Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET
ABSTRACT A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed.
Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models.
Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6.
FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.
SourceAvailable from: Yvonne Freund-Levi[Show abstract] [Hide abstract]
ABSTRACT: Purpose The aim of this study was to evaluate AZD2995 side by side with AZD2184 as novel PET radioligands for imaging of amyloid-β in Alzheimer’s disease (AD). Methods In vitro binding of tritium-labelled AZD2995 and AZD2184 was studied and compared with that of the established amyloid-β PET radioligand PIB. Subsequently, a first-in-human in vivo PET study was performed using [11C]AZD2995 and [11C]AZD2184 in three healthy control subjects and seven AD patients. Results AZD2995, AZD2184 and PIB were found to share the same binding site to amyloid-β. [3H]AZD2995 had the highest signal-to-background ratio in brain tissue from patients with AD as well as in transgenic mice. However, [11C]AZD2184 had superior imaging properties in PET, as shown by larger effect sizes comparing binding potential values in cortical regions of AD patients and healthy controls. Nevertheless, probably due to a lower amount of nonspecific binding, the group separation of the distribution volume ratio values of [11C]AZD2995 was greater in areas with lower amyloid-β load, e.g. the hippocampus. Conclusion Both AZD2995 and AZD2184 detect amyloid-β with high affinity and specificity and also display a lower degree of nonspecific binding than that reported for PIB. Overall [11C]AZD2184 seems to be an amyloid-β radioligand with higher uptake and better group separation when compared to [11C]AZD2995. However, the very low nonspecific binding of [11C]AZD2995 makes this radioligand potentially interesting as a tool to study minute levels of amyloid-β. This sensitivity may be important in investigating, for example, early prodromal stages of AD or in the longitudinal study of a disease modifying therapy.European journal of nuclear medicine and molecular imaging 04/2013; 40(4). DOI:10.1007/s00259-012-2322-6 · 5.22 Impact Factor
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ABSTRACT: Brain amyloid can be measured using positron emission tomography (PET). There are mixed reports regarding whether amyloid measures are correlated with measures of cognition (in particular memory), depending on the cohorts and cognitive domains assessed. In Alzheimer's disease (AD) patients and those at heightened risk for AD, cognitive performance may be related to the level and extent of classical AD pathology (amyloid plaques and neurofibrillary angles), but it is also influenced by neurodegeneration, neurocognitive reserve, and vascular health. We discuss what recent neuroimaging research has discovered about cognitive deficits in AD and offer suggestions for future research.Trends in Cognitive Sciences 09/2013; DOI:10.1016/j.tics.2013.08.007 · 21.15 Impact Factor
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ABSTRACT: Normal aging and Alzheimer's disease (AD) cause profound changes in the brain's structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer's disease. We cover studies that employ the three primary methods to analyze functional connectivity-seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.Neuropsychology Review 02/2014; DOI:10.1007/s11065-014-9249-6 · 5.40 Impact Factor