Cytomegalovirus (CMV) establishes lifelong infection and promotes an immunosenescent T cell profile characterized by depletion of na & iuml;ve cells and expansion of terminally differentiated effector memory cells re-expressing CD45RA (TEMRA). We examined whether CMV-related T cell differentiation was associated with brain structure and depressive symptoms in two complementary cohorts. In a neuroimaging cohort of 129 adults, higher CD4(+) TEMRA percentages were associated with lower parahippocampal volume (beta = -0.27, 95% CI [-0.42, -0.11], p(FDR) = 0.022). However, no T cell measure significantly mediated the association between CMV serostatus and parahippocampal volume after FDR correction. In a population-based sample of 5,759 older adults from the Health and Retirement Study, higher CD4(+) TEMRA:na & iuml;ve ratios, reflecting relative expansion of TEMRA cells and depletion of na & iuml;ve CD4(+) T cells, were associated with greater depressive symptom severity across three assessments from 2016 to 2020 (beta = 0.097, 95% CI [0.074, 0.119], p(FDR) < 0.001). Structural equation models indicated significant indirect associations between CMV seropositivity and depressive symptoms through the CD4(+) TEMRA:na & iuml;ve ratio and CD4(+) na & iuml;ve cells. These findings link CMV-related T cell differentiation to reduced parahippocampal volume and increased depressive symptoms. Larger longitudinal studies integrating immunophenotyping, neuroimaging, and clinical outcomes in the same participants are needed to test this proposed pathway.
Maternal perceptions about the importance of motherhood have been found to impact a variety of maternal well-being outcomes, but the impact of the importance of motherhood for the early maternal bonding relationship and on caregiving behaviors has not been explored. Examining attitudinal factors associated with maternal-infant bonding or early caregiving behaviors is essential for the development of effective interventions. Using a clinic-based, urban sample of predominately low-income and diverse pregnant women (N = 177), we used hierarchical linear regression analysis to examine whether perceptions of the importance of motherhood measured during the first trimester predicted maternal-fetal bonding measured during the second trimester, caregiving engagement at two months postpartum, and postpartum bonding measured at six months postpartum, controlling for sociodemographic characteristics and prior outcomes. Perceived importance of motherhood was positively and significantly associated with higher maternal-fetal bonding (b = 1.28, p < .01), greater daily infant engagement (b = 0.57, p < .05), and higher postpartum bonding (b = 0.94, p < .01). Adjusting for prior outcomes to model temporal pathways revealed that the impact of perceived importance of motherhood for early engagement was fully attenuated by maternal-fetal bonding but remained a significant predictor of postpartum bonding. Perceiving motherhood as important promotes early maternal bonding and caregiving behaviors, which are critical for early infant development. Preconception or parenting education programs that strive to promote the importance of motherhood through mindfulness or maternal reflective functioning interventions should be considered. Perceived importance of motherhood is associated with various maternal outcomes, including childbearing intentions and distress following involuntary childlessness and pregnancy loss. Perceptions of the importance of motherhood in early pregnancy are associated with early maternal bonding and caregiving behaviors. Screening for and enhancing women’s attitudes about motherhood and becoming a mother might be a critical target for intervention.
Individuals are increasingly utilizing large language model (LLM)-based tools for mental health guidance and crisis support in place of human experts. While AI technology has great potential to improve health outcomes, insufficient empirical evidence exists to suggest that AI technology can be deployed as a clinical replacement; thus, there is an urgent need to assess and regulate such tools. Regulatory efforts have been made and multiple evaluation frameworks have been proposed, however,field-wide assessment metrics have yet to be formally integrated. In this paper, we introduce a comprehensive online platform that aggregates evaluation approaches and serves as a dynamic online resource to simplify LLM and LLM-based tool assessment: MindBench.ai. At its core, MindBench.ai is designed to provide easily accessible/interpretable information for diverse stakeholders (patients, clinicians, developers, regulators, etc.). To create MindBench.ai, we built off our work developing MINDapps.org to support informed decision-making around smartphone app use for mental health, and expanded the technical MINDapps.org framework to encompass novel large language model (LLM) functionalities through benchmarking approaches. The MindBench.ai platform is designed as a partnership with the National Alliance on Mental Illness (NAMI) to provide assessment tools that systematically evaluate LLMs and LLM-based tools with objective and transparent criteria from a healthcare standpoint, assessing both profile (i.e. technical features, privacy protections, and conversational style) and performance characteristics (i.e. clinical reasoning skills). With infrastructure designed to scale through community and expert contributions, along with adapting to technological advances, this platform establishes a critical foundation for the dynamic, empirical evaluation of LLM-based mental health tools-transforming assessment into a living, continuously evolving resource rather than a static snapshot.
Decisions under risk can be characterized by two complementary but distinct behavioral variables: the propensity to choose the expected value-maximizing option and the tendency to select the risky option. Human reinforcement learning (RL) research has traditionally focused more on the former (maximization), providing many insights into how fundamental features of the learning environment influence behavior. For instance, previous studies have shown that counterfactual feedback about unchosen options exerts a strong influence on maximization. However, comparatively little is known about scenarios in which maximization and risk propensity are assessed orthogonally and simultaneously. To address this gap, we designed a series of RL experiments in which we manipulated key factors, including the feedback information regime (partial vs. complete) and the relative expected values of risky and safe options. Contrary to the conventional assumption that “more information is better,” our results clearly show that, in such contexts, the effect of foregone outcomes on risk preference outweighs its influence on maximization. We further compared RL choices with description-based versions of the same decision problems. While replicating established findings on the description–experience gap, we found that complete information had little impact on its expression, suggesting that this gap does not arise primarily from instant feedback or insufficient outcome sampling.
BACKGROUND:Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS:Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS:BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS:Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS:The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.
