fmri

Do you feel the words? An fMRI analysis on tactile vs non-tactile words in Mandarin Chinese

June 10, 2023

Language consists of many thousands of words which differ in meaning and syntactic category. Some words may refer to reletevely touchable, hence concrete aspects of the world while others are abstract in meaning which do not have physical references. But which brain areas are associated with tactile word processing? This project aims to investigate brain activation of participants when listening to tactile vs non-tactile word stimuli. The preliminary result shows that large areas of parietal lobe is particularly activated to tactile words compared to non-tactile words

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.

Impact of weight loss on fMRI food cue reactivity

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.

Tulpas: invisible friends in the brain.

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.

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

The face of pain: predicting the facial expression of pain from fMRI data

August 27, 2021

What can our brain tells us about our facial expression in response to painful stimulus ? This projects aims to compare different regression algorithms to see if it is possible to predict facial expression of pain from fMRI data in healthy adults.

Can we identify sex using fMRI?

June 12, 2020

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.

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

June 12, 2020

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.

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

June 12, 2020

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

Do you feel the words? An fMRI analysis on tactile vs non-tactile words in Mandarin Chinese

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

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

Impact of weight loss on fMRI food cue reactivity

Tulpas: invisible friends in the brain.

Decoding of painful stimuli using fMRI data

The face of pain: predicting the facial expression of pain from fMRI data

Can we identify sex using fMRI?

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

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

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