Uncovering Alzheimer’s Disease Biomarkers through Motif-Based Analysis of Synthetic Functional Brain Networks

Alzheimer’s disease (AD) is characterized by complex alterations in brain connectivity, making the understanding of these patterns critical for early diagnosis and intervention. This study presents a motif-based analysis of functional brain connectivity utilizing synthetic adjacency matrices of virtual connectomics derived from Alzheimer’s Disease Neuroimaging Initiative (ADNI) data. Our approach involves first sparsifying the functional brain network to enhance the significance of the connections, followed by the construction of a directed network from bidirectional correlation-based edge weights between ROIs (Regions of Interest) to accurately capture the flow of information. By systematically searching for recurrent motifs within these networks, we aim to identify specific patterns of connectivity that may distinguish AD patients from healthy controls. Motifs are fundamental building blocks that provide insights into the functional organization of the brain and can reveal underlying mechanisms of neurodegeneration. This analysis not only contributes to the characterization of brain network alterations in AD but also demonstrates the utility of synthetic connectomes in neuroimaging research. Our findings have implications for developing network-based biomarkers and enhancing our understanding of the pathophysiology of Alzheimer’s disease, highlighting the importance of integrating advanced network analysis techniques to unravel the complexities of brain connectivity in neurodegenerative conditions.