Projects

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

Multimodal Investigation of Neural Correlates of Athletic Performance

By Arunim Guchait

June 10, 2023

This project investigates the neural correlates of athletic performance using fMRI, dMRI, and FSLVBM to compare grey matter volume and white matter connectivity between athletes and non-athletes. The study aims to identify brain regions associated with athletic performance, explore white matter connectivity differences, and examine the relationship between brain structure and specific athletic skills. The dataset included nine Indiana University football players and nine controls.

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Predicting feedback perception in an online language learning task using EEG and machine learning

By Dylan Scott Low Y.J., & Stephanie Fu Pei-Yun

June 10, 2023

In this project, we aim to use machine learning on EEG data from participants’ language learning tasks on Duolingo. Specifically, we ask if EEG features can predict whether the participant has gotten a task right or wrong when they receive feedback. Using a k-nearest neighbours classifier, we achieve 98% accuracy in determining correct or incorrect answers based on EEG voltages from 8 electrodes.

Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data

By Zara Khan, Fariah Sandhu, & Clara Sun

June 9, 2023

The focus of our project was to gain experience using neuroimaging tools to preprocess, analyze, and visualize functional MRI data. We aimed to explore differential variability in brain connectivity among children with and without ADHD. Project reports are incorporated on the BHS website.

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Comparing different methods for mice behavioral analysis

By Pilar López Maggi, Gonzalo Giordano, Ana Pavlova Contreras, & Santiago D'hers

June 9, 2023

Our project aims to develop different methods for the analysis of behavior in mice (in this case, exploration of an object) to determine which is the best approach to this kind of study. We were able to implement and compare three increasingly complex methods to determine exploration time: manual labeling; motion tracking and data analysis using a custom algorithm; training a Machine Learning classifier on our labeled data.

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

By Roqaie moqadam

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.

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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.

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.

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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.

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.

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

Multimodal Investigation of Neural Correlates of Athletic Performance

Predicting feedback perception in an online language learning task using EEG and machine learning

Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data

Comparing different methods for mice behavioral analysis

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

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