Giter Club home page Giter Club logo

snf_adhdsubtypes_project's Introduction

SNF_ADHDsubtypes_project

This project will be conducted for BrainHack school 2020

Background

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurobehavioral disorders among children and adolescents. Subtype classification of ADHD has not reach consensus whithin the litterature and research on the correlates of ADHD subtypes show incoherent findings.Those subtypes are for the majority based on criteria derived from behavioral and-self-report data and lack of neurophysiological assessment is prominent(Hegerl et al. 2016; Olbrich, Dinteren & Arns, 2015).

Project definition

This project will aim to investigate the presence of subtypes of ADHD from the possible associations between different types of measurements, pairing common behavioral and self-reporting measures to electrophysiological (EEG) data, as well as exploring complementary attributes. To do so, a Similarity Network Fusion (SNF) will be used to integrate these different types of data in a non-linear fashion, guided by the tutorial from Ross Markello. SNF permits the construction of networks of samples (ADHD participants) for each data type, that will then all be aggregated into in one novel network (Wang et al., 2014). Integrating data with this method allows the exploration of common and complementary information between these different types of data.

Data

The sample consisted of 101 college students with an ADHD condition. Different types of measurements are included in this data sample. EEG data recording was performed using a 19-channel electrode cap (international 10-20 system) and consisted of eyes-opened at-rest recording of 5-minute duration. Time-frequency analyses were conducted for each electrode in order to extract amplitude means for each frequency band. Neuropsychological assessment measures included were Conners questionnaire (self-report) and IVA-II behavioral test.

Tools

  • Git and GitHub
  • Jupyter Notebook
  • Python : main packages : pandas,MNE-BIDS, SNFpy based on previous markdown
  • Visualization packages via python

Deliverables

At the end of this project, we will have:

  • A Jupyter notebook markdown describing thoroughly all the steps of our project
  • Python script of main analyses .
  • OSF project management
  • Complete published repository access to all commits and changes of our projects

Coming soon !

Progress overview

Coming soon !

Tools I learned during this project

Coming soon !

snf_adhdsubtypes_project's People

Contributors

beapdk avatar penelopepg avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.