Coupling of the spatial distributions between sMRI and PET reveals the progression of Alzheimer’s disease

Abstract

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell‐type

“…This integration, in turn, offers a novel insight into AD mechanisms from a connectome perspective. Our recent studies have demonstrated that R2SN can accurately capture the structural connectome changes in AD (Zhao et al, 2023; Zhao et al, 2021; Zhao et al, 2022). Additionally, our findings show a correlation between RMCS and cognitive ability and microstructural genetics, further emphasizing the significance of investigating brain connectome changes in other disorders using R2SN.…”

“…The employment of interregional similarity networks, particularly in the context of structural covariance networks (SCNs), has demonstrated the capacity to capture synergistic alterations in morphological architecture across distinct brain regions (Bethlehem et al, 2017; Binnewijzend et al, 2014; Dai et al, 2019; Kim et al, 2016; Li et al, 2021; Yao et al, 2010; Yu et al, 2018; Zheng et al, 2015). Furthermore, SCNs have exhibited efficacy in investigating the dysfunction of the connectome within the spectrum of AD, thereby yielding a comprehensive array of anatomical indices that serve to discriminate AD and demarcate subtypes of MCI patients (Fu et al, 2021; Montembeault, Rouleau, Provost, Brambati, & Alzheimer's Disease Neuroimaging, 2016; Tijms et al, 2012; Yu et al, 2018; Zhao et al, 2021; Zhao et al, 2022; Zhao et al, 2023). While the SCN has garnered diverse applications in the context of AD, how the connectome changes in AD and what is related to those variations are not well‐established.…”

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

“…One interesting use of traditional radiomics measures has been their integration into studies of structural connectivity networks. A series of papers published by Zhao and colleagues have introduced regional radiomics similarity networks and utilized them to characterize connectome changes, 17 , 33 , 34 relate them to progression of cognitive decline, 35 and even to gene expression patterns. 33 A similar approach was proposed by Liu et al who constructed structural similarity networks based on radiomics measures.…”