XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease

• Published in: IEEE journal of translational engineering in health and medicine Vol. 12; pp. 569 - 579
• Main Authors: Brusini, Lorenza, Cruciani, Federica, Dall'glio, Gabriele, Zajac, Tommaso, Boscolo Galazzo, Ilaria, Zucchelli, Mauro, Menegaz, Gloria
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Amiloyd-beta; AMURA; Amyloid beta-Peptides - metabolism; Biomedical imaging; Brain - diagnostic imaging; Brain - metabolism; Brain - pathology; Classification; Diffusion Magnetic Resonance Imaging - methods; Diffusion tensor imaging; Explainable AI; Explainable artificial intelligence; Female; Humans; Image acquisition; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Male; Medical imaging; Particle measurements; Pathology; Support Vector Machine; Support vector machines; tau; tau Proteins - analysis; tau Proteins - metabolism; tract-based spatial statistics; AMURA; Amiloyd-beta; tau; eXplainable Artificial Intelligence; tract-based spatial statistics
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2024.3430035

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A<inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-/tau-) and A<inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>+/tau+ or A<inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>+/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations' recurrence across different methods.TBSS analysis revealed significant differences between A<inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results' stability and the central role of the cingulum to show early sign of AD.By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.