Neurophysiological Assessment of Image Quality from EEG Using Persistent Homology of Brain Network

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

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Metabolic Brain Network Analysis of FDG-PET in Alzheimer’s Disease Using Kernel-Based Persistent Features

Molecules ◽

10.3390/molecules24122301 ◽

2019 ◽

Vol 24 (12) ◽

pp. 2301 ◽

Cited By ~ 4

Author(s):

Liqun Kuang ◽

Deyu Zhao ◽

Jiacheng Xing ◽

Zhongyu Chen ◽

Fengguang Xiong ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Algebraic Topology ◽

Fdg Pet ◽

Persistent Homology ◽

Brain Network ◽

Imaging Biomarker ◽

Imaging Data ◽

Significant Group ◽

Preclinical Ad

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer’s disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

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Neural evidence for image quality perception based on algebraic topology

PLoS ONE ◽

10.1371/journal.pone.0261223 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261223

Author(s):

Chang Liu ◽

Dingguo Yu ◽

Xiaoyu Ma ◽

Songyun Xie ◽

Honggang Zhang

Keyword(s):

Image Quality ◽

Image Quality Assessment ◽

Brain Network ◽

Jpeg Compression ◽

Topological Data Analysis ◽

Eeg Signals ◽

Beta Band ◽

Quality Perception ◽

Validation Experiment ◽

Topological Characteristics

In this paper, the algebraic topological characteristics of brain networks composed of electroencephalogram(EEG) signals induced by different quality images were studied, and on that basis, a neurophysiological image quality assessment approach was proposed. Our approach acquired quality perception-related neural information via integrating the EEG collection with conventional image assessment procedures, and the physiologically meaningful brain responses to different distortion-level images were obtained by topological data analysis. According to the validation experiment results, statistically significant discrepancies of the algebraic topological characteristics of EEG data evoked by a clear image compared to that of an unclear image are observed in several frequency bands, especially in the beta band. Furthermore, the phase transition difference of brain network caused by JPEG compression is more significant, indicating that humans are more sensitive to JPEG compression other than Gaussian blur. In general, the algebraic topological characteristics of EEG signals evoked by distorted images were investigated in this paper, which contributes to the study of neurophysiological assessment of image quality.

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Topological Data Analysis for Eye Fundus Image Quality Assessment

Diagnostics ◽

10.3390/diagnostics11081322 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1322

Author(s):

Gener José Avilés-Rodríguez ◽

Juan Iván Nieto-Hipólito ◽

María de los Ángeles Cosío-León ◽

Gerardo Salvador Romo-Cárdenas ◽

Juan de Dios Sánchez-López ◽

...

Keyword(s):

Data Analysis ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Persistent Homology ◽

Computational Cost ◽

Topological Data Analysis ◽

Topological Descriptors ◽

Fundus Images ◽

Topological Data

The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD.

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Topological Learning for Brain Networks

10.1101/2020.11.30.404665 ◽

2020 ◽

Author(s):

Tananun Songdechakraiwut ◽

Moo K. Chung

Keyword(s):

Loss Function ◽

Diffusion Tensor ◽

Persistent Homology ◽

Brain Networks ◽

Imaging Study ◽

Ground Truth ◽

Brain Network ◽

Functional Brain Networks ◽

Structural Brain Network ◽

Computational Bottleneck

AbstractThis paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations with ground truth to assess the effectiveness of the topological loss in discriminating networks with different topology. The method is further applied to a twin brain imaging study in determining if the brain network is genetically heritable. The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI).

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Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

10.1101/2021.02.10.430469 ◽

2021 ◽

Author(s):

Luigi Caputi ◽

Anna Pidnebesna ◽

Jaroslav Hlinka

Keyword(s):

Data Analysis ◽

Brain Connectivity ◽

Effective Connectivity ◽

Persistent Homology ◽

Brain Network ◽

Topological Data Analysis ◽

Support Vector ◽

Technical Problems ◽

Brain States ◽

Topological Data

AbstractDeveloping sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two enigmatic examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure.We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; likely due to topographical, rather than topological, specificity of the differences. In seizure discrimination from scalp electroencephalography data, classification based on directed persistent homology features provided comparable results to other methods, reaching 89 percent accuracy. Specific niche for topological data analysis opens when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

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

Molecules ◽

10.3390/molecules25112472 ◽

2020 ◽

Vol 25 (11) ◽

pp. 2472

Author(s):

Liqun Kuang ◽

Yan Gao ◽

Zhongyu Chen ◽

Jiacheng Xing ◽

Fengguang Xiong ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Diffusion Tensor ◽

Persistent Homology ◽

Brain Structures ◽

Brain Network ◽

Global Network ◽

Network Research ◽

Structural Brain

Despite the severe social burden caused by Alzheimer’s disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

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