Amyloid-beta (Aβ) deposition and altered brain structure are the most relevant neuroimaging biomarkers for Alzheimer’s disease (AD). However, their spatial inconsistency was always confusing and misleading. Furthermore, the relationship between this spatial inconsistency and AD progression is unclear. The current study introduced a regional radiomics similarity network (R2SN) to map structural MRI and Aβ positron emission tomography (PET) images to study their cross-modal interregional coupling. A total of 790 participants (248 normal controls, 390 mild cognitive impaired patients, and 152 AD patients) with their structural MRI and PET images were studied. The results showed that global and regional R2SN coupling significantly decreased according to the severity of cognitive decline, from mild cognitive impairment to AD dementia. The global coupling patterns are discriminative between different APOE ε4, Aβ, and Tau subgroups. R2SN coupling was probed for relationships with neuropsychiatric measures and peripheral biomarkers. Kaplan–Meier analysis showed that lower global coupling scores could reveal worse clinical progression of dementia. The R2SN coupling scores derived from the coupling between Aβ and atrophy over individual brain regions could reflect the specific pathway of AD progression, which would be a reliable biomarker for AD.
Amyloid-beta (Aβ) deposition and altered brain structure are the most relevant neuroimaging biomarkers for Alzheimer’s disease (AD). We introduced a novel network coupling measure based on the regional radiomics similarity network (R2SN) to explore the potential association between the spatial distributions of brain structure and Aβ based on sMRI and Aβ positron emission tomography (PET) imaging. In this study, we systematically demonstrated that the alteration of the coupling between brain networks of brain structure and Aβ accumulation could serve as a predictor for revealing the distinct progression of AD.
Competing Interests: The authors have declared that no competing interests exist.
These authors contributed equally to this work.
Supporting Information: https://github.com/YongLiulab
Handling Editor: Olaf Sporns