White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

Kuang, Liqun; Gao, Yan; Chen, Zhongyu; Xing, Jiacheng; Xiong, Fengguang; Han, And Xie

doi:10.3390/molecules25112472

Cited by 12 publications

(10 citation statements)

References 54 publications

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“…In our prior study on AD brain networks ( Kuang et al, 2019a ), we have proposed an integrated persistent feature (IPF) based on persistent homology that achieves holistic descriptions of spatial dynamics of the brain network. We have also found that the IPF is more robustness than graph theory-based metrics in our prior studies ( Kuang et al, 2019a , b , 2020a , b ). However, all existing studies on persistent homology only focus on the feature invariants in the process of spatial dynamics, but no literature studies have reported the influence of the change of time window on feature invariants.…”

Section: Introductionsupporting

confidence: 63%

“…Because IPF is a monotonically decreasing convergence function, the slope of IPF (SIP) is used as an index to quantify the dynamic research of AD brain network. We have successfully applied the SIP to AD brain network analysis in our prior studies ( Kuang et al, 2019a , b , 2020a , b ). Our opensource code of persistent homology can be downloaded at http://gsl.lab.asu.edu/software/IPF .…”

Section: Methodsmentioning

confidence: 99%

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A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology

Xing

Jia

et al. 2022

Front. Aging Neurosci.

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Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer’s disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer’s Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.

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Section: Introductionsupporting

confidence: 63%

Section: Methodsmentioning

confidence: 99%

A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology

Xing

Jia

et al. 2022

Front. Aging Neurosci.

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“…The network is comprised of information of relations or interconnections that link many elements in the neurobiological system. The neurons of the human brain are interconnected through synaptic connections and anatomical projections mediate the communication among brain areas, forming a highly complex network system in human beings (Bassett and Sporns, 2017 ; Kuang et al, 2020 ). Structural network analysis based on DTI is a newly-developed method to reflect the topological structural alterations and the connectivity of the brain structural network (Bassett et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

White Matter Structural Network Analysis to Differentiate Alzheimer’s Disease and Subcortical Ischemic Vascular Dementia

Feng

Zhang

Liu

et al. 2021

Front. Aging Neurosci.

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To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer’s disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (Eg), and local efficiency (Eloc)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.

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“…The topological features have been well defined, such as small-worldness [ 21 – 23 ], global and local efficiency [ 24 ], and highly connected hubs [ 25 ], which contain node degree, node betweenness, and so on. The brain network features have been characterized in AD [ 26 , 27 ], schizophrenia [ 28 ], multiple sclerosis [ 29 ], and other conditions. Moreover, several analyses have indicated that patients with PACG exhibit signs of changes in brain network properties that were identified in previous resting-state fMRI studies.…”

Section: Introductionmentioning

confidence: 99%

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

et al. 2022

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Objectives. Recent resting-state functional magnetic resonance imaging (fMRI) studies have focused on glaucoma-related neuronal degeneration in structural and spontaneous functional brain activity. However, there are limited studies regarding the differences in the topological organization of the functional brain network in patients with glaucoma. In this study, we aimed to assess both potential alterations and the network efficiency in the functional brain networks of patients with primary angle-closure glaucoma (PACG). Methods. We applied resting-state fMRI data to construct the functional connectivity network of 33 patients with PACG ( 54.21 ± 7.21 years ) and 33 gender- and age-matched healthy controls ( 52.42 ± 7.80 years ). The differences in the global and regional topological brain network properties between the two groups were assessed using graph theoretical analysis. Partial correlations between the altered regional values and clinical parameters were computed for patients with PACG. Results. No significant differences in global topological measures were identified between the two groups. However, significant regional alterations were identified in the patients with PACG, including differences within visual and nonvisual (somatomotor and cognition-emotion) regions. The normalized clustering coefficient and normalized local efficiency of the right superior parietal gyrus were significantly correlated with the retinal fiber layer thickness (RNFLT) and the vertical cup to disk ratio (V C/D). In addition, the normalized node betweenness of the left middle frontal gyrus (orbital portion) was significantly correlated with the V C/D in the patients with PACG. Conclusions. Our results suggest that regional inefficiency with decrease and compensatory increase in local functional properties of visual and nonvisual nodes preserved the brain network of the PACG at the global level.

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White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

Cited by 12 publications

References 54 publications

A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology

A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology

White Matter Structural Network Analysis to Differentiate Alzheimer’s Disease and Subcortical Ischemic Vascular Dementia

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

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