Brain Advance Access originally published online on May 21, 2009
Brain 2009 132(8):2048-2057; doi:10.1093/brain/awp123
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Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease*
1 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA 2 Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA 3 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA 4 Department of Radiology, University of California, San Francisco, CA, USA 5 Veteran Affairs Medical Center, San Francisco, CA, USA 6 Department of Psychology, Harvard University, Cambridge, MA, USA 7 Howard Hughes Medical Institute, Chevy Chase, MD, USA 8 Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA 9 Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA 10 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
Correspondence to: Bruce Fischl, PhD, Massachusetts General Hospital, NMR Center Rm. 2301, Building 149, 13th Street, Charlestown, MA 02129, USA Email: fischl{at}nmr.mgh.harvard.edu
Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T1-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease.
Key Words: MRI; mild cognitive impairment; Alzheimer's disease; diagnostic marker
Abbreviations: AUC, area under curve; CDR, clinical dementia rating; MCI, mild cognitive impairment; OASIS, Open Access Series of Imaging Studies; OC, older control; ROI, region of interest
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Received January 23, 2009. Revised March 24, 2009. Accepted April 3, 2009.
*Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (Alzheimer's disease NI) database (http://www.loni.ucla.edu/Alzheimer's disease NI). As such, the investigators within the Alzheimer's disease NI contributed to the design and implementation of Alzheimer's disease NI and/or provided data but did not participate in analysis or writing of this report. Alzheimer's disease NI investigators include (complete listing available at www.loni.ucla.edu\Alzheimer's disease NI\Collaboration\Alzheimer's disease NI_Citatation.shtml).