Adolescence is a period of heightened neuroimmune plasticity, during which inflammatory activity may be associated with neural processes relevant to depression risk. To obtain a range of symptoms, adolescents (N = 182; 13-18-years-old) were recruited across three groups: remitted major depressive disorder (MDD; n = 88), current MDD (n = 33), and healthy controls (n = 61). At baseline, participants completed clinical assessments and provided resting-state electroencephalography (EEG) data and saliva for inflammatory assays. Clinical follow-up assessments were completed at 6 and 12 months. Confirmatory factor analysis of IL-1β, IL-6, IL-8, and TNF-α supported a latent pro-inflammatory activity factor (χ2(17) = 21.82, p = 0.192; CFI = 0.99; RMSEA = 0.04; ω = 0.86). Periodic theta power was extracted from the power spectrum and averaged across frontal and centroparietal electrode clusters. Linear mixed-effects models showed that neither inflammatory activity nor theta power were prospectively associated with depressive symptoms. However, inflammatory activity interacted with centroparietal theta power to predict future depressive symptoms (B = -0.395, p = 0.010), such that higher inflammatory activity was associated with greater follow-up depressive symptoms among adolescents with lower centroparietal theta power. The interaction between frontal theta power and inflammatory activity was non-significant (B = 0.271, p = 0.054). Among adolescents with remitted MDD, Cox proportional hazards models indicated that higher centroparietal theta interacted with inflammatory activity to predict depression recurrence (Hazard Ratio [HR] = 0.22, 95% CI: 0.07, 0.72), whereas frontal theta did not interact with inflammatory activity to predict recurrence (HR = 2.42, 95% CI: 0.86, 6.82). Findings suggest that spontaneous theta oscillations may provide a mechanistically grounded framework for stratifying risk and guiding targeted interventions in the context of inflammation-related depression among adolescents.

Background:Exposure to stressors during early childhood has been linked to irritability and subsequent mental health problems; however, little work has examined the unique and combined effects of distinct stress exposures on trajectories of childhood irritability or adolescent mental health symptoms. Methods:Six components of family stress were assessed via maternal report at child age 5. Irritability was measured longitudinally at ages 7, 9, and 11. Adolescents self-reported on anxiety, depression, and externalizing symptoms at age 13-20. Latent growth mixture modeling examined classes of irritability. Bayesian kernel machine regression (BKMR) examined unique and shared effects of stress components on class assignment and to continuously measured irritability at individual age points (N = 472). Causal mediation tested if irritability mediated any stress-related mental health problems. Results:Latent growth mixture modeling identified high and low irritability classes. A positive joint mixture effect of stressors on irritability was detected, with additive contributions from intimate partner violence, material hardship, perceived stress, and maternal demoralization. Intimate partner violence independently predicted high irritability over time and irritability at age 7, and maternal perceived stress independently predicted irritability at age 11. Neither childhood irritability nor early stress predicted self-reported adolescent mental health symptoms. Conclusion:Early exposure to familial stressors are risk markers for childhood irritability. Interventions targeting intimate partner violence and co-occurring stressors could help mitigate persistent irritability, a well-known public health concern related to children's development.

Adolescent suicide rates have risen over the past two decades, underscoring the need for improved risk detection strategies. Although natural language processing (NLP) tools are increasingly used to flag suicide-related content, little is known about how such approaches perform on adolescents' smartphone communications. Addressing this gap, this study leverages passively collected smartphone data to identify suicide-related language in adolescents' keyboard usage via NLP. We developed a lexicon of suicide-related adolescent language and validated it with labeled data (N = 171,468 text entries; e.g., messages, web searches), demonstrating higher performance in identifying suicide-related text than few-shot prediction with large language models (LLMs) and lexicons not designed for youth. Across two independent cohorts at elevated suicide risk (Ns = 208 & 257; >6 million text entries), lifetime suicidal thoughts and behaviors (STB) and current suicidal ideation were associated with increased frequency of smartphone suicide-related language. Human coding indicated varied language, including authentic first-person current suicidal ideation (14.5%) and jokes or hyperbole (20.2%). Compared with the lexicon alone, human coding of suicide-related entries with first-person language showed stronger associations with STB history. These findings highlight that effective NLP-based tools for suicide prevention will require more nuanced and context-specific approaches to better distinguish suicidal intent.

Abstract Rising adolescent suicide rates underscore an urgent need for better detection of short-term risk in the days and hours leading up to attempts. Passive smartphone sensing of language offers a promising approach, yet its performance during vulnerable periods remains unclear. This case study examined five adolescents (3 male, 2 female) who were hospitalized for suicidal crises while enrolled in a smartphone sensing study. Participants contributed outgoing text entries over six months (M = 21,000/person), which were analyzed using natural language processing (NLP) to assess suicide-related content, sentiment, and topics (e.g., school, treatment). In addition, clinicians conducted qualitative reviews of the text entries to identify potential risk events. Results showed that 4 of 5 adolescents exhibited increased suicide-related language and negative sentiment during the 10 days prior to psychiatric hospitalization. Especially elevated suicide language was found within 5 days of hospitalization, while negative sentiment peaked between 5–10 days prior to hospitalization. These signals, however, also occurred outside of acute risk periods, highlighting the challenge of separating suicide risk from distress more generally. Clinical annotations revealed that suicidal thoughts and behaviors often co-occurred with NLP signals of suicide-related language, and topic models identified clinically relevant language related to substance use and psychiatric treatment. Clinical annotations of interpersonal conflict and school stressors were not identified by topic models. Discrepancies largely originated from the inability of NLP methods to infer context (e.g., text conversation history). Although smartphone language data showed low missingness and some sensitivity to acute crises, enhancing contextual analysis is essential for personalized risk detection.

LGBTQIA+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, and related identities) individuals in science face unique career challenges. We surveyed a large sample (N = 428) of neuroscientists, uniquely capturing a diverse international population (hundreds of participants from Europe and the USA; more than 60 transgender participants). In the USA, compared to Europe, we found higher institutional support and a higher likelihood of being out of the closet in academic settings. However, participants based in the USA also reported more negative workplace experiences. A concerning 15% of the participants reported experiencing harassment at their workplace. Thematic analysis of qualitative responses showed that reasons for not being out varied by group; for example, asexual people were more likely to mention a lack of understanding, while transgender people reported safety concerns. The majority of participants (67.3%) felt that legislation affected decisions within their scientific career, with most of these participants reporting moving away from locations unsupportive of LGBTQIA+ individuals or forgoing career opportunities in certain locations. Overall, we show differential experiences of neuroscientists between the USA and Europe, as well as between identities. While our results demonstrate the challenges many LGBTQIA+ individuals in neuroscience face, they also put forward actionable recommendations for institutions that could vastly improve the lives and careers of LGBTQIA+ neuroscientists.

Adolescent smartphone language provides a lens into negative self-referential thinking, which is central to major depressive disorder (MDD). Prior studies have linked language features, including negative sentiment and first-person pronouns, to mood and depression, suggesting that naturalistic language may identify who is at risk and when that risk is greatest. However, studies of adolescent smartphone social communication have typically relied on rule-based models not validated for heterogeneous, context-sensitive language. To address this gap, we determined whether transformer models, including large language models, optimized detection of depression risk in extensive adolescent smartphone text data. In this study, 223 adolescents (Mage = 16.43 years, current MDD = 37, remitted MDD = 103, healthy controls = 83) installed a smartphone app, which prompted participants to provide mood ratings once a day and acquired all keyboard inputs over 12 months (mean text entries per participant = 17,683). Ten thousand text entries were double-coded for entry-level sentiment (positive, neutral, negative) and self-reference for training and testing traditional rule-based approaches (VADER, pronoun counts) and transformer approaches, including GPT-4-mini. In the full data set, between-participant associations with depressive symptoms and within-participant relations to daily mood and depressive episodes were examined. Fine-tuned transformer models best aligned with human-coded sentiment labels (F1GPT4-MINI = .84, F1VADER = .59) and accurately detected self-reference (F1T5-BASE = .97). Adolescents with current and remitted MDD exhibited more transformer-based negative self-referential language than healthy controls (ORs = 1.38, 1.26). Increased negative self-referential language predicted worse next-day mood (β = -.033, p < .001) and a higher likelihood of next-week depressive episodes (OR = 1.66, 95% confidence interval [1.06, 2.62], p = .028), although the association with depressive episodes was not robust to sensitivity analyses. Transformer models may be integrated into digital mental health care to detect when youth are at risk for depression. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Major depressive disorder (MDD) among adolescents is recurrent and characterized by persistent emotion-processing biases. Adolescents (N=161; ages 13-18-years-old; remitted MDD=90, healthy controls=71) completed baseline and 6-month clinical assessments. Additionally, at baseline, participants completed the Facial Recognition Task while EEG data were recorded. The late positive potential (LPP) and effective connectivity during negative emotion processing were examined in relation to 6-month depressive symptoms and behavioral withdrawal (homestay from passive GPS). No group differences in LPP emerged. However, remitted MDD showed stronger right precuneus-superior frontal gyrus (theta-alpha) to sad faces and weaker left precuneus-SFG (alpha-beta) to angry faces relative to controls. Greater right precuneus-SFG connectivity during sad faces predicted depressive symptom severity (b=0.221, p=.025) and increased homestay (b=242.78, p=.048). Thus, altered precuneus-SFG connectivity during sad face processing may serve as a neural marker of depression risk, which could support early identification approaches and personalized interventions.

There is growing interest in identifying brain function underlying adolescent cognition, personality, and psychopathology. One promising approach is Precision Functional Mapping (PFM) of MRI functional connectivity, a data-intensive method for characterizing individualized brain networks. Foundational studies suggest that PFM can detect stable, task-responsive, and clinically relevant networks. Studies demonstrate that both functional connectivity reliability and network stability improve with increasing data quantity, although benchmark estimates vary across populations, preprocessing pipelines, and MRI acquisition approaches. Accordingly, it is important to understand how PFM performs in adolescent populations and with multi-echo fMRI acquisition. In a case study of eight youth (ages 10-17), we applied PFM to 80 minutes of combined resting-state and task-based fMRI. The resulting networks were highly modular, consistent with adult templates, and without evidence of structural registration artifacts. Functional connectivity reliability compared favorably to prior single-echo studies, with multivariate similarity and ICC estimates showing early stabilization around 10-15 min despite continued improvement with additional data. Trait-like stability increased gradually with acquisition time, and a Bayesian algorithm (MS-HBM) demonstrated higher stability than Infomap. Across algorithms, stability was greatest in the somatosensory, auditory, visual, and parietal networks. Furthermore, when evaluating task-based responses to threat and attention paradigms, only the auditory network consistently benefited from individualized mapping over group template networks. These findings suggest that, with constrained scanning time, PFM is especially effective for characterizing sensory and perceptual networks in adolescents. Bridging the methodological divide between deeply sampled individual cases and large-scale developmental studies will require further innovation and validation.

Background:Childhood irritability is a transdiagnostic risk marker of neurodevelopmental problems. Polycyclic aromatic hydrocarbons (PAHs) and psychosocial stressors have been independently associated with altered emotional and behavioral symptoms akin to irritability, but the joint effects of PAHs and maternal psychological distress on trajectories of irritability symptoms have not been directly examined. We hypothesized that combined exposure to prenatal PAHs and maternal psychological distress would be associated with higher and more persistent irritability across childhood. Methods:Prenatal PAH was measured via personal air monitoring, and maternal psychological distress was measured via the Psychiatric Epidemiology Research Instrument-Demoralization, both measured during mothers' third trimester. Children's irritability symptoms were measured via a novel composite of item-level responses from the Children's Behavior Checklist and Conner's Parent Report Scale at ages 7, 9, and 11. Psychometric robustness of the irritability composite scores (N = 511) were evaluated. Latent growth curve modeling (LGM) examined interactive effects of PAH and maternal distress on irritability trajectories (N = 394 with complete exposure and outcome data). Follow-up linear regression analyses tested associations at ages 9 and 11. Results:The irritability composite score was unitary and demonstrated good psychometric properties. Combined exposure to prenatal PAH and maternal distress exposure was associated with higher irritability at ages 7, 9 and 11, but not with change in irritability from age 7-11. Conclusions:The novel irritability composite had a unitary factor structure reflecting a single irritability construct. Combined prenatal exposure to PAHs and maternal psychological distress was associated with higher irritability scores at every developmental timepoint suggesting that chemical exposures may exacerbate the effects of maternal distress on child irritability or vice versa. Exposure to either was not associated with change in irritability over time.

Objective Suicide deaths and poor sleep health in adolescents have significantly increased. Smartphone sensors (eg, accelerometry) provide an unobtrusive and scalable way to monitor sleep health over extended time periods. We hypothesized that poor weekly sleep health (ie, shorter and variable time in bed; late and variable bedtime) assessed through smartphone sensors would associate with weekly suicidal thoughts and behaviors (STB). Method High-risk adolescents (N = 145, 13-18 years of age, 112 female) oversampled for STB (n = 114) installed the Effortless Assessment Research System application on their smartphones, allowing access to motion data over 6 months. Simultaneously, the app measured weekly suicidal ideation. Clinical interviews measured suicidal events (ie, attempts, specifc psychiatric hospitalizations, and emergency department events related to suicide risk) at 1-, 3-, and 6-month follow-up assessments. Mixed-effects logistic regression models tested whether the weekly average and variability of time in bed and bedtime related to next-week STB. Results A 1-SD in weekly time in bed, relative to one’s typical weekly time in bed, related to a near 2-fold greater odds of a next-week suicidal event (odds ratio [OR] = 1.85, 95% CI = 1.01, 3.28, p = .045). This was driven by time in bed on weekdays (OR = 2.39, 95% CI = 1.19, 4.44, p = .013). Greater variability of bedtimes (ie, lower bedtime consistency) within a weekend, relative to one’s own typical weekend bedtime variability, predicted next-week suicidal ideation (OR = 1.91, 95% CI = 1.28, 2.69, p = .001). Conclusion Results reveal proximal associations between smartphone-derived sleep measures and STB, highlighting the possibility of developing just-in-time interventions, focusing on which sleep behaviors to modify and when to intervene to reduce suicide risk.

OBJECTIVES:Given concerns regarding health implications of adolescent smartphone use, we tested associations of smartphone ownership and age of smartphone acquisition with depression, obesity, and insufficient sleep in early adolescence. We hypothesized that smartphone ownership, especially at a younger age, would be associated with worse health outcomes. METHODS:The sample included 10 588 participants from the Adolescent Brain Cognitive Development Study. Mixed-effects logistic regression models tested associations of smartphone ownership and age of first smartphone acquisition, reported by caregivers, with depression, obesity, and insufficient sleep at age 12 years. Among participants who did not own smartphones at age 12 years, we tested associations of recent acquisition of smartphones with outcomes in the subsequent year. Models were adjusted for demographic and socioeconomic variables, ownership of other devices, pubertal development, and parental monitoring. RESULTS:At age 12 years, compared with not owning a smartphone (n = 3849), smartphone ownership (n = 6739) was associated with higher risk for depression (odds ratio [OR] 1.31, 95% CI: 1.05-1.63), obesity (OR 1.40, 95% CI: 1.20-1.63), and insufficient sleep (OR 1.62, 95% CI: 1.46-1.79). Younger age of smartphone acquisition was associated with obesity and insufficient sleep (for each earlier year of acquisition, OR 1.09, 95% CI: 1.02-1.16, and OR 1.08, 95% CI: 1.02-1.12, respectively). At age 13 years, among 3486 youth who did not own a smartphone at age 12 years, those who had acquired a smartphone in the past year (n = 1546) had greater odds of reporting clinical-level psychopathology (OR 1.57, 95% CI: 1.12-2.20) and insufficient sleep (OR 1.50, 95% CI: 1.26-1.77) compared with those who had not (n = 1940) after controlling for baseline mental health and sleep. Results were consistent across several sensitivity analyses. CONCLUSIONS:Smartphone ownership was associated with depression, obesity, and insufficient sleep in early adolescence. Findings provide critical and timely insights that should inform caregivers regarding adolescent smartphone use and, ideally, the development of public policy that protects youth.

BACKGROUND:Adolescence is a sensitive period for the emergence and refinement of cortical rhythms, which are shaped by excitatory-inhibitory (E/I) neurotransmission. However, stress-related disruptions in E/I balance may contribute to major depressive disorder (MDD). The aperiodic components of neurophysiological signals-slope and offset-index E/I dynamics and broadband power, respectively. Although altered E/I balance has been implicated in MDD and neurodevelopment more broadly, the role of aperiodic activity in predicting recurrence among adolescents remains unclear. METHODS:Resting-state electroencephalography and structured stress interviews were acquired from adolescents (N = 148; ages 13-18 years) with current (n = 42) or remitted (n = 106) MDD. Aperiodic slope and offset were parameterized across anterior, central, and posterior electrode clusters. In remitted youths, Cox proportional hazards models tested slope-by-stress and offset-by-stress interactions predicting MDD recurrence over the subsequent 12 months. RESULTS:Adolescents with current MDD exhibited significantly flatter aperiodic slopes across anterior, central, and posterior clusters (range F1,143 = 4.56-5.82, ps < .05) and lower offsets in anterior and central clusters (range F1,143 = 5.38-7.25, ps < .023) compared with remitted adolescents. Among remitted youths, significant slope-by-stress interactions predicted recurrence, wherein flatter slopes were protective against recurrence under low stress but conferred elevated risk under high stress. This effect was strongest in the anterior cluster (hazard ratio = 2.13, p < .001). The aperiodic offset did not predict recurrence. CONCLUSIONS:The aperiodic slope may function as a neurophysiological diathesis that amplifies stress-related risk for depression recurrence. Findings support a diathesis-stress model of adolescent MDD and highlight the aperiodic slope as a biomarker that may guide developmentally sensitive personalized prevention strategies.

BACKGROUND: Irritability is transdiagnostic and associated with considerable impairment. The behavioral presentation of irritability may vary with age, sex, and diagnosis. Although inconsistent, clinical evidence indicates that irritability may present as temper tantrums associated with neurodevelopmental disorders in young boys, whereas irritability in girls may manifest in adolescence associated with negative mood. Functional activation of subcortical regions is characteristic of irritability, but the structural correlates of irritability in these regions are underexplored. We hypothesized that age, sex, and diagnosis would modify subcortical correlates of irritability. METHODS: False discovery rate-corrected regression models tested whether associations between irritability and subcortical structures were moderated by sex, age, or diagnosis in 1792 youths from the Healthy Brain Network dataset (release 11.0), a cohort weighted for psychiatric problems. Irritability was measured via the Affective Reactivity Index. FreeSurfer 6.0.1 extracted subcortical structures. RESULTS: Effect modification by sex indicated higher irritability associated with smaller reward-related volumes (right nucleus accumbens, bilateral caudate) in boys and with larger threat-related volumes (left amygdala) in girls. Effect modification by age or diagnosis was not significant. CONCLUSIONS: Sex-specific subcortical correlates may explain sex-specific differences in the behavioral presentation of irritability. In models of irritability, the subcortex governs initiation of angry responses, which are dampened by prefrontal cortices. The altered volumes in reward-related regions in boys and threat-related regions in girls reported herein may be markers of early risk for irritability and/or possible targets for brain-informed interventions. Girls may benefit from irritability treatments targeting threat-based pathways, and boys may benefit from treatments targeting reward-based pathways.

Major depressive disorder is a leading cause of disability among adolescents. Perseverative negative self-referential thoughts are a promising treatment target. Mindfulness-based real-time fMRI neurofeedback (mbNF), which guides mindfulness practice with feedback to train the downregulation of the default mode network (DMN), is an intervention targeting such negative self-referential thoughts. This study builds on a registered NIMH-supported trial testing the optimal dosing of mbNF on downregulating DMN activation among depressed adolescents. Adolescents (N=90), ages 13-18-years-old, with major depressive disorder will be randomized to receive either a 15- or 30-minute mbNF session. Before and after mbNF, participants will complete a self-referential encoding fMRI task, wherein they categorize whether trait adjectives describe themselves or a friend. It is hypothesized that a 30-minute versus 15-minute mbNF dose will relate to: (1) larger decreases in behavioral negative self-referential biases and (2) larger decreases in DMN activation during self-referential processing.

Central to major depressive disorder (MDD) onset and maintenance is maladaptive self-focused attention, which can be reliably indexed by greater: (a) usage of first-person singular pronouns (e.g., I ) in natural language and (b) alpha oscillations in resting-state EEG. Integrating these largely parallel bodies of research, the present study sought to explicate the associations between, and prospective predictive utility of, linguistic and neural indicators of self-focused attention in adolescents with remitted MDD over 12 months. At baseline, 126 adolescents (ages 13–18) with ( n  = 66) and without ( n  = 60) remitted MDD completed resting-state EEG. Retrospective interviews determined the occurrence of major depressive episodes (MDEs) during the follow-up period. A total of ~2.3 million messages were passively acquired from adolescents' smartphones, on which the proportion of first-person singular pronouns was derived. During the 12 months, 29 (23.0%) participants developed an MDE (28 remitted MDD, 1 control). Cox regression showed that while greater usage of first-person singular pronouns prior to MDE increased the risk for MDE (hazard ratio [HR] = 2.02, p  < .001), greater resting-state alpha power at baseline decreased the risk for MDE (HR = 0.78, p  = .001). Moreover, greater alpha power predicted subsequent first-person singular pronoun usage ( β  = 0.17, p  = .004). Mediation analysis indicated a marginal suppression effect (bootstrapped indirect effect p  < .10), such that accounting for first-person singular pronoun usage amplified the association between alpha power and MDE risk. Findings highlight functionally distinct alpha mechanisms and provide support for smartphone-based first-person singular pronoun usage as a neurobehavioral risk factor and a potentially promising intervention target for adolescent MDD.

Queer people are still underrepresented both as STEM researchers and participants, partially due to a dearth of accurate data on this demographic. The lack of consideration for queer identities in data collection and dissemination causes a vicious cycle of exclusion. To address this invisibility, it is important to collect and report data in an inclusive and accurate manner across all areas of research, including in studies that are not specifically focused on queer populations. However, STEM researchers are often unsure of how to properly collect data in a manner that fairly represents queer people. We have developed a list of Ten Simple rules to aid researchers to perform data collection on queer individuals, focusing on study design and data dissemination. We address several issues in queer data, such as language use, dealing with small populations, and balancing demands. We also discuss how to extend this inclusive practice for studies on animal populations. These rules are aimed at anybody surveying populations which may contain queer individuals, including for example research studies and inclusivity surveys for conferences. By providing practical tips, we hope to alleviate insecurity and confusion around this topic.