Abstract
Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28–30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.
Highlights
Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition
The functional studies have documented that the AD-related disrupted areas yield a highly consensus estimate of default mode network (DMN), which is a set of brain regions that typically deactivate during performance of cognitive tasks (Buckner et al, 2009)
The main findings are as followed: (1) that significantly affected connections were observed across multiple regions, mainly related to the left entorhinal cortex (ENT); (2) that the profoundly changed Mini Mental State Examination (MMSE) score and Eglobal were noted in the AD patients, as well as the pronounced inter-group distinctions of Eglobal, mElocal and mBC in the higher MMSE score zone; (3) that the reservations and alterations of hubs were both detected in the AD patients
Summary
Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. Recent investigations have exhibited that the cerebral connectomes can be modeled into large-scale brain networks with multiple neuroimaging data and can be further analyzed based on the graph theory (Achard et al, 2006; He et al, 2007; Gong et al, 2009) It allows the quantitative examination of the local and global topological organization of the human brain, such as small world architecture, network efficiency, modularity, and spatial distribution of hubs (Bullmore and Sporns, 2009). The graph-based analysis provides an approach to explore the relationship between network properties and cognitive functioning, which could help researchers to obtain more accurate predictions and diagnoses of AD (Gits, 2016) Such decreased functional connectivity of DMN is found related to the declined cognitive functioning (Binnewijzend et al, 2012), and altered path length of morphological networks in the medial posterior cortex showed the strong relationship with cognitive disruption (Tijms et al, 2013)
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