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

Xing, Jiacheng; Jia, Jiaying; Wu, Xin; Kuang, Liqun

doi:10.3389/fnagi.2022.788571

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…Although the pathological mechanisms underlying the neuropathological changes in AD remain unclear, some neuropathological hallmarks have been reported, such as neuritic extracellular amyloid plaques and intracellular neurofibrillary tangles, which would lead to energy slowing down, decrease of complexity and synchrony, and disconnection of the connectivity (Dauwels et al, 2010a ). By complex networks theory and graph theory, recent research on brain functional networks extracted from fMRI ( Table 7 ) and the structural networks extracted from MRI have shown that AD patients have different degrees of local or even global topology abnormalities and “disconnection” symptoms (Xu et al, 2008 ; Sanz-Arigita et al, 2010 ; Córdova-Palomera et al, 2017 ; Khatri and Kwon, 2022 ; Xing et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network

Wang

Han

et al. 2023

Front. Neurosci.

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BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

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Section: Discussionmentioning

confidence: 99%

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network

Wang

Han

et al. 2023

Front. Neurosci.

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“…Recent studies have illustrated the versatility of persistent homology in analyzing complex networks, including brain networks. [ 10 , 12 ] highlighted the application of persistent homology in evaluating temporal changes in topological network features. [ 13 ] used persistent homology to detect and track the evolution of networks’ clique.…”

Section: Introductionmentioning

confidence: 99%

Topological state-space estimation of functional human brain networks

Chung,

Huang,

Carroll

et al. 2024

PLoS Comput Biol

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We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

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“…In C. Liu et al (2021) , persistent homology has been applied to characterize the neuropsychological properties of the brain. In Xing, Jia, Wu, and Kuang (2022) , persistent homology has been used to study the evolution of a spatiotemporal brain network of AD. They have also proposed that persistent homology can be considered as a framework to assess the neurophysiological properties of image quality.…”

Section: Introductionmentioning

confidence: 99%

Altered topological structure of the brain white matter in maltreated children through topological data analysis

Chung,

Azizi,

Hanson

et al. 2024

Network Neuroscience

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Childhood maltreatment may adversely affect brain development and consequently behavioral, emotional, and psychological patterns during adulthood.In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter structure in maltreated and typically developing children.We perform topological data analysis (TDA) to assess the alteration in global topology of the brain white-matter structural covariance network in children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing controls, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly demonstrate that TDA can be used as a baseline framework to model altered topological structures of the brain. The MATLAB codes and processed data used to perform this study can be found at https://github.com/laplcebeltrami/maltreated.

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

Cited by 8 publications

References 43 publications

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network

Topological state-space estimation of functional human brain networks

Altered topological structure of the brain white matter in maltreated children through topological data analysis

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