Norman A. S. Farb

Does physiological arousal shape emotion only when consciously perceived? Existing paradigms confound detection with signal magnitude, rendering this question intractable. The Breath Change Awareness Task (BCAT) resolves this confound by orthogonally manipulating signal magnitude and detection. Across five studies of undergraduate and community samples (total N = 787), detected changes produced arousal scaled to magnitude; equivalent undetected changes produced no differential response. In a pre-registered laboratory study with physiological verification (Study 5; N = 206), belt recordings confirmed physical compliance, and arousal gating was stronger during interoceptive engagement than visual tracking. Self-reported interoceptive awareness (MAIA) predicted detection confidence but not objective threshold, reflecting a disposition toward bodily awareness rather than detection ability. Conscious detection gates interoceptive arousal transfer: physiological change moves us only when we notice it. Findings are based on self-reported arousal and predominantly undergraduate samples, and warrant replication with clinical populations and objective physiological measures.

Flow is a state of optimal engagement. Autistic individuals and individuals with attention-deficit/hyperactivity disorder (ADHD) might have variations in flow-like experiences, such as hyperfocus, autistic inertia, and monotropism, that diverge from traditional or typical definitions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we systematically reviewed 43 publications, including empirical studies, reviews, and other literature, to examine these constructs. Results showed that attentional experiences associated with neurodivergence overlap with, but also differ notably from, typical flow. Autistic inertia hinders task initiation and transitions, in contrast to flow’s seamless engagement. Monotropism involves rigid, directed focus and repetitive, tenacious engagement, whereas flow allows for flexible adaptations to challenges. Hyperfocus is like flow regarding intense concentration and immersion but is often involuntary, lacks clear goals, and is associated with emotional dysregulation. Although commonalities exist across these attentional experiences, the greatest overlap occurs within autistic inertia, monotropism, and hyperfocus. Attention in autistic and ADHD individuals might be more dynamic than previously believed, with potential for transitions between these experiences. This fluidity can enhance focus and productivity but may also lead to negative outcomes, such as overly rigid fixation or difficulty engaging with and disengaging from tasks. Clarifying these differences and overlaps can inform tailored interventions and supports, helping neurodivergent individuals manage focus, reduce task-related stress, and optimize productivity.

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg’s Behavior Model and four design principles informed by literature, the goal of these interventions was to provide triggers that encourage actions so simple that even people with low motivation would be willing to complete them. We examined the utility of these prompts by conducting a 14-day study with 22 participants interested in making small lifestyle improvements in at least one of three domains: physical activity, healthy eating, and mental well-being. Although we found evidence supporting the intent and design of our one-minute interventions, we also observed that participants encountered four types of barriers that hindered follow-through: temporal, physical, resource, and cognitive friction. When combined with insights drawn from participants’ rewrites of our prompts, our findings suggest that intentional personalization through co-authorship could be a lightweight personalization mechanism that balances relevance with low friction.

· Background. Negative self-referential bias (NSB) is a hallmark of depression, yet its neural signature and link to relapse vulnerability remain unclear. We investigated whether NSB and its associated neural activity predict relapse and depressive symptoms following prophylactic psychotherapy.· Methods. This is a two-year prospective neuroimaging study nested within a randomized controlled trial of Mindfulness-Based Cognitive Therapy and Cognitive Behavior Therapy with a Well-Being focus. Remitted depressed outpatients (N = 81) completed the Self-Referential Encoding Task during fMRI pre- and post-treatment and were followed bi-monthly. Study preregistration: https://osf.io/s4n8j.· Results. NSB predicted higher depressive symptoms (b = .10; 95% CI, .06 to .14; p = <.001) but not relapse. Greater NSB neural activity within frontal DMN and SN was associated with relapse status (DMN: b = .32; 95% CI, .09 to .55; SN: b = .36, 95% CI, .09 to .63), whereas DMN–SN connectivity was not. Static whole brain relapse biomarkers included elevated prefrontal activation and somatosensory deactivation. In non-relapsers, treatment-related attenuation of somatosensory deactivation predicted lower risk. Subgenual reactivity was the best prefrontal relapse indicator (Hazard Ratio = 1.84, 95% CI, 1.23 to 2.77), but somatosensory deactivation was the best overall relapse indicator (Hazard Ratio = .16; 95% CI, .05 to .52).· Conclusions. Relapse vulnerability reflects not only heightened prefrontal negative self-processing but also insufficient recruitment of somatosensory systems supporting embodied self-experience, providing a more comprehensive model of depression relapse vulnerability.· Trial registration: ClinicalTrials.gov Identifier: NCT01178424

Background: Major depressive disorder (MDD) is the leading cause of disability worldwide, affecting roughly 322 million people. Recently, doses of psilocybin have shown promise in treating mood disorders, sparking interest in other dosing practices. According to anecdotal reports and observational studies, microdosing psilocybin yields benefits to mental health; however, rigorously controlled trials have failed to produce compelling evidence for this.Aims: To conduct a phase II, double-blind, placebo-controlled, randomized partial crossover trial to compare microdosing psilocybin to placebo for MDD, evaluating its safety, tolerability, and preliminary antidepressant effects.Method: 40 adults with MDD will be randomized to four doses of psilocybin (2 mg) or placebo (maltodextrin) once weekly over four weeks, then four doses of psilocybin (2 mg) once weekly for an additional four weeks. The primary efficacy endpoint will be change in depression symptoms, as measured at baseline (0 weeks), after the experimental phase (4 weeks), and after the open-label phase (8 weeks). A battery of mood, well-being, attention, creativity, mindfulness, and pro-sociality measures will be administered at each time point. Follow ups will occur every six months for up to two years after the trial start date, as part of a long-term extension study.Conclusions: Findings will inform future research on microdosing psilocybin for MDD, regarding dose regimens, effect sizes, and expectancy bias. Findings will also facilitate discussions on the comparable benefits of sub- versus threshold doses of psilocybin, and the therapeutic value of radically altered perception.

Objective: Mental health concerns are rising, particularly among post-secondary students, who may lack access to traditional therapeutic resources due to barriers like long wait times and high costs. To help address these challenges, we explored the potential of large language model-based chatbots for supporting mental health and wellbeing in student populations.Methods: We conducted two studies, lasting one week and four weeks, to examine the effectiveness of chatbot interventions over different durations. Both studies compared two chatbot interventions—one mindfulness-focused and one value-focused—against an active check-in-only control condition. The primary outcome measure was the improvement in wellbeing through the mindfulness-to-meaning (MM) pathway, a process in which enhanced decentering, the ability to see one’s experience from a wider perspective, leads to improved positive reappraisal, the ability to find constructive and empowered interpretations of experience.Results: All conditions showed evidence of stress reduction. However, compared to the active control group, both intervention styles at both durations resulted in improved wellbeing via the MM pathway. This effect was primarily driven by significant improvements in decentering. For the longer duration only, we also observed enhanced reappraisal. Conclusions: These results emphasize the potential of chatbot-based interventions to support the development of regulatory skills by leveraging the MM pathway to enhance mental health. Educational institutions and mental health providers might consider integrating such tools into scalable and accessible student support systems, addressing a broader and more diverse audience while promoting sustained wellbeing through skills development.

Background: Before meaningful progress toward precision psychiatry is possible, objective (unbiased) assessment of patient mental well-being must be validated and adopted broadly. Objective: This study aims to compare the fidelity of a precision psychiatry therapy recommendation algorithm when trained with an objective quantification of psychological stress versus subjective ecological momentary assessments (EMAs) of stress and mood. Methods: From 2786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and postsession self-report and facial biometric data via a mobile health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 “super users” that completed a minimum of 28 sessions with all pre- and postsession measures. The platform used has previously demonstrated reduced psychiatric symptom severity and improved overall mental well-being. Psychotherapy recordings (“sessions”) within the platform, available asynchronously and on demand, span mindfulness, meditation, cognitive behavioral therapy, client-centered therapy, music therapy, and self-hypnosis. The platform also has the unusual ability to rapidly assess mental well-being without bias via an easy-to-use objective measure of psychological stress derived from artificial intelligence–based analysis of facial biomarkers (objective stress level [OSL]). In tandem with the objective measure, EMAs obtain self-reported values of stress (SRS) and mood (SRM). ∆OSL, ∆SRS, and ∆SRM (with delta referring to the presession subtracted from the postsession measurement) were used to independently train a therapy recommendation algorithm designed to predict what future sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual’s self-selected sessions. Results: The objective measure of psychological stress provided a differentiated delta for the measurement of therapeutic efficacy compared to the 2 EMA deltas, as shown by clear divergence in ∆OSL vs ∆SRS or ∆SRM (r<0.03), while the EMA deltas showed significant convergence (r=0.53, P<.01). The recommendation algorithm selected increasingly efficacious therapy sessions as a function of training data when trained with either ∆OSL (F1,16=5.37, P=.03) or ∆SRM data (F1,16=3.69, P<.05). However, the sequential improvement in prediction efficacy only surpassed the efficacy of self-selected therapy when the algorithm was trained using objective data (P<.01). Training the algorithm with EMA data showed potential trends that did not reach significance (∆SRS: P=.09; ∆SRM: P=.12). As a final insight, self-selected therapy sessions were overrepresented among the algorithmically recommended sessions, an effect most pronounced when the algorithm was trained with ∆OSL data (F1,14=30.94, P<.001). Conclusions: These prospective data demonstrate that a rapid, scalable, and objective measure of psychological stress, in combination with a robust recommendation algorithm, can autonomously identify clinically meaningful therapy for individuals. More broadly, this work illustrates the potential for objective data on mental well-being to improve precision psychiatry and the capacity for mental health care professionals to match global demand. Trial Registration: ClinicalTrials.gov NCT06265909; https://clinicaltrials.gov/ct2/show/NCT06265909

Growing global concerns around mental health require innovative and adaptive solutions. Technological support tools such as web applications provide an opportunity for such innovation, but even the most efficacious platform is limited by its ability to promote user engagement. This study explored the impact of user engagement in technology-supported self-monitoring for wellbeing in a cohort (N=196) of university undergraduate students. We aimed to identify an efficient measurement model for daily wellbeing, examining the impact of optimizing user-centered design to promote engagement and enhance user experience. To this end, participants were randomly assigned to either a passive control condition or to one of two daily self-reflection conditions. Self-reflection prompts were delivered using either an online questionnaire platform (Qualtrics), or an app designed specifically to engage users in wellbeing reflection (UpBeing). Results indicated that including general appraisal items alongside momentary affect assessment improved model fit compared to affect measurement alone. Furthermore, wellbeing-centered design (UpBeing) significantly enhanced self-reported wellbeing compared to both Qualtrics and the control conditions, accompanied by decreased negative mood and improved decentering skills. These findings suggest that app-based self-reflection tools like UpBeing can effectively support individuals’ wellbeing and coping abilities in daily life. Analysis of user experience reports suggested that the design of the UpBeing app likely contributed to its superior performance. Given that many apps only measure affect/mood without general appraisals of wellbeing, the study highlights the importance of validating measurements models in wellbeing support tools. While research into longitudinal and clinical outcomes is needed, implementing user-centered design in mental health support tools may be a promising method for enhancing health benefits through increased user engagement.

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.