Relating Connectivity and Graph Analysis to Cognitive Function in Alzheimer’s Disease

Gits, Hunter C.

doi:10.3998/mjm.13761231.0001.111

Cited by 5 publications

(5 citation statements)

References 89 publications

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“…In general, graph theory in brain research presents "a network as a set of nodes, which are the components of a system (represented in the brain network perspective as a region of interest -ROI, and a number of edges, representing the connection (functional connectivity -FC) between each pair of nodes" (Sanz-Arigita et al 2010;Eid and Saleh 2013;Gits 2016). Several neuroimaging tools (e.g., MRI, EEG, etc) have been extensively used in order to extract different network properties.…”

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confidence: 99%

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

Lazarou

Nikolopoulos

Dimitriadis

et al. 2019

Clinical Neurophysiology

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confidence: 99%

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

Lazarou

Nikolopoulos

Dimitriadis

et al. 2019

Clinical Neurophysiology

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“…While, a volume-based morphological study has demonstrated that patients with mild cognitive impairment and AD could retain their hub regions in the frontal lobe but not in the temporal lobe (Yao et al, 2010). Furthermore, 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).…”

Section: Introductionmentioning

confidence: 99%

Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer’s Disease Revealed by Individual Morphological Brain Network

Yang

et al. 2018

Front. Neurosci.

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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.

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“…A recent survey by Gits ( 2016 ) of studies investigating graph properties in AD reveals no clear and systematic differences between heath conditions. For example, the mean clustering coefficient was found to be both significantly smaller (Supekar et al, 2008 ) and larger (Zhao et al, 2012 ) in AD compared to the aged-matched control group.…”

Section: Resultsmentioning

confidence: 99%

“…Although many fMRI studies have investigated changes of functional activity in AD (for a review see Dennis and Thompson, 2014 ), there is no consensus about which information should be used. Such studies typically examine disrupted cortical connectivity, either locally, considering single brain areas (e.g., Dillen et al, 2017 ) and their embedding in the network, or globally, analyzing the entire constructed brain graph and the statistics of its graph properties (Gits, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease

et al. 2018

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The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer.

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Relating Connectivity and Graph Analysis to Cognitive Function in Alzheimer’s Disease

Abstract: As a mathematical tool, graph analysis provides a way to understand changes in the brains of individuals with Alzheimer's disease and its associated devastating cognitive decline.

Cited by 5 publications

References 89 publications

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer’s Disease Revealed by Individual Morphological Brain Network

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease

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