title: Automated MRI measures predict progression to Alzheimer's disease.
abstract: The prediction of individuals with mild cognitive impairment (MCI) destined to develop Alzheimer's disease (AD) is of increasing clinical importance. In this study, using baseline T1-weighted MRI scans of 324 MCI individuals from two cohorts and automated software tools, we employed factor analyses and Cox proportional hazards models to identify a set of neuroanatomic measures that best predicted the time to progress from MCI to AD. For comparison, cerebrospinal fluid (CSF) assessments of cellular pathology and positron emission tomography (PET) measures of metabolic activity were additionally examined. By 3 years follow-up, 60 MCI individuals from the first cohort and 58 MCI individuals from the second cohort had progressed to a diagnosis of AD. Cox models on the first cohort demonstrated significant effects for the medial temporal factor [Hazards Ratio (HR) = 0.43{95% confidence interval (CI), 0.32-0.55}, p < 0.0001], the fronto-parietoccipital factor [HR = 0.59{95% CI, 0.48-0.80}, p < 0.001], and the lateral temporal factor [HR = 0.67 {95% CI, 0.52-0.87}, p < 0.01]. When applied to the second cohort, these Cox models showed significant effects for the medial temporal factor [HR = 0.44 {0.32-0.61}, p < 0.001] and lateral temporal factor [HR = 0.49 {0.38-0.62}, p < 0.001]. In a combined Cox model, consisting of individual CSF, PET, and MRI measures that best predicted disease progression, only the medial temporal factor [HR = 0.53 {95% CI, 0.34-0.81}, p < 0.001] demonstrated a significant effect. These findings illustrate that automated MRI measures of the medial temporal cortex accurately and reliably predict time to disease progression, outperform cellular and metabolic measures as predictors of clinical decline, and can potentially serve as a predictive marker for AD.
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA. rahul@nmr.mgh.harvard.edu
authors.forename: Rahul S
authors.initials: RS
authors.lastname: Desikan
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Cabral
authors.forename: Fabio
authors.initials: F
authors.lastname: Settecase
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Hess
authors.forename: William P
authors.initials: WP
authors.lastname: Dillon
authors.forename: Christine M
authors.initials: CM
authors.lastname: Glastonbury
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Schmansky
authors.forename: David H
authors.initials: DH
authors.lastname: Salat
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000473:pathology* / Q000523:psychology; D057205:Automation, Laboratory / Q000379:methods; D003072:Cognition Disorders / Q000134:cerebrospinal fluid / Q000473:pathology* / Q000523:psychology; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D011237:Predictive Value of Tests; D011446:Prospective Studies