Dementia-related disorders—including Alzheimer’s disease (AD), vascular dementia (VD), and mild cognitive impairment (MCI)—remain among the most challenging neurological conditions due to their overlapping clinical characteristics and complex neurocognitive manifestations. The difficulty in distinguishing these disorders continues to hinder therapeutic interventions, underscoring the need for analytical tools capable of revealing subtle but meaningful differences in brain network organization.
This book presents an in-depth exploration of brain functional network analysis as a means to address these challenges. Leveraging phase-based mutual information as the connectivity measure, we examine the functional architecture of the brain across healthy controls and individuals diagnosed with MCI, AD, and VD. Using a carefully selected reactive frequency band, we identify consistent patterns of hypoconnectivity across the dementia groups, providing insights into shared and divergent network alterations.
Two complementary computational frameworks form the core of this work: a frequent subgraph mining approach for the extraction of recurrent network motifs, and a minimum spanning tree (MST) method for structural characterization of both unweighted and weighted brain networks. The frequent subgraphs derived from dementia-specific networks are further applied for classification tasks, while MST-based features and additional graph-theoretic measures are used to delineate disease-specific network signatures.
Table of Contents
- Authors and Acknowledgment
- Dedication
- Preface
- List of Figures
- 1 Introduction
- 2 Time-Frequency Mapping of Phase-based Mutual Information
- 2.1 Instantaneous Amplitude and Phase Extraction
- 2.1.1 Short-Time Fourier Transformation
- 2.1.2 Hilbert Transform
- 2.1.3 Continuous Wavelet Transform
- 2.2 Phase-based Mutual Information
- 2.3 Reactive Band Identification
- 2.1 Instantaneous Amplitude and Phase Extraction
- 3 Functional Connectivity Analysis
- 3.1 Loss of Functional Connection in Dementia Groups
- 3.2 Inter-Channel Connection Variation Analysis
- 4 Brain Functional Network Analysis
- 4.1 Unweighted Brain Network Analysis Using Frequent Subgraph Approach
- 4.1.1 Formation of Unweighted Brain Functional Network
- 4.1.2 Frequent Edges Search
- 4.2 Weighted Brain Network Analysis Using Minimum Spanning Tree Approach
- 4.2.1 Minimum Spanning Tree Formation
- 4.2.2 Network Topology Quantification
- 4.1 Unweighted Brain Network Analysis Using Frequent Subgraph Approach
- 5 Results
- 5.1 Data Description
- 5.2 Instantaneous Amplitude and Phase Extraction
- 5.3 Synchronization Analysis in Time-Frequency
- 5.3.1 Phase-based Mutual Information in Time Frequency
- 5.3.2 Common Reactive Band
- 5.4 MI-based Functional Connectivity Analysis
- 5.4.1 Loss of Connection in Dementia Related Groups Compared to HC Group
- 5.4.2 Average inter-channel connectivity analysis
- 5.5 Unweighted Brain Functional Network Analysis
- 5.5.1 Common Threshold Identification
- 5.5.2 MI-Based Frequent Network Edges Search
- 5.5.3 Classification
- 5.6 Weighted Brain Functional Network Analysis
- 5.6.1 Minimum Spanning Tree Formation
- 5.6.2 Quantification of MST Topology
- 5.7 Discussion
- 6 Conclusion
- References
Objective & Thematic Focus
This book presents an in-depth exploration of brain functional network analysis to address the challenges in distinguishing various dementia-related disorders like Alzheimer's disease (AD), vascular dementia (VD), and mild cognitive impairment (MCI). The primary objective is to develop and apply analytical tools capable of revealing subtle but meaningful differences in brain network organization to support timely and effective therapeutic interventions and improve diagnostic precision.
- Analysis of brain functional networks for differentiating dementia disorders from healthy controls.
- Leveraging phase-based mutual information from EEG signals as a functional connectivity measure.
- Development and application of two computational frameworks: frequent subgraph mining and minimum spanning tree (MST) methods.
- Identification of consistent patterns of hypoconnectivity and disease-specific network signatures.
- Quantification of network topology using graph-theoretic measures.
- Demonstration of computational neuroengineering's role in clinical decision-making and improved diagnostic accuracy.
Excerpt from the Book
1 Introduction
Ever since the of its exploration, the brain has been found to be arguably the most important organ in the nervous system [1]. It controls and coordinates the activities of other organs in the body system. It is also the most complex body organ consisting of billions of tiny cells called nerve cells which are the building blocks of the brain functionalities [2], [3]. The nerve cells are highly interconnected together in networks. In fact, the brain has several specialized regions designated for carrying out specific activities and functionalities [4], [5], [6]. Naturally, human brain is programmed into functional networks that actively enhance brain functioning such as the processing of sensory stimuli, cognition as well as some goal-directed tasks [7]. A lot of such networks have been identified using neuroimaging modalities at resting stage [8]. This has enabled the bridging of the gap in understanding the mechanisms behind some of those brain functionalities. Nevertheless, a lot of the underlie mechanisms of brain functionalities especially in terms of the various brain disorders are yet to be fully understood [9]. Therefore, the field of computation neuroscience continues to gain multiple interest day in, day out.
Neurological disorders are the abnormalities that affect the central nervous system as a result of loss of neurons and or glial cells [10]. Neural disorders could arise as a result of physical, environmental, genetic or some other factors [11]. Moreso, some of these disorders are age related, thus, they mostly affect people within a specific age group. Neurodegenerative disorders are the forms of neural disorders associated with neuronal loss [12]. Dementia are forms of neurodegenrative disorders that affect the aged adults whose ages are 65 years and above. Cases of dementia have been rising globally due to increase in the rate at which the world population tends toward aging and super-aging recently [13]. In 2016, the total estimated world population living with dementia is about 50 million, amongst whom about 63% live in low- and middle-income countries [13], [14]. Also, it has been estimated that there is going to be an exponential rise in the proportion of dementia cases, specifically to about 115.4 million cases by 2050 [15].
Summary of Chapters
1 Introduction: This chapter introduces the brain's complexity and functional networks, the challenges posed by neurodegenerative disorders like dementia, and the necessity for advanced analytical tools to understand and diagnose these conditions using neuroimaging.
2 Time-Frequency Mapping of Phase-based Mutual Information: This section details the methods for extracting instantaneous amplitude and phase from neurophysiological signals using Short-Time Fourier Transform, Hilbert Transform, and Continuous Wavelet Transform, and how phase-based mutual information is then used to identify reactive frequency bands.
3 Functional Connectivity Analysis: This chapter explores the loss and gain of functional connections in dementia subjects compared to healthy controls, employing differential mutual information and inter-channel connectivity analysis to pinpoint regions of hypoconnectivity on the scalp.
4 Brain Functional Network Analysis: This section describes the application of unweighted and weighted brain network analysis, including frequent subgraph mining for recurrent network motifs and minimum spanning tree (MST) methods for topological characterization, to distinguish dementia disorders.
5 Results: This chapter presents the experimental findings, including data description, instantaneous amplitude and phase extraction, synchronization analysis, MI-based functional connectivity, unweighted brain functional network analysis (including classification accuracy), weighted brain functional network analysis, and discussion of the outcomes.
6 Conclusion: This chapter summarizes the comprehensive framework developed for analyzing dementia-related disorders using EEG-derived brain functional networks, highlighting the effectiveness of mutual information and MST-based analyses in distinguishing disorder stages and supporting diagnostic precision.
Keywords
EEG, computational neuroengineering, dementia diagnosis, Alzheimer's disease (AD), vascular dementia (VD), mild cognitive impairment (MCI), brain functional networks, mutual information (MI), phase-based connectivity, frequent subgraph mining, minimum spanning tree (MST), hypoconnectivity, neurodegenerative disorders, graph theory, classification.
Frequently Asked Questions
What is this work fundamentally about?
This work fundamentally explores the use of EEG-based computational neuroengineering to diagnose and differentiate various dementia-related disorders by analyzing brain functional networks.
What are the central thematic areas?
The central thematic areas include brain functional network analysis, phase-based mutual information, frequent subgraph mining, minimum spanning tree methods, and the application of these techniques for dementia diagnosis and classification.
What is the primary objective or research question?
The primary objective is to develop analytical tools that can reveal subtle but meaningful differences in brain network organization to aid in the timely and effective diagnosis and intervention for dementia-related disorders.
Which scientific method is used?
The scientific method used involves analyzing neurophysiological signals (EEG) using time-frequency mapping, phase-based mutual information for functional connectivity, and graph-theoretic approaches like frequent subgraph mining and minimum spanning tree analysis.
What is covered in the main part?
The main part of the book covers the detailed methodology for time-frequency mapping and mutual information calculation, functional connectivity analysis, brain functional network analysis (both unweighted and weighted), and presents the results and discussion of these analyses in differentiating dementia groups.
Which keywords characterize this work?
Key terms characterizing this work include EEG, computational neuroengineering, dementia diagnosis, Alzheimer's disease, vascular dementia, mild cognitive impairment, brain functional networks, mutual information, frequent subgraph mining, minimum spanning tree, hypoconnectivity, graph theory, and classification.
What role does the "reactive band" play in the analysis?
The "reactive band" is identified as the frequency band where connectivity measures show the most significant variation between healthy controls and dementia conditions, focusing subsequent analysis to this specific range (e.g., alpha and lower beta bands) to highlight relevant alterations.
What insights did the Minimum Spanning Tree (MST) analysis provide regarding neuronal communication in dementia?
MST analysis revealed that healthy control subjects tend to have more "leaves" in their networks, indicating easier neuronal communication, while different dementia groups showed varying topological characteristics that could differentiate pathological networks from healthy ones, suggesting potential network-based biomarkers.
What was the classification accuracy achieved by the proposed technique?
When using phase-based mutual information features alone for classification, an accuracy of 81% was obtained. Combining MI-based features with PLV features further improved the classification accuracy to 96.47%.
How are unweighted and weighted brain networks analyzed in this study?
Unweighted networks are formed by thresholding MI values and characterized using frequent subgraph mining to find recurrent motifs. Weighted networks are analyzed using Minimum Spanning Tree (MST) approaches, with MI values as edge weights, to quantify network topology using metrics like leaf fraction, degree distribution, diameter, and eccentricity.
- Citar trabajo
- Abdulyekeen T. Adebisi (Autor), Kalyana C. Veluvolu (Autor), 2026, EEG-Based Computational Neuroengineering for Dementia Diagnosis, Múnich, GRIN Verlag, https://www.grin.com/document/1689170
