Functional mapping of the nervous system helps to identify the activity in the human brain during certain tasks. The difference between the two conditions helps you to understand the electrode nodes that cause a particular change in the neural state. Therefore, in this book, we will witness the functional difference between healthy people, Alzheimer’s (AD) patients, multiple cognitive disorder (MCI) patients, and dementia patients. This gives the insight into the pair of electrodes that cause the progression of the disease from MCI to AD to dementia. This will help us in designing the biomarker in the future.
Table of Contents
1 Introduction
2 Neural Disorders, Dataset Description and Preliminary Signal Processing
2.1 Alzheimer’s Disease
2.1.1 Attention deficit/hyperactive disorder (ADHD)
2.2 Dataset description
2.3 Signal Decomposition
3 EEG functional connectivity methodologies
3.1 Synchronization Studies
3.1.1 Amplitude and Phase synchronization
3.1.2 Phase Locking Value (PLV)
3.2 Frequency dependent studies
3.2.1 Approximate (AE) and Transfer Entropy(TE)
3.2.2 Spectral Entropy
4 Phase Locking Value
4.1 Time-Frequency PLV spectrum
4.2 Identification of Differential Scalp Maps
4.3 Network Measures
4.3.1 Path length (PL) and clustering-coefficient explanation
5 Wavelet Spectral Entropy
5.1 Wavelet Spectral Entropy Time-Frequency Spectrum
5.2 Wavelet Spectral Entropy
5.3 Identification of Reactive band (RB) and Most Reactive Pairs (MRP)
5.4 Functional connectivity and Network Measures
5.5 Discussion
6 Conclusion and Future works
Research Objectives and Topics
The primary objective of this work is to analyze neural signal patterns and functional connectivity in patients with Alzheimer’s disease, Mild Cognitive Impairment (MCI), and dementia compared to healthy subjects to establish potential future biomarkers. By applying advanced information theory and graph theoretical measures to EEG data, the study aims to quantify brain network degradation across these conditions.
- Application of Phase Locking Value (PLV) for synchronization analysis.
- Utilization of Wavelet Spectral Entropy (WSE) to measure signal randomness and predictability.
- Use of graph theory to map functional connectivity and network topology.
- Identification of "Reactive Bands" and "Most Reactive Pairs" to differentiate clinical states.
Excerpt from the Book
2.3 Signal Decomposition
Wavelet transform has proven to be one of the promising techniques that provide more stability between time and frequency [14]. Wavelets are created for the complete frequency range mentioned earlier with varying scaling factor. Therefore, the EEG signal is convoluted with the family of wavelets of different frequency scales and length providing a well-sequenced Time-Frequency spectrum of the EEG signals. The wavelet transform can be depicted as:
X(t, f) = \int x(t)Ψ(S,T)(t)dt (1)
where Ψ(S,T)(t) are wavelets of different scales S and transition T, the frequencies scales are considered for the range of frequencies mentioned before.
Summary of Chapters
1 Introduction: Provides an overview of electrophysiology, neural engineering, and the rising global burden of Alzheimer’s disease and dementia.
2 Neural Disorders, Dataset Description and Preliminary Signal Processing: Details the clinical context of neural disorders, describes the dataset of 31 subjects, and explains the pre-processing steps including band-pass filtering and ICA.
3 EEG functional connectivity methodologies: Discusses theoretical approaches for synchronization and entropy-based measures to analyze brain activity.
4 Phase Locking Value: Focuses on PLV as a synchronization metric, including Time-Frequency spectrum analysis and the identification of differential scalp maps.
5 Wavelet Spectral Entropy: Explores WSE for evaluating neural connectivity, identifying reactive bands, and applying network measures to classify clinical states.
6 Conclusion and Future works: Summarizes the study findings and proposes future integration with machine learning and longitudinal data for improved clinical diagnostics.
Keywords
EEG, Alzheimer’s disease, Dementia, Mild Cognitive Impairment, Neural engineering, Phase Locking Value, Wavelet Spectral Entropy, Functional connectivity, Graph theory, Biomarker, Signal decomposition, Time-frequency analysis, Small-world networks, Neural degeneration, Electrophysiology.
Frequently Asked Questions
What is the core focus of this publication?
The book focuses on the functional mapping of the nervous system to identify activity patterns in the human brain, specifically differentiating between healthy subjects, Alzheimer's (AD) patients, MCI patients, and dementia patients.
What are the primary scientific fields involved?
The research sits at the intersection of biomedical engineering, computational neuroscience, and electrophysiology.
What is the main goal of the research?
The goal is to analyze EEG signals to provide insights into electrode node interactions that drive the progression from MCI to AD and dementia, ultimately aiming to design a reliable clinical biomarker.
Which methodologies are employed in the study?
The authors use signal processing techniques such as complex Morlet wavelet transforms, Phase Locking Value (PLV) for synchronization, Wavelet Spectral Entropy (WSE) for predictability analysis, and graph theoretical measures like path length and clustering coefficients.
What topics are covered in the main section of the book?
The main sections cover EEG signal pre-processing, synchronization studies (PLV), frequency-dependent studies, and the application of Wavelet Spectral Entropy to form functional connectivity networks.
Which keywords best characterize this work?
Key terms include EEG, Alzheimer's disease, dementia, functional connectivity, Phase Locking Value, Wavelet Spectral Entropy, and graph theory.
Why is Wavelet Spectral Entropy (WSE) significant in this study?
WSE is highlighted because it is independent of signal amplitude, making it a robust measure for evaluating functional neural connectivity and the randomness of signals regardless of their strength.
How is the "Reactive Band" defined and used?
The Reactive Band is the frequency range where entropy shows significant changes; it is used to isolate the specific neural responses that provide the most information regarding condition changes between clinical groups.
What limitation is noted regarding the subject data?
The current analysis is limited to condition-specific data rather than longitudinal tracking of individual patients over time, which is identified as an area for future research.
- Citation du texte
- Kalyana Veluvolu (Auteur), Padma Priya Vijayakumaran (Auteur), 2020, Brain Mapping for Dementia Disorder, Munich, GRIN Verlag, https://www.grin.com/document/957909
