A Reduced Astrocyte Response to β-Amyloid Plaques in the Ageing Brain Associates with Cognitive Impairment

Aims β-amyloid (Aβ) plaques are a key feature of Alzheimer’s disease pathology but correlate poorly with dementia. They are associated with astrocytes which may modulate the effect of Aβ-deposition on the neuropil. This study characterised the astrocyte response to Aβ plaque subtypes, and investigated their association with cognitive impairment. Methods Aβ plaque subtypes were identified in the cingulate gyrus using dual labelling immunohistochemistry to Aβ and GFAP+ astrocytes, and quantitated in two cortical areas: the area of densest plaque burden and the deep cortex near the white matter border (layer VI). Three subtypes were defined for both diffuse and compact plaques (also known as classical or core-plaques): Aβ plaque with (1) no associated astrocytes, (2) focal astrogliosis or (3) circumferential astrogliosis. Results In the area of densest burden, diffuse plaques with no astrogliosis (β = -0.05, p = 0.001) and with focal astrogliosis (β = -0.27, p = 0.009) significantly associated with lower MMSE scores when controlling for sex and age at death. In the deep cortex (layer VI), both diffuse and compact plaques without astrogliosis associated with lower MMSE scores (β = -0.15, p = 0.017 and β = -0.81, p = 0.03, respectively). Diffuse plaques with no astrogliosis in layer VI related to dementia status (OR = 1.05, p = 0.025). In the area of densest burden, diffuse plaques with no astrogliosis or with focal astrogliosis associated with increasing Braak stage (β = 0.01, p<0.001 and β = 0.07, p<0.001, respectively), and ApoEε4 genotype (OR = 1.02, p = 0.001 and OR = 1.10, p = 0.016, respectively). In layer VI all plaque subtypes associated with Braak stage, and compact amyloid plaques with little and no associated astrogliosis associated with ApoEε4 genotype (OR = 1.50, p = 0.014 and OR = 0.10, p = 0.003, respectively). Conclusions Reactive astrocytes in close proximity to either diffuse or compact plaques may have a neuroprotective role in the ageing brain, and possession of at least one copy of the ApoEε4 allele impacts the astroglial response to Aβ plaques

A systematic approach to modeling, capturing, and disseminating proteomics experimental data.

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository

Long-term adaptation to change in implicit contextual learning

The visual world consists of spatial regularities that are acquired through experience in order to guide attentional orienting. For instance, in visual search, detection of a target is faster when a layout of nontarget items is encountered repeatedly, suggesting that learned contextual associations can guide attention (contextual cuing). However, scene layouts sometimes change, requiring observers to adapt previous memory representations. Here, we investigated the long-term dynamics of contextual adaptation after a permanent change of the target location. We observed fast and reliable learning of initial context–target associations after just three repetitions. However, adaptation of acquired contextual representations to relocated targets was slow and effortful, requiring 3 days of training with overall 80 repetitions. A final test 1 week later revealed equivalent effects of contextual cuing for both target locations, and these were comparable to the effects observed on day 1. That is, observers learned both initial target locations and relocated targets, given extensive training combined with extended periods of consolidation. Thus, while implicit contextual learning efficiently extracts statistical regularities of our environment at first, it is rather insensitive to change in the longer term, especially when subtle changes in context–target associations need to be acquired

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce '. functional connectivity'; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3. T, leading to whole brain coverage with 2. mm isotropic resolution in 0.7. s for fMRI, and 1.25. mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7. T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. © 2013 Elsevier Inc