python

Regression-Modelling ERPs (and More!)

June 11, 2023

Emotion perception is contextualized. However, how emotional context modulates word processing is unclear. We regression-fitted raw EEG data to test for emotional valence effects. The results revealed a widespread effect of context valnece, as well as a plausibility N400 waveform, well replicating the past ERP literature. Moreover, as we plan on conducting a subsequent experiment to follow up on the findings the present study has revealed, this project also includes the code for constructing experimental stimuli.

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

June 9, 2023

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

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.

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

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.

Validating χ-separation using phantom simulations

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.

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

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.

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.

Twin normative modelling project

July 22, 2022

In this project I am trying to train and test a normative model on a neuroimaging data set containing twin longitudinal data. I want to look at both changes of deviations in z-scores over time and differences in z-scores between twins

Decoding of painful stimuli using fMRI data

July 16, 2022

Painful experience involves a distributed pattern of brain activity. With hypnosis, it’s possible to increase or decrease pain. This project aims to decode fMRI pain-evoked brain activity and identify pattern of activity that are associated with specific hypnotic conditions

An easy guide to not throwing your expensive computer out the window because you can't run a Python neuroimaging tool

July 5, 2022

The goal of this project was to learn how to create code that would be easy to use for unexperienced users but also to be as more open as possible while also being replicable. So I took a code already written and scripted it, packaged it, made a Docker container for it, and finally created a guide on how to use it.

Regression-Modelling ERPs (and More!)

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

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

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

Validating χ-separation using phantom simulations

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

Spinal Cord Segmentation Generalizable Across Datasets

Twin normative modelling project

Decoding of painful stimuli using fMRI data

An easy guide to not throwing your expensive computer out the window because you can't run a Python neuroimaging tool

For participants:

Some statistics

Filter by keywords