Dementia, particularly Alzheimer’s disease and its prodromal stage, mild cognitive impairment (MCI), is a major global health challenge. Early detection of MCI is crucial because it often precedes irreversible neurodegeneration, yet distinguishing it from later-stage dementia remains difficult due to overlapping symptoms and subtle early changes in brain function.
This book, "Graph Convolution Networks for EEG-Based Brain Network Biomarkers in Alzheimer’s Disease Progression", addresses this challenge by proposing analytical frameworks that reveal the underlying pathophysiological mechanisms of dementia-related disorders. Electroencephalography (EEG), with its accessibility and high temporal resolution, offers a practical window into neural activity, but its full potential emerges only when interpreted from a network-centric perspective.
Adopting a complex network approach, this work investigates EEG-derived brain functional networks (BFNs) in dementia. Using the Phase Lag Index (PLI) as the core connectivity metric, it constructs frequency-specific functional networks and applies a data-driven thresholding technique for robust, unbiased topology estimation. Quantitative and statistical network analyses show that graph-theoretic measures such as rich-club organization, transitivity, and assortativity provide effective biomarkers for differentiating MCI, Alzheimer’s disease, and vascular dementia.
Building on these insights, the BFNs are then used as structured graph inputs to a Graph Convolution Network (GCN) model. Integrating network neuroscience with deep learning, the proposed GCN framework achieves high classification accuracy (around 95%), highlighting the power of graph-learning methods for dementia staging.
Combining methodological rigor, theoretical depth, and practical evaluation, this book presents a unified framework for EEG-based brain network biomarker discovery. It is intended for researchers, clinicians, and students in computational neuroscience, biomedical signal processing, machine learning, and neurodegenerative disease research, and aims to contribute to earlier detection, better tracking, and deeper understanding of Alzheimer’s disease progression.
Table of Contents
- Authors and Acknowledgment
- Dedication
- Preface
- List of Figures
- List of Tables
- 1 Introduction
- 1.1 Study Background
- 1.1.1 Complex Networks in Medicine
- 1.1.2 Brain Functional Network
- 1.1.3 Dementia and Neurological Disorders: A Complex Network Perspective
- 1.2 Motivations
- 1.3 Problem Statements
- 1.4 Contributions
- 1.5 Organization
- 1.1 Study Background
- 2 Literature Review
- 2.1 Complex Network Theory and its Interdisciplinary Applications
- 2.1.1 Graph theory approach
- 2.1.2 Connectivity in brain networks
- 2.1.3 Nodes/Vertices definition in brain network construction
- 2.1.4 Edges definition in brain network construction
- 2.1.5 Graph theoretical metrics for brain network analysis
- 2.2 Functional Brain Networks from Electrophysiological Signals
- 2.2.1 EEG as a tool for brain connectivity
- 2.2.2 Preprocessing and epoching strategies
- 2.2.3 Functional connectivity measures
- 2.3 Applications to Dementia-Related Disorders
- 2.3.1 Forms of dementia: A multifaceted spectrum
- 2.3.1.1 Alzheimer's Disease (AD)
- 2.3.1.2 Vascular Dementia
- 2.3.1.3 Lewy Body Dementia (LBD)
- 2.3.1.4 Frontotemporal Dementia (FTD)
- 2.3.1.5 Mixed Dementia
- 2.3.2 Mild cognitive impairment and progression to AD
- 2.3.2.1 Mild cognitive impairment (MCI)
- 2.3.3 Challenges in dementia diagnosis
- 2.3.3.1 Diagnosing dementia
- 2.3.1 Forms of dementia: A multifaceted spectrum
- 2.4 Identification of Dementia Disorders using Learning Techniques
- 2.4.1 Machine learning approaches for dementia recognition
- 2.4.2 Deep learning approaches for dementia recognition
- 2.4.3 Graph convolution network for dementia recognition
- 2.5 Summary
- 2.1 Complex Network Theory and its Interdisciplinary Applications
- 3 EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset
- 3.1 Overview
- 3.2 Materials and Methods
- 3.2.1 Phase lag index, PLI
- 3.2.2 Identification of band-specific most significant connections
- 3.2.3 Threshold selection and brain functional network formulation
- 3.2.4 Threshold validation
- 3.2.5 Brain functional network quantification
- 3.2.5.1 Rich-club coefficient:
- 3.2.5.2 Transitivity (functional segregation):
- 3.2.5.3 Assortativity coefficient (network resilience):
- 3.2.6 Graph convolution network
- 3.2.6.1 Graph learning
- 3.2.6.2 General representation of graph
- 3.2.6.3 Spectral graph filtering
- 3.2.6.4 Model architecture and initialization
- 3.3 Results
- 3.3.1 Phase lag index
- 3.3.2 Most significant electrode(s) and most significant connections identification
- 3.3.3 Threshold selection
- 3.3.4 Network topology quantification
- 3.3.5 Graph convolution network classification
- 3.3.5.1 Implementation details
- 3.3.5.2 Model performance
- 3.4 Discussion
- 3.5 Summary
- 4 Conclusions
- References
Objective & Thematic Focus
This book aims to integrate complex network theory with deep learning models to analyze and identify neurological disorders, specifically Alzheimer's disease (AD), mild cognitive impairment (MCI), and various stages of neuropathic pain. The primary research question revolves around how graph convolutional networks (GCNs) applied to EEG-based brain functional networks (BFNs) can effectively facilitate the early detection, tracking, and improved understanding of dementia progression and other neurological conditions.
- Graph Convolution Networks (GCNs) for neurological disorder classification.
- EEG-based Brain Functional Networks (BFNs) as biomarkers.
- Diagnosis and progression tracking of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI).
- Application of complex network theory and graph-theoretic measures.
- Development of data-driven methodological frameworks for network analysis.
- Exploration of neuropathic pain using brain functional networks.
Excerpt from the Book
Preface
Dementia, particularly Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI), remains one of the most pressing global health challenges. Despite extensive clinical research, diagnosing and managing these conditions is difficult due to overlapping symptoms, diverse etiologies, and subtle early-stage manifestations. Early detection of MCI is critical, as it often precedes irreversible neurodegeneration. Yet, distinguishing it from later-stage dementia continues to demand tools that move beyond traditional approaches and provide deeper insight into the brain's functional organization.
This book, Graph Convolution Networks for EEG-Based Brain Network Biomarkers in Alzheimer's Disease Progression, is motivated by the need for analytical frameworks capable of revealing the underlying pathophysiological mechanisms of dementia-related disorders. Electroencephalography (EEG), with its wide accessibility and temporal precision, provides a valuable window into neural activity. However, interpreting EEG in the context of dementia requires more than signal analysis—it requires a network-centric understanding of how brain regions interact.
The work presented here adopts a complex network perspective to investigate EEG-derived brain functional networks (BFNs) in dementia. Using the Phase Lag Index (PLI) as the principal connectivity measure, the study constructs functional networks that capture relevant neural interactions across frequency bands. To ensure robust and unbiased network characterization, a data-driven thresholding technique is introduced, enabling reliable topological comparisons through quantitative and statistical network analysis.
The analyses reveal that graph-theoretic measures such as rich-club organization, transitivity, and assortativity serve as effective biomarkers for differentiating MCI, Alzheimer's disease, and vascular dementia. Building on these insights, the BFNs are further leveraged as structured graph inputs to a Graph Convolution Network (GCN) model. This integration of network neuroscience and deep learning demonstrates notable performance, with GCNs achieving high classification accuracies—95.07. This book brings together methodological rigor, theoretical depth, and practical evaluations to present a unified framework for EEG-based brain network biomarker discovery. It is intended for researchers, clinicians, and students interested in computational neuroscience, biomedical signal processing, machine learning, and neurodegenerative disease analysis. By blending traditional network measures with emerging graph-learning techniques, the content aims to support ongoing efforts toward earlier detection, better tracking, and improved understanding of Alzheimer's disease progression.
Summary of Chapters
Chapter 1: Introduction: This chapter introduces complex network theory as a fundamental framework for understanding interconnected systems, emphasizing its transformative potential in neuroscience for modeling and diagnosing neurological disorders like dementia as disruptions in brain networks.
Chapter 2: Literature Review: This chapter provides an in-depth exploration of complex network theory applied to the diagnosis of neurological disorders, with a specific focus on dementia and neuropathic pain, reviewing state-of-the-art methodologies including graph-theoretical analysis and graph convolutional networks for disease classification.
Chapter 3: EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset: This chapter details the methodology of the study, focusing on the identification and analysis of EEG-based brain functional networks (BFNs) in dementia, utilizing Phase Lag Index (PLI) and a data-driven thresholding technique, and leveraging GCNs for classification.
Chapter 4: Conclusions: This chapter summarizes the book's main contributions, highlighting advancements in understanding brain functional networks in dementia-related disorders through integrated GCNs and connectivity analysis, and extending these network-based methodologies to chronic neuropathic pain assessment for more precise diagnostics.
Keywords
Graph Convolution Networks, EEG, Brain Functional Networks, Biomarkers, Alzheimer's Disease, Dementia Progression, Mild Cognitive Impairment, Complex Network Theory, Neuroscience, Deep Learning, Neuropathic Pain, Functional Connectivity, Graph Theory Metrics, Classification, Electroencephalography
Frequently Asked Questions
What is this work fundamentally about?
This work is fundamentally about advancing the diagnosis and understanding of dementia-related disorders and neuropathic pain by integrating complex network theory with deep learning models, particularly Graph Convolutional Networks (GCNs), applied to EEG-based brain functional networks.
What are the central thematic areas?
The central thematic areas include the application of Graph Convolution Networks (GCNs) for neurological disorder classification, EEG-based Brain Functional Networks (BFNs) as biomarkers, diagnosis and progression tracking of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI), and the use of complex network theory for analyzing brain connectivity.
What is the primary objective or research question?
The primary objective is to develop and validate analytical frameworks, specifically GCNs for EEG-based BFNs, to accurately identify and classify various dementia stages and neuropathic pain conditions, thereby aiding in earlier detection and improved patient outcomes.
Which scientific method is used?
The scientific method employed combines network neuroscience (complex network theory, graph-theoretical analysis) with machine learning and deep learning techniques (Graph Convolutional Networks), utilizing electroencephalography (EEG) data for functional connectivity analysis.
What is covered in the main part?
The main part of the book covers the detailed methodology for constructing and quantifying EEG-based brain functional networks, the application of various graph-theoretic metrics, the development and implementation of Graph Convolutional Networks for differential classification of dementia stages, and a comprehensive analysis of the results.
Which keywords characterize the work?
Key terms characterizing this work are: Graph Convolution Networks, EEG, Brain Functional Networks, Biomarkers, Alzheimer's Disease, Dementia Progression, Mild Cognitive Impairment, Complex Network Theory, Neuroscience, Deep Learning, Neuropathic Pain, Functional Connectivity, Graph Theory Metrics, Classification, Electroencephalography.
How are Brain Functional Networks (BFNs) formulated in this study?
BFNs are formulated by extracting Phase Lag Index (PLI) values from pre-processed EEG signals across different frequency bands (delta, theta, alpha, beta, gamma) to measure connectivity between brain regions. A data-driven threshold selection based on eigenvector centrality is then applied to these connectivity matrices to construct the BFNs.
What is the role of Graph Convolutional Networks (GCNs) in this research?
GCNs are leveraged to process the structured graph inputs (BFNs) and learn discriminative network patterns. They capture both local and global dependencies within brain networks, enhancing classification accuracy for identifying different dementia-related disorders.
What specific graph theory metrics are used to differentiate dementia stages?
The study uses three key graph-theoretic measures: the rich-club coefficient, transitivity (functional segregation), and assortativity coefficient (network resilience) to quantify BFN topology and distinguish between Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Vascular Dementia (VD).
How does the study address the challenge of early dementia diagnosis?
The study addresses early dementia diagnosis by proposing a novel framework that integrates network neuroscience and deep learning to identify subtle network disruptions in EEG-based brain functional networks. This approach aims to detect dementia at earlier stages, potentially before overt clinical symptoms appear, through advanced classification accuracies.
- Quote paper
- Abdulyekeen T. Adebisi (Author), Kalyana C. Veluvolu (Author), 2025, EEG-Based Brain Network Biomarkers in Alzheimer's Disease Progression, Munich, GRIN Verlag, https://www.grin.com/document/1676601
