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American Indian and Alaska Native (AIAN) populations have suffered a history of exploitation and abuse within the context of mental health research and related fields. This history is rooted in assimilation policies, historical trauma, and cultural loss, and is promulgated through discrimination and disregard for traditional culture and community knowledge. In recognition of this history, it is imperative for researchers to utilize culturally sensitive approaches that consider the context of tribal communities to better address mental health issues for AIAN individuals. The public availability of data from large-scale studies creates both opportunities and challenges when studying mental health within AIAN populations. This manuscript has two goals; first, showcase an example of problematic use of Adolescent Brain Cognitive Development (ABCD) StudySM data to promulgate stereotypes about AIAN individuals and, second, in partnership with collaborators from Cherokee Nation, we provide five recommendations for utilizing data from publicly available datasets to advance health research in AIAN populations. Specifically, we argue for the consideration of (1) the heterogeneity of the communities represented, (2) the importance of focusing on AIAN health and well-being, (3) engagement of relevant communities and AIAN community leaders, (4) consideration of historical and ongoing injustices, and (5) engagement with AIAN regulatory agencies or review boards. These recommendations are founded on principles from broader indigenous research efforts emphasizing community-engaged research and principles of Indigenous Data Sovereignty and Governance.

BACKGROUND: Repetitive negative thinking (RNT) is a symptom dimension of depression that is associated with a poorer prognosis in terms of higher recurrence, treatment resistance, residual symptoms, and disability. This investigation examined whether RNT is associated with aberrant reward processing and fear learning. METHODS: Very high RNT (VH-RNT) (n = 60) and high RNT (H-RNT) (n = 60) propensity-matched individuals with depression (age, sex, race/ethnicity, income/employment, body mass index, depressive and anxiety symptom severity) participated in this study along with matched healthy comparison volunteers (n = 30). This propensity-matched sample was selected from the larger Tulsa 1000 study. Participants performed two functional magnetic resonance imaging tasks: the monetary incentive delay task probing reward processing and the fear conditioning task probing aversive learning and extinction. RESULTS: Both VH-RNT and H-RNT groups showed lower neural activity than healthy comparison subjects in reward circuitry, including the inferior frontal gyrus (VH-RNT: β = −1.24, H-RNT: β = −1.28) and the cerebellum (VH-RNT: β = −0.93, H-RNT: β = −1.14). However, individuals with VH-RNT exhibited lower activation than those with H-RNT in central autonomic network components during fear conditioning (β = −0.84) and continued conditioned responses during early extinction in the postcentral cortex (β = 0.71). CONCLUSIONS: VH-RNT showed aberrant processing in fear conditioning during both learning and extinction phases compared with H-RNT. These findings demonstrate that dysfunctions of negative valence associated with RNT may be domain specific, which should be taken into account for identifying potential specific targets of intervention.

Unmedicated individuals with major depressive disorder (MDD) show abnormal interoception, but it is unclear whether antidepressant treatment via serotonergic medication alters this relationship. The current cross-sectional study examined associations between neural and behavioral indices of interoceptive processing and chronic serotonergic medication administration in MDD. 47 selective serotonin reuptake inhibitor (SSRI)-medicated MDD (MDD-SSRI) individuals were propensity-matched with 48 unmedicated current MDD (MDD-UnMed) and 41 healthy comparison (HC) participants on demographics including age, sex, body mass index, education, as well as on dimensional scales of symptom severity including depression and anxiety. All participants completed an interoceptive attention task during functional magnetic resonance imaging, and a behavioral heartbeat tapping task under three conditions: Guessing, No Guessing, and Breath Hold. Relative to HC, both MDD groups: (1) exhibited lower mid-insula, amygdala, putamen, and caudate activation during interoceptive versus exteroceptive attention; and (2) showed poorer heartbeat tapping performance during the Breath Hold condition. However, the MDD-SSRI group reported higher intensity ratings of heartbeat and stomach sensations than MDD-UnMed and HC during the interoceptive attention task. These findings suggest that the attenuated patterns of neural activation observed in depressed individuals during interoceptive attention are not ameliorated by the chronic administration of serotonergic medications. However, amplified interoceptive sensation ratings suggest a potential impact of chronic serotonergic medication on conscious experiences of internal body states. Future investigations will need to determine the extent to which serotonergic medications acutely influence interoceptive processing, and whether such changes play a role in therapeutic responses during treatment initiation.

Mindfulness training (MT) reduces self-referential processing and promotes interoception, the perception of sensations from inside the body, by increasing one’s awareness of and regulating responses to them. The posterior cingulate cortex (PCC) and the insular cortex (INS) are considered hubs for self-referential processing and interoception, respectively. Although MT has been consistently found to decrease PCC, little is known about how MT relates to INS activity. Understanding links between mindfulness and interoception may be particularly important for informing mental health in adolescence, when neuroplasticity and emergence of psychopathology are heightened. We examined INS activity during real-time functional magnetic resonance imaging neurofeedback-augmented mindfulness training (NAMT) targeting the PCC. Healthy adolescents (N = 37; 16 female) completed the NAMT task, including Focus-on-Breath (MT), Describe (self-referential processing), and Rest conditions, across three neurofeedback runs and two non-neurofeedback runs (Observe, Transfer). Regression coefficients estimated from the generalized linear model were extracted from three INS subregions: anterior (aINS), mid (mINS), and posterior (pINS). Mixed model analyses revealed the main effect of run for Focus-on-Breath vs. Describe contrast in aINS [R2 = 0.39] and pINS [R2 = 0.33], but not mINS [R2 = 0.34]. Post hoc analyses revealed greater aINS activity and reduced pINS activity during neurofeedback runs, and such activities were related to lower self-reported life satisfaction and less pain behavior, respectively. These findings revealed the specific involvement of insula subregions in rtfMRI-nf MT.

Background Early life stress (ELS) has been linked to poor mental and physical health outcomes in adolescence and adulthood. Mindfulness reduces symptoms of depression and anxiety and improves cognitive and social outcomes in both youth and adults. However, little is known whether mindfulness can mitigate against the adverse neurobiological and psychological effects of ELS. This study aimed to examine the feasibility of conducting a group mindfulness intervention in adolescents with ELS and provide preliminary indication of potential effects on stress-related biomarkers and mental health symptoms. Methods Forty adolescents were randomized to receive either eight sessions of Mindfulness-Based Stress Reduction for Teens in group format (MBSR-T; n = 21) or Control (CTRL; n = 17). Outcomes were assessed at baseline and follow-up and included measures associated with neurobiological functioning (immune and endocrine biomarkers) and self-reported mental health symptoms. Linear mixed effects models were used to assess the effects of group and time on these outcome measures. ResultsSixteen of the 21 adolescents completed the intervention, attending an average of 6.5 sessions. The model examining depressive symptoms revealed a medium effect for symptom reduction [Cohen’s d = .69] in the MBSR-T relateive to CTRL groups.ConclusionsThis study demonstrated feasibilility of conducting a group-based MBSR intervention for adolescents with ELS. There was some evidence for efficacy on a symptom level with potential subtle changes on a biological level. Future larger studies are needed to determine the efficacy of group-based mindfulness interventions in this population.

Background: An estimated 1100 college students die by suicide each year. Our ability to predict who is at risk for suicide, as well as our knowledge of resilience factors protecting against it, remains limited. We used a machine learning (ML) framework in conjunction with a large battery of self-report and demographic measures to select features contributing most to observed variability in suicidal thoughts and behaviors (STBs) in college.Method: First-year university students completed demographic and clinically-relevant self-report measures at the beginning of the first semester of college (baseline; n=356), and at end-of-year (n=228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A ML pipeline with 55 and 57 variables using stacking and nested cross-validation to avoid overfitting was conducted to examine predictors of baseline and end-of-year STBs, respectively. Results: For baseline SBQ-R score, the identified ML algorithm explained 28.3% of variance (95%CI: 28-28.5%), with depression severity, meaning and purpose in life, and social isolation among the most important predictors. For end-of-year SBQ-R score, the identified algorithm explained 5.6% of variance [95%CI: 5.1-6.1%], with baseline SBQ-R score, emotional suppression, and positive emotional experiences among the most important predictors.Limitations: External validation of the model with another independent sample is needed for further demonstrating its replicability.Conclusions: ML analyses replicated known factors contributing to STBs, and identified novel, potentially modifiable risk and resilience factors. Intervention programing on college campuses aiming to reduce depressive symptomatology, promote positive affect and social connectedness, and foster a sense of meaning and purpose, may be effective in reducing STBs.

PurposeFluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations, is instrumental for academic success, psychological well-being, and adoption of healthy behaviors. Our knowledge concerning factors associated with FCF, including those that may be targeted with interventions to improve outcomes, remains limited. MethodsWe used a machine learning (ML) framework in conjunction with a large battery of measures from 9,718 youth from the Adolescent Brain Cognitive Development (ABCD) study to identify factors associated with the observed variability in FCF performance. Youth age-corrected composite FCF score was derived from the National Institutes for Health Toolbox Neurocognitive Battery. A ML pipeline using a stack ensemble of multiple ML algorithms and nested cross-validation to avoid overfitting was conducted to examine factors associated with FCF. Results The identified ML algorithm explained 14.74% of variance (95%CI: 14.53-14.88%) in FCF. Among the most important factors were those that replicated previous research (e.g., socioeconomic factors), as well as novel, potentially modifiable factors, including extracurricular involvement, screen media activity, and sleepConclusionsPragmatic and scalable interventions targeting these behaviors may not only enhance cognitive performance but may also protect against the negative impact of socioeconomic and mental health factors on cognitive performance in at-risk youth. The longitudinal data from ABCD will be able to begin to assess causality by examining how changes in these factors affect subsequent cognitive performance.

Biological Psychiatry, 3 (6), ISSN:0006-3223, ISSN:1873-2402