» Articles » PMID: 27178195

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease

Abstract

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Citing Articles

Reorganized brain functional network topology in stable and progressive mild cognitive impairment.

Xue C, Zheng D, Ruan Y, Cao X, Zhang X, Qi W Front Aging Neurosci. 2024; 16:1467054.

PMID: 39624168 PMC: 11609165. DOI: 10.3389/fnagi.2024.1467054.


Axonal damage and inflammation response are biological correlates of decline in small-world values: a cohort study in autosomal dominant Alzheimer's disease.

Vermunt L, Sutphen C, Dicks E, de Leeuw D, Allegri R, Berman S Brain Commun. 2024; 6(5):fcae357.

PMID: 39440304 PMC: 11495221. DOI: 10.1093/braincomms/fcae357.


Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis.

Chen W, Zhao H, Feng Q, Xiong X, Ke J, Dai L J Headache Pain. 2024; 25(1):177.

PMID: 39390381 PMC: 11468853. DOI: 10.1186/s10194-024-01861-9.


The brain network hub degeneration in Alzheimer's disease.

Jin S, Wang J, He Y Biophys Rep. 2024; 10(4):213-229.

PMID: 39281195 PMC: 11399886. DOI: 10.52601/bpr.2024.230025.


Alteration in temporal-cerebellar effective connectivity can effectively distinguish stable and progressive mild cognitive impairment.

Xue C, Zheng D, Ruan Y, Guo W, Hu J Front Aging Neurosci. 2024; 16:1442721.

PMID: 39267723 PMC: 11390694. DOI: 10.3389/fnagi.2024.1442721.


References
1.
Chen Z, He Y, Rosa-Neto P, Germann J, Evans A . Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex. 2008; 18(10):2374-81. PMC: 2733312. DOI: 10.1093/cercor/bhn003. View

2.
Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K . Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006; 112(4):389-404. PMC: 3906709. DOI: 10.1007/s00401-006-0127-z. View

3.
Chua T, Wen W, Slavin M, Sachdev P . Diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease: a review. Curr Opin Neurol. 2008; 21(1):83-92. DOI: 10.1097/WCO.0b013e3282f4594b. View

4.
Tijms B, Wink A, de Haan W, van der Flier W, Stam C, Scheltens P . Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging. 2013; 34(8):2023-36. DOI: 10.1016/j.neurobiolaging.2013.02.020. View

5.
Yao Z, Hu B, Zheng J, Zheng W, Chen X, Gao X . A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers. PLoS One. 2015; 10(7):e0132300. PMC: 4498596. DOI: 10.1371/journal.pone.0132300. View