Gurevich, Pavel and Stuke, Hannes and Kastrup, Andreas and Stuke, Heiner and Hildebrandt, Helmut (2017) Neuropsychological testing and machine learning distinguish Alzheimer’s disease from other causes for cognitive impairment. Frontiers in aging neuroscience, 9. ISSN 1663-4365

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With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1–42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer’s Disease—Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Uncontrolled Keywords: Alzheimer’s disease, MCI, total tau to Ab(1–42) ratio, neuropsychological testing, dementia, machine learning
Subjects: Philosophy and psychology > Psychology
Technology, medicine, applied sciences > Medicine and health
Divisions: Faculty of Medicine and Health Sciences > Department of Psychology
Date Deposited: 26 Sep 2017 13:14
Last Modified: 22 Nov 2017 07:47
URN: urn:nbn:de:gbv:715-oops-34011
DOI: 10.3389/fnagi.2017.00114

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