Lower cerebral blood flow is associated with faster cognitive decline in Alzheimer's disease

OBJECTIVE: To determine whether lower cerebral blood flow (CBF) is associated with faster cognitive decline in patients with Alzheimer's disease (AD). METHODS: We included 88 patients with dementia due to AD from the Amsterdam Dementia Cohort. Mean follow-up was 2 ± 1 years. Linear mixed models were used to determine associations of lower whole brain and regional pseudo-continuous arterial spin labelling measured CBF with rate of cognitive decline as measured with repeated mini-mental state examination (MMSE). Model 1 was adjusted for age, sex, and education. Model 2 was additionally adjusted for normalized gray matter volume, medial temporal lobe atrophy, white matter hyperintensities, microbleeds, and lacunes. Analyses were repeated after partial volume correction (PVC) of CBF. Statistical significance was set at p ≤ 0.05. RESULTS: Patients were 65 ± 7 years old, 44 (50 %) were women, and mean baseline MMSE was 22 ± 4. Annual decline (β[SE]) on the MMSE was estimated at -2.11 (0.25) points per year. Lower whole brain (β[SE]-0.50[0.25]; p ≤ 0.05) and parietal (β[SE]-0.59[0.25]; p < 0.05) CBF were associated with faster cognitive decline. PVC cortical CBF was not associated with cognitive decline. CONCLUSIONS: Lower CBF, in particular in the posterior brain regions, may have value as a prognostic marker for rate of cognitive decline in AD. KEY POINTS: • In AD, lower CBF is associated with more rapid cognitive decline. • Decreasing CBF does not reach a plateau early in AD. • PcASL-CFB has additive value to conventional structural MRI measures in AD

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

PURPOSE: To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. MATERIALS AND METHODS: Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. RESULTS: Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P .05). CONCLUSION: With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values

Gray matter network measures are associated with cognitive decline in mild cognitive impairment

Gray matter networks are disrupted in Alzheimer's disease and related to cognitive impairment. However, it is still unclear whether these disruptions are associated with cognitive decline over time. Here, we studied this question in a large sample of patients with mild cognitive impairment with extensive longitudinal neuropsychological assessments. Gray matter networks were extracted from baseline structural magnetic resonance imaging, and we tested associations of network measures and cognitive decline in Mini-Mental State Examination and 5 cognitive domains (i.e., memory, attention, executive function, visuospatial, and language). Disrupted network properties were cross-sectionally related to worse cognitive impairment. Longitudinally, lower small-world coefficient values were associated with a steeper decline in almost all domains. Lower betweenness centrality values correlated with a faster decline in Mini-Mental State Examination and memory, and at a regional level, these associations were specific for the precuneus, medial frontal, and temporal cortex. Furthermore, network measures showed additive value over established biomarkers in predicting cognitive decline. Our results suggest that gray matter network measures might have use in identifying patients who will show fast disease progression

Experimental and Numerical Investigation of Metal Type and Thickness Effects on the Impact Resistance of Fiber Metal Laminates

The impact response of fiber metal laminates (FMLs), has been investigated with experiments and numerical simulations, which is reported in this article. Low-velocity impacts were carried out to study the effects of metal type and thickness within FMLs. Glare5-3/2 laminates with two aluminum layer thicknesses and a similar FML containing magnesium sheets were impacted by drop weight tests. Also, a major part of this study was to accomplish a dynamic non-linear transient analysis to study the impact response of FMLs using the commercial finite element (FE) analysis code ABAQUS. By reviewing different approaches of modeling constituents of an FML, it is shown that the appropriate selection of elements hasmore significant role than failure criterion to predict acceptable results for this type of laminate and loading. The good agreement obtained between experimental and numerical results verifies the possibility of relatively simpler simulation by FE-analysis to predict overall response of FMLs under impact loading.Aerspace Structures and MaterialsAerospace Engineerin

Prediction of poor clinical outcome in vascular cognitive impairment: TRACE-VCI study

10.1002/dad2.12077Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring121e12077