Journal of Medical Internet Research
September 26, 2022
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson et al.
26 citations
A data-driven algorithm can predict which individuals are most likely to benefit from a 4-week meditation app (Healthy Minds Program) compared to no intervention. The algorithm, called a Personalized Advantage Index, was developed using machine learning on baseline data from 662 school system employees in a randomized controlled trial. It significantly moderated group differences in distress reduction, meaning it identified people who improved more with the app versus the control condition. Repetitive negative thinking alone predicted benefit nearly as well. Such an algorithm could help individuals make informed decisions about whether a meditation app is appropriate for them.
Journal of Consulting and Clinical Psychology
September 28, 2023
Christian A. Webb, Matthew J. Hirshberg, Oscar González et al.
6 citations
Mechanisms explaining why meditation training works may differ across patient subgroups. Prior research often collapsed heterogeneous groups, obscuring these differences. Using data from 662 participants, researchers developed a Personalized Advantage Index (PAI) to identify individuals likely to benefit more from a meditation app. A moderated mediation analysis showed that mindfulness acquisition mediated better outcomes only for those with higher PAI scores. This suggests that subgroup-specific mediators should be considered to clarify how psychosocial interventions work and to match individuals to the most beneficial treatment.
Psychophysiology
May 1, 2025
Isaac N Treves, Anna O Tierney, Simon B. Goldberg et al.
5 citations
A breath-counting task designed to measure mindfulness in adults was tested in 78 adolescents with high rumination. The task showed fair reliability but did not correlate positively with self-reported mindfulness, either as a trait or in daily life. Unexpectedly, more mindful adolescents performed worse on breath counting, and the task showed negative correlations with observing emotions and body sensations and with nonreactivity. Breath-counting performance was also unrelated to clinical, personality, and executive functioning measures. The findings indicate that, in this population, breath counting may measure only a narrow form of sustained attention and may not capture broader mindfulness qualities or have predictive validity.
Brain Imaging and Behavior
April 1, 2025
Jovan Jande, Isaac N Treves, Samantha L Ely et al.
4 citations
A systematic narrative review of neuroimaging studies on mindfulness-based interventions in youth (ages 5–18) found that such interventions may alter brain connectivity and activity. Analyzing 13 studies with 467 participants, most used a pre-post design with resting-state fMRI. Consistent patterns included increased functional connectivity within and between the salience, frontoparietal, and default mode networks, enhancements in white matter microstructure, and decreased default mode network activation with heightened salience network reactivity during mindfulness practice. These changes may support self-regulation and cognitive control, but methodological variability and small sample sizes limit generalizability.
medRxiv
August 28, 2024
Zishan Jiwani, Simon B. Goldberg, Jack Stroud et al.
3 citations
preprint
Most meditators who use psychedelics perceive them as beneficial for their meditation practice. Among 863 regular meditators (practicing at least three times weekly for the past year) who also used psychedelics, machine learning identified four factors most likely to predict this positive perception: greater frequency of psychedelic use, setting intentions before use, higher agreeableness, and having used N,N-Dimethyltryptamine (DMT). The model explained about 27% of the variance. The findings suggest that intentional and personality factors may shape how psychedelics influence meditation, but causality remains unestablished.
PLoS One
February 12, 2025
Zishan Jiwani, Simon B. Goldberg, Jack Stroud et al.
1 citation
Most meditators who also use psychedelics report that the drugs improve their meditation practice. In a survey of 863 regular meditators who had used psychedelics, 73.5% said psychedelics positively influenced the quality of their meditation. Machine learning analysis of 53 variables identified the strongest predictors of this perceived benefit: greater frequency of psychedelic use, setting intentions before taking psychedelics, having an agreeable personality, and having used N,N-Dimethyltryptamine (N,N-DMT). The results suggest that individual traits and patterns of use shape whether psychedelics are seen as helpful for meditation, but causality cannot be established from this cross-sectional data.
Cerebral cortex (New York, N.Y. : 1991)
February 5, 2025
Isaac N Treves, Aaron Kucyi, Anna O Tierney et al.
1 citation
During a breath-counting task, 72 adolescents showed increased static connectivity within attention-direction and orienting networks and anticorrelations between attention networks and the default mode network compared to rest. Dynamic connectivity analysis revealed four distinct brain states, including one anticorrelated with the default mode network that was proportionally more present during the task. Brain state markers distinguished breathing tasks from rest and momentary on-task from off-task attention, but no brain states reflected between-individual behavioral variability.
October 17, 2023
Christian A. Webb, Lori M. Hilt, Caroline M. Swords et al.
1 citation
preprint
Ecological momentary assessment (EMA) measures of rumination are only modestly correlated with conventional self-report measures, especially for change over time, partly due to lower reliability of EMA. Changes in rumination were larger for conventional self-report than EMA. Both types of measures accounted for unique variance in depressive symptom improvement, showing incremental predictive validity. The findings suggest that EMA and conventional self-report provide distinct, clinically meaningful information. Researchers using EMA should consider psychometric properties and the precise construct they intend to capture.
July 31, 2022
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson et al.
1 citation
An algorithm was developed to predict who benefits most from a meditation app. Using data from a randomized controlled trial of a 4-week meditation app versus a control condition in 662 school system employees, a machine learning model created a Personalized Advantage Index (PAI) that estimated each person's expected reduction in distress. The PAI scores significantly predicted which individuals improved more with the app than without. A simpler model using only repetitive negative thinking as a predictor performed similarly well. The algorithm could help individuals make informed decisions about whether a meditation app is right for them.
July 29, 2021
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson et al.
1 citation
preprint
A meditation app called the Healthy Minds Program (HMP) can reduce psychological distress, but its benefits vary by individual. Using data from a randomized controlled trial of 662 school system employees who used the app for four weeks or served as controls, researchers built an algorithm that predicts who will benefit most. The algorithm, called a Personalized Advantage Index, was based on baseline clinical and demographic traits and successfully identified individuals who showed greater distress reduction with the app versus no intervention. A simpler model using only repetitive negative thinking worked nearly as well. The approach could help people decide whether to try a meditation app.
bioRxiv (Cold Spring Harbor Laboratory)
February 10, 2026
Isaac N Treves, Clare Shaffer, Alexandra Decker et al.
Lapses in attention to internal bodily sensations, such as the breath, are predicted by behavioral and neural markers that overlap with those for lapses in external attention but also involve distinct brain systems. In 93 adolescents with depressive symptoms performing a breath-counting task during fMRI, the strongest predictors of attention lapses were the timing and variability of button responses. Breathing variability and coupling between breathing and behavior showed weaker predictive value. Brain connectivity models, incorporating default-mode, dorsal attention, ventral attention, and somatomotor networks, predicted lapses with modest accuracy and marginally outperformed behavior-only models. These findings suggest shared and unique mechanisms for interoceptive attention failures.
Behaviour Research and Therapy
April 1, 2025
Nur Hani Zainal, Christian A. Webb, Lauren S. Hallion
A proof-of-concept study with 40 high-worry adults (80% with an anxiety disorder) tested whether features of worry predict which cognitive strategy works best to regulate it. Participants rated their worries on five dimensions and tried mindful acceptance, focused attention meditation, or thought suppression during brain scanning. The preregistered hypotheses were not supported, but exploratory analyses showed that mindfulness-based strategies were more effective than thought suppression for worries rated as more uncontrollable. The authors call for larger studies with more varied perseverative thoughts.
Cognitive, affective & behavioral neuroscience
June 23, 2026
Isaac N Treves, Clare Shaffer, Alexandra Decker et al.
During mind-body practices like meditation, attention to breathing often lapses. This study examined behavioral, physiological, and neural signals preceding such interoceptive lapses in 93 adolescents with elevated anxiety and depression symptoms, who performed a breath-counting task during fMRI. The strongest predictors of lapses were the timing and variability of button responses. Breathing variability and breathing-behavior synchronization had smaller predictive value. Whole-brain connectivity models, incorporating default-mode, attention, and somatomotor networks, predicted lapses moderately and marginally outperformed behavior-only models. Some markers overlapped with known exteroceptive lapses (reaction time variability), while others were unique (perceptual coupling with attention networks). These brain-body markers may aid real-time monitoring during mind-body interventions.
Journal of the American Academy of Child and Adolescent Psychiatry
June 12, 2020
Christian A. Webb, Elana S. Israel, Emily L. Belleau et al.
Adolescents with anhedonia (AH) experience mind-wandering more frequently (70.0% of sampled moments) than typically developing (TD) peers (59.2%), and their mind-wandering tends toward unpleasant content. Mind-wandering is linked to higher concurrent negative affect, even after accounting for current activity, social context, and rumination. Bidirectional time-lagged analyses show mind-wandering and positive affect influence each other over time. Among AH adolescents, more mind-wandering is associated with stronger resting-state functional connectivity between the medial prefrontal cortex (a core Default Mode Network hub) and networks for attentional control and salience detection, suggesting heightened influence of affective and sensory salience on internally-oriented thought.