#39;wt&#39;) or is an MeCP2 negative or MeCP2 positive RTT cell. This value&nbsp;under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies&nbsp;of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on [&#39;wt&#39;, &#39;mecp2_pos&#39;, &#39;mecp2_neg&#39;] representing whether the pyramidal cell was recorded from a wild-type mouse (&#39;wt&#39;) or is an MeCP2 negative or MeCP2 positive RTT cell. This value&nbsp;under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype (&#39;wt&#39;, &#39;het&#39;), the mouse_id, the treatment (&#39;NA&#39;=not applicable since no DREADD used), and the cell index starting from 0. The columns are [&#39;centroid&#39;, &#39;cell_boundary&#39;, &#39;annulus_boundary&#39;]. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region&nbsp; excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype (&#39;wt&#39;, &#39;het&#39;), the mouse_id, the treatment (&#39;NA&#39;=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are&nbsp;[&#39;channels&#39;, &#39;channel&#39;, &#39;num_pages&#39;, &#39;width&#39;, &#39;height&#39;, &#39;bits&#39;, &#39;Train_signals&#39;, &#39;Fear_signals&#39;, &#39;Neutral_signals&#39;, &#39;Cue_signals&#39;, &#39;Fear_2_signals&#39;, &#39;Neutral_2_signals&#39;, &#39;Cue_2_signals&#39;, &#39;Train_spikes&#39;, &#39;Fear_spikes&#39;, &#39;Neutral_spikes&#39;, &#39;Cue_spikes&#39;, &#39;Fear_2_spikes&#39;, &#39;Neutral_2_spikes&#39;, &#39;Cue_2_spikes&#39;, &#39;sample_rate&#39;]. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals&#39; are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope.&nbsp;The&nbsp;&#39;*_spikes&#39; are the inferred spikes for each cell stored as an image index. This signal and spike indices&nbsp;can be converted to time using the&nbsp;sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows&nbsp;indexed by&nbsp;genotype (&#39;wt&#39; or &#39;het&#39;) and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=(&#39;Train&#39;, &#39;Fear&#39;, &#39;Neutral&#39;, &#39;Fear_2&#39;, &#39;Neutral_2&#39;). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong>&nbsp;<strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys [&#39;som&#39;, &#39;som_rett_pos&#39;, &#39;som_rett_neg&#39;] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used&nbsp;in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong>&nbsp;<strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys [&#39;som&#39;, &#39;som_rett_pos&#39;, &#39;som_rett_neg&#39;] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used&nbsp;in Figure 4E-G.</p> <p><strong>som_signals_df.pkl:&nbsp;A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype (&#39;wt&#39;, &#39;het&#39;), the mouse_id, the treatment (&#39;NA&#39;=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710&nbsp;cells. The columns are&nbsp;[&#39;channels&#39;, &#39;channel&#39;, &#39;num_pages&#39;, &#39;width&#39;, &#39;height&#39;, &#39;bits&#39;, &#39;Train_signals&#39;, &#39;Fear_signals&#39;, &#39;Neutral_signals&#39;, &#39;Cue_signals&#39;, &#39;Fear_2_signals&#39;, &#39;Neutral_2_signals&#39;, &#39;Cue_2_signals&#39;, &#39;Train_spikes&#39;, &#39;Fear_spikes&#39;, &#39;Neutral_spikes&#39;, &#39;Cue_spikes&#39;, &#39;Fear_2_spikes&#39;, &#39;Neutral_2_spikes&#39;, &#39;Cue_2_spikes&#39;, &#39;sample_rate&#39;]. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals&#39; are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope.&nbsp;The&nbsp;&#39;*_spikes&#39; are the inferred spikes for each cell stored as an image index. This signal and spike indices&nbsp;can be converted to time using the&nbsp;sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz:&nbsp;a dict containing three&nbsp;numpy arrays representing the basis images for mouse ssn33&nbsp;of genotype sst-cre.</strong></p> <p>This dict has three&nbsp;arrays stored under the variable names &#39;U&#39;,&nbsp;&#39;sigma&#39; and &#39;img_shape&#39;. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A&nbsp;of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019&nbsp;of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86&nbsp;source images, height=516&nbsp;pixels and width=654&nbsp;pixels. This data was used to construct Figure 5A&nbsp;of the paper.</p>

opencc-by-4.0Feb 2022View details →
zenodo44/100

Andreas Rett (r1328)

<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Andreas Rett<br><u>musiXplora-ID</u>: r1328<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/r1328">https://musixplora.de/mxp/r1328</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 03 May 1870<br><u>Place of Birth</u>: Bamberg<br><u>First Mentioned</u>: 1905<br><u>Sectors</u>: Kirche, Orgelbau<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Other Places of Activity</u>: Bamberg, Fürth<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Sohn</td><td>Peter Rett</td><td><a href="https://musixplora.de/mxp/r1327">r1327</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr><tr><td>Related</td><td>Fischer &amp; Wohnhaas 1994</td><td>Lexikon süddeutscher Orgelbauer</td><td><a href="https://musixplora.de/mxp/5001133">5001133</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br>&nbsp;&nbsp;- v0.0.1: Initial Upload.<br>

opencc-by-4.0Jun 2024View details →
zenodo40/100

Dataset related to article "Fluoxetine rescues rotarod motor deficits in Mecp2 heterozygous mouse model of Rett syndrome via brain serotonin"

<p><em>The&nbsp;file contains raw data related to the article&nbsp;&quot;Fluoxetine rescues rotarod motor deficits in Mecp2 heterozygous mouse model of Rett syndrome via brain serotonin&quot;, available from&nbsp;</em><a href="https://doi.org/10.1016/j.neuropharm.2020.108221">https://doi.org/10.1016/j.neuropharm.2020.108221</a><em>.</em></p> <p>&nbsp;</p> <p><strong>Abstract of the manuscript</strong></p> <p>&nbsp;Motor skill is a specific area of disability of Rett syndrome (RTT), a rare disorder occurring almost exclusively in girls, caused by loss-of-function mutations of the X-linked methyl-CpG-binding protein2 (MECP2) gene, encoding the MECP2 protein, a member of the methyl-CpG-binding domain nuclear proteins family. Brain 5-HT, which is defective in RTT patients and Mecp2 mutant mice, regulates motor circuits and SSRIs enhance motor skill learning and plasticity. In the present study, we used heterozygous (Het) Mecp2 female and Mecp2-null male mice to investigate whether fluoxetine, a SSRI with pleiotropic effects on neuronal circuits, rescues motor coordination deficits. Repeated administration of 10 mg/kg fluoxetine fully rescued rotarod deficit in Mecp2 Het mice regardless of age, route of administration or pre-training to rotarod. The motor improvement was confirmed in the beam walking test while no effect was observed in the hanging-wire test, suggesting a preferential action of fluoxetine on motor coordination. Citalopram mimicked the effects of fluoxetine, while the inhibition of 5-HT synthesis abolished the fluoxetine-induced improvement of motor coordination. Mecp2 null mice, which responded poorly to fluoxetine in the rotarod, showed reduced 5-HT synthesis in the prefrontal cortex, hippocampus and striatum, and reduced efficacy of fluoxetine in raising extracellular 5-HT as compared to female mutants. No sex differences were observed in the ability of fluoxetine to desensitize 5-HT<sub>1A</sub>&nbsp;autoreceptors upon repeated administration. These findings indicate that fluoxetine rescues motor coordination in Mecp2 Het mice through its ability to enhance brain 5-HT and suggest that drugs enhancing 5-HT neurotransmission may have beneficial effects on motor symptoms of RTT.</p>

opencc-by-4.0Sep 2021View details →
zenodo36/100

Effects of immersive virtual reality with treadmill in subjects with Rett Syndrome: a pilot study

<p>This dataset is related to a group of 9 girls affected by Rett Syndrome that performed three/four sessions of virtual reality based activity by means of the GRAIL system (Motek).</p><p>Participants underwent an initial assessment of the cognitive and motor aspects using the Modified Raven matrix, the&nbsp;Rett Assessment Rating Scale (RARS) and the Rett Syndrome Gross Motor Scale (GMS). These baseline assessments were performed by an experienced therapist prior to the access to the GRAIL.</p><p>The project consisted of 4 sessions of GRAIL activities in 1 week. During the first 3 sessions, exergames and augmented feedback activities were proposed. During the fourth session a gait analysis was conducted for the subjects with a suitable motor and behavioral level .</p><p>During all the sessions, a video recording was conducted to detect facial expression of the subjects, in order to use this data to compute the "happiness index". The exergames were administered until the therapists detected any sign of discomfort by the subject.</p><p>The outcome of the training, reported in the database were:</p><ul><li>happiness index</li><li>endurance time</li><li>performance speed</li><li>attention focus on scenario</li><li>suitability evaluation questionnaire</li><li>multistep gait analysis with the GRAIL (only 4 patients).</li></ul><p>The Happiness Index and the Suitability Evaluation Questionnaire investigated the feasibility and tolerability of the training with positive results.&nbsp;Additionally, an increase in walking speed, endurance time and focus attention was detected.&nbsp;When possible, subjects underwent a gait assessment whose results&nbsp;provide insights, although preliminary, concerning differences in gait pattern amongst the recruited subjects. Despite the small sample size, the dataset suggests new strategies for Rett rehabilitation.</p><p>&nbsp;</p>

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

Rett Syndrome Omics Datasets integrated into RettDb

<p>This datasets collection is part of the web resource: RettDb, the Rett Syndrome Omics Database.</p> <p>RettDb is fully described in this paper submitted to DATABASE: Cill&agrave;ri, N., Neri, G., Pisanti, N., Milazzo, P., Borello, U. "RettDb: the Rett Syndrome Omics Database to navigate the Rett Syndrome genomic landscape"&nbsp;</p> <p>&nbsp;</p> <p>The table below contains a brief description of the data</p> <p>For a full description of the data, refer to the web resourse or this publication</p> <p>&nbsp;</p> <table> <tbody> <tr> <td><strong>filenames</strong></td> <td><strong>description</strong></td> <td><strong>file-type</strong></td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K4me3 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K4me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K9me3 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K9me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz</td> <td>H3K27ac ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K27ac ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K36me3 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K36me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz</td> <td>H4K20me3 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H4K20me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph&nbsp;</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_3.bw.gz.bw</td> <td>Mecp2 ChipSeq</td> <td>bigwig</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz</td> <td>Mecp2 RNASeq&nbsp;</td> <td>bed</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz.tbi</td> <td>Mecp2 RNASeq Index</td> <td>bed index</td> </tr> <tr> <td>Mecp2_wt.hic</td> <td>Mecp2 WT chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>Mecp2_ko.hic</td> <td>Mecp2 KO chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>CpG_island_mm10.bed.gz</td> <td>CpG island</td> <td>bed&nbsp;</td> </tr> <tr> <td>CpG_island_mm10.bed.gz.tbi</td> <td>CpG island Index</td> <td>bed index</td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz</td> <td>FIMO CISBP TFBS Predictions</td> <td>bed&nbsp;</td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz.tbi</td> <td>FIMO CISBP TFBS Predictions Index</td> <td>bed index</td> </tr> <tr> <td>remap2022_CRMs.bb</td> <td>Remap2022 Cis Regulatory Modules</td> <td>bigbed</td> </tr> <tr> <td>remap2022_non_redundant_peak.bb</td> <td>Remap2022 Non Redundant Peaks</td> <td>bigbed</td> </tr> <tr> <td>hub.config.json</td> <td>Tracks configuration file for WashU</td> <td>json</td> </tr> </tbody> </table> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
ClinicalTrials.gov36/100

Pharmacological Treatment of Rett Syndrome With Glatiramer Acetate (Copaxone)

ClinicalTrials.gov study NCT02153723. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Evaluation of the Efficacy, Safety, and Tolerability of Sarizotan in Rett Syndrome With Respiratory Symptoms

ClinicalTrials.gov study NCT02790034. IPD Sharing: Not stated. Countries: 5. Publications: 27.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

An Open-Label Study of Trofinetide for the Treatment of Girls Two to Five Years of Age Who Have Rett Syndrome

ClinicalTrials.gov study NCT04988867. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Study of Trofinetide for the Treatment of Girls and Women With Rett Syndrome (LAVENDER™)

ClinicalTrials.gov study NCT04181723. IPD Sharing: Not stated. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

A Study to Evaluate Ketamine for the Treatment of Rett Syndrome

ClinicalTrials.gov study NCT03633058. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Open-Label Extension Study of Trofinetide for the Treatment of Girls and Women With Rett Syndrome

ClinicalTrials.gov study NCT04279314. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Open-Label Extension Study of Trofinetide for Rett Syndrome

ClinicalTrials.gov study NCT04776746. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Treatment of Rett Syndrome With Recombinant Human IGF-1

ClinicalTrials.gov study NCT01777542. IPD Sharing: Not stated. Countries: 1. Publications: 57.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Label-free three-photon imaging of intact human cerebral organoids: Tracking early events in brain development and deficits in Rett Syndrome

<p>Human cerebral organoids are unique in their development of progenitor-rich zones akin to ventricular zones from which neuronal progenitors differentiate and migrate radially. Analyses of cerebral organoids thus far have been performed in sectioned tissue or in superficial layers due to their high scattering properties. Here, we demonstrate label-free three-photon imaging of whole, uncleared intact organoids (~2 mm depth) to assess early events of early human brain development. Optimizing a custom-made three-photon microscope to image intact cerebral organoids generated from Rett Syndrome patients, we show defects in the ventricular zone volumetric structure of mutant organoids compared to isogenic control organoids. Long-term imaging of live organoids reveals that shorter migration distances and slower migration speeds of mutant radially migrating neurons are associated with more tortuous trajectories. Our label-free imaging system constitutes a particularly useful platform for tracking normal and abnormal development in individual organoids, as well as for screening therapeutic molecules via intact organoid imaging.</p>

opencc-zeroJun 2022View details →
zenodo32/100

Peter Rett (r1327)

<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Peter Rett<br><u>musiXplora-ID</u>: r1327<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/r1327">https://musixplora.de/mxp/r1327</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 07 July 1832<br><u>Place of Birth</u>: Bad Dürkheim<br><u>Date of Death</u>: 27 October 1901<br><u>Place of Death</u>: Würzburg<br><u>First Mentioned</u>: 1866<br><u>Sectors</u>: Kirche, Orgelbau<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Bamberg<br><u>Other Places of Activity</u>: Gremsdorf<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Vater</td><td>Andreas Rett</td><td><a href="https://musixplora.de/mxp/r1328">r1328</a></td></tr></tbody></table><br><u>Arbeitsumfeld:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>ArbeitgeberInnen</td><td>Arbeitnehmer</td><td>Friedrich Wiedemann</td><td><a href="https://musixplora.de/mxp/b3817">b3817</a></td></tr><tr><td>ArbeitgeberInnen</td><td>Arbeitnehmer</td><td>Joseph Wiedemann</td><td><a href="https://musixplora.de/mxp/w0597">w0597</a></td></tr><tr><td>VorgängerInnen</td><td>Nachfolger</td><td>Friedrich Wiedemann</td><td><a href="https://musixplora.de/mxp/b3817">b3817</a></td></tr></tbody></table><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Rett</td><td><a href="https://musixplora.de/mxp/3030679">3030679</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr><tr><td>Related</td><td>Fischer &amp; Wohnhaas 1994</td><td>Lexikon süddeutscher Orgelbauer</td><td><a href="https://musixplora.de/mxp/5001133">5001133</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br>&nbsp;&nbsp;- v0.0.1: Initial Upload.<br>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Boosting serotonin synthesis is not sufficient to improve motor coordination of Mecp2 heterozygous mouse model of Rett syndrome

<p>Excel file related to the article</p>

opencc-by-4.0Jul 2024View details →
ClinicalTrials.gov32/100

A Safety Study of NNZ-2566 in Pediatric Rett Syndrome

ClinicalTrials.gov study NCT02715115. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Natural History of Rett Syndrome & Related Disorders

ClinicalTrials.gov study NCT02738281. IPD Sharing: YES. Countries: 1. Publications: 75.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

A Validation of Current Hospital Triage Performance System Versus RETTS

ClinicalTrials.gov study NCT02303613. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Neurophysiologic Biomarkers in Rett Syndrome

ClinicalTrials.gov study NCT05932589. IPD Sharing: YES. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →

ScienceDex guides

Understand access before you commit

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

Compare curated 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.

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