Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning.

We examined the impact of training-induced improvements in self-regulation, operationalized in terms of response inhibition, on longitudinal changes in self-reported adaptive socioemotional functioning. Data were collected from participants undergoing 3 months of intensive meditation training in an isolated retreat setting (Retreat 1) and a wait-list control group that later underwent identical training (Retreat 2). A 32-min response inhibition task (RIT) was designed to assess sustained self-regulatory control. Adaptive functioning (AF) was operationalized as a single latent factor underlying self-report measures of anxious and avoidant attachment, mindfulness, ego resilience, empathy, the five major personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience), diffi-culties in emotion regulation, depression, anxiety, and psychological well-being. Participants in Retreat 1 improved in RIT performance and AF over time whereas the controls did not. The control participants later also improved on both dimensions during their own retreat (Retreat 2). These improved levels of RIT performance and AF were sustained in follow-up assessments conducted approximately 5 months after the training. Longitudinal dynamic models with combined data from both retreats showed that improvement in RIT performance during training influenced the change in AF over time, which is consistent with a key claim in the Buddhist literature that enhanced capacity for self-regulation is an important precursor of changes in emotional well-being

Self-reported mindfulness and cortisol during a Shamatha meditation retreat.

Objective: Cognitive perseverations that include worry and rumination over past or future events may prolong cortisol release, which in turn may contribute to predisease pathways and adversely affect physical health. Meditation training may increase self-reported mindfulness, which has been linked to reductions in cognitive perseverations. However, there are no reports that directly link self-reported mindfulness and resting cortisol output. Here, the authors investigate this link. Methods: In an observational study, we measured self-reported mindfulness and p.m. cortisol near the beginning and end of a 3-month meditation retreat (N = 57). Results: Mindfulness increased from pre- to post-retreat, F(1, 56) = 36.20, p < .001. Cortisol did not significantly change. However, mindfulness was inversely related to p.m. cortisol at pre-retreat, r(53) = −.31, p < .05, and post-retreat, r(53) = −.30, p < .05, controlling for age and body mass index. Pre to postchange in mindfulness was associated with pre to postchange in p.m. cortisol, β = −.37, t(49) = 2.30, p < .05: Larger increases in mindfulness were associated with decreases in p.m. cortisol, whereas smaller increases (or slight decreases) in mindfulness were associated with an increase in p.m. cortisol. Conclusions: These data suggest a relation between self-reported mindfulness and resting output of the hypothalamic-pituitary-adrenal system. Future work should aim to replicate this finding in a larger cohort and determine stronger inference about causality by using experimental designs that include control-group conditions

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach

BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking. OBJECTIVE: We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults. METHODS: Participants from the Health and Retirement Study (N = 9,979; aged 50-98 years) were followed for up to 10 years (M = 6.85 for cognitive impairment; M = 7.67 for dementia). Using a split-sample methodology, we first estimated the relative importance of predictors using machine learning (random forest survival analysis), and we then used semi-parametric survival analysis (Cox proportional hazards) to estimate effect sizes for the most important variables. RESULTS: African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia. Sociodemographic (lower education, Hispanic ethnicity) and health variables (worse subjective health, increasing BMI) were comparatively strong predictors for cognitive impairment. Cardiovascular factors (e.g., smoking, physical inactivity) and polygenic scores (with and without APOEɛ4) appeared less important than expected. Post-hoc sensitivity analyses underscored the robustness of these results. CONCLUSIONS: Higher-order factors (e.g., emotional distress, subjective health), which reflect complex interactions between various aspects of an individual, were more important than narrowly defined factors (e.g., clinical and behavioral indicators) when evaluated concurrently to predict cognitive impairment and dementia

Selected Psychographic Characteristics and their Relationship with Sexual-Risk Outcomes.

<p>These partial regression plots <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099987#pone.0099987-Brehney1" target="_blank">[34]</a> show the predicted influence of significant behavioral characteristics (x-axes) on sexual risk-taking outcomes (y-axes), controlling for demographic variables. Sexual risk-taking outcomes, by panel, include (a) number of lifetime sexual partners, (b) probability of previous HIV testing, and (c) probability of non-virgin status.</p

Predictors of Sexual-Risk Taking.

<p><i>Note</i>. Raw regression weights are reported with standard errors (in parentheses). Dashes (−) reflect sets of items that were not included in an analysis due to their negligible contribution to improvement in model fit. * <i>p</i><.05</p>a<p>Regression weights represent change in log odds (e.g.,.77 gives <i>e⁰</i>^.77 = 2.16× increase in odds of engaging in sexual behavior for females relative to males, given other covariates in the model.</p>b<p>Analyses carried out on data from the subset of individuals who reported previous sexual activity.</p>c<p>Exponentiated coefficients show the multiplicative increase in expected number of lifetime sex partners (e.g.,.34 gives <i>e⁰</i>^.34 = 1.4× increase in number of sexual partners for youth with primary education relative to those without).</p>d<p>Coefficients represent change in log odds of incremental probability of virginity loss. As examples, holding other variables constant, [A] completion of secondary education reduces the incremental (yearly by age) hazard of virginity loss by a factor of <i>e</i>^−0.73 = 0.48 or 52% (1–.48) relative to those who have not completed primary school, and [B] Maasai have an increased yearly hazard of virginity loss equal to <i>e</i>^0.35 = 1.42 or 42% relative to non-Maasai.</p>e<p>Age was not included in the set of basic demographic predictor variables for this analysis.</p>f<p>Number of villages (out of 7, excluding reference village) showing a significant positive relationship to outcome.</p>g<p>Media consumption is included for consistency, despite having been excluded as a predictor set in each of the best-fitting models in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099987#pone-0099987-t002" target="_blank">Table 2</a>.</p

Hierarchical Regression Analyses.

<p><i>Note</i>. Sets of predictors were added in sequence from M0 to M7. M0  =  Null Model (intercept only), M1 =  demographic variables (age, gender, highest education achieved, employment status, Maasai vs. other ethnicity), M2 =  village membership, M3 =  weekly media consumption (radio, television, print media), M4 =  HIV knowledge, M5 =  psychographic factors. Change in model fit was assessed via likelihood ratio testing: i.e., change in deviance relative to change in degrees of freedom. Sets shown to improve model fit (<i>* p</i><.05) were carried forward in subsequent analyses.</p>a<p>Deviance (-2*log-likelihood) is reported for each model. R² is reported in brackets for best-fitting logistic models.</p>b<p>Analyses were carried out on data from the subset of individuals who reported previous sexual activity.</p>c<p>Age was not included in the set of basic demographic variables in this analysis.</p

Map Showing the Villages in which the Youth Survey was Conducted.

<p>Red filled circles indicate primary study villages (#s 7–14), and blue filled circles indicate pilot study villages (#s 1–6).</p

Participant Demographics.

a<p>Almost invariably this is in addition to farming.</p>b<p>Non-responders were included in calculation of percentage as 'untested'.</p>c<p>Only those who reported being sexually active were included in the calculation.</p

Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training

Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations. •Brain mechanisms associated with shamatha meditation training are modeled.•A novel approach to analyze longitudinal changes in model parameters is presented.•A new method to model lateral connectivity in thalamic reticular nucleus is shown.•Modeled intrathalamic gain & corticothalamic delay change with meditation training