Probing connections between social connectedness, mortality risk, and brain age: A preregistered study.

Many lifestyle and psychosocial factors are associated with a longer lifespan; central among these is social connectedness, or the feeling of belongingness, identification, and bond as part of meaningful human relationships. Decades of research have established that social connectedness is related not only to better mental health (e.g., less loneliness and depression) but also to improved physical health (e.g., decreased inflammatory markers, reduced cortisol activity). Recent methodological advances allow for the investigation of a novel marker of biological health by deriving a predicted "age of the brain" from a structural neuroimaging scan. Discrepancies between a person's algorithm-predicted brain-age and chronological age (i.e., the brain-age gap) have been found to predict mortality and psychopathology risk with accuracy rivaling other known measures of aging. This preregistered investigation uses the Midlife in the United States (MIDUS) study to examine connections between the quality of social connections, the brain-age gap, and markers of mortality risk to understand the longevity-promoting associations of social connectedness from a novel biological vantage point. While social connectedness was associated with markers of mortality risk (number of chronic conditions and ability to perform activities of daily living), our models did not find significant links between social connectedness and the brain-age gap, or the brain-age gap and mortality risk. Supplemental and sensitivity analyses suggest alternate approaches to investigating these associations and overcoming limitations. While plentiful evidence underscores that being socially connected is good for the mind, future research should continue to consider whether it impacts neural markers of aging and longevity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integrating data science and neuroscience in developmental psychopathology: Formative examples and future directions.

This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We first review elements of our research program investigating how early life adversity shapes neurodevelopment and may convey risk for psychopathology. We then illustrate three ways that data science techniques (e.g., machine learning) can support developmental psychopathology research, such as by distinguishing between common and diverse developmental outcomes after stress exposure. Finally, we discuss logistical and conceptual refinements that may aid the field moving forward. Throughout the piece, we underscore the profound impact of Dr Dante Cicchetti, reflecting on how his work influenced our own, and gave rise to the field of developmental psychopathology.

Pathways to adolescent social anxiety: Testing interactions between neural social reward function and perceived social threat in daily life.

Recent theories suggest that for youth highly sensitive to incentives, perceiving more social threat may contribute to social anxiety (SA) symptoms. In 129 girls (ages 11-13) oversampled for shy/fearful temperament, we thus examined how interactions between neural responses to social reward (vs. neutral) cues (measured during anticipation of peer feedback) and perceived social threat in daily peer interactions (measured using ecological momentary assessment) predict SA symptoms two years later. No significant interactions emerged when neural reward function was modeled as a latent factor. Secondary analyses showed that higher perceived social threat was associated with more severe SA symptoms two years later only for girls with higher basolateral amygdala (BLA) activation to social reward cues at baseline. Interaction effects were specific to BLA activation to social reward (not threat) cues, though a main effect of BLA activation to social threat (vs. neutral) cues on SA emerged. Unexpectedly, interactions between social threat and BLA activation to social reward cues also predicted generalized anxiety and depression symptoms two years later, suggesting possible transdiagnostic risk pathways. Perceiving high social threat may be particularly detrimental for youth highly sensitive to reward incentives, potentially due to mediating reward learning processes, though this remains to be tested.

Altered topological structure of the brain white matter in maltreated children through topological data analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

We employ topological data analysis (TDA) to investigate altered topological structures in the white matter of children who have experienced maltreatment. Persistent homology in TDA is utilized to quantify topological differences between typically developing children and those subjected to maltreatment, using magnetic resonance imaging and diffusion tensor imaging data. The Wasserstein distance is computed between topological features to assess disparities in brain networks. Our findings demonstrate that persistent homology effectively characterizes the altered dynamics of white matter in children who have suffered maltreatment.

Examining the reliability of brain age algorithms under varying degrees of participant motion.

Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years.

Background: A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth. Methods: To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Results: Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. Conclusion: Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.

Cortisol's diurnal rhythm indexes the neurobiological impact of child adversity in adolescence.

Adverse early life experiences, such as child maltreatment, shapes hypothalamic-pituitary-adrenal (HPA) activity. The impact of social context is often probed through laboratory stress reactivity, yet child maltreatment is a severe form of chronic stress that recalibrates even stable or relatively inflexible stress systems such as cortisol's diurnal rhythm. This study was designed to determine how different social contexts, which place divergent demands on children, shape cortisol's diurnal rhythm. Participants include 120 adolescents (9-14 years), including 42 youth with substantiated child physical abuse. Up to 32 saliva samples were obtained in the laboratory, on days youth stayed home, and on school days. A 3-level hierarchical linear model examined cortisol within each day and extracted the diurnal rhythm at level 1; across days at level 2; and between-individual differences in cortisol and its rhythm at level 3. While cortisol's diurnal rhythm was flattened when youth were in the novel laboratory context, the impact of maltreatment was observed within the home context such that maltreated children had persistently flattened diurnal rhythms. The effect of maltreatment overlapped with current chronic interpersonal family stress. Results are consistent with the idea that maltreatment exerts a robust, detrimental impact on the HPA axis and are interpreted in the context of less flexibility and rhythmicity. The HPA axis adapts by encoding signifiers of relevant harsh or unpredictable environments, and the extreme stress of physical abuse in the family setting may be one of these environments which calibrates the developing child's stress responsive system, even throughout a developmental stage in which the family takes on diminishing importance.