Functional MRI Analysis of Connectivity and Network Dynamics of central fatigue in Multiple Sclerosis disease

Title

Leaders

Alberto Benelli

Collaborators

No response

Brainhack Global 2024 Event

Brainhack Lucca

Project Description

Introduction Fatigue is a debilitating symptom of multiple sclerosis (MS), affecting over 80% of patients and significantly impairing their quality of life. Despite its prevalence, the neural mechanisms underlying fatigue in MS remain poorly understood. Functional magnetic resonance imaging (fMRI) offers a powerful tool to investigate the brain’s intrinsic connectivity and dynamic network behavior. This project aims to analyze fMRI time-series data to uncover differences in functional connectivity and network dynamics among MS patients with fatigue, MS patients without fatigue, and healthy controls. By identifying distinct connectivity patterns and their relationships with structural MRI findings, this study seeks to advance our understanding of MS-related fatigue and identify potential neuroimaging biomarkers.

Aims Primary Objective: To investigate differences in functional connectivity and network dynamics among: MS patients with fatigue MS patients without fatigue Healthy controls Secondary Objectives: To explore correlations between fMRI connectivity metrics and clinical measures of fatigue severity. To integrate structural MRI findings (e.g., lesion load, gray matter atrophy) with functional connectivity results to provide a comprehensive view of the neural underpinnings of fatigue in MS.

Methodology

Participants The study will include three groups: Group 1: MS patients with clinically significant fatigue (assessed using FSS). Group 2: MS patients without fatigue. Group 3: Healthy controls matched for age and sex.
Data Acquisition Resting-state fMRI (rs-fMRI) will be used to capture intrinsic brain activity. Structural MRI scans will be acquired to assess lesion load and gray matter atrophy.
Preprocessing fMRI time-series data have been preprocessed already using CONN toolbox: Motion correction and slice timing correction. Spatial normalization to a standard brain atlas (e.g., MNI space). Denoising steps, including removal of motion-related artifacts and physiological noise using techniques like CompCor.
Functional Connectivity Analysis Seed-Based Analysis: Define regions of interest (ROIs) in fatigue-related networks (e.g., default mode network, sensorimotor network). Correlate time-series data from these ROIs with other brain regions. Whole-Brain Connectivity: Compute pairwise correlations between all brain regions using atlases like AAL or Desikan-Killiany. Dynamic Functional Connectivity: Employ sliding-window approaches to capture time-varying connectivity patterns and assess network flexibility.
Network Dynamics and Graph Theory Metrics Compute graph theory metrics such as: Global efficiency: Overall integration across the brain. Modularity: Segregation of brain networks. Node centrality: Influence of specific brain regions in the network. Compare network metrics across the three groups to identify fatigue-related alterations.
Integration With Structural MRI Use lesion maps and gray matter volume from structural MRI to examine their relationship with functional connectivity alterations in MS patients.
Statistical Analysis Compare connectivity and network metrics across the three groups using statistical tests (e.g., ANOVA with post hoc corrections). Correlate connectivity patterns and graph metrics with fatigue severity scores and structural MRI measures. Employ machine learning techniques to classify participants based on fatigue status using fMRI-derived features.

Expected Outcomes Functional Connectivity Differences: Identification of altered connectivity patterns in key networks (e.g., default mode network, sensorimotor network) associated with fatigue in MS. Dynamic Connectivity Insights: Insights into the temporal variability of brain networks and how these dynamics differ in fatigued MS patients. Integration of Structural and Functional Findings: A comprehensive understanding of how structural brain changes in MS contribute to functional connectivity alterations and fatigue. Biomarkers for Fatigue: Potential neuroimaging biomarkers for diagnosing and monitoring fatigue in MS patients.

Link to project repository/sources

No response

Goals for Brainhack Global

Increase knowledge about the programming language in Python.
Running fMRI time-series

Good first issues

issue one:
issue two:

Communication channels

I'll update this information during the event

Skills

python code fMRI MRI segmentation

Onboarding documentation

No response

What will participants learn?

Increase knowledge about the programming language in Python.
Get to know new libraries in Python that can achieve the goal.
Reconstructing fMRI time series

Data to use

We can use the data I have collected for my PhD project. I have T1, T2, Flair, fMRI, and DTI from 62 participants

Number of collaborators

1-3

Credit to collaborators

No response

Image

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Type

coding_methods, data_management, pipeline_development, visualization

Development status

1_basic structure

Topic

connectome, data_visualisation, diffusion, EEG_source_modelling, MR_methodologies, physiology

Tools

DIPY, Freesurfer, FSL, Nipype, SPM

Programming language

Python

Modalities

DWI, fMRI, MRI

Git skills

0_no_git_skills

Anything else?

No response

Things to do after the project is submitted and ready to review.

[X] Add a comment below the main post of your issue saying: Hi @brainhacklucca my project is ready!
[X] Twitter-sized summary of your project pitch.

brainhacklucca / brainhacklucca.github.io