ArticleAge and ageing2024
Small World derived index to distinguish Alzheimer's type dementia and healthy subjects.
Article in Age and ageing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Wavelet event-related EEG phase coherence as a discriminant biomarker of the cognitive status in Parkinson's and Lewy body disease.Frontiers in human neuroscience · 2026Article
- CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for EEG-Based Dementia Classification.Sensors (Basel, Switzerland) · 2025Article
- Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project.Diagnostic and prognostic research · 2024Article
- Brain network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThis article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns.
methodsElectroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones.
resultsResults showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones.
conclusionsThese findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.
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