Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

Abstract

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown.Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters.Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%.Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

“…Our results exhibited that the functional networks of the SCD and aMCI patients also showed a small-world topology, which accords with previous studies ( Sun et al, 2014 , 2018 ). Compared with the HC, the aMCI showed significantly lower small-worldness, lower normalized clustering coefficient, and higher characteristic path length.…”

“…The altered σ, γ, and L p in the SCD and aMCI groups indicated the disturbed balance between local specialization and global integration ( Sun et al, 2014 ). Moreover, the aMCI group showed a significant decline in global efficiency compared with the HC, which was consistent with previous studies, also proving the potential mechanisms of disconnection in the AD spectrum ( Sun et al, 2018 ). More severe disruptions of network metrics were found in the aMCI patients relative to the SCD patients.…”

“…The normalized rich-club coefficient of AD was higher than that of HC, which can be an important biomarker of AD (Daianu et al, 2013). Additionally, the rich-club connection strength already decreased in the SCD group, and the distinct disrupted topological phenomenon of brain connectome can be beneficial to distinguishing the aMCI converters from non-converters (Sun et al, 2018). Pusil et al (2019) applied functional connectivity to magnetoencephalographic (MEG) data and found network failure in progressive MCI.…”

Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

“…Recent developments in non-invasive neuroimaging techniques, including functional and structural imaging, have given rise to a variety of commonly used neuroimaging biomarkers for AD. Among the multiple neuroimaging modalities, structural magnetic resonance imaging (sMRI) has attracted significant interest due to its ready availability for mildly symptomatic patients and its high spatial resolution ( Cuingnet et al, 2011 ; Salvatore et al, 2015 ; Wei et al, 2016 ; Long et al, 2017 ; Gupta et al, 2019b , c ; Sun et al, 2019 ). sMRI can also reveal abnormalities in a wide range of brain areas, including gray matter (GM) atrophy in the medial temporal lobe and hippocampal/entorhinal cortex, which are identified as valuable AD-specific biomarkers for the discrimination or classification of AD patients ( Cuingnet et al, 2011 ).…”

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

“…In other neurologic diseases, it has already been shown that network features are predictive of outcomes. For subjects with mild cognitive impairment, network features were predictive of volumetric atrophy in 6 months and conversion to Alzheimer's Disease (Nir et al, 2015;Sun et al, 2019). Furthermore, progressive deterioration of the rich club organization dynamically reflects the progression of Alzheimer's Disease (Yan et al, 2018).…”