Age Correction in Dementia – Matching to a Healthy Brain

Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
PLoS ONE (Impact Factor: 3.53). 07/2011; 6(7):e22193. DOI: 10.1371/journal.pone.0022193
Source: PubMed

ABSTRACT In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.

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Available from: Matthias L Schroeter, Aug 16, 2015
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    • "For example, classification studies often control for effects of covariate variables using a matched sample design, as in the present study. It is possible, however, that greater classification accuracy could be obtained if the effects of covariate demographic variables such as age are appropriately estimated (and regressed out) prior to classification analysis (Barnes et al., 2010; Cobia et al., 2012; Dukart et al., 2011). While possible, such approaches are not yet commonly implemented (e.g., Schrouff et al., 2013 "
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    • "These pathological changes include precocious and/or accelerated brain aging (Fotenos et al., 2008; Driscoll et al., 2009; Sluimer et al., 2009; Wang et al., 2009; Spulber et al., 2010; Clark et al., 2012). Recently, atrophic regions detected in AD patients were found to largely overlap with those regions showing a normal age-related decline in healthy control subjects (Dukart et al., 2011). Hence, early identification of neuroanatomical changes deviating from the normal age-related atrophy pattern has the potential to improve clinical outcomes in the disease course through early treatment or prophylaxis (Ashburner et al., 2003). "
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    • "However, for inference in elderly subjects, the approaches often do not address the underlying developmental process, e.g. age-related effects in the control sample (see also Dukart et al., 2011), as well as variations due to other relevant covariates, e.g. global volume differences (Peelle et al., 2012). "
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