scholarly journals Spatial relationships between white matter degeneration, amyloid load and cortical volume in amnestic mild cognitive impairment

2018 ◽  
Author(s):  
Ileana O Jelescu ◽  
Timothy M Shepherd ◽  
Dmitry S Novikov ◽  
Yu-Shin Ding ◽  
Benjamin Ades-Aron ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Diffusion Mri ◽  
Amnestic Mild Cognitive Impairment ◽  
Amyloid Deposition ◽  
Cortical Volume ◽  
Cortical Areas ◽  
Healthy Control ◽  
Β Amyloid

The spatial-temporal relationships between gray and white matter (WM) degeneration during preclinical and early symptomatic Alzheimer's disease are poorly understood. We characterized β-amyloid deposition, cortical volume and WM degeneration in 44 subjects including healthy control (N=23), amnestic mild cognitive impairment (aMCI) (N=19), and early Alzheimer's subjects (N=2). Integrated PET-MRI provided simultaneous measurement of 18F-Florbetapir uptake in cortical areas, regional brain volumes from structural MRI, and WM tract integrity metrics from diffusion MRI using biophysical modeling. Across the cohort of healthy control and aMCIs, cortical volumes correlated poorly with β-amyloid deposition in the same area (p < 0.05 only in the posterior cingulate and parietal lobe). WM degeneration correlated significantly with both amyloid and volume of connected cortical areas, but more strongly with volume. Diffusion MRI metrics for WM demyelination and/or axonal loss could therefore provide new biomarkers associated with clinical Alzheimer's conversion. These WM changes may represent sequential propagation of Alzheimer's neurodegeneration between functionally connected regions, and/or evidence of direct WM injury during the Alzheimer's pathology cascade.

Identifying Brain Connectivity Using Network-Based Statistics in Amnestic Mild Cognitive Impairment Stratified by β-Amyloid Positivity

2018 ◽  
Vol 34 (2) ◽  
pp. 104-111 ◽  
Author(s):  
Ji Eun Kim ◽  
Sung-Woo Kim ◽  
Minsuk Choi ◽  
Joon-Kyung Seong ◽  
Jae-Hong Lee
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Amnestic Mild Cognitive Impairment ◽  
Control Group ◽  
Whole Brain ◽  
Brain White Matter ◽  
Significant Difference ◽  
Β Amyloid ◽  
Structural Networks

Background: The aim of this study was to identify white matter structural networks of amnestic mild cognitive impairment (aMCI) dichotomized by β amyloid (Aβ) status and compare them using network-based statistics (NBS). Methods: Patients underwent whole-brain diffusion-weighted magnetic resonance imaging, detailed neuropsychological test and [18F]-Florbetaben amyloid positron emission tomography. We performed the NBS analysis to compare the whole-brain white matter structural networks extracted from diffusion tensor images. Results: One hundred sixteen participants (Aβ− cognitively normal [CN], n = 35; Aβ− aMCI, n = 42; Aβ+ aMCI, n = 39) were included. There was no subnetwork showing significant difference between Aβ+ aMCI and Aβ− aMCI. However, by comparing each aMCI group with control group, we found that supplementary motor areas were common hub regions. Intriguingly, Aβ+ aMCI showed reduced connectivity mainly in the medial frontal regions, while Aβ− aMCI showed somewhat uniform disruption when compared to CN. Conclusion: Structural network analysis using network-based approach in aMCI may shed light on further understanding of white matter disruption in the prodromal stage of Alzheimer’s disease.

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
pp. 1-14
Author(s):  
Fangmei He ◽  
Yuchen Zhang ◽  
Xiaofeng Wu ◽  
Youjun Li ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Microstructure Changes ◽  
Disease Trajectory ◽  
Development Trajectory ◽  
The Right

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer’s disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient’s Mini-Mental State Examination scores. Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

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