ArticleBMC neuroscience2024
Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis.
Article in BMC neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Nurse-led non-pharmacological interventions for cognitive improvement in dementia patients: a systematic review and meta-analysis of randomized controlled trials.Frontiers in aging neuroscience · 2026Pooled it
- rTMS modulates early AD progression via synergistic brain network reorganization and peripheral biomarker dynamics.GeroScience · 2026Trial
- Electroencephalography Functional Network Responses to Immersive Virtual Reality in Alzheimer Disease and Mild Cognitive Impairment: Exploratory Single-Session 3-Group Repeated-Measures Study.JMIR serious games · 2026Article
- The electrophysiological basis of resting-state fMRI hyperconnectivity in early Alzheimer's disease.Alzheimer's research & therapy · 2026Article
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.Entropy (Basel, Switzerland) · 2026Article
- Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.Journal of neuroengineering and rehabilitation · 2026Article
- Lean-NET-Based Local Brain Connectome Analysis for Autism Spectrum Disorder Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Spinal cord stimulation for concurrent improvement of consciousness and spasticity following severe traumatic brain injury: a case study.Frontiers in neuroscience · 2026Article
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- Non-invasive brain stimulation in frontotemporal dementia: syndrome-specific signals and priorities for future trials.Frontiers in aging neuroscience · 2026Review
- Brain Topology Disruption in Early-Onset Dementia: Review of Current Findings and the Need for Network Resilience Focused Models.Brain and behavior · 2025Review
- A dual path graph neural network framework for dementia diagnosis.Scientific reports · 2025Article
- A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer's Disease and Mild Cognitive Impairment.Biosensors · 2025Article
- Altered Brain Functional Connectivity and Topological Structural in Girls with Idiopathic Central Precocious Puberty: A Graph Theory Analysis Based on Resting-State fMRI.Children (Basel, Switzerland) · 2025Article
- A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.Journal of personalized medicine · 2025Article
- The role of quantitative EEG biomarkers in Alzheimer's disease and mild cognitive impairment: applications and insights.Frontiers in aging neuroscience · 2025Review
Corrections and comments
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6 authors.
Funding
Abstract
backgroundAlzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.
methodsWe analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.
resultsBoth AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.
conclusionsOur findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.