Original ArticlesHigh-throughput, Fully Automated Volumetry for Prediction of MMSE and CDR Decline in Mild Cognitive ImpairmentKovacevic, Sanja PhD*; Rafii, Michael S. MD, PhD* †; Brewer, James B. MD, PhD* †and the Alzheimer's Disease Neuroimaging InitiativeAuthor Information Departments of *Radiology †Neurosciences, University of California, San Diego, La Jolla, CA Supported by NINDS K23 NS050305. Data collection and sharing for this project was funded by the ADNI (principal investigator: Michael Weiner; NIH grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions from the following: Pfizer Inc, Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Co, GlaxoSmithKline, Merck and Co Inc, AstraZeneca AB, Novartis Pharmaceuticals Corp, Alzheimer's Association, Eisai Global Clinical Development, Elan Corp plc, Forest Laboratories, and the Institute for the Study of Aging, with participation from the US Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles. Alzheimer's Disease Neuroimaging Inititative: Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). 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 is available at http://www.loni.ucla.edu/ADNI/Data/ADNI_Authorship_List.pdf. Authors report no conflicts with CorTechs, manufacturers of NeuroQuant software used for automated segmentations. CorTechs provided authors with free access to their proprietary software to analyze the data for the purposes of this paper, and had no input in the data analysis or the preparation of this manuscript. Reprints: James B. Brewer, MD, PhD, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093-0949 (e-mail: email@example.com). Received for publication July 24, 2008; accepted October 27, 2008 Alzheimer Disease & Associated Disorders: April-June 2009 - Volume 23 - Issue 2 - p 139-145 doi: 10.1097/WAD.0b013e318192e745 Buy SDC Metrics AbstractIn Brief Medial temporal lobe (MTL) atrophy is associated with increased risk for conversion to Alzheimer disease, but manual tracing techniques and even semiautomated techniques for volumetric assessment are not practical in the clinical setting. In addition, most studies that examined MTL atrophy in Alzheimer disease have focused only on the hippocampus. It is unknown the extent to which volumes of amygdala and temporal horn of the lateral ventricle predict subsequent clinical decline. This study examined whether measures of hippocampus, amygdala, and temporal horn volume predict clinical decline over the following 6-month period in patients with mild cognitive impairment (MCI). Fully automated volume measurements were performed in 269 MCI patients. Baseline volumes of the hippocampus, amygdala, and temporal horn were evaluated as predictors of change in Mini-mental State Examination and Clinical Dementia Rating Sum of Boxes over a 6-month interval. Fully automated measurements of baseline hippocampus and amygdala volumes correlated with baseline delayed recall scores. Patients with smaller baseline volumes of the hippocampus and amygdala or larger baseline volumes of the temporal horn had more rapid subsequent clinical decline on Mini-mental State Examination and Clinical Dementia Rating Sum of Boxes. Fully automated and rapid measurement of segmental MTL volumes may help clinicians predict clinical decline in MCI patients. Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal's Web site (www.alzheimerjournal.com). © 2009 Lippincott Williams & Wilkins, Inc.