Introduction:The hypothalamus-pituitary-adrenal (HPA) axis plays an important role in regulating behavior, neuroplastic responses to the environment during childhood and adolescent development, and highly implicated in stress-related mental disorders. However, due to the small size of hypothalamic structures and the limited availability of automated segmentation tools, there are relatively few neuroimaging studies examining hypothalamic involvement in mental health in human populations. Using a semi-automated segmentation approach, we conducted an exploratory study examining associations between hypothalamic volume and mental health-related behaviors in typically developing youth. Methods:T1-weighted magnetic resonance imaging (MRI) scans and behavioral measures [Behavioral Assessment System for Children-2 Parent Report Scale (BASC-2 PRS)] were collected from 71 youth (aged 6-16 years). T1-weighted MRI data were quality checked and processed, and hypothalamic volumes semi-automatically delineated. Left, right and total hypothalamus volumes were tested across age using linear mixed effects models, as well as tested separately for associations with clinical T scores using a general linear model. Results:Left (T = -2.0; p = 0.04) and total (T = -2.6, p = 0.01) hypothalamic volume decreased with age. A trend-level association was observed between left hypothalamic volume and adaptability scores (T = 1.8; p = 0.08), which did not reach conventional statistical significance. No significant associations were observed for internalizing or externalizing scores. Conclusion:Decreased ability to adapt to one's environment may be a predictor of mental illness. In this exploratory study, we observed significantly decreasing hypothalamus volume across this age range. There was a trend-level association between hypothalamic volume and adaptability, suggesting that structural variation in this region may be relevant to stress-related functioning in youth. However, this finding should be interpreted cautiously and requires replication in larger, longitudinal samples.

Prenatal psychological distress is associated with altered brain development and adverse outcomes in offspring. The cerebellum is particularly vulnerable, and structural alterations associated with developmental deficits in children. This study examined the relationships between prenatal anxiety and depression, cerebellar asymmetry, and infant developmental outcomes. A prospective cohort study was conducted with 90 pregnant participants and their infants from Calgary, Alberta, Canada. Maternal prenatal depression and anxiety were assessed using the Edinburgh Perinatal/Postnatal Depression Scale (EPDS) and the Patient-Reported Outcomes Measurement Information System Anxiety-Adult Short Form (PROMIS Anxiety). Infant cerebellar volumes were obtained via magnetic resonance imaging at three months, and hemispheric asymmetry was calculated. Developmental domains (gross motor, fine motor, personal-social, problem solving, communication) were assessed with the Ages and Stages Questionnaire (ASQ-3) at 12 months. Prenatal depression predicted leftward cerebellar asymmetry in infants (B = −111.44, 95% CI = −191.28 – −31.59, p = 0.006), while prenatal anxiety predicted rightward cerebellar asymmetry (B = 76.98, 95% CI = 4.92 – 149.04, p = 0.036). These effects persisted when modeling prenatal anxiety and depression separately. Rightward asymmetry at three months was a predictor of better communication abilities at 12 months (B = 0.003, 95% CI = 0.001– 0.005, p=0.003), but did not predict other developmental outcomes. Findings indicate that prenatal anxiety and depression are differentially associated with cerebellar development in infancy and that cerebellar asymmetry predicts communication at 12 months. These results underscore the importance of considering specific forms of psychological distress during pregnancy and their potential impact on brain development and offspring behaviour.

Abstract Background Establishment of the gut microbiome during the first years of life is critically important for long-term health and development. In preterm infants, microbial colonization is disrupted due to their developmental immaturity and the myriad of pre- and postnatal exposures. Unfortunately, this places them at an elevated risk for adverse health outcomes into childhood (eg, asthma and impaired neurodevelopment). Understanding the developmental trajectory of the microbiome in preterm infants, the factors predicting these microbial patterns, and the links to childhood health can offer opportunities to develop interventions and optimize health. Objective BLOOM (Begin a Life of Health With Observation and Optimization of the Microbiome) is a prospective, observational cohort study that aims to examine how early-life microbiomes shape the health and development of preterm infants into childhood (eg, asthma and neurodevelopment). Methods Infants (<37 weeks gestation) and their families are recruited from 4 neonatal intensive care units in Calgary, Alberta, Canada. Maternal stool and weekly infant stool, urine, and human milk samples are collected during the first 2 months postnatally; participants are then followed up at 3 months, 1 year, and 3 years corrected age for continued data (eg, nutrition, medications, medical history, and home environment) and sample collection (stool, urine, nasal swabs, hair, and blood). Additional clinical testing (eg, allergen skin prick test) and questionnaires (eg, Ages & Stages Questionnaires, Third Edition) are administered to assess health outcomes and developmental milestones. Results Study recruitment and data/sample collection for BLOOM commenced in 2019; as of May 2026, we have enrolled 245 participants, and recruitment is ongoing. An initial characterization of early-life microbiome profiles for the first 105 infants enrolled in BLOOM was published online in December 2025. Conclusions BLOOM is a large, comprehensive prospective cohort study and biobank aimed at measuring the microbiome development of preterm-born children. This cohort will address significant research priorities through characterizing the patterns of microbiome development in preterm infants over the first 3 years postnatally and correlating these with health outcomes (eg, immune development, asthma and allergy risks, and neurodevelopment). It is anticipated that the results can be leveraged to identify factors and altered microbiome patterns underlying disease risk, design intervention strategies (eg, microbial therapeutics) for later clinical testing, and generate novel hypotheses of possible underlying mechanisms linking microbiome development to health.

The present study examined if the associations between patterns of exposure to adversechildhood experiences (ACEs) and prenatal alcohol, tobacco, and cannabis use remainafter controlling for sociodemographic factors, current hardship, and other prenatalsubstance use. Participants (n57,471) were from the population-based PregnancyDuring the COVID-19 Pandemic cohort study conducted across all provinces and terri-tories of Canada. Eligible participants were 17 & thorn;years old, 35 weeks gestation or less,living in Canada, and able to read/write in English or French. Self-reported ACEsincluded emotional, physical, and sexual abuse/neglect, and exposure to family vio-lence, substance abuse, mental illness, divorce, or incarceration. Alcohol, tobacco, andcannabis use during pregnancy were self-reported. Latent class analyses and logisticregressions were conducted. Four ACEs classes were identified: low adversity (68% ofparticipants), parental divorce/mental illness (16%), emotional abuse (11%), and highadversity across all ACEs (5%). Prenatal tobacco use was associated with high adver-sity and emotional abuse, which was explained by sociodemographic factors. Althoughalcohol and cannabis use were associated with ACEs, childhood emotional abuse andhigh adversity were specifically associated with prenatal legal cannabis use, with posthoc analyses showing associations for alcohol were explained by co-occurring prenatalcannabis use. The associations of ACEs with prenatal cannabis use and co-use of canna-bis and alcohol support the importance of early adversity prevention, of clearer messag-ing regarding the safety of prenatal cannabis use, and of researching the efficacy oftherapy approaches for prenatal substance use in the context of ACEs exposure.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for early intervention and prevention. The current study uses machine learning to build a model of risk prediction and transition based on multi-modal neuroimaging, clinical, and behavioral data from youth at transdiagnostic risk. Participants aged 12–25 were recruited at two sites in Canada, and followed for 4 years. Symptom severity was measured using the Scale of Psychosis-Risk Symptoms (SOPS) and K10 Distress Scale, and a range of cognitive and behavioral measures were collected, as well as magnetic resonance imaging (MRI) data. Participants were assigned to one of 5 groups: healthy controls (HC; n = 42), familial risk (stage 0; n = 40), mild symptoms (stage 1a; n = 48), attenuated syndromes (stage 1b; n = 82), or discrete disorder (transition; n = 31). Constrained spherical deconvolution was used to generate whole brain tractography maps from diffusion MRI, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional MRI signal between pairs of brain regions. All measures were evaluated in a model to predict transition between groups. Random Forest analysis identified diffusion MRI-derived nodal metrics of betweenness centrality in the angular gyrus, inferior temporal gyrus, amygdala and calcarine fissure as potential features which can discriminate between the groups. Additionally, SOPS and K10 Distress Scales were useful behavioral predictors of transdiagnostic risk. Our findings show that combining neuroimaging with clinical characteristics may result in a promising predictive model for transdiagnostic risk and transition to SMI.

BACKGROUND:Prenatal alcohol exposure (PAE) occurs in ~10% of pregnancies in North America and can alter brain development. Prior research on brain morphology in children with PAE shows mixed findings for cortical thickness, and only a few studies have investigated more complex morphological features, showing decreased gyrification with PAE. Here, we use four anatomical measures to provide a comprehensive characterization of cortical morphology in youth with PAE compared to unexposed youth. METHODS:T1-weighted Magnetic Resonance Imaging (MRI) was used to examine cortical morphology metrics in 163 scans from 121 youth (56 with PAE) aged 7-21 years. Each participant had between 1 and 3 scans. Images were processed to segment 98 cortical brain regions. Linear mixed-effects models were used to test the effects of PAE on cortical gyrification index, thickness, sulcal depth, and shape complexity index, as well as their relationships with age. RESULTS:Individuals with PAE had significantly lower gyrification (β=-0.17, q = 0.028) and sulcal depth (β=-0.60, q = 0.013) in the right pars triangularis, lower shape complexity in the right occipital pole (β=-0.019, q = 0.012), and higher shape complexity in the right medial orbital gyrus (β=0.017, q = 0.012), compared to unexposed youth. PAE moderated the relationship between age and both gyrification and sulcal depth in several regions such that sulcal depth and gyrification decreased with age in the PAE group but slightly increased with age (or remained stable) in the unexposed group. CONCLUSION:These findings build upon previous reports of structural and developmental differences in youth with PAE, suggesting that gyrification may be more sensitive to the effects of PAE than cortical thickness.

High-angular resolution diffusion imaging (HARDI) is an advanced method for characterizing brain microstructure and function. However, HARDI is time-consuming limiting real-world applications, especially in children. We aimed to address the challenge by creating non-acquired HARDI data through deep learning and testing utility in neurodevelopment. Brain diffusion magnetic resonance imaging from 95 children aged 2-10 years (49 females) were examined. Each subject included two source b-value datasets: 750 s/mm2 and 2000 s/mm2, 30 directions each with five b0 volumes. Deep learning was voxel-wise, approximately 1,620,000 voxels from 12-subject each matched on age and sex for training and validation, respectively, to predict b2000 s/mm2 from b750 s/mm2 dataset. Different training samples, window sizes, and tissue inputs were also assessed. The remaining 71 individuals were used for tract-based analysis of neurodevelopment, which involved eight brain white matter tracts. Results showed that model performance improved with an increasing training sample, large window size, and additional brain segmentation input. HARDI outcomes including predicted data were similar in pattern to source-only data across tracts and metrics, with all appeared to increase with age. Additionally, tract volumes showed consistent sex differences between prediction-integrated and source-only datasets in all but pyramidal tract. Collectively, deep learning-enabled HARDI is feasible for pediatric imaging, which may help reduce scan time by half and characterize sex-specific neurodevelopment.

Abstract Early childhood development is scaffolded by rapid maturation of brain white matter structure, believed to support the emergence of cognitive and socioemotional functions. Previous whole-tract studies have suggested patterns of white matter development occurring along posterior-anterior, deep-superficial and inferior-superior axes. However, little is known as to whether these patterns are evident within tracts. Using longitudinal diffusion imaging data from 133 children (4-8 years; 76 females), the present work characterizes along-tract patterns of white matter development across association, commissural and projection bundles using fixel-based analyses of microstructure and macrostructure. Within long range association bundles, faster age-related changes were observed for segments adjacent to the visual cortices relative to segments located near association regions, supporting a sensorimotor-association axis of brain development. An inferior-superior pattern was found for projection tracts, with faster age-effects observed for segments near the brainstem. Lastly, while several association and commissural bundles exhibited faster maturation within central segments; indicative of a deep-superficial axis, effects were mixed between micro- and macrostructure, underscoring the unique developmental timing of these different fiber properties. Our findings provide evidence that within-tract white matter maturation unfolds along key spatiotemporal axes and suggests that increased spatial precision can advance our understanding of early childhood brain development.

In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies introduce additional variability through heterogeneous collection materials, such as scanner and acquisition protocols, which confound with biologically relevant signals. Neuroscientists often utilize statistical methods on image-derived metrics, such as volume of regions of interest, after all image processing to minimize site-related variance. HACA3, a deep learning harmonization method, offers an opportunity to harmonize image signals prior to metric quantification; however, HACA3 has not yet been validated in a pediatric cohort. In this work, we investigate HACA3's ability to remove site-related variance and preserve biologically relevant signal compared to a statistical method, neuroCombat, and pair HACA3 processing with neuroCombat to evaluate the efficacy of multiple harmonization methods in a pediatric (age 7 to 21) population across three unique scanners with controls and cases of PAE with downstream MaCRUISE volume metrics. We find that HACA3 qualitatively improves inter-site contrast variations, but statistical methods reduce greater site-related variance within the MaCRUISE volume metrics following an ANCOVA test, and HACA3 relies on follow-up statistical methods to approach maximal biological preservation in this context.

Background Due to systemic discrimination and social exclusion, sexual minority populations have elevated rates of mental health challenges, adverse birth and child health outcomes, and barriers to perinatal care, yet are largely overlooked in perinatal research. Sexual minority pregnant people also have intersecting identities, which may include racialized ethnicity, which has a well-established association with perinatal mental health outcomes. The current study investigated the intersecting impacts of sexual orientation and ethnicity on symptoms of depression and anxiety during pregnancy. We also investigated perceived social support as a potential protective factor for perinatal mental health. Methods Data collected from a longitudinal, pan-Canadian cohort study of pregnant people, Pregnancy During the COVID-19 Pandemic (PdP), between 2020 and 2021 was used. Measures included self-reported demographics and mental health symptom questionnaires. Logistic regression analyses were conducted to assess differences across sexual orientation in clinically significant symptoms of depression and anxiety while controlling for covariates. The intersecting impact of sexual orientation and ethnicity on perinatal mental health was also examined. Social support was considered as a protective factor in secondary analyses. Results The current study included 7,220 PdP participants. Identifying as a sexual minority was associated with increased odds of clinically elevated symptoms of depression and anxiety during pregnancy beyond other known correlates. Models including ethnicity demonstrated significant main effects of sexual orientation and ethnicity on depressive symptoms. Interactions between sexual orientation and perceived social support were not associated with a statistically significant difference in odds of depression or anxiety in adjusted models, but there was a significant main effect of perceived social support on symptoms of depression and anxiety. Conclusion These findings inform understanding of perinatal mental health for diverse sexual minority individuals, including protective factors and areas for intervention. Future research should examine social stressors and supports unique to sexual minority perinatal populations to further our understanding of their experiences and needs.

Background The COVID-19 pandemic and associated stressors contributed to increased mental health concerns (e.g., anxiety and depression). Objective stress among expectant parents poses a risk for child development, with previous studies demonstrating that exposure to stress in-utero can have negative implications for social-emotional development. Objective To assess the association between pandemic-related objective hardship and psychological distress (i.e., anxiety and depression) during pregnancy and postpartum and child social-emotional development at 24 months. Study design Participants were from the longitudinal Pregnancy during the Pandemic (PdP) cohort. They were largely White, educated, and in married or common-law relationships. Pandemic-related objective hardship was assessed during pregnancy, psychological distress was assessed during pregnancy and postpartum, and child social-emotional development was assessed at 24 months of age. Serial Mediation Analysis was used to examine the strength of the association between objective hardship, psychological distress during pregnancy and postpartum, and child social-emotional outcomes. Results Participants who provided social-emotional development outcome data at 24 months were included in the analysis (n = 3889). Greater exposure to pandemic-related objective hardship was associated with worse social-emotional development at 24 months through the effects of prenatal and postpartum psychological distress (B=1.33; SE=0.22; p < .001). Conclusions Exposure to more pandemic-related objective hardship was associated with a higher likelihood of child social-emotional developmental concerns. Greater psychological distress following hardship in the prenatal and postpartum periods partially explains this association.

Symptoms of depression, anxiety and anger are common amongst parents and are associated with increased risk of offspring mental health problems and differences in child brain development. Many parents do not access treatment, in part due to expense, wait times, and stigma. Increasing access to treatment may improve quality of life for parents and their children. eHealth and peer-supported interventions may offer accessible and effective treatment avenues for parents of young children. The primary aim of this non-inferiority trial is to estimate the effect of the Building Emotional Awareness and Mental Health (BEAM) intervention, a digital and peer-supported mental health and parenting program, administered by trained peer paraprofessionals, on parental depression, anxiety and anger symptoms relative to a treatment-as-usual (TAU) waitlist control group. The secondary aims are to compare child internalizing and externalizing behaviours between BEAM and TAU groups to estimate the effect of the BEAM program on child behavioural outcomes. The final aim is to estimate differences in child structural and functional brain connectivity 6 months post intervention between the BEAM and TAU control groups. A two-arm, single-blinded, parallel groups randomized controlled trial (RCT) with a 1:1 allocation ratio and repeated measures will be conducted in Western Canada. Parents and primary caregivers (aged 18 +) who self-report moderate to severe symptoms of anger, anxiety, or depression at time of enrolment and reside in the provinces of British Columbia or Alberta, Canada, will be eligible to participate from the larger Pregnancy During the Pandemic (PdP) cohort and invited to enrol. PdP research coordinator will distribute invitations to individuals who previously consented to be contacted for future studies and meet eligibility criteria. Parents will also be recruited from the community through social media advertisements to reach a total sample of 240 participants. Assessments will be conducted at baseline (T1), post-intervention (T2), and 6 months post treatment (T3) and will include self-report questionnaires, and neuroimaging. Findings from this study will provide data on the potential efficacy of the BEAM program in supporting parental mental health, child well-being, and neurodevelopment. Results may inform future large-scale trials and may guide the development of accessible, community-based interventions aimed at improving family mental health and child developmental outcomes. This trial was registered on ClinicalTrials.gov on September 20, 2023. The first planned enrolment date was April 24, 2024. The registration number is NCT06046989. https://clinicaltrials.gov/study/NCT06046989.

Asians may face elevated risk for postpartum depression (PPD), though findings are mixed. Perceived discrimination, a known mediator of health outcomes, remains understudied in this group. To examine (1) the likelihood of probable PPD using culturally informed Edinburgh Postnatal Depression Scale (EPDS) cut-offs among Asian and non-Hispanic White (NHW) participants and (2) whether perceived everyday discrimination mediated the association between ethnicity and probable PPD. A secondary aim compared postpartum mental health treatment-seeking between groups. This secondary analysis used data from the Pregnancy during the Pandemic (PdP) study, a longitudinal study that followed Canadians throughout pregnancy and the postpartum (2020–2021). Asian (n = 170) and NHW (n = 170) participants were matched on demographics (N = 340). Online surveys assessed demographics (< 35 weeks’ gestation), perceived discrimination (pregnancy to 9 months postpartum), depressive symptoms, and treatment-seeking (12 months postpartum). Data were analyzed using logistic regression and path analysis. Sensitivity analyses examined the impact of alternative EPDS threshold specifications. Asian participants were more likely to meet criteria for probable PPD when using a culturally informed threshold (EPDS ≥ 9 vs. ≥ 13 for NHW). However, this difference was not observed under alternative or uniform threshold specifications. A significant indirect effect was identified, such that Asian participants reported higher perceived everyday discrimination, which was associated with increased likelihood of PPD symptoms. No group differences were observed in mental health treatment-seeking. Observed differences in probable PPD between Asian and NHW participants were dependent on EPDS threshold specification, underscoring the sensitivity of prevalence estimates to cut-off selection. While culturally informed thresholds may improve detection, optimal EPDS cut-offs for Asian populations remain uncertain. These findings highlight the importance of culturally responsive screening approaches and the role of perceived discrimination in postpartum mental health.

Mathematics is a complex skill requiring the coordination of distributed gray matter brain regions connected by white matter tracts. Diffusion tensor imaging (DTI) studies have revealed a network of white matter tracts that support math processing, but the specific microstructural features driving this relationship remain unclear. Other magnetic resonance imaging (MRI) methods-neurite orientation dispersion and density imaging (NODDI), inhomogeneous magnetization transfer (ihMT), multicomponent driven-equilibrium single-pulse observation of T1 and T2 (mcDESPOT), and g-ratio imaging-can probe microstructural features like axon packing, fiber orientation, and myelin more specifically than DTI. We applied these methods alongside DTI to evaluate links between white matter microstructure and math in a longitudinal cohort of 33 6-16 year olds (66 datasets total). Partial correlations between metrics of white matter microstructure and math skill, controlling for age and gender, were carried out in the left superior longitudinal and inferior longitudinal fasciculi, corticospinal tract, and the splenium. Cross-sectionally, fiber coherence of the corticospinal tract and superior longitudinal fasciculus correlated to mathematics performance. Longitudinally, change in markers of axonal packing and myelin were linked to changes in both math skill and fluency in a regionally-specific manner, with links to myelin-sensitive metrics most prevalent. Notably, decreases in myelin were linked to improvements in mathematics over time, suggesting ongoing refinement of the math network. Findings presented here did not survive multiple comparisons corrections, but provide insight for future work elaborating upon these associations in larger samples.

White matter segmentation methods from diffusion magnetic resonance imaging range from streamline clustering-based approaches to bundle mask delineation, but none have proposed a pediatric-specific approach. White matter tract segmentation tools optimized for adults have shown inconsistent performance in pediatric populations. We explore optimization of manually corrected masks integrated into a direct-set, deep learning framework for generalizable pediatric white matter tract segmentation. We hypothesize that a deep learning model with a similar approach to TractSeg will improve similarity between an algorithm-generated mask and an expert-labeled ground truth. Given a cohort of 56 manually labelled white matter bundles, we take inspiration from TractSeg's 2D UNet architecture, and we modify (a) inputs to match bundle definitions as determined by pediatric experts; (b) evaluation to use k-fold cross validation instead of a train/validation/test split; (c) the loss function to masked Dice loss. We evaluate Dice score, volume overlap, and volume overreach of 16 major regions of interest compared to the expert-labeled dataset. To test whether our approach offers statistically significant improvements over TractSeg, we compare Dice voxels, volume overlap, and adjacency voxels with a Wilcoxon signed-rank test followed by false discovery rate (FDR) correction. We find statistical significance (p < 0.05) across all bundles for all metrics with one exception in volume overlap. After we run TractSeg and our model, we combine their output masks into a 60-label atlas to evaluate if TractSeg and our model combined can generate a robust, individualized atlas, and observe smoothed, continuous masks in cases that TractSeg did not produce an anatomically plausible output. With the improvement of white matter pathway segmentation masks, we can further understand neurodevelopment on a population-level scale, and we can produce reliable estimates of individualized anatomy in pediatric white matter diseases and disorders.

Emotional knowledge (EK) is broadly defined as the ability to identify and understand emotions. Despite being recognized as a foundational component of socioemotional development in childhood, early emergence of EK is seldom explored before age 3. Similarly, research has yet to explore the feasibility of assessing young children's EK via online assessment, despite the growing relevance and potential of this modality in expanding research accessibility. To address these research gaps, a sample of 92 toddlers (age range: 18-36-months, 52 female and 40 male) and their mothers (M_age = 32.6 years; 50% White Canadian) participated in this exploratory study investigating the feasibility of assessing toddler EK using virtual assessment tools. Results provide evidence for future potential assessing toddler EK online, as most toddlers (66.7%) were able to participate in at least one component of the virtual assessment task. Methodological strengths and suggestions are discussed to facilitate future research that incorporates online assessments with younger children.

Importance: Exposure to maternal mental illness in the first 3 years of life is associated with poor child outcomes. Maternal mental health problems increased dramatically during the COVID-19 pandemic with many parents not having access to evidence-based treatments. Mobile health (mHealth) treatments show promise for adult mood and anxiety disorders but rarely include parenting strategies and have high dropout rates. Objective: This study aimed to evaluate the efficacy of the Building Emotion Awareness and Mental Health (BEAM) app-based program, which responds to maternal mental health and parenting needs while building social connection between participants. Design: A two-arm, phase III randomized controlled trial (RCT) was conducted to evaluate the BEAM intervention compared to unrestricted services-as-usual (US). Participants completed self-report measures at eligibility screening (baseline assessment, T0), prior to randomization (pre-intervention, T1) and immediately following the intervention (post-intervention, T2). Setting: Individuals were recruited and completed surveys online. Participants: A final sample of 119 mothers with children aged 18 to 36 months, who self-reported moderate-to-severe symptoms of depression and/or anxiety.Intervention: Individuals randomized to treatment participated in the 10-week BEAM program. It was hypothesized that the treatment group would report decreases in mental health symptoms (anxiety, depression, anger, alcohol use, sleep problems) and harsh parenting (overreactive parenting, conflictual parent-child interactions) compared to the US group. Results: BEAM out-performed the US condition in reducing anxiety symptoms. Participants in both groups experienced significant decreases in depression. Participants with higher levels of anxiety and depression symptoms at screening, experienced significant decreases in mental health symptoms and harsh parenting composite scores, if they received the BEAM program, compared to US. This included specific declines in anxiety, anger, and dysfunctional parenting interactions. There were no significant effects for sleep problems, alcohol misuse, or overactive discipline. Conclusion and Relevance: BEAM is a highly scalable intervention that has the potential to rapidly reach underserved groups in need of mental health and parenting support. Next steps include improving the user interface and exploring engagement and implementation of the program within existing health and social service systems for long-term improvements in family health and well-being.