Projects

This project aims to investigate the functional connectivity patterns in individuals with Parkinson’s disease and neuropsychiatric symptoms (PD+NPS) compared to those without the NPS. The study utilizes resting-state fMRI data to analyze the connectivity matrix and identify alterations in functional brain networks associated with PD+NPS. The project also involves familiarizing with data science packages, interpreting neuroimaging data, and advanced visualization techniques. The findings may contribute to understanding the neural mechanisms underlying PD+NPS and inform future research and interventions. The project involves BIDS validation, fMRI preprocessing, functional connectivity analysis, group comparisons, and prediction modeling for mild cognitive impairment.

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

By Hugo Galván, Agustina Boveda, Pablo Koss, & Sebastian Galván

June 9, 2023

Schizophrenia (SZ) involves significant alterations in perception, thoughts, mood, and behavior. This project aims to develop an AI model using machine learning for complementary SZ diagnosis, utilizing prefrontal cortex connectomics and tractography techniques. It focuses on creating scripts for data separation, comparing classification models, and analyzing the connectome of healthy individuals and those with SZ. Early detection and accurate diagnosis through machine learning will enable targeted interventions, improving outcomes for individuals with SZ.

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The Neural mechanism of Nature-based intervention with Environmental information of Forest

By Chun-Yi, & Lee

June 9, 2023

Researchers will use fMRI to study how short-term exposure to nature affects the brain. They hypothesize that nature exposure will improve cognitive function and reduce noisy information processing in the VMPFC.

Using a machine learning model trained on functional connectivity patterns to predict ADHD

By Sebastian Morales

June 9, 2023

This project uses functional magnetic resonance imaging data to study the connectivity of children with Attention Deficit Hyperactivity Disorder (ADHD).A set of children diagnosed with ADHD were given a series of memory tasks while undergoing MRI scans. In this project, data from one of these tasks was used to calculate connectivity matrices for 65 subjects from that data set and a machine learning model was trained. The data was downloaded from Openneuro website.

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Validating χ-separation using phantom simulations

By Daniel Ridani

June 9, 2023

How can we validate χ-separation algorithm? In the absence of ground truth to validate χ-separation, my project aims to validate the χ-separation results using realistic in-silico head phantom simulations. Simulations offer a valuable advantage by providing a controlled environment where we can define and manipulate various parameters with known ground truth values. By choosing specific values for the simulation, we can create a ground truth against which we can compare the results obtained through the χ-separation algorithm.

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

By Behrouz Vejdani Afkham

June 8, 2023

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.

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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.

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.

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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).

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.

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Modulation of functional connectivity in Parkinson’s disease with neuropsychiatric symptoms

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

The Neural mechanism of Nature-based intervention with Environmental information of Forest

Using a machine learning model trained on functional connectivity patterns to predict ADHD

Validating χ-separation using phantom simulations

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

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