Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis
- PMID: 39009557
- PMCID: PMC11250750
- DOI: 10.1111/cns.14859
Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis
Abstract
Objective: The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD.
Methods: Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties.
Results: For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group.
Conclusion: There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.
Keywords: SCD; brain functional networks; early identification; graph theory.
© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.
Conflict of interest statement
The author reports no conflicts of interest in this work.
Figures




Similar articles
-
Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.J Prev Alzheimers Dis. 2025 May;12(5):100079. doi: 10.1016/j.tjpad.2025.100079. Epub 2025 Feb 6. J Prev Alzheimers Dis. 2025. PMID: 39920001 Free PMC article.
-
Cognitive reserve in subjective cognitive decline with worry: DMN-VN connectivity supports episodic memory under structural vulnerability.BMC Geriatr. 2025 Jul 2;25(1):452. doi: 10.1186/s12877-025-06072-8. BMC Geriatr. 2025. PMID: 40604471 Free PMC article.
-
Hyperconnectivity and Connectome Gradient Dysfunction of Cerebello-Thalamo-Cortical Circuitry in Alzheimer's Disease Spectrum Disorders.Cerebellum. 2025 Feb 6;24(2):43. doi: 10.1007/s12311-025-01792-4. Cerebellum. 2025. PMID: 39913059
-
¹⁸F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI).Cochrane Database Syst Rev. 2015 Jan 28;1(1):CD010632. doi: 10.1002/14651858.CD010632.pub2. Cochrane Database Syst Rev. 2015. PMID: 25629415 Free PMC article.
-
The Black Book of Psychotropic Dosing and Monitoring.Psychopharmacol Bull. 2024 Jul 8;54(3):8-59. Psychopharmacol Bull. 2024. PMID: 38993656 Free PMC article. Review.
References
-
- Abbott A. Conquering Alzheimer's: a look at the therapies of the future. Nature. 2023;616(7955):26‐28. - PubMed
-
- 2023 Alzheimer's disease facts and figures. Alzheimers Dement. 2023;19(4):1598‐1695. - PubMed
-
- Self WK, Holtzman DM. Emerging diagnostics and therapeutics for Alzheimer disease. Nat Med. 2023;29(9):2187‐2199. - PubMed
-
- Jessen F, Wolfsgruber S, Kleineindam L, et al. Subjective cognitive decline and stage 2 of Alzheimer disease in patients from memory centers. Alzheimers Dement. 2023;19(2):487‐497. - PubMed
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Medical