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.
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.
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.
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.
An introduction to brain decoding and comparing the results of the seven different classifier on Haxby dataset
Brain decoding is a neuroscience field that concerned about different types of stimuli from information that has already been encoded and represented in the brain by networks of neurons. My goal for this project is learning the fundamentals of brain decoding. Moreover, I compared the performance of seven different common classification approaches including Naive Bayes, Nearest Neighbours, Neural Networks, Logistic Regression, Support vector machine, Decision tree and finally the Artificial Neural Network on Haxby dataset.
This project is about diffusion magnetic resonance (MR) data processing and analysis. It mainly consists of three parts: brain diffusion MR data preprocessing, diffusion MRI images reconstruction, data visualization and left and right hemispherical preprocessed MR images classification. The whole procedures can be found in this Jupyter Notebook file. Explanations about procedures results and other details are given in it. With reproducibility being a primary concern, this project was completed by using open-source softwares/tools (Python, FSL, DIPYPE…) and dataset (dHCP and PRIME).
Multisite data is becoming increasingly common in MRI-based studies with the proliferation of open datasets. This brings the benefit of increased statistical power, but there is a pitfall: increased variability due to site-specific effects. This project evaluates three methods of harmonizing multi-site data.
Do artificial neural networks process visual images similarly to our brain? If so, how? In this project, we bridge deep learning and brain EEG signals as we aim to understand more about our ability to process common visual stimuli such as objects, faces, scenes and animals.
Each project repository should have a markdown file explaining the background and objectives of the project, as well as a summary of the results, and links to the different deliverables of the project. Project reports are incorporated in the BHS website.