ArticleBrain topography2019
Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer's Disease: Graph Theory Applied to Resting State EEG.
Article in Brain topography, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The neurophysiology of healthy and pathological aging: a comprehensive systematic review.Brain structure & function · 2025Pooled it
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- The electrophysiological basis of resting-state fMRI hyperconnectivity in early Alzheimer's disease.Alzheimer's research & therapy · 2026Article
- Multimodal non-invasive approaches for early Alzheimer's disease detection: a review of neuroelectrophysiological and neuroimaging techniques.Frontiers in psychiatry · 2026Review
- 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
- The network neuropsychology of juvenile myoclonic epilepsy.Cortex; a journal devoted to the study of the nervous system and behavior · 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
- EEG-based graph network analysis in relation to regional tau in asymptomatic Alzheimer's disease.Brain communications · 2025Article
- Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.Physical and engineering sciences in medicine · 2024Article
- Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis.BMC neuroscience · 2024Article
- Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease.Journal of clinical neurology (Seoul, Korea) · 2024Article
- Hub overload and failure as a final common pathway in neurological brain network disorders.Network neuroscience (Cambridge, Mass.) · 2024Review
- Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.Network neuroscience (Cambridge, Mass.) · 2024Article
- Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease.Brain connectivity · 2023Article
- The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition.GeroScience · 2023Article
- Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms.Frontiers in aging neuroscience · 2023Article
- Prefrontal EEG slowing, synchronization, and ERP peak latency in association with predementia stages of Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.Human brain mapping · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph theory analysis on resting state electroencephalographic rhythms disclosed topological properties of cerebral network. In Alzheimer's disease (AD) patients, this approach showed mixed results. Granger causality matrices were used as input to the graph theory allowing to estimate the strength and the direction of information transfer between electrode pairs. The number of edges (degree), the number of inward edges (in-degree), of outgoing edges (out-degree) were statistically compared among healthy controls, patients with mild cognitive impairment due to AD (AD-MCI) and AD patients with mild dementia (ADD) to evaluate if degree abnormality could involve low and/or high degree vertices, the so called hubs, in both prodromal and over dementia stage. Clustering coefficient and local efficiency were evaluated as measures of network segregation, path length and global efficiency as measures of integration, the assortativity coefficient as a measure of resilience. Degree, in-degree and out-degree values were lower in AD-MCI and ADD than the control group for non-hubs and hubs vertices. The number of edges was preserved for frontal electrodes, where patients' groups showed an additional hub in F3. Clustering coefficient was lower in ADD compared with AD-MCI in the right occipital electrode, and it was positively correlated with mini mental state examination. Local and global efficiency values were lower in patients' than control groups. Our results show that the topology of the network is altered in AD patients also in its prodromal stage, begins with the reduction of the number of edges and the loss of the local and global efficiency.
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