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By Stephanie Alley
June 12, 2020
Machine learning models are often used to analyze fMRI data, whether it be a simple classification or regression problem or something more complex. While the focus of a study is often centered on the model architecture, data preprocessing also plays a vital role in a model’s success. This project will explore the effect that various preprocessing options may have on the prediction performance of a machine learning model for age prediction using resting state fMRI.
By Isabelle Arseneau-Bruneau
This project is a tutorial. It aims for you to learn how to use the scripts of a machine-learning classifier (the Hidden Markov Model). The codes were written in MATLAB. They classify an auditory neural signal called the Frequency Following Responses (FFR), which represents how well the brain represents and process complexe sounds, such as speech or music.
By Frederic St-Onge
This project aimed to understand how to pre-process fMRI data using fMRIPrep. Through this learning experience, a tutorial was created.
By Jonathan Gallego
In this project I employed some of the tools we learned at the Brainhack school to generate interactive figures to display functional connectivity from MEG and fMRI resting state data from the Human Connectome Project.
By Béatrice P.De Koninck & Pénélope Pelland-Goulet
June 11, 2020
ADHD subtypes are a controversial aspect of ADHD literature. Most subtypes classifications are based on behavioral and cognitive data but lack biomarkers. Using a multimodal dataset comprised of EEG data as well as self-reported symptoms and behavioral data, we tried to predict the DSM subtypes of each of our 96 participants. Since ADHD has been noted to present itself differently across sexes, we also tried to predict sex. At-rest eeg data and behavioral data proved to be poor predictors of the DSM subtypes. However, self-reported symptoms were a rich predictor of ADHD subtype. Additionally, predicting sex using EEG data yielded the highest decoding accuracies.
By Marcel Farres Franch
In this project I aim to combine data from different modalities (fMRI, EEG, and behavioral) to understand more about sound and music processing. My main focus in this project was to try to reproduce some of the results from a published paper starting form raw data.
By Alexander Albury
Computational Psychiatry is growing trend that applies machine learning methods to psychological disorders. How well can we predict schizophrenia diagnosis from brain activity? This project uses neuroimaging tools from Nilearn, and machine learning tools from scikit-learn to differentiate patients diagnosed with schizophrenia from healthy controls using resting state fmri data.
By Kendra Oudyk
A Python package that create a dynamic visualization the use of methods in citation networks over time.
By Emily Chen, Andréanne Proulx, & Mikkel Schöttner
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.
By Annabelle Harvey, & Liz Izakson
June 10, 2020
Are neuropsychiatric disorders extreme cases of connectivity patterns that are found in the overall population? Using personality traits as a measure of individual variation and knowing that neuroticism is especially linked with mental disorders we wanted to see if neuroticism in a healthy population was linked with specific patterns of connectivity that could be compared to those common to neuropsychiatric disorders.
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