I have calcium imaging data in mice while they performed behavioural learning tasks in a touchscreen chamber. I want to figure a way to consolidate the neural data (activity of ~100 individual cells over time (~30,000 x ~30ms time bins)) with behavioural data (time-stamped actions and decisions made by the animal during their behavioural task)
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).
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.
The focus of this project was to combine, use and present a set of tools to organize, preprocess, analyze and visualize diffusion MRI data. The overarching goal is to investigate the consequences of cortical blindness on structural connectivity using diffusion MRI.
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.
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.
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.
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.