Graph Theory and Brain Connectivity in Alzheimer’s Disease

delEtoile, Jon; Adeli, Hojjat

doi:10.1177/1073858417702621

Cited by 154 publications

(58 citation statements)

References 85 publications

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“…At present, a growing number of neuroimaging studies have suggested that patients with SCD show atrophy of gray matter volume, degeneration of white matter fiber structure, and reduction of spontaneous functional activity in the frontal, lateral temporal, and parietal cortices ( Fan et al, 2018 ; Shu et al, 2018 ; Lin et al, 2019 ). The brain connectome, including functional connections and graph theory topological metrics, is based on functional network and has attracted increasing attention owing to the complex brain network mechanism and various diagnostic information ( Biswal et al, 2010 ; delEtoile and Adeli, 2017 ; Filippi et al, 2018 ; Gao et al, 2020 ). In a series of resting-state functional brain network researches, Wang Y. et al (2013) compared the functional connections among individuals with SCD, MCI, and normal controls (NCs) and found that the strength of the functional connection between the default mode network (DMN) and right hippocampus in the SCD group was stronger than that in the MCI group but weaker than that in the NCs.…”

Section: Introductionmentioning

confidence: 99%

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Tao

et al. 2020

Front. Neurosci.

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Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

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Section: Introductionmentioning

confidence: 99%

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Tao

et al. 2020

Front. Neurosci.

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“…3 Cognitive and behavioral symptoms in patients with dementia are considered to arise from a disruption in the neuronal interactions required for normal cognitive integration. [4][5][6] Changes in functional brain networks inferred from fMRI or PET data have been found to distinguish behavioral variant frontotemporal dementia (bvFTD) 7 from Alzheimer's disease (AD) and mild cognitive impairment (MCI) 8,9 in small studies in research settings. Similarly, longitudinal changes in MEG and EEG networks have been explored over short periods.…”

Section: Introductionmentioning

confidence: 99%

Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia

Vuksanovic

Staff

Ahearn

et al. 2019

Int. J. Neur. Syst.

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Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.

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“…Moreover, the graph theoretical approaches can also identify highly connected regions in a network, so-called hub nodes, which play central roles in integrating diverse information sources and supporting fast communication with minimal energy cost [11]. These network properties can be used to demonstrate fundamental aspects of normal brain organization and highlight important aspects of underlying brain pathology in various neurologic diseases such as Alzheimer disease [12], Parkinson disease [13], and epilepsy [14]. These studies have suggested that many neurologic diseases could be produced by the dysfunction of a brain network.…”

Section: Introductionmentioning

confidence: 99%

Is Transient Global Amnesia a Network Disease?

2018

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Background: We evaluated a brain network using graph theoretical analysis and microstructural abnormalities of the white matter in patients with transient global amnesia (TGA). Methods: Twenty patients with TGA and healthy control subjects were recruited, and they underwent diffusion tensor imaging (DTI) scans. Graph theory was applied to obtain network measures based on DTI data. We investigated the network measures and microstructural abnormalities of white matter using tract-based spatial statistics (TBSS) analysis in the patients with TGA. Results: Measures of global topology were not different between the patients with TGA and healthy subjects. However, there were significant differences of hubs organization; the strength of the right superior and inferior orbitofrontal, the right inferior frontal operculum, the left superior parietal, and left postcentral gyrus, the cluster coefficient of the right middle orbitofrontal and left inferior parietal gyrus, the betweenness centrality of the left angular gyrus, and the pagerank centrality of the right superior and inferior orbitofrontal, right inferior frontal operculum, left superior parietal, and left postcentral gyrus in the patients with TGA were significantly lower than those in healthy subjects. Regarding the analysis of the white matter microstructure with TBSS, there were no differences in the fractional anisotropy and mean diffusivity values between the 2 groups. Conclusions: We newly identify a reorganization of network hubs of the brain network in patients with TGA, especially in the regions of the default-mode network. These alterations of the brain network may play a role in the pathophysiologic mechanism underlying TGA and suggest that TGA is a network disease.

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Graph Theory and Brain Connectivity in Alzheimer’s Disease

Cited by 154 publications

References 85 publications

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia

Is Transient Global Amnesia a Network Disease?

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