Brainhack school

Automated detection of focal epilepsy based on brain morphometrics

Fri, 20 Jun 2025 00:00:00 +0000

Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer.

g-Ratio mapping of the optic nerve

Fri, 20 Jun 2025 00:00:00 +0000

This project aims to accurately compute the optic nerve g-ratio, a key neuroimaging marker reflecting myelin integrity in nerve fibers. Using advances quantitative MRI techniques, it analyses the human optic nerve’s microstructure non-invasively. By integrating MP2RAGE and diffusion MRI, it seeks to improve assessment of myelin and nerve health, contributing to better understanding, early detection, and diagnosis of neurological diseases like multiple sclerosis (MS).

Age-Dependent EEG patterns for Predicting Treatment Response in ADHD

Thu, 19 Jun 2025 00:00:00 +0000

In this project we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups. Project reports are incorporated in the BHS website.

EEG Athlete Project: Brain Activity During Golf Performance

Thu, 19 Jun 2025 00:00:00 +0000

Using EEG band power to investigate cognitive states during golf swings and correlate them with subjective performance ratings.

fMRI Stats Exploration

Thu, 19 Jun 2025 00:00:00 +0000

This project aimed to further my intuitive understanding of fMRI data. Around 20 interactive/static figures of various statistics of raw fMRI data, confounds and atlased data were produced. Special efforts have been made to make the analysis highly and easily reproducible.

Increasing Accessibility of BrainAGE Open Access Calculators

Thu, 19 Jun 2025 00:00:00 +0000

This project aims to increase the accessibility and reproducibility of open-access BrainAGE calculators. We developed RMarkdown documents designed to help standardize diverse structural MRI outputs into the specific formats required by different BrainAGE calculators. By simplifying these complex input requirements and centralizing these tools in a public GitHub repository, our work helps reduce technical barriers, enabling more researchers to easily leverage BrainAGE models in their neuroimaging studies and promote broader scientific use.

Brian Tumor Detection in MRI Using Faster R-CNN

Wed, 18 Jun 2025 00:00:00 +0000

This project presents a deep learning-based pipeline for detecting brain tumors in MRI scans using a customized Faster R-CNN architecture.

CWAS4fMRI: a python package to perform Connectome Wide Association Study

Wed, 18 Jun 2025 00:00:00 +0000

This project aimed to develop a BIDS App for performing Connectome-Wide Association Studies (CWAS) on fMRI connectivity matrices. The result is a GitHub repository that can be installed via pip, enabling analyses on BIDS-formatted connectomes. Integration tests ensure the pipeline runs reliably, and a dedicated website provides full documentation and example outputs.

Images10K Compendium

Wed, 18 Jun 2025 00:00:00 +0000

A web-based tool for exploring visual datasets across real-world categories using carousels, tables, and interactive views.

The Friends Compendium: A brief visual and statistical description of FRIENDS

Tue, 17 Jun 2025 00:00:00 +0000

The main goal of this brainhack project and the website is to do basic statistical analyses to the friends annotations data and to present the results in the form of visualisations. These analyses will provide better context and information about the stimuli on different levels of analyses. Here is the github for the website.

The Many Faces of Fear: Univariate, Predictive and Representational Perspectives on Fearful Neuroimaging

Tue, 17 Jun 2025 00:00:00 +0000

This project explores how different fMRI analysis methods reveal distinct aspects of the neural representation of fear, including a mass univariate approach (GLM), a machine learning (decoding) approach, and representational similarity analysis (RSA) approach. While GLM identified some expected activation patterns and machine learning failed to decode fear ratings reliably, RSA revealed modest but significant structure in frontal regions, highlighting the value of methodological triangulation in cognitive neuroscience.

Predicting general cognition from resting-state functional brain connectivity

Mon, 16 Jun 2025 00:00:00 +0000

The purpose of the project is to compare various predictive models to compare the effectiveness of each predictive model and to identify important features that best contribute towards predicting general cognition. Additionally, the ideal number of features were also explored for each model.

Analysing Variability in Frontoparietal Activity in Children with and without ADHD

Sun, 15 Jun 2025 00:00:00 +0000

This study examines dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) connectivity and dlPFC BOLD time series in ADHD versus typically developing (TD) children during the cued stop-signal task (CSST) using fMRI data from OpenNeuro ds005899. It is hypothesised that stronger dlPFC-PPC connectivity will be found in the ADHD group.

BHS_trRSA_project

Sun, 15 Jun 2025 00:00:00 +0000

Implementing Time-Resolved Representational Similarity Analysis to Track Cortical Representation of Consciousness Report

Decoding Depression via EEG Biomarkers: A Neurocomputational Approach using Machine and Deep Learning

Sun, 15 Jun 2025 00:00:00 +0000

This project was conducted as part of Brainhack School 2025. It aimed to classify major depressive disorder (MDD) using temporal-domain EEG features (i.e., band power), applying both machine learning (SVM) and deep learning (EEGNet) models.

Intrinsic Connectivity of Task-Defined Language Regions

Sun, 15 Jun 2025 00:00:00 +0000

This project examines how language-related brain regions connect with DMN, FPN, and SN during rest, using fMRI data to explore links between functional connectivity and language comprehension.

Practice extracting functional signals from specific brain region

Sun, 15 Jun 2025 00:00:00 +0000

This project aims to extract and analyze fMRI signals from the hippocampus during spatial navigation, using a reproducible workflow based on open tools and data formats.

2025 Brainhack School (Mini-)Project - Cognitive Dispersion and Its Neural Correlates

Sat, 14 Jun 2025 00:00:00 +0000

This mini-project for the 2025 Brainhack School is part of my PhD dissertation on Late-Life Cognitive Heterogeneity, where I examine the neural correlates of cognitive dispersion – a measure of within-individual variability – using neuropsychological and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224).

Exploring Emotional Modulation of the P300 in EEG Data

Sat, 14 Jun 2025 00:00:00 +0000

This project explores how deviant auditory tones in a cross-modal oddball paradigm elicit a stronger P300 component using EEG data from the MNE sample dataset. The analysis focuses on ERP comparison and difference waves, setting the stage for future investigations on emotional modulation of P300.

Functional Brain Activation During a Memory Encoding and Retrieval Task: Discovering Tools and Techniques for Analysis

Sat, 14 Jun 2025 00:00:00 +0000

This project aimed to explore tools and techniques used to analyze fMRI brain activation at the first level using data from an open-access dataset. We produced a comprehensive Jupyter Notebook that provides a step-by-step guide to running the analyses, including applying the GLM, defining contrasts, and generating various brain activation maps.

MEG Meets Stories - TRF Analysis of Continuous Speech in SMN4Lang

Sat, 14 Jun 2025 00:00:00 +0000

This project explores how the brain responds to natural stories via TRF modeling on the SMN4Lang dataset, using acoustic envelope and word-aligned features – and includes an exploratory attempt at word classification using machine learning.

Understanding Learning Trajectories in VRIT: Dynamic Behavioral and Neural Signatures of Inference

Sat, 14 Jun 2025 00:00:00 +0000

This project aims to examine how the brain supports two types of inference—active and passive—during the process of posterior belief integration. I applied trial-by-trial analysis to track participants’ learning trajectories over time.

CVAE-based ADHD neuroimaging analysis

Fri, 13 Jun 2025 00:00:00 +0000

This project applies a contrastive variational autoencoder (CVAE) to Burner-preprocessed MRI data from the ADHD-200 dataset to disentangle ADHD-specific brain features from shared anatomical variation. We explore latent representations using RSA and clustering to better understand neuroanatomical heterogeneity in ADHD.

fMRI Signatures and Behavioural Correlates of Self and Empathic Pain

Fri, 13 Jun 2025 00:00:00 +0000

This project examined neural activation and functional connectivity during self-experienced and empathic pain using an open-source fMRI dataset. Analyses focused on several regions of interest, including the anterior cingulate (ACC) and insular (IC) cortices, exploring links to loneliness and social connectedness. Results showed greater activation during self-experienced pain compared to empathic pain, with loneliness predicting ACC activation in the meditation group. No significant differences in connectivity between conditions were found, though some associations with social connectedness emerged. The workflow and results are available in a reproducible GitHub repository.

Brainbeats: Classifying Music Genre with fMRI Connectivity

Thu, 12 Jun 2025 00:00:00 +0000

Can we predict music genres based on fMRI connectivity patterns alone? This project explores a single-subject decoding approach using ROI-to-ROI correlation matrices and machine learning classifiers on OpenNeuro dataset ds003720.

Decoding Perceived Emotion from BOLD data using Machine Learning

Thu, 12 Jun 2025 00:00:00 +0000

This project applies machine learning to decode perceived emotions from fMRI data using ROI-based features. Data from the ds003548 OpenNeuro dataset are analyzed, with task labels extracted from events files. ROI time series are extracted using the MIST 64-ROI atlas, and mean signals during emotion blocks are classified using linear SVM. The goal is to distinguish between six conditions (happy, sad, angry, neutral, blank, scrambled), demonstrating key concepts and challenges in neuroimaging-based classification.

EEG-Based Odor Preference Modeling 🌹🧀️🪷🍃

Thu, 12 Jun 2025 00:00:00 +0000

The human sense of smell plays a crucial role in emotional experience. Previous research has shown that EEG can distinguish between pleasant and unpleasant odors at an individual level (Kroupi et al.,2014), but the consistency of these preferences across individuals remain open questions. OPPD dataset: www.epfl.ch/labs/mmspg/downloads/page-119131-en-html

Univariate analysis on melody evaluation test

Thu, 12 Jun 2025 00:00:00 +0000

The project focused on extracting activities from functional images in a previous study about neural representation of melody-transposition. Using parcellated brain atlas as a mask, the BOLD signals underwent univariate analysis to look for effects in error detection or music-like stimulus-related brain regions/

Dynamic Functional Connectivity of the Default Mode Network in ADHD

Wed, 11 Jun 2025 00:00:00 +0000

This project examines dynamic functional connectivity (dFC) within the Default Mode Network (DMN) in children with ADHD using the ADHD-200 dataset. Key methods of analyses include time-varying correlation, clustering of connectivity states, and group comparisons to understand how brain network dynamics differ in ADHD

Age-Dependent EEG patterns for Predicting Treatment Response in ADHD

Mon, 09 Jun 2025 00:00:00 +0000

This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response.

Replication Analysis of Brain Correlates of Speech Perception in Schizophrenia Patients with and without Auditory Hallucinations

Mon, 09 Jun 2025 00:00:00 +0000

An attempt to replicate the study by Soler-Vidal et al. (2022), using the study’s dataset available on OpenNeuro. Attempted preprocessing of the first participant, sub-01, using FSL (FEAT files in ‘sub-01’ folder) and fMRIPrep (material found in ‘code’ and ‘derivatives’ folders). Attempted creation of timing files, found in ‘Ideal_Time_Series’ folder.

Singapore

Fri, 06 Jun 2025 00:00:00 +0000

This course focuses on advanced data preprocessing and analytical techniques for human brain neurophysiological data including electroencephalography/event-related potentials (EEG/ERP), magnetoencephalography (MEG), and structural/functional magnetic resonance imaging (MRI).

Functional Connectivity in ADHD: Group Differences and Predictive Modeling During Spatial Working Memory Task

Tue, 03 Jun 2025 00:00:00 +0000

Firstly, this project investigates differences in frontoparietal brain connectivity between individuals diagnosed with ADHD and control participants during Spatial Working Memory Task, using fMRI-based connectivity data. In the second part of this project, To classify individuals as either having ADHD or being in control group based on functional connectivity data features machine learning models was tested by using k-fold cross validation.

Taiwan

Tue, 03 Jun 2025 00:00:00 +0000

BrainHack School Taiwan is conducted under three institute courses: The Graduate Institute of Brain and Mind Sciences (GIBMS7021) and Graduate Institute of Linguistics (LING7430) at National Taiwan University, and the Institute of Cognitive Neuroscience at National Central University (NS5126).

Deep Electrode Mapper: Localizing EEG Electrodes from 3D Point Clouds Using Deep Learning

Tue, 20 May 2025 00:00:00 +0000

Accurate EEG source localization depends on precise electrode coordinates, yet current methods are often manual, costly, and technically demanding. Deep Electrode Mapper attempts to address this by applying deep learning to segment electrodes from 3D head models—derived from MRI or 3D scans—and localize their coordinates using clustering. Although the project remains incomplete, it demonstrates a proof-of-concept pipeline, and progress is documented in the public repository.

Multimodal spine multiple sclerosis segmentation

Fri, 16 May 2025 00:00:00 +0000

This project presents an open-source pipeline for segmenting multiple sclerosis lesions in the spinal cord using multimodal MRI data. Built for the MS-Multi-Spine Challenge, it combines nnUNet, the Spinal Cord Toolbox, Docker, and Boutiques for reproducibility and ease of use. The pipeline includes preprocessing, inference, and post-processing steps, and is packaged with full documentation and containerization to support future research and clinical applications in spinal cord imaging.

Université de Montréal (Psychology), Montréal, Québec, Canada

Wed, 14 May 2025 00:00:00 +0000

The brainhack school site at Université de Montréal is a graduate credited course at the deparment of Psychology (PSY6983), also open to students from other departments such as neuroscience, psychiatry or computer science.

University of Toronto, Toronto, Canada

Mon, 05 May 2025 00:00:00 +0000

The BrainHack Toronto School is co-hosted by five sites, sponsored by the Ontario Brain Institute and the University of Toronto!

Investigating the effect of alpha band on TRFs

Tue, 25 Jun 2024 00:00:00 +0000

In our study of three participants, removing the alpha band affected TRFs, with some features being suppressed and others enhanced. This simplification highlighted local signals, making brain activity clearer. However, it’s unclear if these enhanced signals represent true brain activity or noise, requiring further analysis for validation.

Voice and Linguistic Analysis to Determine Emotions in Friends' Interactions

Tue, 25 Jun 2024 00:00:00 +0000

Analyzes emotional dynamics in social interactions using voice and linguistic analysis on the Friends TV show dataset. Utilizing tools like Praat, OpenSmile, NLTK, and Hugging Face Transformers, it detects emotions from audio and text. Deliverables include code, documentation, datasets, and analysis workflows. Achievements encompass integrated emotion detection models and a robust analysis pipeline, with future plans to enhance models and expand datasets.

ADHD diagnosis prediction using machine learning

Fri, 21 Jun 2024 00:00:00 +0000

This project trains machine learning classification models to make predictions of adhd diagnosis from brain fRMI connectivity measures which are obtained from a resting state ADHD dataset . The main goals of this project are to get more practice with machine learning tools and to learn how work with brain data more precisely fMRI data.

Evaluating ANNs of the Visual System with Representational Similarity Analysis

Fri, 21 Jun 2024 00:00:00 +0000

This project aims to evaluate the similarity between the representations of artificial neural networks (ANNs) and the visual system in the mouse brain using Representational Similarity Analysis (RSA).

Fine-Tuning a Video Large Language Model (Vid-LLM) for Automatic Annotation of Gameplay

Fri, 21 Jun 2024 00:00:00 +0000

This project aims to develop a preprocessing pipeline to fine-tune a Video Large Language Model (Vid-LLM) for automatic annotation of gameplay recordings in cognitive neuroscience studies. Leveraging the Gym Retro ecosystem and the Courtois NeuroMod dataset, we convert event logs into video format and generate detailed annotations and timestamps to train the Vid-LLM. Deliverables include cleaned datasets, documentations and Jupyter notebooks.

Grandpa is moody, will his cognition decline? Predicting cognitive decline in Parkinson's disease from non-motor symptoms evolution

Fri, 21 Jun 2024 00:00:00 +0000

While Parkinson’s disease (PD) is recognized by its motor symptoms, it is also characterized by non-motor symptoms (NMS), such as anxiety, depression, pain, etc. NMS often precede cognitive decline, making them potential predictors of such decline. This project aims to investigate the longitudinal association between non-motor symptoms and cognitive and neural decline in patients with PD. Early identification of individuals at higher risk of cognitive decline through their NMS presentation can facilitate timely interventions.

Sleep detection using fMRI data

Wed, 19 Jun 2024 00:00:00 +0000

This project utilizes fMRI data and machine learning to predict sleep states, aiming to enhance understanding of sleep patterns and disorders. By analyzing brain activity during different sleep stages, it seeks to improve diagnostics and develop personalized treatments for sleep disorders. The primary goal is to determine whether a participant is asleep or awake using resting-state fMRI data.

Brain Decoding Using Connectivity Informed Models

Sat, 15 Jun 2024 00:00:00 +0000

Brain Decoding is the reconstruction of the sensory and other stimuli form the information that has already been encoded and represented in the brain. For example, image genration, and task classification, from brain activity signals could be covered under this topic. In this project, a graph neural netwrok appriach is used to learn the representation of the brain regions activities, and do image classification for the end-user (patient).

Detecting ADHD through fMRI signals using ML classification models

Fri, 14 Jun 2024 00:00:00 +0000

We used the ADHD-200 Sample dataset to implement various machine learning classification models, aimed at diagnosing ADHD through resting-state fMRI signals.

Detection of Alzheimer's through Acoustic and Semantic Markers

Fri, 14 Jun 2024 00:00:00 +0000

Our project aims to identify acoustic and semantic markers from the speech of Alzheimer’s patients to detect the disease and estimate MMSE scores using machine learning models. This approach offers a scalable and cost-effective method for early diagnosis.

École Polytechnique, Montréal, Québec, Canada

Fri, 10 May 2024 00:00:00 +0000

Brainhack School is offered as a for-credit course at Polytechnique (GBM6332E). The course is organized by the Neuropoly lab of the electrical engineering department at Polytechnique Montreal, under the supervision of Pr Eva Alonso Ortiz.

Regression-Modelling ERPs (and More!)

Sun, 11 Jun 2023 00:00:00 +0000

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.

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

Sat, 10 Jun 2023 00:00:00 +0000

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

Sat, 10 Jun 2023 00:00:00 +0000

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.

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

Sat, 10 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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.

Comparing different methods for mice behavioral analysis

Fri, 09 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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

Fri, 09 Jun 2023 00:00:00 +0000

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

Thu, 08 Jun 2023 00:00:00 +0000

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

Wed, 07 Jun 2023 00:00:00 +0000

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

Wed, 07 Jun 2023 00:00:00 +0000

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

Tue, 06 Jun 2023 00:00:00 +0000

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

Mon, 05 Jun 2023 00:00:00 +0000

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.

Thu, 01 Jun 2023 00:00:00 +0000

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.

Working Memory in Children with and without ADHD

Sun, 21 May 2023 00:00:00 +0000

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

Fri, 19 May 2023 00:00:00 +0000

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.

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

Sun, 07 Aug 2022 00:00:00 +0000

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

Sat, 06 Aug 2022 00:00:00 +0000

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.

Can we classify men and women based on the connectivity profile of their language network?

Wed, 03 Aug 2022 00:00:00 +0000

Sex differences in the language network is a long lasting and unresolved debate in the neuroscience field. Clinical studies have shown that pathologies or developmental conditions affecting language functions can differently affect individuals based on their sex. Although the language network is bilaterally organized, the left hemisphere is dominant for language in most individuals. However, this lateralisation tends to vary between sexes.In the present project, we address the research question on whether young adults present differences in the pattern of rs-fMRI functional connectivity within the language network based on their sex. To address this issue, we propose to determine whether we can classify healthy young adults, men and women, based on their rs-fMRI functional connectivity profiles within the language network.

Exploratory Work on the Predictive Clinical Neuroscience (PCN) Toolkit

Fri, 29 Jul 2022 00:00:00 +0000

My project consists of exploring the predictive normative modelling (PCN) toolkit via their numerous tutorials. It contains a markdown file for future new users of this package. It also includes steps on how to format your own data to use this toolkit. Finally, some cloud computing user guides will be touched upon.

Twin normative modelling project

Fri, 22 Jul 2022 00:00:00 +0000

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

Sat, 16 Jul 2022 00:00:00 +0000

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

Tue, 05 Jul 2022 00:00:00 +0000

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.

Using ALE algorithm and machine learning to classify need and desire states

Fri, 03 Sep 2021 00:00:00 +0000

The goal of this project is to make a classification of the needs and desires states from studies fMRI data

rs-fMRI Workflow from Preprocessing to Machine Learning Classification

Fri, 27 Aug 2021 00:00:00 +0000

Can functional connectivity predict sensory deprivation? This project 1. explores neuroimaging data organization and preprocessing using open science tools and 2. uses a predictive model to classify whether a participant is hearing or not. For better visualization, the most contributing coefficients in the classifier are displayed on the brain.

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

Fri, 27 Aug 2021 00:00:00 +0000

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.

Generating a BIDS compatible dataset and interactive graphs from the Projet Courtois NeuroMod's hearing test data

Wed, 25 Aug 2021 00:00:00 +0000

In a longitudinal study, the amount of data and figures to manage and create quickly becomes too massive to be manually handled. The goal of this project is to create tools to take an auditory test database and automatically format it into a BIDS compatible dataset and generate interactive graphs.

Experimenting with Occlusion methods to visualize the features learned by a CNN from audio or visual inputs

Fri, 07 Aug 2020 00:00:00 +0000

This project has for goal to explore, understand and learn how to create comprehensive visualizations of the features learned by a convolution neural network, whether the model is specialized in auditory or visual input.

Exploring machine learning tools for modelling calcium imaging data to behavioural events

Sat, 13 Jun 2020 00:00:00 +0000

I have calcium imaging data in mice while they performed behavioural learning tasks in a touchscreen chamber. I want to figure a way to consolidate the neural data (activity of ~100 individual cells over time (~30,000 x ~30ms time bins)) with behavioural data (time-stamped actions and decisions made by the animal during their behavioural task)

Biosignal processing for automatic emotion recognition

Fri, 12 Jun 2020 00:00:00 +0000

Can we automatically detect changes in emotions given a user’s biosignals? In this project, we used multimodal biosignal data to predict the target emotion of audiovisual stimuli.

Brain Learning Unicorn Project

Fri, 12 Jun 2020 00:00:00 +0000

Can a model predict the genetic profile of an individual based on brain regions volumes? There is growing evidence suggesting that genetic variations formally associated to neurodevelopmental disorders have significant effects on brain structures. In this project, the performance of three classifiers will be compared when predicting the genetic status of individuals from brain region volumes in a highly imbalanced dataset (UK BioBank cohort).

Can we identify sex using fMRI?

Fri, 12 Jun 2020 00:00:00 +0000

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.

Diffusion MRI- From raw data to mapping brain connectomes.

Fri, 12 Jun 2020 00:00:00 +0000

The focus of this project was to combine, use and present a set of tools to organize, preprocess, analyze and visualize diffusion MRI data. The overarching goal is to investigate the consequences of cortical blindness on structural connectivity using diffusion MRI.

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

Fri, 12 Jun 2020 00:00:00 +0000

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.

EEG-FFR Classification in MATLAB: A Tutorial

Fri, 12 Jun 2020 00:00:00 +0000

This project is a tutorial. It aims for you to learn how to use the scripts of a machine-learning classifier (the Hidden Markov Model). The codes were written in MATLAB. They classify an auditory neural signal called the Frequency Following Responses (FFR), which represents how well the brain represents and process complexe sounds, such as speech or music.

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

Fri, 12 Jun 2020 00:00:00 +0000

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

Visualization of functional connectivity from multiple neuroimaging modalities

Fri, 12 Jun 2020 00:00:00 +0000

In this project I employed some of the tools we learned at the Brainhack school to generate interactive figures to display functional connectivity from MEG and fMRI resting state data from the Human Connectome Project.

Classifying ADHD subtypes and sex using multimodal data

Thu, 11 Jun 2020 00:00:00 +0000

ADHD subtypes are a controversial aspect of ADHD literature. Most subtypes classifications are based on behavioral and cognitive data but lack biomarkers. Using a multimodal dataset comprised of EEG data as well as self-reported symptoms and behavioral data, we tried to predict the DSM subtypes of each of our 96 participants. Since ADHD has been noted to present itself differently across sexes, we also tried to predict sex. At-rest eeg data and behavioral data proved to be poor predictors of the DSM subtypes. However, self-reported symptoms were a rich predictor of ADHD subtype. Additionally, predicting sex using EEG data yielded the highest decoding accuracies.

Combine EEG/MRI/Behavioral data-sets to learn more about Music/Auditory system

Thu, 11 Jun 2020 00:00:00 +0000

In this project I aim to combine data from different modalities (fMRI, EEG, and behavioral) to understand more about sound and music processing. My main focus in this project was to try to reproduce some of the results from a published paper starting form raw data.

Diagnosing Schizophrenia from Brain Activity

Thu, 11 Jun 2020 00:00:00 +0000

Computational Psychiatry is growing trend that applies machine learning methods to psychological disorders. How well can we predict schizophrenia diagnosis from brain activity? This project uses neuroimaging tools from Nilearn, and machine learning tools from scikit-learn to differentiate patients diagnosed with schizophrenia from healthy controls using resting state fmri data.

MethNet: Visualizing methods in citation networks

Thu, 11 Jun 2020 00:00:00 +0000

A Python package that create a dynamic visualization the use of methods in citation networks over time.

Using fMRI Data to Predict Autism Diagnoses with Various Machine Learning Models and Cross-Validation Methods

Thu, 11 Jun 2020 00:00:00 +0000

Is autism associated with a distinct neurofunctional signature? If so, how accurately are we able to predict the diagnosis based on fMRI data? In this project, we set out to compare different machine learning models and cross-validation methods to see how well each one was able to predict autism from resting state fMRI data in the ABIDE dataset.

Predicting Neuroticism and Personality Traits from fMRI Data

Wed, 10 Jun 2020 00:00:00 +0000

Are neuropsychiatric disorders extreme cases of connectivity patterns that are found in the overall population? Using personality traits as a measure of individual variation and knowing that neuroticism is especially linked with mental disorders we wanted to see if neuroticism in a healthy population was linked with specific patterns of connectivity that could be compared to those common to neuropsychiatric disorders.

An introduction to brain decoding and comparing the results of the seven different classifier on Haxby dataset

Sat, 16 May 2020 00:00:00 +0000

Brain decoding is a neuroscience field that concerned about different types of stimuli from information that has already been encoded and represented in the brain by networks of neurons. My goal for this project is learning the fundamentals of brain decoding. Moreover, I compared the performance of seven different common classification approaches including Naive Bayes, Nearest Neighbours, Neural Networks, Logistic Regression, Support vector machine, Decision tree and finally the Artificial Neural Network on Haxby dataset.

Diffusion MRI reconstruction Project

Sat, 16 May 2020 00:00:00 +0000

This project is about diffusion magnetic resonance (MR) data processing and analysis. It mainly consists of three parts: brain diffusion MR data preprocessing, diffusion MRI images reconstruction, data visualization and left and right hemispherical preprocessed MR images classification. The whole procedures can be found in this Jupyter Notebook file. Explanations about procedures results and other details are given in it. With reproducibility being a primary concern, this project was completed by using open-source software/tools (Python, FSL, DIPYPE…) and dataset (dHCP and PRIME).

Harmonizing Multisite MRI Data Using ComBat

Sat, 16 May 2020 00:00:00 +0000

Multisite data is becoming increasingly common in MRI-based studies with the proliferation of open datasets. This brings the benefit of increased statistical power, but there is a pitfall: increased variability due to site-specific effects. This project evaluates three methods of harmonizing multi-site data.

Resting State Functional network connectivity changes in reward network of adoloscents who are at risk for addiction.

Sat, 16 May 2020 00:00:00 +0000

This project will walk you through visualizing functional network connectivity based on a custom mask of ROIs of interest and visualize those network changes across time

Revealing similarities between deep learning models and brain EEG representations

Sat, 16 May 2020 00:00:00 +0000

Do artificial neural networks process visual images similarly to our brain? If so, how? In this project, we bridge deep learning and brain EEG signals as we aim to understand more about our ability to process common visual stimuli such as objects, faces, scenes and animals.