type	date	title	names	github_repo	website	tags	summary	image
project	2024-07-18	Analyzing Functional Connectivity by Region of Interest (ROI) using HCP rs-fMRI Data	Ismail	https://github.com/yourusername/yourproject		brain connectivity functional MRI ROI analysis neuroscience	This project analyzes functional connectivity using HCP rs-fMRI data, focusing on specific brain regions of interest (ROI). It involves signal fluctuation analysis, correlation studies, and explores cognitive implications using advanced neuroimaging techniques.

Project definition

Background

Analyzing Functional Connectivity by Region of Interest (ROI) to explore the connections between specific regions in the brain using the HCP resting-state fMRI (rs-fMRI) data in relationship to cognition. This project involves selecting predefined functional regions as ROIs, and then measuring the signal fluctuations over time between these regions. This includes selection of the desired ROI’s, assigning of signals from the timeseries, preprocessing and a correlation analysis. The statistical analysis and interpretation will be in relation to cognition.

Tools

The project will rely on the following technologies:

• Software: Jupyter, Python, R, GitHub.
• Libraries: Nilearn, Scikit-learn.
• Data Analysis Tools: Human Connectome Workbench, FreeSurfer, mriqc, xcp-d, qsiprep.

Dataset

The Human Connectome Project (HCP) aims at mapping the neural connections in the human brain, including examining neural network pathways and circuits and their connection to cognitive functions, behaviours, and neurological disorders. The HCP Adult dataset includes data from multiple imaging methods such as diffusion MRI (dMRI), resting-state functional MRI (rs-fMRI), and task-based functional MRI (task-fMRI), providing comprehensive insights into brain connectivity and variability.

Deliverables

At the end of this project, we will have:

• Jupyter script for analysis.
• Python code for running analysis.

Results

Deliverables

At the end of this project, the following deliverables will be provided:

1. Individual Connectivity Matrix for 5 Participants:

   • Detailed connectivity matrices depicting the strength of connections between brain regions for each of the five selected participants.

2. Overall Connectivity Matrix for the Population:

   • A consolidated connectivity matrix summarizing the average connectivity patterns across all participants in the study.

3. Demographic and Scores Tables:

   • Tables presenting demographic information (e.g., age, gender) and cognitive scores (e.g., IQ scores, memory test results) for each participant.

4. Cognitive Scores Table:

   • A specific table highlighting the cognitive scores of the participants, showcasing variability in scores and potential correlations with connectivity patterns.

Results

The primary findings of this study include:

• Comparison of Individual Connectivity Scores: Analysis of individual connectivity matrices revealed distinct patterns in connectivity strengths across participants.

• Correlation with Cognitive Scores: Examination of cognitive scores alongside connectivity data indicated potential correlations between specific connectivity patterns and cognitive performance.

These results provide insights into how individual differences in brain connectivity may relate to cognitive abilities, highlighting the potential for personalized neuroscientific insights and interventions.

Conclusion and Acknowledgement

The development of this project has been enriched my understanding in the field of neuroscience and inspires me into furthering research in personalized cognitive assessments and interventions.