ABSTRACT
OBJECTIVE: To use multivariate statistical analysis of EEG data in order to separate EEGs of patients with Alzheimer's disease (AD) from controls. A group of individuals with mild cognitive impairment (MCI) was evaluated using the same methodology. Additionally, the effects of scopolamine on this separation are studied. METHODS: Statistical pattern recognition (SPR) is used in conjunction with information extracted from EEGs before and after administration of scopolamine. RESULTS: There was complete separation of the AD group and controls before administration of scopolamine. The separation increased after scopolamine had been given. Of the 10 MCI individuals, five seemed to belong to the AD group. Three of those progressed to AD within 1 year and one after 3 years. CONCLUSIONS: Using SPR on EEG recordings it is possible to separate AD from controls. This separation can be increased by the use of scopolamine but the medication is not a prerequisite for classification. The results indicate that SPR is useful for predicting progress of MCI to AD. SIGNIFICANCE: EEG registration is a simple and noninvasive method. If these results are confirmed in other studies, this method could be more widely applied than the highly specialized methods used today in detection of early AD.
Subject(s)
Alzheimer Disease/diagnosis , Cerebral Cortex/physiopathology , Cognition Disorders/diagnosis , Electroencephalography/methods , Scopolamine , Aged , Aged, 80 and over , Alzheimer Disease/physiopathology , Brain Mapping , Cognition Disorders/physiopathology , Disease Progression , Female , Humans , Male , Middle Aged , Neuropsychological Tests , Patient Selection , Pattern Recognition, Automated , Pilot Projects , Signal Processing, Computer-AssistedABSTRACT
We show that density functional theory calculations have reached an accuracy and speed making it possible to use them in conjunction with an evolutionary algorithm to search for materials with specific properties. The approach is illustrated by finding the most stable four component alloys out of the 192 016 possible fcc and bcc alloys that can be constructed out of 32 different metals. A number of well known and new "super alloys" are identified in this way.
