Brain Connectome Graphs based Machine Learning for Diagnosis of Alzheimer’s Disease

The findings of our research contribute to the burgeoning field of neuroinformatics by offering a novel and effective approach to AD diagnosis. The integration of machine learning with brain connectome graphs has potential to provide early and accurate identification of individuals at risk of AD, and pave the way for timely interventions and personalized treatment strategies. Moreover, this study sheds light on the intricate connectome-level changes associated with AD and fosters a deeper understanding of the disease's pathophysiology. Ultimately, this research has established a significant step towards leveraging advanced computational techniques to enhance our ability to diagnose and manage AD.