Enhancement of cognitive and neural functions through complex reasoning training: evidence from normal and clinical populations

Public awareness of cognitive health is fairly recent compared to physical health. Growing evidence suggests that cognitive training offers promise in augmenting cognitive brain performance in normal and clinical populations. Targeting higher-order cognitive functions, such as reasoning in particular, may promote generalized cognitive changes necessary for supporting the complexities of daily life. This data-driven perspective highlights cognitive and brain changes measured in randomized clinical trials that trained gist reasoning strategies in populations ranging from teenagers to healthy older adults, individuals with brain injury to those at-risk for Alzheimer’s disease. The evidence presented across studies support the potential for Gist reasoning training to strengthen cognitive performance in trained and untrained domains and to engage more efficient communication across widespread neural networks that support higher-order cognition. The meaningful benefits of Gist training provide compelling motivation to examine optimal dose for sustained benefits as well as to explore additive benefits of meditation, physical exercise, and/or improved sleep in future studies

Theta and Alpha Alterations in Amnestic Mild Cognitive Impairment in Semantic Go/NoGo Tasks

Growing evidence suggests that cognitive control processes are impaired in amnestic mild cognitive impairment (aMCI); however the nature of these alterations needs further examination. The current study examined differences in electroencephalographic theta and alpha power related to cognitive control processes involving response execution and response inhibition in 22 individuals with aMCI and 22 age-, sex-, and education-matched cognitively normal controls. Two Go/NoGo tasks involving semantic categorization were used. In the basic categorization task, Go/NoGo responses were made based on exemplars of a single car (Go) and a single dog (NoGo). In the superordinate categorization task, responses were made based on multiple exemplars of objects (Go) and animals (NoGo). Behavioral data showed that the aMCI group had more false alarms during the NoGo trials compared to controls. The EEG data revealed between group differences related to response type in theta (4–7 Hz) and low-frequency alpha (8–10 Hz) power. In particular, the aMCI group differed from controls in theta power during the NoGo trials at frontal and parietal electrodes, and in low-frequency alpha power during Go trials at parietal electrodes. These results suggest that alterations in theta power converge with behavioral deterioration in response inhibition, whereas alterations in low-frequency alpha power appear to precede behavioral changes in response execution. Both behavioral and electrophysiological correlates combined provide a more comprehensive characterization of cognitive control deficits in aMCI

Disrupted Value-Directed Strategic Processing in Individuals with Mild Cognitive Impairment: Behavioral and Neural Correlates

Value-directed strategic processing involves attending to higher-value information while inhibiting lower-value information. This preferential processing is relatively preserved in cognitively normal older adults but is impaired in individuals with dementia. No studies have investigated whether value-directed strategic processing is disrupted in earlier stages of cognitive decline, namely, mild cognitive impairment (MCI). The current study examined behavioral and EEG differences in value-directed strategic processing between 18 individuals with MCI and 18 cognitively normal older controls using a value-directed list learning task. Behaviorally, individuals with MCI recalled fewer total and high-value words compared to controls, but no group differences were observed in low-value word recall. Neurally, individuals with MCI had reduced theta synchronization relative to controls between 100 and 200 ms post-stimulus. Greater alpha desynchronization was observed for high- versus low-value words between 300 and 400 ms in controls but not in the MCI group. The groups showed some processing similarities, with greater theta synchronization for low-value words between 700 and 800 ms and greater alpha desynchronization for high-value words between 500 and 1100 ms. Overall, value-directed strategic processing was compromised in individuals with MCI on both behavioral and neural measures relative to controls. These findings add to the growing body of literature on differences between typical cognitive aging and MCI

A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression

<div><p>To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer’s Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3–6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated disease states. A sixteen-analyte signature was identified which differentiated AD vs. NL with an area under the receiver operating characteristic curve of 0.89, which was better than any combination of amyloid beta (1–42), tau, and phospho-181 tau. This same signature, when applied to a new and independent data set, also strongly predicted both probability and rate of future progression of MCI subjects to AD, better than traditional markers. These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.</p></div

Normal vs. Alzheimer Disease, univariate analysis.

<p>Shown are the analytes with a q-value < 0.05.</p

Receiver-operator curves.

<p>Receiver-operator curves comparing the 16-peptide multivariate signature (red dotted line) to combinations of Aβ42, tau and p-tau 181 (black line) as well as the 16-peptide signature + combinations of Aβ42, tau and p-tau 181 (blue dashed line) for the prediction of 36-month conversion from MCI to AD.</p

Disease-state demographics.

<p>Disease-state demographics.</p

Performance of AD vs. NL multivariate signatures to differentiate the Time to Progression of MCI subjects to AD.

<p>Performance of AD vs. NL multivariate signatures to differentiate the Time to Progression of MCI subjects to AD.</p

Performance of multivariate model to differentiate disease state.

<p>The top row corresponds to all permutations of Ab, tTau and pTau, and the bottom row refers to the 16-peptide signature shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182098#pone.0182098.g002" target="_blank">Fig 2</a>.</p

16-peptide signature.

<p>Relative importance of the contribution of each peptide in the 16-peptide multivariate signature for differentiating AD vs. NL that is subsequently used for predicting progression of MCI subjects to AD. Peptides are plotted in the order of their importance/contribution to this multivariate signature in the logistic regression model. As the 16^th peptide related to CATD appears to provide very little incremental value (noted in red), the data-driven process that led to its inclusion in the signature suggested an overall benefit of retaining it in the signature. The coefficients for each of these markers is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182098#pone.0182098.s002" target="_blank">S2 Table</a>.</p