Graph Network Modeling of Brain Connectivity: An Exploration of Word and Object Recognition Tasks

Understanding the neural mechanisms underlying cognitive processes, such as word and object recognition, is crucial for advancing cognitive neuroscience. This study explores the neural mechanisms underlying word and object recognition using a graph-based approach, BrainGNN, applied to fMRI data. By focusing on the fMRI scans related to these cognitive tasks, we constructed ROI-wise functional connectivity matrices based on the AAL atlas and employed the BrainGNN model for classification. The model effectively distinguished between tasks, achieving an accuracy of 91.67%. Significant differences were observed in brain regions such as the Temporal Pole and Supplementary Motor Area. This research contributes to a deeper understanding of the brain’s functional connectivity and the neural mechanisms that underpin word and object recognition.