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1,681 results for “autism”
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Inter-Chemical Correlation results for the study: HHEARx2016-1449 (Environmental phenols and pesticide levels in relationship to autism)
Title: Environmental phenols and pesticide levels in relationship to autism <br>Species: Homo sapiens <br>Number of samples: 842 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=7 <br>
Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF
<p>This database corresponds to the results of the paper:</p><p>González Sala, F., Pastor-Cerezuela, G., Sanz-Cervera, P., & Tárraga-Mínguez, R. (2023). Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF. <i>Anales de Psicología / Annals of Psychology, 39</i>(1), 81–87. https://doi.org/10.6018/analesps.481531</p>
Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder
<p>This database corresponds to the results of the paper:</p><p>Lacruz-Pérez, I., Pastor-Cerezuela, G., Tárraga-Mínguez, R. & Lüke, T. (2023): Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder, <i>European Journal of Special Needs Education</i>. <a href="https://doi.org/10.1080/08856257.2023.2185858">https://doi.org/10.1080/08856257.2023.2185858</a> </p><p> </p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Raw data for: "Postsynaptic autism spectrum disorder genes and synaptic dysfunction"
<p>Schematic illustration representing postsynaptic proteins associated to ASD. These proteins are involved in different synaptic functions, either directly (ion channels and glutamate receptors), or indirectly, including transmembrane heterophilic (NLGNs) and homophilic (NrCAM) cell-adhesion molecules, and scaffolding proteins (PSD-95, Shank, Homer), that link transmembrane and membrane-associated protein complexes with the underlying actin cytoskeleton. Additional cellular functions may influence synaptic activity in ASD, such as alternative splicing (PTEN, RBFOX1, nSR100/SRRM4), RNA editing (FMR1, FXR1), transcription (FOXP1, FOXP2, TBR1, TSHZ3), translation (FMR1), degradation (UBE3A), and mitochondrial activity (AGC1).</p>
RNA-seq dataset for Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
<p>Data to be used along with <a href="https://github.com/neelroop/asd-development-coexpression-2013">code</a> from 2013 paper that was originally on a site hosted at UCLA, but may no longer be accessible.</p>
Autism Infographic
<p>This infographic was created to visualize data on autism prevalence and the sex ratio between males and females. This infographic represents new research, and how it has changed autism data and diagnosis. Additionally, a word cloud was created to help viewers new to autism and autism data understand what the condition is.</p> <p>The data for the autism prevalence graph came from the Centers for Disease Control and Prevention. The data for autism signs and symptomatology are my own gathered from twenty consumer health websites in October 2023. For this dashboard, I used Python (in Google Colab with Pandas and Plotly Express), Voyant, and Canva for data visualizations. Canva was also used to create the infographic itself. The title was generated by Chat GPT, then modified by the creator. GitHub was used to host the dashboard and supplementary information. The intended audiences for this dashboard are autistic people and their loved ones, autism researchers, and clinicians who want to learn specific data about autism.</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751</p> <p>References:</p> <p>Loomes, R., Hull, L., & Mandy, W. P. L. (2017). What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56(6), 466-474. <a href="https://doi.org/10.1016/j.jaac.2017.03.013">https://doi.org/10.1016/j.jaac.2017.03.013</a></p> <p>Maenner, M. J., Shaw, K. A., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., Furnier, S. M., Hallas, L., Hall-Lande, J., Hudson, A., Hughes, M. M., Patrick, M., Pierce, K., Poynter, J. N., Salinas, A., Shenouda, J., Vehorn, A., Warren, Z., Constantino, J. N., Cogswell, M. E. (2021). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years-Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. Morbidity and Mortality Weekly Report Surveillance Summaries, 70(11), 1-16. <a href="http://dx.doi.org/10.15585/mmwr.ss7011a1">http://dx.doi.org/10.15585/mmwr.ss7011a1</a></p>
[Saliency4ASD] A dataset of eye movements for the children with autism spectrum disorder
<p>Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD</p>
Debunking neuromyths: Pre‐service teachers' insights on autism spectrum disorder.
<p>This database corresponds to the results of the paper:</p> <p>Lacruz-Pérez, I., Pastor-Cerezuela, G., Caurín-Alonso, C., Morales-Hernández, A.J. & Tárraga-Mínguez, R. (in press). Debunking neuromyths: Pre‐service teachers' insights on autism spectrum disorder. <em>Journal of Research in Special Educational Needs. </em></p>
The Time is Ripe for the Renaissance of Autism Treatments: Evidence from Clinical Practitioners
<p>Data from Survey Monkey collected online from 170 Board Certified Behavioral Analysts (BCBAs) in New Jersey and 170 BCBAs outside NJ, in the USA. Data from 170 therapists certified by the International Council on Development and Learning (ICDL) DIR Floortime and by the Profectum Foundation, certified Developmental Therapists (comprising Occupational Therapists, Speech Language Pathologists and Physical Therapists) </p>
BIDS wildtype data selection from "Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories"
<p>Data package selecting wildtype animals from the “Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories” article, formatted corresponding to the Brain Imaging Data Structure. The relevant publication can be found via DOI <a href="https://doi.org/10.1093/cercor/bhy046">10.1093/cercor/bhy046</a> .</p>
Characterization of a loss-of-function NAPB mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected iPSC lines. Author names and affiliations
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from induced pluripoent stem cells (iPSC). There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz) and reverse read (R2_001.fastq.gz).</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01: Proband sample</p> <p>NDD_04: Proband sample</p> <p>NDD_05: Proband sample</p>
Data for: Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells
<p>Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly, despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD subtypes and mTOR modulation rescued ASD phenotypes and reproduced autism NPC-associated phenotypes in control NPCs. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation.</p>
Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia
<p>Data for the manuscript Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia</p> <div> </div>
Sensory-Informed Architectural Design Qualities in Autism
<p><span>This dataset provides a collection of design qualities for autism-friendly designs. The collected data relies on the current literature, including various guidelines and research papers.</span></p>
Single-Cell Autism data stored as sce object
<p>The raw autism dataset is from UCSC Cell Browser Dataset, Autism section (<a href="https://cells.ucsc.edu/">https://cells.ucsc.edu</a>). It is stored as SingleCellExperiment object for further usage. </p>
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Figure 9. The impossible figure (right) is not noticeable as such at first glance-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>In the lexical domain a similar effect of holistic word processing is described in (Anstis,<br> 2005b). The viewers were presented with pairs of three-letter words in quick succession and asked<br> to report if the upper halves of the successively presented words were identical. Surprisingly, even<br> when the upper halves of the words were orthographically identical, the error rate was reliably<br> higher than expected and in comparison with matching identical successive words. As the author of<br> the study Stuart Anstis points out: “students were processing the words not as separable parts, but<br> holistically as perceptual units that could not be perceptually split apart. These results show that in<br> normal circumstances, the visual system cannot, or does not, divide words into upper and lower<br> halves” (Anstis, 2005b, p. 239).The author relates the results of his study to studies of visual<br> perception of faces as evidence that the mechanism of holistic processing in the visual and the<br> lexical domains is essentially the same.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.