• Brain dysfunction assessment i...

Treffer: Brain dysfunction assessment in Alzheimer's disease: A phase-space projection and interactive signal decomposition framework.

Title:
Brain dysfunction assessment in Alzheimer's disease: A phase-space projection and interactive signal decomposition framework.
Authors:
Srimaharaj W; The International College, Payap University, Chiang Mai, 50000, Thailand. Electronic address: wanus_s@payap.ac.th.
Source:
Computers in biology and medicine [Comput Biol Med] 2026 Jan 15; Vol. 201, pp. 111440. Date of Electronic Publication: 2026 Jan 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Imprint Name(s):
Publication: New York : Elsevier
Original Publication: New York, Pergamon Press.
MeSH Terms:
Alzheimer Disease*/physiopathology , Electroencephalography*/methods , Signal Processing, Computer-Assisted* , Brain*/physiopathology, Humans ; Male ; Female ; Aged ; Middle Aged ; Aged, 80 and over ; Frontotemporal Dementia/physiopathology
Comments:
Erratum in: Comput Biol Med. 2026 Jan 19:111483. doi: 10.1016/j.compbiomed.2026.111483.. (PMID: 41554659)
Contributed Indexing:
Keywords: Alzheimer's disease; Biomarkers; Electroencephalography (EEG); Functional connectivity; Interactive signal decomposition (ISD); Nonlinear dynamics; Phase-space projection; Signal processing
Entry Date(s):
Date Created: 20260102 Date Completed: 20260109 Latest Revision: 20260119
Update Code:
20260120
DOI:
10.1016/j.compbiomed.2025.111440
PMID:
41481966
Database:
MEDLINE

Weitere Informationen

This study introduces and evaluates a signal processing framework to identify electrophysiological biomarkers for neurodegenerative diseases from resting-state electroencephalography (EEG) data. The objective is to quantify distinct patterns of brain dysfunction in Alzheimer's Disease (AD) by analyzing nonlinear signal dynamics and functional connectivity. The analysis was performed on a dataset of 65 participants, including individuals with AD, Frontotemporal Dementia (FTD), and Healthy Controls (HC). A two-stage analytical method was applied to the EEG signals. The first stage, Phase-Space Projection (PSP), transforms the EEG time-series into a signal representing its deviation from a stable reference state. The second stage, Interactive Signal Decomposition (ISD), separates this deviation signal into oscillatory components and a nonlinear residual. Functional connectivity was assessed using the directed Phase Lag Index (dPLI). The AD group exhibited significantly lower functional connectivity compared to the HC group. Concurrently, the mean residual energy, a measure of signal complexity derived from the ISD framework, was significantly lower in AD patients. A direct positive correlation was found between residual energy and Mini-Mental State Examination (MMSE) scores, linking reduced nonlinear signal complexity to greater cognitive impairment. The combined PSP-ISD framework provides a quantitative measure of local nonlinear signal complexity, which is reduced in Alzheimer's Disease. When used alongside functional connectivity analysis, this approach offers a method for characterizing the neuropathology of AD through both network-level degradation and local reduced nonlinear signal complexity, supplying a new analytical tool for clinical neuroscience.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.