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Diagnosis ADHD with Convolutional-LSTM Model


  • Diagnosing mental disorders is a considerably complex task for behavioral health professionals
  • Many factors complicate the process:
    • People exhibit individual behaviors with the symptoms of their disorder(s)
    • No objective biological markers associated with mental disorders
    • Mental disorders often overlap with one to many others
    • Similarity of symptoms among different diseases can lead to inaccurate diagnosis 
  • Potential Solution lies with functional Magnetic Resonance Imaging (fMRI) technology

functional Magnetic Resonance Imaging

  • Measures brain activity by detecting changes associated with blood flow
    • Known as blood-oxygen-level dependent (BOLD) method
  • This technique relies on the fact that blood flow and neuronal activation are coupled.
  • When an area of the brain is in use, blood flow to that region also increases
  • Does so to provide energy to the neurons, which do not have internal reserves of energy.
  • fMRI machine captures blood flow and “lights up” brain areas in images
  • Indicates that part of the brain is responsible for handling a certain activity


  • Construct a hybrid model that captures and analyzes both the spatial and temporal aspects of an fMRI dataset
  • This model will consist of a Convolutional Neural Network and Recurrent Neural Network
  • Convolutional Neural Network:
    • Retrieves spatial features of the data
    • Extracts the details of active areas in the brain
  • Recurrent Neural Network
    • Retrieves the temporal features of the data
    • Model the flow of the blood that is associated to certain disorders (or activities)
  • Goal: Construct a 3D Convolutional Neural Network + LSTM-based RNN to diagnose the ADHD disorder with the provided fMRI data sample

Currently In Progress

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