ReviewHeliyon2023
Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review.
Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Altered Driving Structures and Controllability Patterns of Brain Effective Networks in Mild Cognitive Impairment and Alzheimer's Disease.Brain sciences · 2026Article
- Early Microstructural Changes in the Left Inferior Fronto-Occipital Fasciculus: A Key Factor in Probable Alzheimer's Disease Patients With Slow Gait Speed.Brain and behavior · 2026Article
- Association of neighborhood disadvantage with cognitive function and cortical disorganization in an unimpaired cohort: An exploratory study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Bridging Reduced Grip Strength and Altered Executive Function: Specific Brain White Matter Structural Changes in Patients with Alzheimer's Disease.Clinical interventions in aging · 2024Article
- A new network representation for time series analysis from the perspective of combinatorial property of ordinal patterns.Heliyon · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
Indexed as
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Registered trials
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.