💻 🧠 Brainhack school is taking a break, we'll be back in 2022! 🧠 💻:

fMRI

Can we identify sex using fMRI?

Does functional connectivity between brain regions differ in male and female? If yes then fMRI data can be used to distinguish sex on the basis of the difference in functional connectivity. I applied supervised Machine Learning algorithms on the fMRI data to classify sex.

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Does rs-fMRI preprocessing matter for prediction performance in machine learning?

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.

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fMRIPrep 101 - Pre-processing fMRI data and extracting connectivity matrices

This project aimed to understand how to pre-process fMRI data using fMRIPrep. Through this learning experience, a tutorial was created.

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Visualization of functional connectivity from multiple neuroimaging modalities

In this project I employed some of the tools we learned at the Brainhack school to generate interactive figures to display funtional connectivity from MEG and fMRI resting state data from the Human Connectome Project.

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Combine EEG/MRI/Behavioral data-sets to learn more about Music/Auditory system

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.

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Diagnosing Schizophrenia from Brain Activity

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.

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MethNet: Visualizing methods in citation networks

A Python package that create a dynamic visualization the use of methods in citation networks over time.

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Predicting Neuroticism and Personality Traits from fMRI Data

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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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.

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