This report presents a detailed analysis of the Flanker Dataset using functional magnetic resonance imaging (fMRI) to explore the neural basis of cognitive control. The study focuses on investigating differences in the BOLD signal between Congruent and Incongruent Flanker trials. Various analyses, including cognitive control assessment, quality control, preprocessing, statistical modeling, and post-analysis, were conducted using the FSL software. The findings contribute valuable insights into the neural mechanisms underlying cognitive control and response inhibition.
- Introduction
- Experimental Design
- Data Collection
- Used Tools
- Analysis
- Results and Interpretation
- Discussion
- Conclusion
- References
- Submission
- Scripts
Cognitive control processes, particularly attention and response inhibition, were investigated using the Flanker Dataset and fMRI. This report details the experimental design, data collection procedures, and a comprehensive analysis pipeline.
The Flanker task, a well-established paradigm for studying cognitive control, was employed. Participants were presented with Congruent and Incongruent Flanker trials, requiring a button press based on the central arrow's direction.
Twenty-six adult participants underwent fMRI scans while performing the Flanker task. High-resolution anatomical images and functional scans were acquired.
Utilize FSL for preprocessing, statistical analysis, and ROI analysis to identify brain regions involved in cognitive control.
Employ bash scripting to automate large-scale data processing and ROI analysis, streamlining the workflow.
Integrate R scripts for advanced analysis of brain region activity during task performance.
To ensure reliable results, cognitive control was assessed by analyzing reaction times, demonstrating robust differences between congruent and incongruent conditions.
Quality control procedures identified motion artifacts in specific subjects, addressing potential confounds in the subsequent analysis.
Anatomical and functional images underwent preprocessing steps, including brain extraction, temporal filtering, motion correction, and registration to the MNI152 template.
The first-level analysis included GLM modeling with contrasts for congruent and incongruent conditions. Post-stats analysis identified activated pixels and clusters.
Different algorithms, including Fixed Effect, Flame 1, and Simple OLS, were applied in the second-level analysis to determine the most effective approach.
Exploratory group analysis was performed using Flame 1 based on the Fixed Effect algorithm to assess the effectiveness of different algorithms.
Regions of interest (ROIs) were identified and confirmed using a confirmation test to ensure the significance of the identified clusters.
Results and Interpretation discussed in the report
The results revealed indirect relationships between confirmed clusters and the flanker task. The discussion interprets these findings in the context of existing literature and theoretical frameworks.
Despite indirect relationships observed, the study provides valuable insights into the neural mechanisms underlying cognitive control and response inhibition during the Flanker task.
Kelly, A.M., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39(1):527-37
- Soares JM, Magalhães R, Moreira PS, Sousa A, Ganz E, Sampaio A, Alves V, Marques P and Sousa N (2016) A Hitchhiker’s Guide to Functional Magnetic Resonance Imaging. Front. Neurosci. 10:515. doi: 10.3389/fnins.2016.00515
- AndysBrainBook website (https://andysbrainbook.readthedocs.io/en/latest/)
This Report, authored by Omar Saad Elgharbawy, was submitted as the final project for the "Neuroimaging" course, under the guidance of Prof. Meena Makary.
The repository includes all scripts utilized in the analysis. The entire analysis was conducted on Linux Ubuntu 22.04 using WSL 2.0 from a Windows environment.