ArticleQuantitative imaging in medicine and surgery2023
Topological properties of individual gray matter morphological networks in identifying the preclinical stages of Alzheimer's disease: a preliminary study.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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4 citing papers in PubMed, 9 citations in OpenAlex.
- Similar minds age alike: an MRI similarity approach for predicting age-related cognitive decline.npj aging · 2026Article
- Development of a Predictive Model for the Progression of Subjective Cognitive Decline: A Longitudinal Study.Brain and behavior · 2025Article
- Research progress on brain network imaging biomarkers of subjective cognitive decline.Frontiers in neuroscience · 2025Review
- Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters.Heliyon · 2024Article
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Authors and funding
13 authors at 5 institutions in 1 country.
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Abstract
Background: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are preclinical stages of Alzheimer's disease (AD). Individual biomarkers are essential for evaluating altered neurological outcomes at both SCD and MCI stages for early diagnosis and intervention of AD. In this study, we aimed to investigate the relationships between topological properties of the individual brain morphological network and clinical cognitive performances among healthy controls (HCs) and patients with SCD or MCI. Methods: The topological measurements of individual morphological networks were analyzed using graph theory, and inter-group differences of standard graph topology were correlated and regressed to scores of clinical cognitive functions. Results: Compared with HCs, the topology of the individual morphological networks in SCD and MCI patients was significantly altered. At the global level, altered topology was characterized by lower global efficiency, shorter characteristics path length, and normalized characteristics path length [all P<0.05, false discovery rate (FDR) corrected]. In addition, at the regional level, SCD and MCI patients exhibited abnormal degree centrality in the caudate nucleus and nodal efficiency in the caudate nucleus, right insula, lenticular nucleus, and putamen (all P<0.05, FDR corrected). Conclusions: The topological features of individual gray matter morphological networks may serve as biomarkers to improve disease prognosis and intervention in the early stages of AD, namely SCD and MCI. Moreover, these findings may further elucidate the relationships between brain morphological alterations and cognitive dysfunctions in SCD and MCI.
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