A framework for identification of a resting-bold connectome associated with cognitive reserve.

The concept of cognitive reserve proposes that specific life experiences result in more flexible or resilient cognitive processing allowing some people to cope better with age- or disease-related brain changes than others. Imaging studies seeking to understand the neural implementation of cognitive reserve have most often used task-related fMRI studies. Using that approach, we recently described a task-invariant cognitive-reserve network whose expression correlated with IQ and that moderated between cortical thickness and cognitive performance. Here we sought to identify a pattern of resting BOLD connectivity related to cognitive reserve. We identified a connectome pattern whose connectivity correlated with IQ in both the derivation sample and a separate replication sample. The majority of the edges showing positive relationships with IQ implicate frontal regions. In the derivation sample, connectivity either moderated the relationship between mean cortical thickness and a set of cognitive outcomes or accounted for unique variance in cognitive performance after accounting for cortical thickness. In a replication sample we found that expression of this connectome correlated significantly with the primary endpoint of IQ, and also accounted for unique variance in cognitive performance beyond cortical thickness. Our findings represent an intermediate level of replication and are unlikely to have arisen purely by type-I error. This connectivity pattern therefore meets some of our theoretical criteria for a cognitive reserve-related network and provides insight into the neural implementation of cognitive reserve. Further, expression of this connectome could potentially be used as a direct measure of cognitive reserve, and as an outcome measure for intervention studies that seek to influence cognitive reserve. Future validation of and re-derivation of the pattern in expanded data sets by our and other groups will lead to further improved estimates of cognitive reserve in resting functional connectivity.

A task-invariant cognitive reserve network.

The concept of cognitive reserve (CR) can explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or disease-related brain changes. Epidemiologic evidence indicates that CR helps maintain performance in the face of pathology across multiple cognitive domains. We therefore tried to identify a single, "task-invariant" CR network that is active during the performance of many disparate tasks. In imaging data acquired from 255 individuals age 20-80 while performing 12 different cognitive tasks, we used an iterative approach to derive a multivariate network that was expressed during the performance of all tasks, and whose degree of expression correlated with IQ, a proxy for CR. When applied to held out data or forward applied to fMRI data from an entirely different activation task, network expression correlated with IQ. Expression of the CR pattern accounted for additional variance in fluid reasoning performance over and above the influence of cortical thickness, and also moderated between cortical thickness and reasoning performance, consistent with the behavior of a CR network. The identification of a task-invariant CR network supports the idea that life experiences may result in brain processing differences that might provide reserve against age- or disease-related changes across multiple tasks.

Online detection and quantification of particles of ergot bodies in cereal flour using near-infrared hyperspectral imaging.

The objective of this study is to assess near-infrared (NIR) hyperspectral imaging for the detection of ergot bodies at the particle level in cereal flour. For this study, ground ergot body samples and wheat flour samples as well as mixtures of both from 100 to 500,000 mg kg-1 were analysed. Partial least squares discriminant analysis (PLS-DA) models were developed and applied to spectral images in order to detect the ergot body particles. Ergot was detected in 100% of samples spiked at more than 10,000 mg kg-1 and no false-positives were obtained with non-contaminated samples. A correlation of 0.99 was calculated between the reference values and the values predicted by the PLS-DA model. For the cereal flours containing less than 10,000 mg kg-1 of ergot, it was possible for some samples spiked as low as 100 mg kg-1 to detect enough pixels of ergot to conclude that the sample was contaminated. However, some samples were under- or overestimated, which can be explained by the lack of homogeneity in relation to the sampling issue and the thickness of the sample. This study has demonstrated the potential of NIR hyperspectral imaging combined with chemometrics as an alternative solution for discriminating ergot body particles from cereal flour.