This research article explores whether brain activity related to reward and loss before treatment can predict how well adults with generalized anxiety disorder (GAD) improve with either behavioral activation (BA) or exposure therapy (EXP). The study used fMRI to measure neural responses during a monetary incentive task and then randomized participants to ten sessions of BA or EXP. Findings suggest that specific pre-treatment brain activity patterns, particularly in regions like the left caudate and fronto-parietal regions of the cortex, were associated with different outcomes depending on the type of therapy received, highlighting the potential for neural predictors to inform GAD treatment approaches.

This research investigated the effectiveness of real-time fMRI neurofeedback (rtfMRI-NF) for treating major depressive disorder (MDD) by analyzing whole-brain activity patterns during training. The study identified distinct subtypes of brain activation during self-regulation and responses to feedback, which were significantly associated with symptom reduction. Notably, the clinical response was more related to these large-scale brain patterns than the activity within the targeted amygdala region. These findings suggest that successful neurofeedback therapy for MDD involves specific patterns of brain activity, including control regions and areas related to self-referential thinking. The research highlights the potential for tailoring neurofeedback training to these subtypes to enhance its therapeutic impact on depression.

The provided sources describe BRAINIAC, a novel Bayesian statistical model designed for neuroimaging research to analyze the complex relationships between whole-brain data (primarily resting-state fMRI) and cognitive or behavioral traits. This model addresses the challenges of replicating findings from smaller studies by simultaneously considering all brain features and assessing the contribution of predefined brain feature groupings called annotations. BRAINIAC estimates the total variance in a cognitive trait explained by brain data and identifies whether certain annotated feature groups are particularly enriched for these associations, as demonstrated in its application to the ABCD Study data for crystallized intelligence and psychopathology, with validation using the HCP-D dataset. The method aims to offer a more reliable and comprehensive understanding of brain-behavior links by moving beyond traditional single-feature or sparsity-assuming analyses.

This research article investigates how weather conditions influence social media activity by analyzing billions of posts from Facebook and Twitter between 2009 and 2016 alongside meteorological data. The study reveals that both extreme temperatures and precipitation independently lead to increased online engagement, with compounded weather events causing even greater surges. Notably, these effects persist at an individual level and surpass the social media activity seen during major social events like New Year's Eve in New York City. The authors highlight that environmental factors play a significant and often overlooked role in shaping digital social interactions, affecting both weather-related and general online posting. The findings suggest that adverse weather drives people to engage more online, potentially due to reduced offline opportunities. 
https://pubmed.ncbi.nlm.nih.gov/39903688/

This paper by White et al. (2022) discusses ethical considerations and best practices for using large-scale, publicly available datasets, such as the ABCD Study®, for research on American Indian and Alaska Native (AIAN) populations, who have a history of exploitation in mental health research1 .... The authors aim to highlight problematic data use that could perpetuate stereotypes and offer five recommendations, developed with the Cherokee Nation, to promote health research in AIAN communities. The paper critiques a study by Assari (2020) as an example of inappropriate interpretation of ABCD Study® data. The authors identify three main issues: lack of appropriate theoretical rationale, as Assari's model on protective factors was not validated with AIAN communities and ignored existing research; inappropriate interpretation of statistical analyses, including an imbalanced sample size, unreliable mean estimates due to small cell sizes, aggregation of the heterogeneous AIAN/NHPI group, and misinterpreting associations as causal effects; and lack of community engagement or regulatory review. The authors, in partnership with the Cherokee Nation, propose five recommendations for researchers using publicly available AIAN participant data:

(1) Consider heterogeneity of large-scale AIAN samples: Recognize the diverse cultures, histories, languages, and traditions among the 574 federally recognized AIAN nations to avoid overgeneralization.

(2) Prioritize advancement of health and well-being in AIAN communities: Ensure research benefits these communities and consider its impact on public policy and perceptions. Study design should include elements that increase resources and sustainability for tribal research collaborators and build AIAN communities' capacity to engage with publicly available data.

(3) Facilitate community engagement at each stage of the research process: Involve tribal communities early to ensure research relevance, incorporate community knowledge in interpretations, and disseminate findings beyond scientific outlets with bidirectional information sharing.

(4) Consider the impact of social injustices on study variables: Acknowledge the effects of colonization, historical trauma, intergenerational trauma, and discrimination on AIAN mental health and contextualize findings accordingly, especially with biological data as race is not a biological or causal variable.

(5) Engage with tribal research regulatory infrastructure: Comply with tribal regulations (e.g., IRBs, tribal councils) and consult with regulatory bodies for oversight. Future studies should aim to establish coordinated regulatory bodies informed by Indigenous Data Governance (IDG) and Indigenous Data Sovereignty (IDS) principles.
The authors emphasize that both data generators and users share the responsibility for applying these recommendations, along with research reviewers, editors, and publishers. These recommendations are based on principles of community-engaged research and Indigenous Data Sovereignty and Governance, driven by the ethical principle of solidarity. The paper concludes that ethical and culturally appropriate research is crucial to advance understanding of AIAN health and well-being using large-scale publicly available data.

This podcast discusses a recent study that tested how heartbeat perception might be affected within several different mental health conditions. The results suggested that, in depression, anxiety, substance use, and eating disorders, the brain may be less flexible in how it processes signals received from the body. This could be one reason for the emotional difficulties experienced by individuals with these conditions.

​Our lab analyzed questionnaire and blood-based biomarker data collected as part of the Tulsa 1000 study funded by LIBR. We compared three groups of people: (1) those with pure depression, (2) those with comorbid depression + generalized anxiety disorder, and (3) non-depressed/non-anxious individuals. Both depression groups reported higher eating disorder symptoms than the non-depressed group but they did not differ in insulin, adiponectin, or leptin levels. Disordered eating may contribute to daily impairments experienced by people struggling with depression, and could be one future area of intervention.

This is a project from Dr. Stewart's lab using LIBR's Tulsa 1000 dataset.
​We compared people with amphetamine use disorder to those without any substance use disorder (who were allowed to have past depression) on brain activation while they performed a reward anticipation task. ​

IMPORTANCE
​Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable burdens. The etiological factors contributing to TRD are complex and not fully understood.

OBJECTIVE
To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits and explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us (AoU) Research Program.

DESIGN, SETTING, AND PARTICIPANTS
This study was a cohort design with observational data from participants in the AoU Research Program who have both electronic health records and genomic data. Data analysis was performed from March 27 to October 24, 2024.

EXPOSURES
PGS for 61 unique traits from 7 domains.

MAIN OUTCOMES AND MEASURES
Logistic regressions to test if PGSwas associated with treatment-resistant depression (TRD) compared with treatment-responsive major depressive disorder (trMDD). Cox proportional hazard model was used to determine if the progressions from MDD to TRD were associated with PGS.

RESULTS
A total of 292,663 participants (median [IQR] age, 57 (41-69) years; 175 981 female [60.1%]) from the AoU Research Program were included in this analysis. In the discovery set (124 945 participants), 11 of the selected PGS were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia (odds ratio [OR], 1.11; 95%CI, 1.07-1.15) and specific neuroticism (OR, 1.11; 95%CI, 1.07-1.16) traits were associated with increased TRD risk, whereas higher education (OR, 0.88; 95%CI, 0.85-0.91) and intelligence
(OR, 0.91; 95%CI, 0.88-0.94) scores were protective. The associations held across different TRD definitions (meta-analytic R2 >83%) and were consistent across 2 other independent sets within AoU (the whole-genome sequencing Diversity dataset, 104 388, and Microarray dataset, 63 330). Among 28,964 individuals followed up over time, 3,854 developed TRD within a mean of 944 days (95%CI, 883-992 days). All 11 previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD.

CONCLUSIONS AND RELEVANCE
Results of this cohort study suggest that genetic predisposition related to neuroticism, cognitive function, and sleep patterns had a significant association with the development of TRD. These findings underscore the importance of considering psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance understanding of pathways leading to treatment resistance.

Approximately 1/3 of individuals with major depressive disorder (MDD) exhibit low-grade systemic
inflammation based on levels of C-reactive protein (CRP); the American Heart Association defines high risk as a CRP as >3mg/L (1). At the behavioral level, anhedonia appears to be the symptom most closely tied to inflammation (2-4). In healthy participants, inflammatory challenge with low-dose lipopolysaccharide (LPS) - a reliable, safe, and well-validated method of inducing robust peripheral and CNS inflammation (5-8) - transiently increases motivational anhedonia and decreases ventral striatal response to anticipatory reward (9-11).
However, to our knowledge, no study has tested whether depressed individuals with and without systemic inflammation show differential anhedonic responses to an acute inflammatory exposure. Such experimental substantiation would constitute clear evidence of a biological subtype of MDD and corroborate previously proposed mechanistic links between inflammation and anhedonia (2-4). This would also have implications for treatment as it has been proposed that clinical trials of immune-modulating therapies in depression are confounded by a failure to selectively recruit participants with evidence of inflammation (12-15). Here, we present results from a randomized controlled trial (NCT03142919) designed to test whether MDD participants with elevated serum CRP levels (≥3mg/L), as compared to those with non-elevated CRP (≤1.5mg/L), display a greater increase in anhedonic symptoms at the approximate peak of the inflammatory response to LPS, i.e. ~1.5 hours post infusion.