Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer's disease?
ABSTRACT Patients with mild cognitive impairment (MCI) were assessed, and a metabolic profile associated with conversion to AD at 18-month follow-up was sought. As compared with nonconverters (n = 10), converters (n = 7) had lower fluorodeoxyglucose uptake in the right temporoparietal cortex (p = 0.02, corrected for cluster size), without individual overlap. Awaiting replication in an independent sample, these findings suggest that among patients with MCI, fluorodeoxyglucose PET may accurately identify rapid converters.
SourceAvailable from: Flavio NobiliThe Open Nuclear Medicine Journal 11/2010; 2(1):46-52. DOI:10.2174/1876388X01002010046
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ABSTRACT: The accurate diagnosis of Alzheimers disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer aided diagnosis (CAD) of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multi-modal neuroimaging features in one setting and has the potential to require less labelled data. A performance gain was achieved in both binary classification and multi-class classification of AD. The advantages and limitations of the proposed framework are discussed.IEEE transactions on bio-medical engineering 11/2014; DOI:10.1109/TBME.2014.2372011 · 2.23 Impact Factor
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ABSTRACT: Alzheimer's disease (AD) has a complex and progressive neurodegenerative phenotype, with hypometabolism and impaired mitochondrial bioenergetics among the earliest pathogenic events. Bioenergetic deficits are well documented in preclinical models of mammalian aging and AD, emerge early in the prodromal phase of AD, and in those at risk for AD. This review discusses the importance of early therapeutic intervention during the prodromal stage that precedes irreversible degeneration in AD. Mechanisms of action for current mitochondrial and bioenergetic therapeutics for AD broadly fall into the following categories: 1) glucose metabolism and substrate supply; 2) mitochondrial enhancers to potentiate energy production; 3) antioxidants to scavenge reactive oxygen species and reduce oxidative damage; 4) candidates that target apoptotic and mitophagy pathways to either remove damaged mitochondria or prevent neuronal death. Thus far, mitochondrial therapeutic strategies have shown promise at the preclinical stage but have had little-to-no success in clinical trials. Lessons learned from preclinical and clinical therapeutic studies are discussed. Understanding the bioenergetic adaptations that occur during aging and AD led us to focus on a systems biology approach that targets the bioenergetic system rather than a single component of this system. Bioenergetic system-level therapeutics personalized to bioenergetic phenotype would target bioenergetic deficits across the prodromal and clinical stages to prevent and delay progression of AD.Journal of the American Society for Experimental NeuroTherapeutics 12/2014; 12(1). DOI:10.1007/s13311-014-0324-8 · 3.88 Impact Factor