Qualitative estimates of medial temporal atrophy as a predictor of progression from mild cognitive impairment to dementia
ABSTRACT Individuals diagnosed as having mild cognitive impairment (MCI) have a high likelihood of progressing to dementia within 3 to 5 years, but not all individuals with MCI progress to dementia. Prognostic uncertainty suggests the need for additional measures to assist the clinician.
To assess the added value of qualitative measures of medial temporal atrophy (MTA) to estimate the relative risk of progressing from MCI to dementia.
A 3-year, double-blind, placebo-controlled Alzheimer's Disease Cooperative Study initially designed to evaluate the efficacy of donepezil hydrochloride or vitamin E vs placebo to delay progression of MCI to dementia.
Memory assessment centers.
A total of 190 individuals with MCI.
Ratings of MTA performed using magnetic resonance images obtained at baseline. Log-rank tests and Cox proportional hazards ratios examining the significance of MTA estimates in predicting progression of MCI to dementia.
A mean MTA score greater than 2.0 was associated with a greater than 2-fold increased likelihood of progression to dementia during the observation period (hazards ratio, 2.30; 95% confidence interval, 1.09-4.92; P = .03) after controlling for age, education, sex, and baseline Mini-Mental State Examination score.
Adjusted estimates of MTA were associated with significantly increased risk of developing dementia within 3 years, suggesting that obtaining a magnetic resonance image during the evaluation of MCI may offer additional independent information about the risk of progression to dementia. Given the relatively high prevalence of MCI in the general population, use of this method as part of routine clinical evaluation may help identify individuals who might benefit from increased surveillance and future treatment.
clinicaltrials.gov Identifier: NCT00000173.
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ABSTRACT: Background: In the Netherlands, dementia syndromes are diagnosed in specialized memory outpatient clinics (MC). Many radiologists are not trained to assess magnetic resonance imaging (MRI) scans with respect to possible radiological changes that may indicate neurodegenerative disease. Methods: This is a cross-sectional descriptive study. A survey was sent to all Dutch MC and included questions as to how MRI scans are assessed by radiologists and how these assessments are used in the diagnostic process. Results: In most MC, radiologists report on typical Alzheimer pathology and large vessel disease. Small vessel disease and other anatomical changes signifying neurodegenerative disease frequently are not assessed. In the majority of MC, the radiological assessment is not standardized, and physicians assess MRI for themselves to use this information to discuss the consensus diagnosis subsequently. Conclusion: MRI assessment by radiologists in Dutch MC probably underestimates the presence of cerebrovascular and neurodegenerative disease. The validity of standardized assessment protocols in routine clinical practice deserves further study, as the implementation of standardization outside research settings could improve diagnostic accuracy. © 2014 S. Karger AG, Basel.Dementia and Geriatric Cognitive Disorders 07/2014; 38(5-6):281-285. DOI:10.1159/000363499 · 2.81 Impact Factor
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ABSTRACT: Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global grey matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local grey matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease.NeuroImage 04/2014; 97(100). DOI:10.1016/j.neuroimage.2014.04.018 · 6.13 Impact Factor
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ABSTRACT: In this paper, we review studies that have investigated whether neuropsychological, neuropsychiatric, and neuroimaging measures predict decline to Alzheimer's disease (AD). Prospective neuropsychological studies indicate that cognitive performance may be an excellent indicator of future progression from mild cognitive impairment (MCI) to AD, particularly when episodic memory is combined with tasks relying on executive control and language tasks. Research on neuropsychiatric symptoms reveal that depression, apathy, anxiety, and sleep disturbances can contribute to predictive models, though their sensitivity is typically lower than that found with cognitive measures. Finally, different structural brain imaging markers reveal excellent predictive accuracy. The paper discusses issues that will have to be addressed in future studies. First, it will be necessary to increase the evaluation of combined markers, as this may considerably improve predictive accuracy. Second, it will be necessary to move to earlier stages than MCI in order to expand the detection window. Third, processes of compensation and plasticity will have to be better investigated as research moves into earlier stages. The Consortium for the early identification of AD-Quebec (CIMA-Q) is presented as an instance of this approach, and potential batteries of measures are proposed.Journal of Alzheimer's disease: JAD 09/2014; 42. DOI:10.3233/JAD-141470 · 3.61 Impact Factor