Projects

This project aimed to create a detailed tutorial on B0 field mapping principles in MRI and demonstrate the estimation methods interactively. Additionally, it explored the importance of B0 field maps in MRI by comparing the connectivity matrix of rs-fMRI with and without using the B0 field map in the processing pipeline.

Impact of weight loss on fMRI food cue reactivity

By Justine Daoust

June 7, 2023

Functional MRI studies examining reactivity to food cues in obesity have shown BOLD differences in brain regions involved in the regulation of food intake. This project aims to characterize brain reactivity to food cues in individuals with severe obesity and to examine changes in brain reactivity to food cues by fMRI after weight loss induced by bariatric surgery.

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The Effects of Word Frequency, Orthographic Neighborhood and Part of Speech in Word Processing: A MEG Study

By Ruo-Chi Yao

June 7, 2023

Word processing is influenced by different attributes of words, which can be observed vis brain imaging techniques such as MEG. Let’s find evidence of the influence of word attributes by analyzing a dataset.

Effects of Sleepiness on Resting-State Connectivity

By Thomas Perrin

June 6, 2023

Can functional connectivity predict sleep deprivation? This project aims to explore neuroimaging data organization to build a workflow from the acquisition of an open dataset to the visualization of brain connectivity. The pipeline will be detailed and carried out for one subject, using resting state fMRI to compare the result between normal sleep and sleep deprivation (less than 3 hours of sleep the previous night).

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Unveiling Children's Theory of Mind with rs-fMRI

By Wei-Hung Lin, & Syuan-Yu Lin

June 5, 2023

Can functional connectivity bed used to predict children’s theory of mind (ToM)? This project utilizes supervised machine learning algorithms on the fMRI data to predict children’s ToM ability. For better visualization, the most contributing brain region connections are displayed on the brain.

Tulpas: invisible friends in the brain.

By Jonas Mago

June 1, 2023

Tulpas are invisible friends that can be cultivated on will by so called Tulpamancers. This fMRI dataset comprises scans of Tulpamancers, comparing periods where there is an experiential presence of such Tulpa and where there is not. The aim of this proejct is to study the neurophysiological signature of Tulpas using GLMs, functional connectivity measrues, machine learning, and deep neural networks. website.

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Working Memory in Children with and without ADHD

By Wei-Chen Chang, & Shan-San Huang

May 21, 2023

This project aims to using both fMRI data and the behavioral data during a n-back task to compare the difference between ADHD children and the heathly control ones.

Spinal Cord Segmentation Generalizable Across Datasets

May 19, 2023

My idea is to train a deep learning model on multiple (4) spinal cord segmentation datasets to improve generalizability to new contrasts, vendors, pathologies, etc… My project aims to train the nnU-Net model architecture, a state-of-the-art deep learning architecture for biomedical segmentation, on four aggregated datasets and compare its generalizability capabilities with the four specific models trained on each individual dataset. I will conclude by comparing the two approaches on a fifth and sixth dataset outside of the training domain.

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Workflow of resting state connectivity from a raw dataset to longitudinal visualization

By Kevin Jamey

August 7, 2022

To aim of this project was to provide a full neuroimaging workflow from preprocessing of raw data to visualisation of results, to explore longitudinal analysis between two treatments in this dataset and to visualise resting-state networks linked to the default mode network and attention. In my github repository you will find scripts and documentation about the the BIDS to NiFTY conversion, fMRI prep as well as resting-state visualisation of a single participant. There is also a powerpoint presentation slide to guide you through the work.

Classifying Neuropsychiatric Disorder Diagnoses Using Resting State BOLD fMRI Connectivity Data

By Peter Brotherwood

August 6, 2022

Can functional connectivity data be used as a predictor for neuropsychiatric diagnosis? This project explores the usefulness of connectivity data in predicting ADHD, Bipolar Disorder, and Schizophrenia diagnoses using machine learning classification methods.

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Unveiling the Power of B0 Field Mapping: A Comprehensive Tutorial and Analysis in MRI

Impact of weight loss on fMRI food cue reactivity

The Effects of Word Frequency, Orthographic Neighborhood and Part of Speech in Word Processing: A MEG Study

Effects of Sleepiness on Resting-State Connectivity

Unveiling Children's Theory of Mind with rs-fMRI

Tulpas: invisible friends in the brain.

Working Memory in Children with and without ADHD

Spinal Cord Segmentation Generalizable Across Datasets

Workflow of resting state connectivity from a raw dataset to longitudinal visualization

Classifying Neuropsychiatric Disorder Diagnoses Using Resting State BOLD fMRI Connectivity Data

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