Dataset Information

Whither the hippocampus? FDG-PET hippocampal hypometabolism in Alzheimer disease revisited.

ABSTRACT:

Background and purpose

The hippocampus is a widely recognized area of early change in AD, yet voxelwise analyses of FDG-PET activity differences between AD and CN controls have consistently failed to identify hippocampal hypometabolism. In this article, we propose a high-dimensional PET-specific analysis framework to determine whether important hippocampal metabolic FDG-PET activity differences between patients with AD and CN subjects are embedded in the Jacobian information generated during spatial normalization.

Materials and methods

Resting FDG-PET data were obtained from 102 CN and 92 participants with AD from the ADNI data base. A PET-study-specific template was constructed using symmetric diffeomorphic registration. Spatially normalized raw FDG maps, Jacobian determinant maps, and modulated maps were generated for all subjects. Statistical parametric mapping and tensor-based morphometry were performed, comparing patients with AD with CN subjects.

Results

Whole-brain spatially normalized raw FDG maps demonstrated robust hypometabolism in cingulate gyrus and bilateral parietal areas. No hippocampal differences were present, except on ROI-based analyses with a hippocampal mask. Whole-brain modulated maps demonstrated robust bilateral hippocampal hypometabolism, and some hypometabolism in the posterior cingulate. Tensor-based morphometry demonstrated robust hippocampal differences only.

Conclusions

These results demonstrate that hippocampal metabolic differences are embedded in the Jacobian information from the spatial normalization procedure. We introduce a voxelwise PET-specific analysis framework based on the use of a PET-population-specific template, high-dimensional symmetric diffeomorphic normalization, and the use of Jacobian information, which can provide substantially increased statistical power and an order of magnitude decrease in imaging costs.

SUBMITTER: Maldjian JA

PROVIDER: S-EPMC7964612 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:UnlabelledIn the recently revised diagnostic criteria for Alzheimer disease (AD), the National Institute on Aging and Alzheimer Association suggested that confidence in diagnosing dementia due to AD and mild cognitive impairment (MCI) due to AD could be improved by the use of certain biomarkers, such as (18)F-FDG PET evidence of hypometabolism in AD-affected brain regions. Three groups have developed automated data analysis techniques to characterize the AD-related pattern of hypometabolism in a single measurement. In this study, we sought to directly compare the ability of these three (18)F-FDG PET data analysis techniques--the PMOD Alzheimer discrimination analysis tool, the hypometabolic convergence index, and a set of meta-analytically derived regions of interest reflecting AD hypometabolism pattern (metaROI)--to distinguish moderate or mild AD dementia patients and MCI patients who subsequently converted to AD dementia from cognitively normal older adults.MethodsOne hundred sixty-six (18)F-FDG PET patients from the AD Neuroimaging Initiative, 308 from the Network for Efficiency and Standardization of Dementia Diagnosis, and 176 from the European Alzheimer Disease Consortium PET study were categorized, with masking of group classification, as AD, MCI, or healthy control. For each AD-related (18)F-FDG PET index, receiver-operating-characteristic curves were used to characterize and compare subject group classifications.ResultsThe 3 techniques were roughly comparable in their ability to distinguish each of the clinical groups from cognitively normal older adults with high sensitivity and specificity. Accuracy of classification (in terms of area under the curve) in each clinical group varied more as a function of dataset than by technique. All techniques were differentially sensitive to disease severity, with the classification accuracy for MCI due to AD to moderate AD varying from 0.800 to 0.949 (PMOD Alzheimer tool), from 0.774 to 0.967 (metaROI), and from 0.801 to 0.983 (hypometabolic convergence index).ConclusionThe 3 tested techniques have the potential to help detect AD in research and clinical settings. Additional efforts are needed to clarify their ability to address particular scientific and clinical questions. Their incremental diagnostic value over other imaging and biologic markers makes them easier to implement by other groups for these purposes.

Project description:PurposeTo quantify the effect sizes of regional metabolic and morphometric measures in patients with preclinical and mild Alzheimer disease (AD) to aid in the identification of noninvasive biomarkers for the early detection of AD.Materials and methodsThe study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from each participant or participant's legal guardian. Fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) imaging data were analyzed from 80 healthy control (HC) subjects, 68 individuals with AD, and 156 with amnestic mild cognitive impairment (MCI), 69 of whom had single-domain amnestic MCI. Regions of interest (ROIs) were derived after coregistering FDG PET and MR images by using high-throughput, subject-specific procedures. The Cohen d effect sizes were calculated for 42 predefined ROIs across the brain. Statistical comparison of the largest overall effect sizes for MR imaging and PET was performed. Metabolic effect sizes were determined with and without accounting for regional atrophy. Discriminative accuracy of ROIs showing the largest effect sizes were compared by calculating receiver operating characteristic curves.ResultsFor all disease groups, the hippocampus showed the largest morphometric effect size and the entorhinal cortex showed the largest metabolic effect size. In mild AD, the Cohen d effect size for hippocampal volume (1.92) was significantly larger (P < .05) than that for entorhinal metabolism (1.43). Regression of regional atrophy substantially reduced most metabolic effects. For all group comparisons, the areas under the receiver operating characteristic curves were significantly larger for hippocampal volume than for entorhinal metabolism.ConclusionThe current results show no evidence that FDG PET is more sensitive than MR imaging to the degeneration occurring in preclinical and mild AD, suggesting that an MR imaging finding may be a more practical clinical biomarker for early detection of AD.