{
  "fetched_at": "2026-10-19T07:20:56Z",
  "source_url": "https://www.kaggle.com/competitions/abide/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Subject data has been preprocessed using the cpac pipeline and globally filtered.\n\npheno_train, pheno_test -- Contain SUB_ID and labels ( DX_GROUP) for training  + lots and lots of metadata -- most important columns have self-explanatory names.\n\nabide.hdf5: contains functional connectivity data for all subjects in efficient binary format\n\ntrain, test : contain original functional connectivity data in 1D format\nsample_submission: example submission format\n\nGood luck!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "This competition is organised by the QMUL Data Science Society. The goal is to work together to improve the results obtained on the ABIDE dataset by this [2017 study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635344/) utilising state-of-the-art techniques. If you enjoy this initiative, please support future competitions and events by buying a membership on our [SU](https://www.qmsu.org/groups/dsandml/) page.\n\n\" The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.\"",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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      "mimeType": "",
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    },
    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition will be the Area Under Receiver Operating Characteristics Curve (AUC).\n\nSUBMISSION CSV FORMAT:\nColumns: SUB_ID (string), DX_GROUP(float)\nno index\nsee: sample_submission.csv",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Prizes",
      "content": "1st 100 pounds from our membership fund\n2nd-5th Society merchandise\nMost upvoted EDA: Society merchandise",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}