P2-222: Classification of normal elderly, MCI, and mild AD using neuropsychological tests and self-reported memory function

Abstract

Like most diseases, the most effective management in AD, and the ultimate hope for cure, will depend on early recognition and intervention. The amnestic form of mild cognitive impairment (MCI) is a frequent precursor of AD and may be the earliest clinical stage of AD. Using magnetoencephalography (MEG) in a simple 45–sec long fixation task, we demonstrated reliable changes in the strength of synchronous positive (Lewis et al., Soc. Neurosci. Abstr. 542: 6, 2005) and negative interactions (McCarten et al., Soc. Neurosci. Abstr. 542: 7, 2005) between neuronal ensembles in normal elderly (NE), amnestic MCI and mild AD subjects, and provided support for the concept that MCI represents early AD. In the present analysis, we focused on neuropsychological tests and self–reporting memory function to find out whether a set of such variables can yield correct classification of subjects to these diagnostic categories. Subjects were in good general health (NE; N=4; Age± SD =76.67±5.03; MMSE±SD= 28.50±1.29), had amnestic MCI (N=4; 75.0±7.87; 26.50±1.29) or mild AD (N=4; 79.0±0.82; 25.50±1.00) based on a comprehensive history, physical and neuropsychological evaluation. All subjects were tested on a battery of neuropsychological tests (yielding 42 variables), and also completed a 64 item self–report memory questionnaire before and after neuropsychological testing. A linear discriminant classification analysis was performed using the robust leave–one–out method for cross validation. We found that a combination of 7 specific neuropsychological and self–report indices (Digit Span forward, list recall, story recall, and 4 self–report ratings of the seriousness of one's memory impairment) was able to correctly classify all subjects even using the leave–one–out method. In addition, a different set of 6 self–reported memory functioning items, on their own, were able to correctly classify 92% of cases. We are currently assessing the power of MEG–derived synchronous dynamic networks (Langheim et al., PNAS USA 103: 455, 2006) to correctly classify NE, MCI, and AD subjects with highly promising results. These findings demonstrate that brief and non–invasive tests can be used successfully to classify subjects into one of these diagnostic categories.