Autism Connectome Analysis

[Remi-Gau]

Project leader

Name: Varun Kumar
Github username:

Project Description

Theoretical Motivation:

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction, and by restrictive repetitive patterns of behaviours. ASD heterogeneity is huge, symptom severity varies from severe impairment to mild impairment, and from requiring substantial support to requiring support. Consequently, a categorical approach and a behavioural assessment of the ASD core symptoms fail to detect ASD subjects in the upper spectrum. ASD subjects with mild impairments are susceptible of a misdiagnosis or a late diagnosis.

Aim:

This project aims to find a functional connectivity based biomarker that can use a resting state fMRI scan for the following 2 purposes:

1. To predict the severity scores such as - ADOS Total Score or SRS Score using these biomarkers.
2. Trying to cluster the subjects into separable groups using the available phenotypic scores and those connectivity biomarkers and to discover multiple subtypes to which an autistic individual can belong to.
   For this we will use resting state fMRI scans from ABIDE.

Research Design:

1. Employing a seed based functional connectivity analysis approach to build the connectome.
2. Finding group level differences in functional connectivity between autistic and healthy subjects.
3. Using those differences for the purpose of subject clustering or severity score prediction.

Code Development (Python):

Using nipype and scikit learn/nilearn to do the following:

1. Making a preprocessing pipeline using nipype.
2. Building functional connectivity maps.
3. Using scikit learn/ nilearn to for clustering subjects and predicting severity scores

Calling all the Neuroscientists and Programmers:

It would be great if neuroscientists join this project as they know about brain anatomy and can hypothesize what brain connections might be impaired in Autism as a disorder of social communication. They can help in deciding which seed regions to focus on and inspect. They can also help in interpreting the results.
Also, everyone can benefit a lot from programmers by learning efficient coding practices.

It's a great opportunity for both to come together and learn from each other.

Type

method_development, pipeline_development

Development status

1_basic structure

Topic

machine_learning

Tools

Nipype

Programming language

Python

Modalities

other

Git skills

1_commit_push