Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET
Department of Biomathematics, David Geffen School of Medicine at UCLA, CA 90095, USA. NeuroImage
(Impact Factor: 6.36).
02/2012; 61(4):749-60. DOI: 10.1016/j.neuroimage.2012.02.056
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.
Available from: Emily L. Dennis
- "There is conflicting evidence on what effect amyloid (Aβ -beta-amyloid) build-up might have in cognitively normal individuals (Jack et al. 2009; Mormino et al. 2009). Amyloid burden can be detected by PET imaging (positron emission tomography) using the 11 C-labeled Pittsburgh Compound B (PiB), among other ligands (Johnson et al. 2007; Rabinovici et al. 2007), and some types of PET scans are sensitive to both tau and amyloid pathology (Braskie et al. 2010; Protas et al. 2012). Hedden et al. (2009) used a seed-based approach (PCC seed) to investigate effects of Aβ burden in 38 healthy elderly individuals . "
[Show abstract] [Hide abstract]
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; 24(1). DOI:10.1007/s11065-014-9249-6 · 4.59 Impact Factor
Available from: Yvonne Freund-Levi
- "Nevertheless, [11C]AZD2995 showed a greater effect size than [11C]AZD2184 in the hippocampus and the lateral temporal cortex, supporting the notion that this radioligand might be useful for studying areas with small increments in amyloid-β pathology, taking advantage of the low background binding. [18F]FDDNP has so far been the only amyloid ligand for which it has been possible to relate pathological progression and cognitive decline [10, 37], although these findings were not replicated in a study showing no relationship between increased levels of FDDNP and progression from mild cognitive impairment to AD. Recent large studies using [11C]PIB have begun to show patterns between amyloid deposition and early cognitive changes  as well as a markedly greater conversion rate in [11C]PIB-positive patients with mild cognitive impairment than in [11C]PIB-negative patients . "
[Show abstract] [Hide abstract]
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).
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.
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.
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 · 5.38 Impact Factor
Available from: Wolfgang Froestl
[Show abstract] [Hide abstract]
ABSTRACT: Cognitive enhancers (nootropics) are drugs to treat cognition deficits in patients suffering from Alzheimer's disease, schizophrenia, stroke, attention deficit hyperactivity disorder, or aging. Cognition refers to a capacity for information processing, applying knowledge, and changing preferences. It involves memory, attention, executive functions, perception, language, and psychomotor functions. The term nootropics was coined in 1972 when memory enhancing properties of piracetam were observed in clinical trials. In the meantime, hundreds of drugs have been evaluated in clinical trials or in preclinical experiments. To classify the compounds, a concept is proposed assigning drugs to 18 categories according to their mechanism(s) of action, in particular drugs interacting with receptors, enzymes, ion channels, nerve growth factors, re-uptake transporters, antioxidants, metal chelators, and disease-modifying drugs meaning small molecules, vaccines, and monoclonal antibodies interacting with amyloid-β and tau. For drugs, whose mechanism of action is not known, they are either classified according to structure, e.g., peptides, or their origin, e.g., natural products. The review covers the evolution of research in this field over the last 25 years.
Journal of Alzheimer's disease: JAD 08/2012; 41(4). DOI:10.3233/JAD-2012-121186 · 4.15 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.