Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs.

BACKGROUND: Visual assessment of medial temporal lobe atrophy (MTA; range 0-4, from no atrophy to increasing atrophy of the choroid fissure, temporal horns and hippocampus) is a sensitive radiological marker of Alzheimer's disease (AD). One of the critical elements for visual MTA assessment is the cut-off score that determines deviation from normality. METHODS: In this study, we assessed the sensitivity and specificity of different MTA cut-off scores to classify control subjects, individuals with mild cognitive impairment (MCI) and AD patients from two large independent cohorts, AddNeuroMed and Alzheimer's Disease Neuroimaging Initiative. Of note, we evaluated the effects of clinical, demographic and genetic variables on the classification performance according to the different cut-offs. RESULTS: A cut-off of ≥1.5 based on the mean MTA scores of both hemispheres showed higher sensitivity in classifying patients with AD (84.5%) and MCI subjects (75.8%) who converted to dementia compared to an age-dependent cut-off. The age-dependent cut-off showed higher specificity or ability to correctly identify control subjects (83.2%) and those with MCI who remained stable (65.5%). Increasing age, early-onset disease and absence of the ApoE ε4 allele had a stronger influence on classifications using the ≥1.5 cut-off. Above 75 years of age, an alternative cut-off of ≥2.0 should be applied to achieve a classification accuracy for both patients with AD and control subjects that is clinically useful. CONCLUSION: Clinical, demographic and genetic variables can influence the classification of MTA cut-off scores, leading to misdiagnosis in some cases. These variables, in addition to the differential sensitivity and specificity of each cut-off, should be carefully considered when performing visual MTA assessment.

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment.

BACKGROUND: Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index. METHODS: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated. RESULTS: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like. CONCLUSION: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.

Differences in brain cholesterol metabolism and insulin in two subgroups of patients with different CSF biomarkers but similar white matter lesions suggest different pathogenic mechanisms.

Investigate possible associations of white matter hyperintensities (WMHs) with the metabolism of cholesterol and insulin in two subgroups of patients with memory complaints and different CSF Aß42 and CSF tau levels. 59 patients from the memory clinic at Karolinska Hospital were included. Degree of WMHs was rated using the ARWMC scale and the following biomarkers were measured in CSF and plasma: insulin, cholesterol, lanosterol, lathosterol, and oxidized cholesterol metabolites. The WMHs in CSF control-like group correlated with increased brain cholesterol synthesis and reduced efflux of oxysterols and insulin in CSF. In the CSF AD-like group, the WMHs correlated with increased peripheral cholesterol metabolism. Despite having similar appearance on FLAIR images, the pathogenic mechanisms of WMHS are likely to be different in the two groups investigated.