open science

rs-fMRI Workflow from Preprocessing to Machine Learning Classification

Can functional connectivity predict sensory deprivation? This project 1. explores neuroimaging data organization and preprocessing using open science tools and 2. uses a predictive model to classify whether a participant is hearing or not. For better visualization, the most contributing coefficients in the classifier are displayed on the brain.

Continue reading

Brain Learning Unicorn Project

Can a model predict the genetic profile of an individual based on brain regions volumes? There is growing evidence suggesting that genetic variations formally associated to neurodevelopmental disorders have significant effects on brain structures. In this project, the performance of three classifiers will be compared when predicting the genetic status of individuals from brain region volumes in a highly imbalanced dataset (UK BioBank cohort).

Continue reading

Using fMRI Data to Predict Autism Diagnoses with Various Machine Learning Models and Cross-Validation Methods

Is autism associated with a distinct neurofunctional signature? If so, how accurately are we able to predict the diagnosis based on fMRI data? In this project, we set out to compare different machine learning models and cross-validation methods to see how well each one was able to predict autism from resting state fMRI data in the ABIDE dataset.

Continue reading