Individual variability in neural representations of mind-wandering

Consumer-Grade Neurofeedback With Mindfulness Meditation: Meta-Analysis

BACKGROUND

There is burgeoning interest in the application of neuroscientific technology to facilitate meditation and lead to beneficial psychological outcomes. One popular approach is using consumer-grade neurofeedback devices to deliver feedback on brain targets during meditation (mindfulness-based neurofeedback). It is hypothesized that optimizing brain targets like alpha and theta band activity may allow meditators to experience deeper mindfulness and thus beneficial outcomes.

OBJECTIVE

This study aimed to systematically review and meta-analyze the impacts of consumer-grade mindfulness-based neurofeedback compared with control conditions. Included studies involved mindfulness practice operationalized as open monitoring or focused attention meditation. This study was preregistered.

METHODS

A total of 16 randomized controlled training trials, as well as 5 randomized within-participant designs were included, encompassing 763 and 167 unique participants, respectively. Effects were categorized outcomes (ie, psychological distress, cognitive function, and physiological health) and process variables (ie, state mindfulness and brain measures). Study risk of bias, reporting bias, and publication bias were assessed.

RESULTS

Samples were typically small (n=30-50), and the majority of studies used mindfulness apps as controls. To deliver neurofeedback, most studies used the Muse device (11/16 randomized controlled trials [RCTs]). There was a modest effect for decreases in psychological distress compared with controls (k=11, g=-0.16, P=.03), and heterogeneity was low (I2< 0.25). However, there was no evidence for improvements in cognition (k=7, g=0.07, P=.48), mindfulness (k=9, g=0.02, P=.83), and physiological health (k=7, g=0.11, P=.57) compared to controls. Mechanistic modulation of brain targets was not found in RCTs or within-participant designs. Sex (male or female), age, clinical status, study quality, active or passive controls, sample size, and neurofeedback duration did not moderate effects. There was some evidence for reporting bias, but no evidence of publication bias. Adverse effects were not assessed in 19 out of 21 studies and not found in the 2 studies that assessed them.

CONCLUSIONS

Assertions that consumer-grade devices can allow participants to modulate their brains and deepen their meditations are not currently supported. It is possible that neurofeedback effects may rely on "neurosuggestion" (placebo effects of neurotechnology). Future research should examine more extensive calibration and individualization of devices, larger sample sizes, and gold-standard sham-controlled RCTs.

Neural sensitivity to the heartbeat is modulated by spontaneous fluctuations in subjective arousal during wakeful rest

Spontaneous thoughts, occupying much of one's awake time in daily time, are often colored by emotional qualities. While spontaneous thoughts have been associated with various neural correlates, the relationship between subjective qualities of ongoing experiences and the brain's sensitivity to bodily signals (i.e., interoception) remains largely unexplored. Given the well-established role of interoception in emotion, clarifying this relationship may elucidate how processes relevant to mental health, such as arousal and anxiety, are regulated. We used EEG and ECG to measure the heartbeat evoked potential (HEP), an index of interoceptive processing, while 51 adult participants (34 male, 20 female) visually fixated on a cross image and let their minds wander freely. At pseudo-random intervals, participants reported their momentary level of arousal. This measure of subjective arousal was highly variable within and between individuals but was statistically unrelated to several markers of physiological arousal, including heart rate, heart rate variability, time on task, and EEG alpha power at posterior electrodes. A cluster-based permutation analysis revealed that the HEP amplitude was increased during low relative to high subjective arousal in a set of frontal electrodes during the 0.328 s - 0.364 s window after heartbeat onset. This HEP effect was more pronounced in individuals who reported high, relative to low, levels of state anxiety. Together, our results offer novel evidence that at varying levels of state anxiety, the brain differentially modulates sensitivity to bodily signals in coordination with the momentary, spontaneous experience of subjective arousal-a mechanism that may operate independently of physiological arousal.

Significance Statement

Our findings highlight the relationships between spontaneous fluctuations in subjective arousal, brain-body interactions, and anxiety, offering new insights into how interoception fluctuates with changes in internal states. By showing that interoceptive processing is heightened during lower subjective arousal and that this effect is amplified in individuals with higher state anxiety, our study suggests the brain adaptively downregulates interoceptive sensitivity in response to fluctuating internal states. These results have implications for understanding how spontaneous thoughts shape interoception and emotion, particularly in clinical contexts where dysregulated interoception is linked to anxiety and mood disorders. More broadly, our work underscores the need to distinguish between different forms of arousal, advancing understanding of the taxonomy and ways of measuring arousal.

Connectome-Based Predictive Modeling of Trait Mindfulness

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. To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome-based predictive modeling analysis in 367 meditation-naïve adults across three samples collected at different sites. 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. 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.

Minimal Variation in Functional Connectivity in Relation to Daily Affect

Reported associations between functional connectivity and affective disorder symptoms are minimally reproducible, which can partially be attributed to difficulty capturing highly variable clinical symptoms in cross-sectional study designs. "Dense sampling" protocols, where participants are sampled across multiple sessions, can overcome this limitation by studying associations between functional connectivity and variable clinical states. Here, we characterized effect sizes for the association between functional connectivity and time-varying positive and negative daily affect in a nonclinical cohort. Data were analyzed from 24 adults who attended four research visits, where participants self-reported daily affect using the PANAS-X questionnaire and completed 39 min of functional magnetic resonance imaging across three passive viewing conditions. We modeled positive and negative daily affect in relation to network-level functional connectivity, with hypotheses regarding within-network connectivity of the default mode, salience/cingulo-opercular, frontoparietal, dorsal attention, and visual networks and between-network connectivity of affective subcortical regions (amygdala and nucleus accumbens) with both default mode and salience/cingulo-opercular networks. Effect sizes for associations between affect and network-level functional connectivity were small and nonsignificant across analyses. We additionally report that functional connectivity variance is largely attributable to individual identity with small relative variance (<3%) accounted for by within-subject daily affect variation. These results support previous reports that functional connectivity is dominated by stable subject-specific connectivity patterns, while additionally suggesting relatively minimal influence of day-to-day affect. Researchers planning studies examining functional connectivity in relation to daily affect, or other varying stable states, should therefore anticipate small effect sizes and carefully consider power in study planning.

Connectome predictive modeling of trait mindfulness

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.