Automated MRI measures predict progression to Alzheimer's disease - PubMed (original) (raw)

Comparative Study

Automated MRI measures predict progression to Alzheimer's disease

Rahul S Desikan et al. Neurobiol Aging. 2010 Aug.

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.

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Figures

Figure 1

Multivariate Cox model results for the four neuroanatomic factors derived from the training cohort displayed on the gray matter surface (only one hemisphere is shown) in (a) lateral and (b) medial views, and (c) in the coronal view of a T1-weighted MRI image. These factors include the 1) medial temporal factor (individual ROIs illustrated in red), 2) fronto-parietoccipital factor (individual ROIs illustrated in orange), 3) lateral temporal factor (individual ROIs illustrated in bright yellow), and 4) fronto-cingulate factor (individual ROIs illustrated in faded yellow). The color scale at the bottom illustrates the magnitude of risk (hazard ratio) associated with progressing from MCI to AD, with faded yellow indicating regions with the lowest risk and red indicating regions with the highest risk (please see text for specific hazard ratios for each of the factors).

Figure 2

T1-weighted MRI images in the coronal view showing marked differences in the medial temporal lobe, specifically hippocampal volume and entorhinal cortex thickness, for a representative (a) MCI-Converter and (b) MCI-Nonconverter. The red overlay shows the gray/CSF boundary and the white overlay shows the gray/white matter boundary and the distance between these surfaces represents the cortical thickness.

Figure 3

Predicted survival plots estimating the probability of progressing from MCI to AD in the training (a) and validation (b) cohorts based on the value of the medial temporal factor (volume and thickness), shown at the mean (red lines) and one standard deviation above (green lines) and below (blue lines) the mean.

Figure 3

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Automated MRI measures predict progression to Alzheimer's disease - PubMed (original) (raw)