Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia

Abstract

The value of 18 F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: 18 F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18 F-FDG uptake (regions with significant hypo-and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P , 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18 F-FDG PET. Principal Components Analysis of Brain Metabolism PredictsDevelopment of http://jnm.snmjournals.org/content/60/6/837 This article and updated information are available at: http://jnm.snmjournals.org/site/subscriptions/online.xhtml Information about subscriptions to JNM can be found at: http://jnm.snmjournals.org/site/misc/permission.xhtml

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia

“…Indeed, the KLSE method outperformed the traditional group-level approach for revealing patterns of altered metabolic connectivity predictive of conversion. Our findings of salient connectivity patterns in the parietal and occipital lobes, recapitulate results of previous FDG-PET group-based comparisons [8,25]. In addition, we found that KLSE has considerable intra-group stability for resolving metabolic network organization, and highlights an especially pronounced inter-individual dissimilarity of metabolic connectivity of the parietal lobe of the pMCI group (Fig.…”

“…The Z-statistic for each continuous covariate was used to calculate a hazard ratio (HR) of the risk for conversion as a function of the number of standard deviations of an increase in the covariates. We then performed four multivariable Cox model analyses with age and sex as factors, including the following variables: (1) clinical (MMSE, APOE ε4), (2) metabolic connectome expression (MCE), (3) group-level pattern expression score (PES) [8], and (4) combination of all variables (MMSE, APOE ε4, MCE). To assess the model's validity, each Cox model was applied independently to the test dataset.…”

“…Applying an optimized semi-quantitative FDG-PET method, Pagani et al correctly identified 89% of MCI patients who converted to AD within 5 years of follow-up [7]. Notably, Blazhenets et al proposed a novel method with spatial covariance mapping of the FDG-PET signal to identify an AD conversion-related pattern of cerebral metabolism [8]. These methods emphasize regional FDG uptake or quantitative characteristics of metabolically abnormal regions in PET images, without fully considering the metabolic interactions of between-regions, thus potentially losing relevant information related to concerning individual differences in metabolic topology.…”

Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia

“…Stratifying mild cognitive impairment (MCI) subjects according to their conversion risk is of great interest for clinical practice and clinical trials (e.g., patient counseling, initiation of pharmacologic and nonpharmacologic treatments, and inclusion in trials). A recent study by our group evaluated 18 F-FDG PET by voxelwise principle-components analysis (PCA) and validated a PCA-derived AD conversion-related pattern (ADCRP) that showed high accuracy in prediction of conversion from MCI to AD (4). This study was in contrast to other studies (5)(6)(7).…”

“…18 F-FDG PET scans were spatially normalized to an in-house 18 F-FDG PET template in Montreal Neurological Institute space (8) and smoothed with an isotropic gaussian kernel of 12 mm in full width at half maximum. We assessed the pattern expression score (PES) of the previously validated ADCRP as described before (4).…”