Hypometabolism exceeds atrophy in presymptomatic early-onset familial Alzheimer's disease.
ABSTRACT The aim of the present study is to compare brain atrophy with hypometabolism as preclinical markers of Alzheimer's disease (AD) by studying presymptomatic individuals from families with known early-onset autosomal dominant AD (FAD) carrying mutations in the Presenilin 1 gene.
Seven asymptomatic at-risk FAD individuals (age, 35-49 y; 4 women; education >/= 12 y) and 7 matched healthy control subjects received complete clinical, neuropsychologic, MRI, and (18)F-FDG PET examinations. Regions of interest (ROIs: whole brain [WB], hippocampus [Hip], entorhinal cortex [EC], posterior cingulate cortex [PCC], inferior parietal lobule [IPL], and superior temporal gyrus (STG]) were drawn on the MRI scans of all subjects and used to measure volumes on MRI and glucose metabolism (MRglc) from the MRI-coregistered, atrophy-corrected PET scans.
Compared with controls and after correcting for head size, MRI volume reductions in FAD subjects were restricted to the IPL (18%, P < 0.02). After atrophy correction and adjusting for pons MRglc, PET MRglc reductions were found in all FAD subjects compared with controls in the WB (13%), bilaterally in the IPL (17%) and in the STG (12%), and in the left EC (21%), PCC (20%), and Hip (12%) (P values < 0.05). PET MRglc measurements were consistently less variable than MRI measures, yielding significantly larger effect sizes in separating FAD from controls.
Presymptomatic FAD individuals show widespread MRglc reductions consistent with the typical AD PET pattern in the relative absence of structural brain atrophy. These data further suggest that PET MRglc measures may serve as biomarkers for the preclinical diagnosis of AD.
Full-textDOI: · Available from: Benedetta Nacmias, Jun 30, 2015
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ABSTRACT: Functional brain imaging is a common tool in monitoring the progression of neurodegenerative and neurological disorders. Identifying functional brain imaging derived features that can accurately detect neurological disease is of primary importance to the medical community. Research in computer vision techniques to identify objects in photographs have reported high accuracies in that domain, but their direct applicability to identifying disease in functional imaging is still under investigation in the medical community. In particular, Serre et al. (: In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR-05). pp 994-1000) introduced a biophysically inspired filtering method emulating visual processing in striate cortex which they applied to perform object recognition in photographs. In this work, the model described by Serre et al.  is extended to three-dimensional volumetric images to perform signal detection in functional brain imaging (PET, SPECT). The filter outputs are used to train both neural network and logistic regression classifiers and tested on two distinct datasets: ADNI Alzheimer's disease 2-deoxy-D-glucose (FDG) PET and National Football League players Tc99m HMPAO SPECT. The filtering pipeline is analyzed to identify which steps are most important for classification accuracy. Our results compare favorably with other published classification results and outperform those of a blinded expert human rater, suggesting the utility of this approach. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.Human Brain Mapping 01/2014; 35(1). DOI:10.1002/hbm.22149 · 6.92 Impact Factor
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