Dispositional mindfulness co-varies with smaller amygdala and caudate volumes in community adults.

<p>Mindfulness, a psychological process reflecting attention and awareness to what is happening in the present moment, has been associated with increased well-being and decreased depression and anxiety in both healthy and patient populations. However, little research has explored underlying neural pathways. Recent work suggests that mindfulness (and mindfulness training interventions) may foster neuroplastic changes in cortico-limbic circuits responsible for stress and emotion regulation. Building on this work, we hypothesized that higher levels of dispositional mindfulness would be associated with decreased grey matter volume in the amgydala. In the present study, a self-report measure of dispositional mindfulness and structural MRI images were obtained from 155 healthy community adults. Volumetric analyses showed that higher dispositional mindfulness is associated with decreased grey matter volume in the right amygdala, and exploratory analyses revealed that higher dispositional mindfulness is also associated with decreased grey matter volume in the left caudate. Moreover, secondary analyses indicate that these amygdala and caudate volume associations persist after controlling for relevant demographic and individual difference factors (i.e., age, total grey matter volume, neuroticism, depression). Such volumetric differences may help explain why mindful individuals have reduced stress reactivity, and suggest new candidate structural neurobiological pathways linking mindfulness with mental and physical health outcomes.</p

Greater dispositional mindfulness is associated with decreased grey matter volume in the right amygdala.

<p>The right amygdala is shown here in red.</p

Multiple regression analysis relating dispositional mindfulness (Trait MAAS) and grey matter volumes.

<p><i>Notes: MAAS = Mindfulness Attention Awareness Scale, BDI = Beck Depression Inventory.</i></p

Bivariate correlations between MAAS and Psychosocial Affectivity Measures.

<p><i>Notes: MAAS = Mindfulness Attention Awareness Scale, BDI = Beck Depression Inventory, PANAS = Positive and Negative Affect Scale, STAI = State Trait Anxiety Inventory.</i></p

Health Neuroscience: Defining a New Field.

<p>Health neuroscience is a new field that is at the interface of health psychology and neuroscience. It is concerned with the interplay between the brain and physical health over the lifespan. This review provides a conceptual introduction to health neuroscience, focusing on its major themes, representative studies, methodologies, and future directions.</p

Social Network Diversity and White Matter Microstructural Integrity in Humans

Diverse aspects of physical, affective and cognitive health relate to social integration, reflecting engagement in social activities and identification with diverse roles within a social network. However, the mechanisms by which social integration interacts with the brain are unclear. In healthy adults ( N = 155), we tested the links between social integration and measures of white matter microstructure using diffusion tensor imaging. Across the brain, there was a predominantly positive association between a measure of white matter integrity, fractional anisotropy (FA), and social network diversity. This association was particularly strong in a region near the anterior corpus callosum and driven by a negative association with the radial component of the diffusion signal. This callosal region contained projections between bilateral prefrontal cortices, as well as cingulum and corticostriatal pathways. FA within this region was weakly associated with circulating levels of the inflammatory cytokine interleukin-6 (IL-6), but IL-6 did not mediate the social network and FA relationship. Finally, variation in FA indirectly mediated the relationship between social network diversity and intrinsic functional connectivity of medial corticostriatal pathways. These findings suggest that social integration relates to myelin integrity in humans, which may help explain the diverse aspects of health affected by social networks

PINES.

<p>Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at <i>p</i> < 0.001 uncorrected. Blowout sections show the spatial topography of the pattern in the left amygdala, right insula, and posterior cingulate cortex. Panel B shows the predicted affective rating compared to the actual ratings for the cross validated participants (<i>n</i> = 121) and the separate holdout test data set (<i>n</i> = 61). Accuracies reflect forced-choice comparisons between high and low and high, medium, and low ratings. Panel C depicts an average peristimulus plot of the PINES response to the holdout test dataset (<i>n</i> = 61). This reflects the average PINES response at every repetition time (TR) in the timeseries separated by the rating. Panel D illustrates an item analysis which shows the average PINES response to each photo by the average ratings to the photos in the separate test dataset (<i>n</i> = 61). Error bars reflect ±1 standard error.</p

Region of interest analysis.

<p>Panel A illustrates the spatial distribution of the three anatomical ROIs used in all analyses (amygdala = yellow, insula = red, ACC = cyan). Panel B depicts the average activation within each ROI across participants for each level of emotion and pain in the emotion hold out (<i>n</i> = 61) and pain test datasets (<i>n</i> = 28). Error bars reflect ±1 standard error. Panel C illustrates the spatial topography of the PINES and NPS patterns within each of these anatomical ROIs. While these plots show one region, correlations reported in the text reflect bilateral patterns.</p

PINES clustering based on shared patterns of connectivity.

<p>This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the <i>p</i> < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.</p

Experimental paradigm and analysis overview.

<p>Panel A depicts the sequence of events for a given trial. Participants view an initial fixation cross and then are instructed to look at the picture (compared to reappraise). Participants then see a photo and are asked to rate how negative they feel on a likert scale of 1–5. Panel B illustrates the temporal data reduction for each rating level using voxel-wise univariate analysis and an assumed hemodynamic response function. Panel C: these voxels are then treated as features and trained to predict ratings using LASSO-PCR with leave-one-subject-out cross validation. Subject’s data for each rating is concatenated across participants. Panel D: this multivoxel weight map pattern can be tested on new data using matrix multiplication to produce a scalar affective rating prediction. Panel E: we calculated two different types of classification accuracy: (a) the ability to discriminate between high (rating = 5) and low (rating = 1) affective ratings and (b) the ability to discriminate between high affective and high pain data.</p