Section 1: Resting-State fMRI Analysis with the CONN Toolbox
This session provides an in-depth walkthrough of the entire process of resting-state fMRI analysis using the CONN toolbox, from initial preprocessing to connectivity analysis. We start with preprocessing steps, including brain extraction, slice timing correction, segmentation realignment, normalization, and smoothing, essential for preparing fMRI data for analysis. Following preprocessing, we cover denoising procedures to remove unwanted noise and artifacts, enhancing the quality and interpretability of connectivity results. The session then dives into connectivity analysis, where we demonstrate how to extract and interpret seed-based connectivity and ROI-to-ROI connectivity measures. This comprehensive guide is designed to equip participants with the knowledge and technical skills needed to effectively perform resting-state fMRI analyses in academic and research settings.
Section 2: Task-Based fMRI Analysis with the CONN Toolbox
In this session, we extend our exploration of fMRI data processing in the CONN toolbox, focusing on task-based fMRI analysis. We begin with preprocessing steps tailored to task-based data, including brain extraction, slice timing correction, segmentation, alignment, normalization, and smoothing, to ensure accurate spatial registration and signal integrity. Next, we address denoising methods to filter out motion artifacts and other sources of noise, crucial for improving data quality and reliability in task-based studies. Finally, we conduct connectivity analyses, showcasing methods for both seed-based connectivity and ROI-to-ROI connectivity analysis. This session provides a rigorous, step-by-step framework for processing task-based fMRI data and interpreting connectivity patterns, aimed at academic researchers and students seeking to build expertise in fMRI data analysis using the CONN toolbox.
