Richard J. Davidson’s research while affiliated with University of Wisconsin–Madison and other places

This cohort study examines the association of a college course designed to strengthen skills for mental health taken in 2018 or 2019 with measures of anxiety, depression, and flourishing during the early months of the SARS-CoV-2 pandemic in 2020.

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualised meditation practices and designed various meditation interventions (MIs), that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has examined the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and non-clinical populations. However, the generalizability and replicability of current neuroscientific models of meditation are yet to be established, as they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural MRI). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights, accounting for multi-domain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms underlying therapeutic action of meditation practices on psychological and cognitive attributes, advancing the field of meditation and contemplative neuroscience.

Objective Recent theoretical work suggests the expression of emotions may differ among Black and White Americans, such that Black Americans engage more frequently in expressive suppression to regulate emotions and avoid conflict. Prior work has linked expressive suppression usage with increases in cardiovascular disease risk, suggesting that racialized differences in expressive suppression usage may be one mechanism by which racism “gets under the skin” and creates heath disparities. Method To examine racialized differences in expressive suppression and blood pressure (a measure of cardiovascular disease risk), we used self-report and facial electromyography (fEMG) data from two cohorts of Black and White Americans from the Midlife in the United States (MIDUS) longitudinal study (MIDUS 2, n = 271, 34.7% Black, collected from 2004-2009; MIDUS Refresher 1, n = 114, 31.6% Black, collected from 2012-2016; total N = 385, 33.9% Black). Results Black Americans reported engaging in expressive suppression more frequently than White Americans ( t (260.95) = 2.18, p = .002) and showed less corrugator fEMG activity during negative images( t (969) = 2.38, pFDR = .026). Less corrugator activity during negative images was associated with higher systolic blood pressure only for Black Americans ( b = -4.63, t(375) = 2.67, p = .008). Conclusion Overall, results are consistent with theoretical accounts that Black Americans engage more frequently in expressive suppression, which in turn is related to higher cardiovascular risk. Additional research is needed to further test this claim, particularly in real-world contexts and self-reports of in-the-moment usage of expressive suppression.

The neurodevelopmental epoch from fetal stages to early life embodies a critical window of peak growth and plasticity in which differences believed to be associated with many neurodevelopmental and psychiatric disorders first emerge. Obtaining a detailed understanding of the developmental trajectories of the cortical gray matter microstructure is necessary to characterize differential patterns of neurodevelopment that may subserve future intellectual, behavioral, and psychiatric challenges. The neurite orientation dispersion density imaging (NODDI) Gray-Matter Based Spatial Statistics (GBSS) framework leverages information from the NODDI model to enable sensitive characterization of the gray matter microstructure while limiting partial volume contamination and misregistration errors between images collected in different spaces. However, limited contrast of the underdeveloped brain poses challenges for implementing this framework with infant diffusion MRI (dMRI) data. In this work, we aim to examine the development of cortical microstructure in infants. We utilize the NODDI GBSS framework and propose refinements to the original framework that aim to improve the delineation and characterization of gray matter in the infant brain. Taking this approach, we cross-sectionally investigate age relationships in the developing gray matter microstructural organization in infants within the first month of life and reveal widespread relationships with the gray matter architecture.

Despite growing popularity, associations between dosage and outcomes in meditation-app interventions have not been established. We examined this relationship using a range of operationalizations of dosage (e.g., minutes of use, days of use, number and type of activities completed) and strategies for modeling outcomes (e.g., ordinary least squares regression, multilevel modeling, latent class analysis). We used data from a recently completed randomized controlled trial that tested a meditation app ( N = 662; 80.4% with elevated depression/anxiety) that included psychological distress as its preregistered primary outcome. Across 41 models, whether an association was detected and the shape and direction of this association varied. Although several models indicated that higher dosage was associated with larger decreases in psychological distress, many models failed to show this relationship, and some even showed the opposite. These results may have implications for optimizing and studying dosage in meditation apps and for open-science practices.

Objective: Meditation apps are the most widely used mental health apps. The precise mechanisms underlying their effects remain unclear. In particular, the degree to which affect experienced during meditation is associated with outcomes has not been established. Method: We used the meditation app arm of a recently completed randomized controlled trial comparing a self-guided meditation app (Healthy Minds Program) to a waitlist control. Predominantly distressed public school employees (n = 243, 80.9% with clinically elevated depression and/or anxiety) reported positive and negative affect during meditation practice. Data were analyzed using two-level multivariate latent growth curve models (observations nested within participants) that simultaneously attended to both positive and negative affect. We examined whether positive and negative affect during meditation changed over time and whether these changes were associated with changes in psychological distress (parent trial's preregistered primary outcome) at posttest or 3-month follow-up. Results: On average, participants reported decreased negative affect but no change in positive affect during meditation over time. Increased positive affect and decreased negative affect during meditation were associated with improvements in distress at posttest and follow-up. Change in positive affect was a stronger predictor of distress at follow-up than change in negative affect. Conclusions: Despite notions embedded within mainstream mindfulness meditation training that deemphasize the importance of the affective experience of practice (i.e., nonjudgmental awareness of present moment experience, regardless of valence), results indicate that these experiences contain signals associated with outcomes. Monitoring affect during meditation may be worthwhile to guide intervention delivery (i.e., measurement-based care, precision medicine). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Introduction Trait mindfulness refers to one’s disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. Methods To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. Results In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging . Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. Conclusions We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

We developed a self-report measure of psychological well-being for teens and adults, the Healthy Minds Index, based on a novel theory that four trainable pillars underlie well-being: awareness, connection, insight, and purpose. Ninety-seven items were developed and revised by experts and guided by qualitative testing with teens (n = 32; average age = 16.0 years). After assessing the internal validity and factor structure in teens (n = 1607; average age = 16.7 years) and adults (n = 420; average age = 45.6 years), we reduced the survey to 17 items. We then validated the factor structure, internal and convergent and divergent validity, and retest reliability of the 17-item Healthy Minds Index in two new teen samples (study 1: n = 1492, average age = 15.7 years; study 2: n = 295, average age = 16.1 years), and one adult sample (n = 285; average age = 45.3 years). The Healthy Minds Index demonstrated adequate validity and provided a comprehensive measure of a novel theory of psychological well-being that includes two domains not found in other conceptualizations of this construct—awareness and insight. This measure will be invaluable for primary research on well-being and as a translational tool to assess the impact and efficacy of widely used behavioral training programs on these core dimensions of wellbeing.