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.

Probing multiple algorithms to calculate brain age: Examining reliability, relations with demographics, and predictive power.

The calculation of so-called "brain age" from structural MRIs has been an emerging biomarker in aging research. Data suggests that discrepancies between chronological age and the predicted age of the brain may be predictive of mortality and morbidity (for review, see Cole, Marioni, Harris, & Deary, 2019). However, with these promising results come technical complexities of how to calculate brain age. Various groups have deployed methods leveraging different statistical approaches, often crafting novel algorithms for assessing this biomarker derived from structural MRIs. There remain many open questions about the reliability, collinearity, and predictive power of different algorithms. Here, we complete a rigorous systematic comparison of three commonly used, previously published brain age algorithms (XGBoost, brainageR, and DeepBrainNet) to serve as a foundation for future applied research. First, using multiple datasets with repeated structural MRI scans, we calculated two metrics of reliability (intraclass correlations and Bland-Altman bias). We then considered correlations between brain age variables, chronological age, biological sex, and image quality. We also calculated the magnitude of collinearity between approaches. Finally, we used machine learning approaches to identify significant predictors across brain age algorithms related to clinical diagnoses of cognitive impairment. Using a large sample (N = 2557), we find all three commonly used brain age algorithms demonstrate excellent reliability (r > .9). We also note that brainageR and DeepBrainNet are reasonably correlated with one another, and that the XGBoost brain age is strongly related to image quality. Finally, and notably, we find that XGBoost brain age calculations were more sensitive to the detection of clinical diagnoses of cognitive impairment. We close this work with recommendations for future research studies focused on brain age.

Autonomic responses to fear conditioning among women with PTSD and dissociation.

BACKGROUND: Individuals with posttraumatic stress disorder (PTSD) demonstrate alterations in autonomic responses to fear conditioning, such as exaggerated startle and poor fear inhibition. However, there is a paucity of research on fear conditioning among individuals with PTSD and dissociative symptoms, which represents 10-30% of those with PTSD. The current study used a fear-potentiated startle (FPS) conditioning paradigm to examine autonomic responses among women with PTSD and a range of dissociative symptoms. METHODS: Participants included 39 women with PTSD and dissociation, and 53 women with PTSD with unknown levels of dissociation. The FPS paradigm consisted of conditioned stimuli associated and not associated with an aversive unconditioned stimulus. FPS response (eyeblink startle), electrocardiogram (ECG), and skin conductance response (SCR) were collected during the FPS paradigm. RESULTS: Compared to the PTSD-unknown dissociation sample, the PTSD-dissociation sample demonstrated significantly lower FPS during the last block of conditioning. Among the PTSD-dissociation sample, higher dissociation scores were associated with decreased FPS and SCR, and higher respiratory sinus arrhythmia (derived from ECG). CONCLUSIONS: Results suggest that autonomic responses to fear conditioning differ depending on the presence and severity of dissociative symptoms. Given that treatment response may differ depending on dissociative symptoms, it is important to understand the mechanisms that underlie different subtypes of PTSD and that may affect treatment response and outcome.

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).

Positive parenting moderates associations between childhood stress and corticolimbic structure.

Childhood stress has a deleterious impact on youth behavior and brain development. Resilience factors such as positive parenting (e.g. expressions of warmth and support) may buffer youth against the negative impacts of stress. We sought to determine whether positive parenting buffers against the negative impact of childhood stress on youth behavior and brain structure and to investigate differences between youth-reported parenting and caregiver-reported parenting. Cross-sectional behavioral and neuroimaging data were analyzed from 482 youth (39% female and 61% male, ages 10-17) who participated in an ongoing research initiative, the Healthy Brain Network (HBN). Regression models found that youth-reported positive parenting buffered against the association between childhood stress and youth behavioral problems (ß = -0.10, P = 0.04) such that increased childhood stress was associated with increased youth behavior problems only for youth who did not experience high levels of positive parenting. We also found that youth-reported positive parenting buffered against the association between childhood stress and decreased hippocampal volumes (ß = 0.07, P = 0.02) such that youth who experienced high levels of childhood stress and who reported increased levels of positive parenting did not exhibit smaller hippocampal volumes. Our work identifies positive parenting as a resilience factor buffering youth against the deleterious impact of stressful childhood experiences on problem behaviors and brain development. These findings underscore the importance of centering youth perspectives of stress and parenting practices to better understand neurobiology, mechanisms of resilience, and psychological well-being.

Childhood adversity and COVID-19 outcomes in the UK Biobank.

OBJECTIVES: This study aims to investigate the association between childhood adversity and COVID-19-related hospitalisation and COVID-19-related mortality in the UK Biobank. DESIGN: Cohort study. SETTING: UK. PARTICIPANTS: 151 200 participants in the UK Biobank cohort who had completed the Childhood Trauma Screen were alive at the start of the COVID-19 pandemic (January 2020) and were still active in the UK Biobank when hospitalisation and mortality data were most recently updated (November 2021). MAIN OUTCOME MEASURES: COVID-19-related hospitalisation and COVID-19-related mortality. RESULTS: Higher self-reports of childhood adversity were related to greater likelihood of COVID-19-related hospitalisation in all statistical models. In models adjusted for age, ethnicity and sex, childhood adversity was associated with an odds ratio (OR) of 1.227 of hospitalisation (95% CI 1.153 to 1.306, childhood adversity z=6.49, p<0.005) and an OR of 1.25 of a COVID-19-related death (95% CI 1.11 to 1.424, childhood adversity z=3.5, p<0.005). Adjustment for potential confounds attenuated these associations, although associations remained statistically significant. CONCLUSIONS: Childhood adversity was significantly associated with COVID-19-related hospitalisation and COVID-19-related mortality after adjusting for sociodemographic and health confounders. Further research is needed to clarify the biological and psychosocial processes underlying these associations to inform public health intervention and prevention strategies to minimise COVID-19 disparities.

Neural signatures of emotional inference and experience align during social consensus.

Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended "ground truth" emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity- the first predicted the "ground truth" (r = 0.50, p < 0.0001) and the second predicted observer inferences (r = 0.53, p < 0.0001). When individuals make more accurate inferences, there is greater moment-by-moment concordance between these two models, suggesting that an observer's brain activity contains latent representations of other people's emotional states. Using naturalistic socioemotional stimuli and machine learning, we developed reliable brain signatures that predict what an observer thinks about a target, what the target thinks about themselves, and the correspondence between them. These signatures can be applied in clinical data to better our understanding of socioemotional dysfunction.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer.

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

Combating the Conspiracy of Silence: Clinician Recommendations for Talking About Racism-Related Events With Youth of Color.

Graphic videos of race-based violence, including police brutality toward Black people and anti-Asian hate crimes, have exploded over the past year. While documentation of these horrific acts has brought visibility to the pervasiveness of racial discrimination, it has also resulted in youth of color being exposed to racial stressors more than ever before across numerous social media and news platforms.1-3 Beyond the significant race-related stress already experienced by youth in school contexts,4 this increased exposure to racism via media is concerning, as both direct and vicarious exposure to racial discrimination can compromise psychological well-being of youth and cause trauma-like symptoms, such as intrusive thoughts, vigilance, and depression.3,5.

Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset.

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.