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BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Caregivers' goals influence their interactions with their children. In this preregistered study, we examined whether directing parents to "teach" their baby versus "learn" from their baby influenced the extent to which they engaged in intrusive (e.g., controlling, adult-centered rather than child-centered), sensitive, warm, or cognitively stimulating caregiving behaviors. Mothers and their 6-month-old infants (N = 66; 32 female infants) from the San Francisco Bay Area participated in a 10-min "free-play" interaction, coded in 2-min epochs for degree of parental intrusiveness. Prior to the final epoch, mothers were randomly assigned to receive instructions to focus on (a) "teaching" something to their infant or (b) "learning" something from their infant. A control group of mothers received no instructions. Analyses of within-person changes in intrusive behavior from before to after receiving these instructions indicated that mothers assigned to teach their infant increased in intrusiveness whereas mothers assigned to learn from their infant and mothers in the control group did not significantly change in intrusiveness. The study provides experimental evidence that caregivers' explicit goals to teach infants result, on average, in more controlling and adult-centered caregiving behavior.

Suicide is the second leading cause of death among adolescents. While clinicians and researchers have begun to recognize the importance of considering multidimensional factors in understanding risk for suicidal thoughts and behaviors (STBs) during this developmental period, the role of puberty has been largely ignored. In this review, we contend that the hormonal events that occur during puberty have significant effects on the organization and development of brain systems implicated in the regulation of social stressors, including amygdala, hippocampus, striatum, medial prefrontal cortex, orbitofrontal cortex, and anterior cingulate cortex. Guided by previous experimental work in adults, we also propose that the influence of pubertal hormones and social stressors on neural systems related to risk for STBs is especially critical to consider in adolescents with a neurobiological sensitivity to hormonal changes. Furthermore, facets of the pubertal transition, such as pubertal timing, warrant deeper investigation and may help us gain a more comprehensive understanding of sex differences in the neurobiological and psychosocial mechanisms underlying adolescent STBs. Ultimately, advancing our understanding of the pubertal processes that contribute to suicide risk will improve early detection and facilitate the development of more effective, sex-specific, psychiatric interventions for adolescents.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Exposure to environmental pollutants has been associated with cellular aging in children and adolescents. Individuals may vary, however, in their sensitivity or vulnerability to the effects of environmental pollutants. Larger hippocampal volume has emerged as a potential index of increased sensitivity to social contexts. In exploratory analyses (N = 214), we extend work in this area by providing evidence that larger hippocampal volume in early adolescence reflects increased sensitivity to the effect of neighborhood pollution burden on telomere length (standardized [Beta] = -0.40, 95% CI[-0.65, -0.15]). In contrast, smaller hippocampal volume appears to buffer this association (standardized [Beta] = 0.02). In youth with larger hippocampal volume, pollution burden was indirectly associated with shorter telomere length approximately 2 years later through shorter telomere length at baseline (indirect standardized [Beta] = -0.25, 95% CI[-0.40, 0.10]). For these youth, living in high or low pollution-burdened neighborhoods may predispose them to develop shorter or longer telomeres, respectively, later in adolescence.

Exposure to ozone is a well-documented risk factor for negative physical health outcomes but has been considered less frequently in the context of socioemotional health. We examined whether levels of neighborhood ozone predicted trajectories of depressive symptoms over a four-year period in 213 adolescents (ages 9-13 years at baseline; 57% female; 53% of minority race/ethnicity). Participants self-reported depressive and other types of psychopathology symptoms up to 3 times, and their home addresses were used to compute ozone levels in their census tract. Possible confounding variables, including personal, family, and neighborhood characteristics, were also assessed. We found that higher ozone predicted steeper increases in depressive symptoms across adolescent development, a pattern that was not observed for other forms of psychopathology symptoms. These findings underscore the importance of considering ozone exposure in understanding trajectories of depressive symptoms across adolescence.

Suicidal ideation (SI) and non-suicidal self-injury (NSSI) are two distinct yet often co-occurring risk factors for suicide deaths in adolescents. Elucidating the neurobiological patterns that specifically characterize SI and NSSI in adolescents is needed to inform the use of these markers in intervention studies and to develop brain-based treatment targets. Here, we clinically assessed 70 adolescents-49 adolescents with depression and 21 healthy controls-to determine SI and NSSI history. Twenty-eight of the depressed adolescents had a history of SI and 29 had a history of NSSI (20 overlapping). All participants underwent a resting-state fMRI scan. We compared groups in network coherence of subdivisions of the central executive network (CEN), default mode network (DMN), and salience network (SN). We also examined group differences in between-network connectivity and explored brain-behavior correlations. Depressed adolescents with SI and with NSSI had lower coherence in the ventral DMN compared to those without SI or NSSI, respectively, and healthy controls (all ps

BackgroundAdolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC).MethodsIn this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence.ResultsNascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping.ConclusionsDelineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.

Adolescence is characterized by rapid brain development in white matter (WM) that is attributed in part to surges in gonadal hormones. To date, however, there have been few longitudinal investigations relating changes in gonadal hormones and WM development in adolescents. We acquired diffusion-weighted MRI to estimate mean fractional anisotropy (FA) from 10 WM tracts and salivary testosterone from 51 females and 29 males (ages 9-14 years) who were matched on pubertal stage and followed, on average, for 2 years. We tested whether interactions between sex and changes in testosterone levels significantly explained changes in FA. We found positive associations between changes in testosterone and changes in FA within the corpus callosum, cingulum cingulate, and corticospinal tract in females (all ps

AimEarly life adversity leads to enduring effects on physical and mental health, school performance and other outcomes. We sought to identify potentially modifiable factors associated with socioeconomic adversity in early life.MethodsWe enrolled 1503 pregnant women aged 16-40 years, without pregnancy complications or pre-existing conditions from Shelby County, Tennessee. Social, familial and economic variables were analysed using principal components (PCs) analyses to generate the Socioeconomic Adversity Index (SAI). This was replicated using the National Survey of Children's Health (NSCH). Health and social outcomes were compared across the quintile groups defined by SAI values at the county, state and national levels.ResultsSignificant differences occurred across the SAI Quintile-1 to Quintile-5 groups in marital status, household structure, annual income, education and health insurance. Significantly worse health and social outcomes occurred in the lower versus higher SAI quintiles, including maternal depression, parental incarceration, child's birthweight and potential for child abuse. Maternal age and race also differed significantly across the SAI quintiles.ConclusionModifiable factors contributing to socioeconomic adversity in early life included marital status, household structure, annual income, education and health insurance. Those exposed to greater socioeconomic adversity as defined by SAI values had significantly worse maternal and child outcomes.

Although behavioral sensitivity to reward predicts the onset and course of mania in bipolar disorder, the evidence for neural abnormalities in reward processing in bipolar disorder is mixed. To probe neural responsiveness to anticipated and received rewards in the context of bipolar disorder, we scanned individuals with remitted bipolar I disorder (n = 24) and well-matched controls (n = 24; matched for age and gender) using Functional Magnetic Resonance Imaging (FMRI) during a Monetary Incentive Delay (MID) task. Relative to controls, the bipolar group showed reduced NAcc activity during anticipation of gains. Across groups, this blunting correlated with individual differences in impulsive responses to positive emotions (Positive Urgency), which statistically accounted for the association of blunted NAcc activity with bipolar diagnosis. These results suggest that blunted NAcc responses during gain anticipation in the context of bipolar disorder may reflect individual differences in Positive Urgency. These findings may help resolve discrepancies in the literature on neural responses to reward in bipolar disorder, and clarify the relationship between brain activity and the propensity to experience manic episodes.