AIMS: Disrupted affective processes are core features of psychosis; yet emotion reactivity and emotion regulation impairments have not been fully characterized in individuals at clinical high-risk for developing psychosis (CHR) or adolescents diagnosed with a psychotic disorder (AOP). Characterizing these impairments may provide a fuller understanding of factors contributing to psychosis risk and psychosis onset. Using cross-sectional and longitudinal data, we evaluated (1) group-level effects of emotion reactivity and regulation, (2) stability of group-level effects over time and age, (3) relationships between emotion reactivity and regulation, and (4) associations between these measures and psychosocial functioning and clinical symptomatology. METHODS: Eighty-seven participants (CHR = 32, TD = 42, AOP = 13; 12-25 years, 1-5 visits) completed the Emotion Reactivity Scale, Difficulties in Emotion Regulation Scale, and Emotion Regulation Questionnaire. We assessed psychotic symptoms with the Structured Interview for Prodromal Syndromes and measured real-world functioning with the Global Functioning: Social and Role Scales. We used analysis of variance to assess Aim 1 and linear mixed models to address Aims 2-4. RESULTS: CHR and AOP endorsed experiencing heightened levels of emotion reactivity and greater difficulty utilizing emotion regulation strategies compared to TD. These impairments were stable across time and adolescent development. Greater levels of emotion reactivity were associated with greater emotion regulation impairments. Greater impairments in emotion regulation were associated with lower social functioning and greater negative symptom severity. CONCLUSION: Therapeutic interventions designed to reduce emotion reactivity and improve ones ability to utilize emotion regulation strategies may be effective in reducing clinical symptomatology and improving real-world functioning in CHR and AOP.

The microbiome of flowers (anthosphere) is an understudied compartment of the plant microbiome. Within the flower, petals represent a heterogeneous environment for microbes in terms of resources and environmental stress. Yet, little is known of drivers of structure and function of the epiphytic microbial community at the within-petal scale. We characterized the petal microbiome in two co-flowering plants that differ in the pattern of ultraviolet (UV) absorption along their petals. Bacterial communities were similar between plant hosts, with only rare phylogenetically distant species contributing to differences. The epiphyte community was highly culturable (75% of families) lending confidence in the spatially explicit isolation and characterization of bacteria. In one host, petals were heterogeneous in UV absorption along their length, and in these, there was a negative relationship between growth rate and position on the petal, as well as lower UV tolerance in strains isolated from the UV-absorbing base than from UV reflecting tip. A similar pattern was not seen in microbes isolated from a second host whose petals had uniform patterning along their length. Across strains, the variation in carbon usage and chemical tolerance followed common phylogenetic patterns. This work highlights the value of petals for spatially explicit explorations of bacteria of the anthosphere.

Background

The neurodevelopmental model of psychosis was established over 30 years ago; however, the developmental influence on psychotic symptom expression - how age affects clinical presentation in first-episode psychosis - has not been thoroughly investigated.

Methods

Using generalized additive modeling, which allows for linear and non-linear functional forms of age-related change, we leveraged symptom data from a large sample of antipsychotic-naïve individuals with first-episode psychosis (N = 340, 12-40 years, 1-12 visits), collected at the University of Pittsburgh from 1990 to 2017. We examined relationships between age and severity of perceptual and non-perceptual positive symptoms and negative symptoms. We tested for age-associated effects on change in positive or negative symptom severity following baseline assessment and explored the time-varying relationship between perceptual and non-perceptual positive symptoms across adolescent development.

Results

Perceptual positive symptom severity significantly decreased with increasing age (F = 7.0, p = 0.0007; q = 0.003) while non-perceptual positive symptom severity increased with age (F = 4.1, p = 0.01, q = 0.02). Anhedonia severity increased with increasing age (F = 6.7, p = 0.00035; q = 0.0003), while flat affect decreased in severity with increased age (F = 9.8, p = 0.002; q = 0.006). Findings remained significant when parental SES, IQ, and illness duration were included as covariates. There were no developmental effects on change in positive or negative symptom severity (all p > 0.25). Beginning at age 18, there was a statistically significant association between severity of non-perceptual and perceptual symptoms. This relationship increased in strength throughout adulthood.

Conclusions

These findings suggest that as maturation proceeds, perceptual symptoms attenuate while non-perceptual symptoms are enhanced. Findings underscore how pathological brain-behavior relationships vary as a function of development.

PURPOSE: We examined whether interindividual differences in naturalistic sleep patterns correlate with any deviations from typical brain aging. METHODS: Our sample consisted of 251 participants without current psychiatric diagnoses (9-25 years; mean [standard deviation] = 17.4 ± 4.52 yr; 58% female) drawn from the Neuroimaging and Pediatric Sleep Databank. Participants completed a T1-weighted structural magnetic resonance imaging scan and 5-7 days of wrist actigraphy to assess naturalistic sleep patterns (duration, timing, continuity, and regularity). We estimated brain age from extracted structural magnetic resonance imaging indices and calculated brain age gap (estimated brain age-chronological age). Robust regressions tested cross-sectional associations between brain age gap and sleep patterns. Exploratory models investigated moderating effects of age and biological gender and, in a subset of the sample, links between sleep, brain age gap, and depression severity (Patient-Reported Outcomes Measurement Information System Depression). RESULTS: Later sleep timing (midsleep) was associated with more advanced brain aging (larger brain age gap), β = 0.1575, puncorr = .0042, pfdr = .0167. Exploratory models suggested that this effect may be driven by males, although the interaction of gender and brain age gap did not survive multiple comparison correction (β = 0.2459, puncorr = .0336, pfdr = .1061). Sleep duration, continuity, and regularity were not significantly associated with brain age gap. Age did not moderate any brain age gap-sleep relationships. In this psychiatrically healthy sample, depression severity was also not associated with brain age gap or sleep. DISCUSSION: Later midsleep may be one behavioral cause or correlate of more advanced brain aging, particularly among males. Future studies should examine whether advanced brain aging and individual differences in sleep precede the onset of suboptimal cognitive-emotional outcomes in adolescents.

Probing naturally-occurring, reciprocal genomic copy number variations (CNVs) may help us understand mechanisms that underlie deviations from typical brain development. Cross-sectional studies have identified prominent reductions in cortical surface area (SA) and increased cortical thickness (CT) in 22q11.2 deletion carriers (22qDel), with the opposite pattern in duplication carriers (22qDup), but the longitudinal trajectories of these anomalies-and their relationship to clinical symptomatology-are unknown. Here, we examined neuroanatomic changes within a longitudinal cohort of 261 22q11.2 CNV carriers and demographically-matched typically developing (TD) controls (84 22qDel, 34 22qDup, and 143 TD; mean age 18.35, ±10.67 years; 50.47% female). A total of 431 magnetic resonance imaging scans (164 22qDel, 59 22qDup, and 208 TD control scans; mean interscan interval = 20.27 months) were examined. Longitudinal FreeSurfer analysis pipelines were used to parcellate the cortex and calculate average CT and SA for each region. First, general additive mixed models (GAMMs) were used to identify regions with between-group differences in developmental trajectories. Secondly, we investigated whether these trajectories were associated with clinical outcomes. Developmental trajectories of CT were more protracted in 22qDel relative to TD and 22qDup. 22qDup failed to show normative age-related SA decreases. 22qDel individuals with psychosis spectrum symptoms showed two distinct periods of altered CT trajectories relative to 22qDel without psychotic symptoms. In contrast, 22q11.2 CNV carriers with autism spectrum diagnoses showed early alterations in SA trajectories. Collectively, these results provide new insights into altered neurodevelopment in 22q11.2 CNV carriers, which may shed light on neural mechanisms underlying distinct clinical outcomes.

STUDY OBJECTIVES: Healthy sleep is important for adolescent neurodevelopment, and relationships between brain structure and sleep can vary in strength over this maturational window. Although cortical gyrification is increasingly considered a useful index for understanding cognitive and emotional outcomes in adolescence, and sleep is also a strong predictor of such outcomes, we know relatively little about associations between cortical gyrification and sleep. We aimed to identify developmentally invariant (stable across age) or developmentally specific (observed only during discrete age intervals) gyrification-sleep relationships in young people. METHODS: A total of 252 Neuroimaging and Pediatric Sleep Databank participants (9-26 years; 58.3% female) completed wrist actigraphy and a structural MRI scan. Local gyrification index (lGI) was estimated for 34 bilateral brain regions. Naturalistic sleep characteristics (duration, timing, continuity, and regularity) were estimated from wrist actigraphy. Regularized regression for feature selection was used to examine gyrification-sleep relationships. RESULTS: For most brain regions, greater lGI was associated with longer sleep duration, earlier sleep timing, lower variability in sleep regularity, and shorter time awake after sleep onset. lGI in frontoparietal network regions showed associations with sleep patterns that were stable across age. However, in default mode network regions, lGI was only associated with sleep patterns from late childhood through early-to-mid adolescence, a period of vulnerability for mental health disorders. CONCLUSIONS: We detected both developmentally invariant and developmentally specific ties between local gyrification and naturalistic sleep patterns. Default mode network regions may be particularly susceptible to interventions promoting more optimal sleep during childhood and adolescence.

Study objectives

Structural brain maturation and sleep are complex processes that exhibit significant changes over adolescence and are linked to many physical and mental health outcomes. We investigated whether sleep-gray matter relationships are developmentally invariant (i.e. stable across age) or developmentally specific (i.e. only present during discrete time windows) from late childhood through young adulthood.

Methods

We constructed the Neuroimaging and Pediatric Sleep Databank from eight research studies conducted at the University of Pittsburgh (2009-2020). Participants completed a T1-weighted structural MRI scan (sMRI) and 5-7 days of wrist actigraphy to assess naturalistic sleep. The final analytic sample consisted of 225 participants without current psychiatric diagnoses (9-25 years). We extracted cortical thickness and subcortical volumes from sMRI. Sleep patterns (duration, timing, continuity, regularity) were estimated from wrist actigraphy. Using regularized regression, we examined cross-sectional associations between sMRI measures and sleep patterns, as well as the effects of age, sex, and their interaction with sMRI measures on sleep.

Results

Shorter sleep duration, later sleep timing, and poorer sleep continuity were associated with thinner cortex and altered subcortical volumes in diverse brain regions across adolescence. In a discrete subset of regions (e.g. posterior cingulate), thinner cortex was associated with these sleep patterns from late childhood through early-to-mid adolescence but not in late adolescence and young adulthood.

Conclusions

In childhood and adolescence, developmentally invariant and developmentally specific associations exist between sleep patterns and gray matter structure, across brain regions linked to sensory, cognitive, and emotional processes. Sleep intervention during specific developmental periods could potentially promote healthier neurodevelopmental outcomes.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.