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11,745 results for “Disorder”
Oral Naltrexone In Pediatric Eating Disorders
ClinicalTrials.gov study NCT05073679. IPD Sharing: NO. Countries: 1. Publications: 7.
Improving Adherence in Adolescents and Young Adults With Bipolar Disorder
ClinicalTrials.gov study NCT04348604. IPD Sharing: YES. Countries: 1. Publications: 1.
A Phase 3 Study of Lu AA21004 in Patients With Major Depressive Disorder
ClinicalTrials.gov study NCT02389816. IPD Sharing: YES. Countries: 1. Publications: 3.
PRimary Care Opioid Use Disorders Treatment (PROUD) Trial
ClinicalTrials.gov study NCT03407638. IPD Sharing: YES. Countries: 1. Publications: 7.
Exposure-Based Treatment for Undifferentiated Somatic Symptom Disorder
ClinicalTrials.gov study NCT04511286. IPD Sharing: NO. Countries: 1. Publications: 1.
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
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Data from: Characterizing the hyperuniformity of disordered network metamaterials
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Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup
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Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
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White matter deficits in cocaine use disorder V1.0
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Development of a high-throughput small molecule screening assay for phenotypical characterization of lysosomal storage disorder-affected cells, with infantile cystinosis as a proof of principle
<p>Together with the Pivot Park Screening Centre we performed a drug screen on CTNS-/- proximal tubule cells. For this we developed an assay to evaluate LC3-II positive puncta, and which may be applied for any disease in which autophagy plays an important role. The screen was optimized by the hotel for a 384 well format, making it useful for high throughput screening. The screen was performed with 1280 compounds from the Prestwick library.</p>
Wikidata Dump Depressive disorders
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> Wikidata dumps<br> <a href="https://tools.wmflabs.org/wdumps/dump/286">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Wikidata Dump Depressive disorders
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> Wikidata dumps<br> <a href="https://tools.wmflabs.org/wdumps/dump/286">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Alzheimer's Disease versus Bipolar Disorder versus Health Control MRI data and processed results
<p><strong>README</strong></p> <p>The data is structured as follows:</p> <p>Clinical_data folder contains the .csv that can be read by spreadsheet software, as well as from Python, Matlab or R. There are separate files for each biomarker. The file "clinical_data_id_age_gender.csv" contains the numerical random key of the patient for anonymity, diagnostic key, age and gender for each entry in the other files. The file "clinical_data_corrected.csv" can be ignored.</p> <p>Diagnostic keywords: "crl" == healthy control, "tb" == bipolar disorder, "ea" == Alheimer's disease</p> <p>Imaging data is nifti encoded. The name of the file starts with the diagnostic key followed by the numerical random key and some nemotechnic for the contents. For instance: "crl_132_diff_dti_FA_FA_to_target.nii.gz" is the spatially normalized FA data of healthy control 132. Imaging data can be read with FSL, SPM, and any other nifti reading soft.</p> <p>Imaging folders contain the following data<br> DWI_origin - > the original diffusion weighted MRI data and their corresponding b-vector values</p> <p>FA - > the FA coefficients computed using FSL</p> <p>FA_to_target - > the FA volumes registered to MNI template using FSL tools</p> <p>T1_preprocessed - > the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools</p> <p>T1_VBM_SPM_1mm - > the results of applying SPM implementation of voxel based morphometry (VBM) on the T1-weighted data, including results of the correlation between biomarkers and the detected clusters . Results can be checked using SPM (https://www.fil.ion.ucl.ac.uk/spm/)</p> <p>TBSS_results -> contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS)</p> <p> </p> <p><strong>Publications using this dataset</strong></p> <p>M. Graña, M. Termenon, A. Savio, A. Gonzalez-Pinto, J. Echeveste, J. M. Pérez, A. Besga, Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation, Neuroscience letters,Volume 502, Issue 3, 20 September 2011, Pages 225-229</p> <p>A. Besga, M. Termenon, M. Graña, J. Echeveste, J. M. Perez, A. Gonzalez-Pinto "Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features, <strong>Neuroscience Letters</strong>, Volume 520, Issue 1, 27 June 2012, Pages 71–76.</p> <p>M. Termenon, M. Graña, A. Besga, J. Echeveste, A. Gonzalez-Pinto, Lattice Independent Component Analysis feature selection on Diffusion Weighted Imaging for Alzheimer’s Disease Classification, Neurocomputing (2013) Volume 114, 19 August 2013, Pages 132–141</p> <p>Ariadna Besga, Itxaso González-Ortega, Enrique Echeburúa, Alexandre Savio, Borja Ayerdi, Darya Chyzhyk, Jose LM Madrigal, Juan C. Leza, Manuel Graña, Ana González-Pinto, "Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis" Frontiers in Aging Neuroscience, 7:231</p> <p>Ariadna Besga-Basterra, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña, Ana González-Pinto, Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s Disease and Late Onset Bipolar Disorder, Current Alzheimer Research, 13(5): 557 - 565 (2016)</p> <p>Ariadna Besga, Darya Chyzhyk, Itxaso Gonzalez Ortega, Jon Echeveste, Marina Grana-Lecuona, Manuel Grana, Ana González-Pinto, White Matter Tract Integrity in Alzheimer’s Disease versus Late Onset Bipolar Disorder and its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers, Frontiers in Aging Neuroscience, 9:179 (2017)</p>
Dataset: Public stigma towards prolonged grief disorder (Gonschor, Eisma, Barke & Doering, 2020)
<p>Dataset from Gonschor, Eisma, Barke & Doering (2020): Public stigma towards prolonged grief disorder: Does diagnostic labeling matter?</p> <p>The README file contains the information as outlined below.</p> <p>The data are available in two formats: (a) Excel file and (b) CSV file. The content of (a) and (b) is identical. In the CSV file, the fields are separated by semicola. Decimal sign is a comma. The file contains a matrix of 35 x 9.<br> Description: The file contains the data as entered into the MANOVA after listwise deletion of missings. The values are provided separately for each of the eight experimental conditions. </p> <p>Naming conventions </p> <p>s: symptoms described in vignette:<br> pgd: symptoms of prolonged grief<br> 000: no symptoms<br> d: diagnostic label assigned in vignette:<br> pgd: prolonged grief disorder<br> mde: major depressive episode<br> 000: no diagnosis<br> f / m: male or female person described in the vignette</p> <p>Thus: s_pgd_d_pgd_m: a vignette describing a person with PGD symptoms, who received a PGD diagnosis and of male gender.</p> <p>Variable names: explanation</p> <p>participants_number: absolute number of participants <br> women_number: absolute number of women<br> men_number: absolute number of men<br> gender_divers_number: absolute number of persons identifying their gender as divers<br> age_mean: mean age <br> age_sd: standard deviation of age<br> higher_education_number: absolute number of participants reporting higher education<br> bereaved_last_2_years_number: number of participants who reported any bereavement in the last two years<br> bereavements_lifetime_median: Median of reported bereavements in the participants’ lifetime</p> <p>Participants indicated their relationship towards the person whose death was most distressing to them:<br> loss_spouse_number: Number of participants who had lost a spouse<br> loss_child_number: Number of participants who had lost a child<br> loss_parent_number: Number of participants who had lost a parent<br> loss_grandparent_number: Number of participants who had lost a grandparent<br> loss_sibling_number: Number of participants who had lost a sibling<br> loss_other_number: Number of participants who reported any other loss </p> <p><br> icg_mean: mean score of the Inventory of Complicated Grief (Prigerson et al. 1995)<br> icg_sd: standard deviation of the Inventory of Complicated Grief <br> competent_mean: rating of the griever as competent (mean)<br> competent_sd: rating of the griever as competent (standard deviation)<br> warm_mean: rating of the griever as warm (mean)<br> warm_sd: rating of the griever as warm (standard deviation)<br> dependent_mean: rating of the griever as dependent (mean)<br> dependent_sd: rating of the griever as dependent (standard deviation)<br> sensitive_mean: rating of the griever as sensitive (mean)<br> sensitive_sd: rating of the griever as sensitive (standard deviation)<br> emotionally_stable_mean: rating of the griever as emotionally stable (mean)<br> emotionally_stable_sd: rating of the griever as emotionally stable (standard deviation)<br> fear_mean: rating of one’s fear towards the griever (mean)<br> fear_sd: rating of one’s fear towards the griever (standard deviation)<br> anger_mean: rating of one’s anger towards the griever (mean)<br> anger_sd: rating of one’s anger towards the griever (standard deviation)<br> prosocial_mean: rating of one’s prosocial emotions towards the griever (mean) <br> prosocial_sd: rating of one’s prosocial emotions towards the griever (standard deviation)<br> social_distance_mean: mean score of social distance scale (Link et al. 1987, higher scores indicate a stronger desire for social distance)<br> social_distance_sd: standard deviation of social distance scale</p>
Transcranial Direct Current Stimulation Improves Action-Outcome Monitoring in Schizophrenia Spectrum Disorder
<p>Data set of the publication: Straube, B., van Kemenade, B.M., Kircher, T. & Schülke, R. (2020). Transcranial Direct Current Stimulation Improves Action-Outcome Monitoring in Schizophrenia Spectrum Disorder. <em>Brain Communications.</em> doi: 10.1093/braincomms/fcaa151</p> <p><strong>Abstract</strong></p> <p><strong>Background:</strong> Patients with schizophrenia spectrum disorder (SSD) often demonstrate impairments in action-outcome monitoring. Passivity phenomena and hallucinations, in particular, have been related to impairments of efference copy-based predictions which are relevant for the monitoring of outcomes produced by voluntary action. Frontal transcranial direct current stimulation (tDCS) has been shown to improve action-outcome monitoring in healthy subjects. However, whether tDCS can improve action monitoring in patients with SSD remains unknown.</p> <p><strong>Objective:</strong> We investigated whether tDCS can improve the detection of temporal action-outcome discrepancies in patients with SSD.</p> <p><strong>Methods:</strong> On 4 separate days, we applied sham or left cathodal/right anodal tDCS in a randomised order to frontal (F3/F4), parietal (CP3/CP4) and frontoparietal (F3/CP4) areas of 19 patients with SSD and 26 healthy control (HC) subjects. Action-outcome monitoring was assessed subsequent to 10 min of sham/tDCS (1.5 mA). After a self-generated (active) or externally generated (passive) key press, subjects were presented with a visual outcome (a dot on the screen), which was presented after various delays (0–417 ms). Participants had to detect delays between the key press and the visual consequence. Symptom subgroups were explored based on the presence or absence of symptoms related to a paranoid-hallucinatory syndrome (SSD phs+/phs-).</p> <p><strong>Results:</strong> In general, delay-detection performance was impaired in the SSD compared to the HC group. Interaction analyses showed group-specific (SSD vs HC) and symptom-specific (SSD phs+ vs SSD phs-) tDCS effects. Post-hoc tests revealed that frontal tDCS improved the detection of long delays in active conditions and reduced the proportion of false alarms in undelayed trials of the passive condition in patients. The SSD phs- group benefited especially from frontal tDCS in active conditions, while improvement in the SSD phs+ group was predominantly reflected in reduced false alarm rates in passive conditions.</p> <p><strong>Conclusion:</strong> These data provide some first evidence for the potential utility of tDCS in improving efference copy mechanisms and action-outcome monitoring in SSD. Current data indicate that improving efference copy-related processes can be especially effective in patients with no or few positive symptoms, while intersensory matching (i.e. task-relevant in passive conditions) could be more susceptible to improvement in patients with paranoid-hallucinatory symptoms.</p> <p> </p> <p><strong>Keywords:</strong> transcranial direct current stimulation; action-perception; action feedback; delay detection; schizophrenia.</p> <p> </p>
Differential ADAR editing landscapes in major depressive disorder and suicide.
<p>Neuropsychiatric disorders including depression and suicide are becoming an increasing public health concern. The current COVID19 pandemic by contributing to the rising rates of both depression and suicide has only hastened our need for objective and reliable diagnostic biomarkers. These can aide clinicians in not only diagnosing, but also in development of treatment plans for depression and suicide. While differential gene expression analysis has highlighted the serotonin signaling cascadeamong other critical neurotransmitter pathways to underly the pathology of depression and suicide, the biological mechanisms remain elusive. Well studied adenosine deaminase action on RNA (ADAR) editing sites such as serotonin and glutamate receptors have been examined individually one at a time. Here we propose a novel approach to better understand molecular underpinnings of neuropsychiatric disorders. We take advantage of publically available RNA-seq datasets to map ADAR editing landscapes in a more global gene-centric view. We use a unique combination of Guttman scaling and random forest classification modeling to create, describe and compare ADAR editing profiles focusing on both temporal and biological sex differences. We use a subset of experimentally confirmed ADAR editing sites located in known protein coding regions, the excitome, to map ADAR editing profiles in Major Depressive Disorder (MDD) and suicide. Using Guttman scaling, we were able to describe significant changes in editing profiles across brain regions in males and females with respect to cause of death (COD) and MDD diagnosis. The temporal distribution of editing sites may provide insight into biological mechanisms under-pinning clinical symptoms associated with MDD and suicidal behavior. Additionally, we use random forest modeling including these differential profiles among other markers of global editing patterns in order to highlight potential biomarkers providing insight into changes in synaptic plasticity. Together these models identify potential prognostic, diagnostic and therapeutic biomarkers for MDD diagnosis and/or suicide.</p>
Basic self-disturbances are associated with sense of coherence in patients with psychotic disorders
<p>Background: The <i>Sense of Coherence </i>(SOC)<i> </i>theory<i> </i>gives a possible explanation of how people can experience subjective good health despite severe illness. Basic self-disturbances (BSDs) are subtle non-psychotic disturbances that may destabilize the person's sense of self, identity, corporeality, and the overall 'grip' of the world. </p> <p>Aim: Our objective was to investigate associations between BSDs and SOC in patients with psychotic disorders.</p> <p>Design: This is a cross-sectional study of 56 patients diagnosed with <em><span>psychotic disorders inside and outside the schizophrenia spectrum (35 schizophrenia, 13 bipolar, and eight other psychoses). SOC was measured using Antonovsky's 13-item SOC questionnaire, and BSDs were assessed using the Examination of Anomalous Self-Experience (EASE) manual. Diagnosis, symptoms, and social and occupational performance were assessed using standardized clinical instruments. </span></em></p> <p>Results: We found a statistically significant correlation (r=) between high levels of BSDs and low levels of SOC (r=-0.64/<i>p</i><0.001). This association was not influenced by diagnostics, clinical symptoms or level of functioning in follow-up multivariate analyses.</p> <p>Conclusion: A statistically significant association between BSDs and SOC indicates that the presence and level of self-disturbances may influence the person's ability to experience life as comprehensive, manageable and meaningful. However, the cross-sectional nature of the study precludes conclusions regarding the direction of this association. </p>
Developmental stress elicits preference for methamphetamine in the spontaneously hypertensive rat model of attention-deficit/hyperactivity disorder
<p>The raw data of the manuscript, "Developmental stress elicits preference for methamphetamine in the spontaneously hypertensive rat model of attention-deficit/hyperactivity disorder".</p>
A series of pathological macromolecular crystallography datasets with twinning and other lattice disorders
<p>These 11 datasets were collected at the Diamond Light Source and belong to the same protein, NAL (N-acetyl neuraminic acid lyase), which crystallised in 4 different crystal forms from the same crystallisation conditions producing crystals with the same morphology. The file names, where applicable (10/11 cases), contain the corresponding PDB deposition code.</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.