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 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
• Analysis
  • Quality Control
  • Preprocessing
  • First-Level Analysis
  • Second-Level Analysis
  • Third-Level Analysis
  • ROI 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.

Figure 1, Flanker Task Trial

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 2 fMRI runs while performing the Flanker task. High-resolution anatomical images and functional scans were acquired.

Figure 1, Project Workflow

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, smoothing, registration of functional to anatomical, and normalization 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 "Fixed Effect".

Whole-brain analysis was performed using Flame 1 based on the Fixed Effect algorithm.

Regions of interest (ROIs) were identified and confirmed using a confirmation test to ensure the significance of the identified clusters.

Figure 2, Lateral Occipital Cortex, Inferior Division

• To decrease false positives, Eklund (2016) suggested accepting only P-values < 0.001.
• Lateral Occipital Cortex, Inferior Division: Associated with visual processing, object recognition, and scene perception.
• Flanker Task: Focuses on cognitive control. However, participants still engage early visual areas while processing visual stimuli.

Figure 3, Lateral Occipital Cortex, Superior Division

• Lateral Occipital Cortex, Superior division: Associated with visual motion information.
• Flanker Task: Focuses on cognitive control. However, participants still process visual motion information while processing visual stimuli.

Figure 4, Precentral Gyrus

• Precentral Gyrus: Associated with motor control and movement initiation.
• Flanker Task: Requires cognitive control. Participants inhibit responses, supported by regions like the dorsolateral prefrontal cortex, which communicate with the precentral gyrus for motor execution, emphasizing its role in response control.
• Acknowledged no direct task link, like activating dorsomedial prefrontal cortex.

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 Mahmoud Yaser Salman, was submitted as the final project for the "fMRI Data Analysis" course, under the guidance of Prof. Meena M. Makary.

The repository includes all scripts utilized in the analysis, many of which were graciously shared by my friend Omar Saad. These scripts, adapted from Andy's Brain Book, proved to be versatile and contributed significantly to achieving similar results. Unfortunately, my own scripts had to be removed due to unexpected issues with my WSL 2.0 setup. The entire analysis was conducted on Linux Ubuntu 22.04 using WSL 2.0 from a Windows environment.