City Research Online

Abstract

Early detection of Alzheimer's disease (AD) is a major focus of current research efforts to guide early interventions. Subtle neural changes might be observed even before symptoms surface. We interrogated brain images obtained with Magnetic Resonance Imaging (MRI) from two large-scale dementia datasets (ADNI and BioFIND) to establish the utility of fractal dimensionality (FD)-an understudied measure that estimates the complexity of 3D structures (in this case, brain regions)-for AD detection. We show that FD measures are consistent across the two datasets, and can be used to detect group differences between patients and controls, as well as for individual-based classification. We further show that the contribution of specific brain regions to individual-based classification adheres to previous literature on the properties of the brain's memory network and how it relates to cognition. Taken together, the study offers novel and interpretable evidence for the utility of FD for the detection of AD.

Publication Type:	Article
Additional Information:	© 2026 The Author(s). Published by Elsevier Inc. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Alzheimer’s disease, Fractal Dimensionality, Machine Learning, Magnetic Resonance Imaging(MRI), Mild Cognitive Impairment, Structure Complexity
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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