This research paper from the Journal of Affective Disorders investigates the relationship between brain activity, measured by P300 amplitude during a reward-based task, and treatment outcomes for individuals with major depressive disorder (MDD). The study found that MDD patients showed smaller P300 amplitudes compared to healthy individuals, indicating reduced neural resource allocation to task stimuli. Furthermore, within the MDD group, individuals with larger P300 amplitudes at the start of treatment were more likely to complete therapy. These findings suggest that P300 amplitude could potentially serve as a predictive marker for treatment adherence in behavioral therapy for depression. 

This research paper investigated how the brains of individuals with both anxiety and depression (AnxMDD) respond differently to fear conditioning compared to those with only major depressive disorder (MDD). Using fMRI, the study found that the AnxMDD group showed greater activation in brain regions associated with fear processing, such as the frontal, insular, and cingulate cortices, when presented with stimuli paired with a threat. Conversely, the MDD group did not exhibit this heightened response to threat cues. These findings suggest that comorbid anxiety and depression is characterised by an exaggerated neural sensitivity to fear, highlighting potential distinct neurobiological underpinnings and treatment targets for this prevalent and often treatment-resistant condition.

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.