Radically different
Background: Curiosity plays a fundamental role in human learning, development, and motivation, and emerging evidence suggests that reduced curiosity is linked to poorer mental health outcomes, including depressive symptoms (DS). However, to date, the majority of curiosity research relies on self-report assessments and thus risks biased reporting. Virtual reality (VR), a novel tool increasingly used within mental health research and treatment, might represent a potent tool for offering ecologically valid insights into curiosity-driven behaviors while circumventing issues related to self-report assessments, including demand characteristics and recall bias. Objective: The study aimed to enhance the assessment of curiosity by using a novel VR environment and to examine its relevance to DS. Specifically, we tested 2 hypotheses using a novel VR environment: first, that curiosity, as assessed through spontaneous exploratory interactions and behaviors in VR, positively correlates with self-reported curiosity, and second, that VR-based curiosity is inversely associated with DS. Methods: This exploratory study used an observational design that included 100 volunteers. All participants completed self-reported assessments of DS and curiosity before engaging in a novel VR scenario. Although progression in the virtual environment required solving cognitive tasks, these were embedded as structural elements rather than framed as the primary objective. Instead, participants’ free explorations and interactions with objects formed the basis for the 4 curiosity metrics used in this study. After VR exposure, participants completed a questionnaire assessing cybersickness symptoms. Results: Hypothesis 1 was not supported, as only one curiosity metric, namely object interactions, was positively associated with one aspect of curiosity relating to motivation to seek new knowledge and experiences. Further, diminishing significance after correction for multiple testing warranted caution. Results relating to hypothesis 2 indicated partial support, in that object interaction was significantly associated with DS while controlling for age, sex, and cybersickness levels. Sensitivity analyses showed no associations between object interactions and self-reported anxiety and stress symptoms. Conclusions: VR may be a potent tool for assessing exploratory behaviors in a controlled, yet ecologically valid, environment that avoids issues related to self-report. However, whether such motivations translate to established curiosity constructs warrants further research. This study also provided preliminary insights into how assessing exploratory interactions in VR may be a promising avenue that could enhance the understanding of the etiology and assessment of DS—particularly its early stages.
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Research led by New York University suggests a marker of epigenetic aging could be linked to depression.
The team found that accelerated aging of a type of white blood cell called a monocyte was significantly associated with the psychological and cognitive expressions of depression in a group of women with and without HIV.
“Depression is not a one-size-fits-all disorder—it can look really different from person to person, which is why it’s so important to consider varied presentations and not just a clinical label,” said lead researcher Nicole Beaulieu Perez, PhD, assistant professor at NYU Rory Meyers College of Nursing, in a press statement.
“Our study reveals unique biological underpinnings of mental health that are often obscured by broad diagnostic categories.”
As reported in The Journals of Gerontology Series A, the researchers analyzed blood samples and depression scores from 440 women, 261 living with HIV and 179 without, from the Women’s Interagency HIV Study. They tested women with HIV as people with this disease and others affecting the immune system are at greater risk of depression than the general public.
The team looked at biological aging using two epigenetic clocks: a broad multi-tissue clock and a monocyte-specific clock that measures chemical modifications to DNA in these cells.
Depression was measured using the CES-D questionnaire, which separates physical, bodily expressions of depression such as fatigue, appetite loss, and agitation from psychological and cognitive expressions of the disorder such as hopelessness, anhedonia, and feelings of failure.
Accelerated monocyte aging was significantly associated with the psychological and cognitive expressions of depression and with anhedonia specifically, even after adjusting for HIV status, race, and ethnicity. The broader multi-tissue Horvath clock showed no association with any depression domain, suggesting it is the monocyte-specific aging signal, not generalized biological aging, that tracks with mood and cognitive symptoms.
Diagnosis of depression relies largely on self-reported symptoms and not a specific physiological test. The finding that monocyte aging maps onto cognitive and mood symptoms rather than physical ones is counterintuitive, since monocytes are inflammatory cells that one might expect to track physical, inflammation-driven complaints like fatigue.
The study is small and cross-sectional, so causality cannot yet be established, but if the claims of the study were validated it could help to personalize treatment for depression in the future.
“The dynamics of monocyte aging and depression warrant further study to clarify mechanistic links,” conclude the authors.
“Our findings bring us a step closer to this goal of precision mental health care, especially for high-risk populations, by providing a biological framework that could guide future diagnosis and treatment,” adds Beaulieu Perez.
The post Biomarker of Epigenetic Aging Could Signal Depression appeared first on Inside Precision Medicine.
Adolescence is characterized by extensive development in neural circuits that support emotion processing and regulation, and by increased risk for depression. While altered frontolimbic functioning during emotion processing has been implicated in youth with depression relative to healthy controls, the directions of the associations have been inconsistent.
Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
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