scholarly journals Probability of Alzheimer's disease in breast cancer survivors based on gray‐matter structural network efficiency

Author(s):  
Shelli R. Kesler ◽  
Vikram Rao ◽  
William J. Ray ◽  
Arvind Rao ◽  
2012 ◽  
Vol 8 (4S_Part_14) ◽  
pp. P534-P535
Author(s):  
Yael Reijmer ◽  
Alexander Leemans ◽  
Karen Caeyenberghs ◽  
Sophie Heringa ◽  
Geert Jan Biessels ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaoning Sheng ◽  
Haifeng Chen ◽  
Pengfei Shao ◽  
Ruomeng Qin ◽  
Hui Zhao ◽  
...  

BackgroundStructural network alterations in Alzheimer’s disease (AD) are related to worse cognitive impairment. The aim of this study was to quantify the alterations in gray matter associated with impaired cognition and their pathological biomarkers in AD-spectrum patients.MethodsWe extracted gray matter networks from 3D-T1 magnetic resonance imaging scans, and a graph theory analysis was used to explore alterations in the network metrics in 34 healthy controls, 70 mild cognitive impairment (MCI) patients, and 40 AD patients. Spearman correlation analysis was computed to investigate the relationships among network properties, neuropsychological performance, and cerebrospinal fluid pathological biomarkers (i.e., Aβ, t-tau, and p-tau) in these subjects.ResultsAD-spectrum individuals demonstrated higher nodal properties and edge properties associated with impaired memory function, and lower amyloid-β or higher tau levels than the controls. Furthermore, these compensations at the brain regional level in AD-spectrum patients were mainly in the medial temporal lobe; however, the compensation at the whole-brain network level gradually extended from the frontal lobe to become widely distributed throughout the cortex with the progression of AD.ConclusionThe findings provide insight into the alterations in the gray matter network related to impaired cognition and pathological biomarkers in the progression of AD. The possibility of compensation was detected in the structural networks in AD-spectrum patients; the compensatory patterns at regional and whole-brain levels were different and the clinical significance was highlighted.


2021 ◽  
Vol 13 ◽  
Author(s):  
Florian U. Fischer ◽  
Dominik Wolf ◽  
Oliver Tüscher ◽  
Andreas Fellgiebel ◽  

Introduction: Functional imaging studies have demonstrated the recruitment of additional neural resources as a possible mechanism to compensate for age and Alzheimer’s disease (AD)-related cerebral pathology, the efficacy of which is potentially modulated by underlying structural network connectivity. Additionally, structural network efficiency (SNE) is associated with intelligence across the lifespan, which is a known factor for resilience to cognitive decline. We hypothesized that SNE may be a surrogate of the physiological basis of resilience to cognitive decline in elderly persons without dementia and with age- and AD-related cerebral pathology.Methods: We included 85 cognitively normal elderly subjects or mild cognitive impairment (MCI) patients submitted to baseline diffusion imaging, liquor specimens, amyloid-PET and longitudinal cognitive assessments. SNE was calculated from baseline MRI scans using fiber tractography and graph theory. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher SNE and the modulation of this association by increased cerebral amyloid, liquor tau or WMHV. Results: For the majority of cognitive outcome measures, higher SNE was associated with higher resilience to cognitive decline (p-values: 0.011–0.039). Additionally, subjects with higher SNE showed more resilience to cognitive decline at higher cerebral amyloid burden (p-values: <0.001–0.036) and lower tau levels (p-values: 0.002–0.015).Conclusion: These results suggest that SNE to some extent may quantify the physiological basis of resilience to cognitive decline most effective at the earliest stages of AD, namely at increased amyloid burden and before increased tauopathy.


2012 ◽  
Vol 8 (4S_Part_2) ◽  
pp. P54-P54
Author(s):  
Yael Reijmer ◽  
Alexander Leemans ◽  
Karen Caeyenberghs ◽  
Sophie Heringa ◽  
Geert Jan Biessels ◽  
...  

2013 ◽  
Author(s):  
Laura Q. Rogers ◽  
R. Trammell ◽  
S. Vicari ◽  
P. Hopkins-Price ◽  
A. Spenner ◽  
...  

2013 ◽  
Author(s):  
Shannon L. Mihalko ◽  
Samantha E. Yocke ◽  
Greg Russell ◽  
Marissa Howard-McNatt ◽  
Edward A. Levine

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