Affiliations: [a] Department of Nuclear Medicine, Charité, Berlin, Germany | [b] jung diagnostics GmbH, Hamburg, Germany | [c] Université Pierre et Marie Curie, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital de la Salpêtrière, Paris, France | [d] Center for Stroke Research Berlin, Charité, Berlin, Germany
Correspondence:
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Correspondence to: Ralph Buchert, PhD, Charité – Universitätsmedizin Berlin, Department of Nuclear Medicine, Charitéplatz 1, 10117 Berlin, Germany. Tel.: +49 30 450627059; Fax: +49 30 4507527959; Ralph.Buchert@charite.de
Note: [1] These authors contributed equally as senior authors.
Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Hippocampus volumetry based on magnetic resonance imaging (MRI) has not yet been translated into everyday clinical diagnostic patient care, at least in part due to limited availability of appropriate software tools. In the present study, we evaluate a fully-automated and computationally efficient processing pipeline for atlas based hippocampal volumetry using freely available Statistical Parametric Mapping (SPM) software in 198 amnestic mild cognitive impairment (MCI) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI1). Subjects were grouped into MCI stable and MCI to probable Alzheimer’s disease (AD) converters according to follow-up diagnoses at 12, 24, and 36 months. Hippocampal grey matter volume (HGMV) was obtained from baseline T1-weighted MRI and then corrected for total intracranial volume and age. Average processing time per subject was less than 4 minutes on a standard PC. The area under the receiver operator characteristic curve of the corrected HGMV for identification of MCI to probable AD converters within 12, 24, and 36 months was 0.78, 0.72, and 0.71, respectively. Thus, hippocampal volume computed with the fully-automated processing pipeline provides similar power for prediction of MCI to probable AD conversion as computationally more expensive methods. The whole processing pipeline has been made freely available as an SPM8 toolbox. It is easily set up and integrated into everyday clinical patient care.
