This project will be conducted with the collaboration of Penelope PG.
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.
This project will aim to investigate 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.
The sample consisted of 93 college students with an ADHD condition. Different types of measurements are included in this data sample. EEG data recording was performed using a 32-channel electrode cap 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), WAIS-IV working memory subscale and IVA-II behavioral test.
- Git and GitHub
- Jupyter Notebook
- Python : main packages : pandas, SNFpy based on previous markdown
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 !
Coming soon !
Coming soon !