The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity—in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI—were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Background:Existing evidence suggests cortical morphometric alterations occur in people with autism and ADHD. However, these findings remain tentative due to small sample sizes, heterogeneous imaging pipelines, varied statistical approaches, and limited harmonization across acquisition sites. Few studies have applied standardized processing to large, clinically enriched datasets or addressed site-related batch effects. Methods:We leveraged six large-scale brain imaging datasets (n = 9,647; male=5,835; female=3,812; ages 5-64 years), including 1,533 individuals with ADHD, 1,080 with autism spectrum disorder, and 7,034 matched controls. All imaging data were processed using the validated ABCD-HCP pipeline, with cortical parcellation into 360 regions based on the Human Connectome Project (HCP) atlas, and ComBat harmonization was applied to account for variability across 67 acquisition sites. Group-level differences in cortical thickness and sulcal curvature were examined with ANCOVAs, controlling for covariates and using Bonferroni correction for multiple comparisons. Results:Our analyses revealed distinct neuroanatomical signatures for both autism and ADHD. Individuals with autism exhibited regionally thinner cortex and curvature alterations particularly in the Cingulo-Opercular network. In contrast, individuals with ADHD displayed regionally thicker cortex, particularly in the default mode and somatomotor networks, alongside curvature differences. Control participants showed intermediate patterns, suggesting that autism and ADHD may represent diverging extremes of cortical maturation. Conclusions:Cortical thickness and curvature emerge as potential biomarkers that can advance understanding of neurodevelopmental conditions and disentangle heterogeneity across diagnostic groups. These findings highlight the value of harmonized, large-scale, standardized analyses for resolving inconsistencies in the literature.

Objectives: Brain tissue segmentation of infant magnetic resonance (MR) images is important for studying typical and atypical brain development. The infant brain undergoes rapid changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. We introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source model for robust and generalizable brain tissue segmentation leveraging data augmentation and a large sample size of manually annotated images. Experimental design: Model training included MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance was assessed by comparing BIBSNet, and joint label fusion (JLF) inferred segmentations to ground truth segmentations, and an ad-hoc analysis with iBeat inferred segmentation, using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to produce anatomical and resting state functional derivatives to further assess model performance on processed derivatives. Principal observations: BIBSNet outperforms JLF based on DSC comparisons especially with gray matter (BIBSNet = 0.849, JLF = 0.713) and white matter (BIBSNet = 0.862, JLF = 0.791). Additionally, with processed derived metrics, BIBSNet inferred segmentations outperforms JLF inferred segmentations across nearly all anatomical and functional metrics. Ad-hoc analyses of cortical segmentations-iBeat does not perform subcortical segmentations-showed that there is no significant difference between iBeat and BIBSNet segmentation for infants 0-5 months, but iBeat performed significantly better for infants 6-8 months. Conclusions: BIBSNet shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF at segmentation inference, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

Dopaminergic (DA) function and basal ganglia neurobiology are central to reward learning, motivation, and cognitive control, and dysregulation of these systems contributes to neuropsychiatric conditions that emerge during development. Adolescence is marked by profound reorganization of DAergic basal ganglia circuitry, yet direct in vivo assessment of the DA system remains limited in youth. Brain tissue iron is a developmentally sensitive marker of DA-related neurobiology that can be measured non-invasively via magnetic resonance imaging (MRI). Iron is an essential co-factor for DA synthesis and a foundational metabolic resource that supports cellular metabolism, myelination, and energetic demands of the basal ganglia. T2*-weighted echo-planar imaging (EPI), collected during functional MRI (fMRI), is sensitive to magnetic susceptibility of non-heme brain iron. Leveraging this property, we demonstrate the validity and broad applicability of an iron-sensitive metric that can be derived from conventional single-echo fMRI: ΔR2*. In a longitudinal developmental dataset (N = 151; age range 12-31), ΔR2* showed high reliability, strong longitudinal stability, and validity via robust convergence with established quantitative relaxometry-based iron measures (R2* and R2'). Critically, ΔR2* can be retrospectively estimated from extant fMRI data and derived in large-scale consortium data repositories, demonstrated here in the Adolescent Brain and Cognitive Development (ABCD) baseline cohort (N = 8,366; ages 9-11). We show that ΔR2* captures known age-related increases in basal ganglia iron, highlighting neurodevelopmental sensitivity at population-scale. Together, these findings establish ΔR2* as a reliable, widely accessible marker of basal ganglia iron, enabling scalable investigation of lifespan trajectories and neuropsychiatric risk in existing and future datasets.

Brain networks that support episodic memory development in the first years of life remain poorly understood. Protracted growth of regions such as the hippocampus have been suggested as a causal role in episodic memory development, but development of these memory brain networks and their role in episodic memory development is not yet fully elucidated. In this study, subcortical memory network regions (hippocampus, thalamus, amygdala) were segmented from MRI images in 835 visits spanning 0-4 years of age across 322 participants in the Baby Connectome Project. Hippocampal segmentations were further subdivided into head, body, and tail subregions manually for 426 visits, which were used to train models that automatically segmented hippocampal subregions for the remaining visits. 58 participants returned for an early school-age follow-up, including two episodic memory tasks. Volumetric growth trajectories differed across regions and across subregions within the hippocampus, with the head of the hippocampus showing steep growth that plateaued months later than the body or tail of the hippocampus. In the right hemisphere's hippocampal head, age- and sex- adjusted volumes positively predicted future early school-age episodic memory performance. After accounting for total brain volume, the right thalamus also predicted memory performance. Total sleep duration at the follow-up visit accounted for performance variance above and beyond brain volume correlations. Altogether, results suggest that trajectories of growth and relationships between volume and episodic memory performance are region and subregion specific, and provide evidence for the important role of sleep in associations between brain networks and early episodic memory development.

BACKGROUND:Population neuroscience datasets provide an opportunity for researchers to estimate reproducible effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. METHODS:We used motion-ordering and motion-ordering+resampling (bagging) to test whether these methods preserve functional magnetic resonance imaging (fMRI) data in the Adolescent Brain Cognitive Development (ABCD) Study (N = 5733). For the 2 methods, brain-behavior associations were computed as the partial Spearman's rank correlations (Rs) between functional connectivity and cognitive performance (NIH Cognition Toolbox) as well as externalizing and internalizing psychopathology (Child Behavior Checklist) while adjusting for participant sex assigned at birth and head motion. RESULTS:Black and Hispanic youth exhibited excess head motion relative to data collected from White youth and were discarded disproportionately when conventional approaches were used. Motion-ordering and bagging methods retained more than 99% of Black and Hispanic youth. Both methods produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. CONCLUSIONS:The motion-ordering and bagging methods are 2 feasible approaches that can enhance sample representation for testing brain-behavior associations and that result in reproducible effect sizes in diverse populations.

Prescription stimulants (e.g., methylphenidate) are thought to improve attention, but evidence from prior fMRI studies is conflicted. We utilized resting-state fMRI data from the Adolescent Brain Cognitive Development Study (n = 11,875; 8-11 years old) and validated the functional connectivity findings in a precision imaging drug trial with highly sampled (n = 5, 165-210 min each) healthy adults (methylphenidate 40 mg). Stimulant-related connectivity differences in sensorimotor regions matched fMRI patterns of daytime arousal, sleeping longer at night, and norepinephrine transporter expression. Taking stimulants reversed the effects of sleep deprivation on connectivity and school grades. Connectivity was also changed in salience and parietal memory networks, which are important for dopamine-mediated, reward-motivated learning, but not the brain's attention systems (e.g., dorsal attention network). The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, improving task effort and persistence without effects on attention networks.

Prescription stimulants such as methylphenidate are being used by an increasing portion of the population, primarily children. These potent norepinephrine and dopamine reuptake inhibitors promote wakefulness, suppress appetite, enhance physical performance, and are purported to increase attentional abilities. Prior functional magnetic resonance imaging (fMRI) studies have yielded conflicting results about the effects of stimulants on the brain's attention, action/motor, and salience regions that are difficult to reconcile with their proposed attentional effects. Here, we utilized resting-state fMRI (rs-fMRI) data from the large Adolescent Brain Cognitive Development (ABCD) Study to understand the effects of stimulants on brain functional connectivity (FC) in children (n = 11,875; 8-11 years old) using network level analysis (NLA). We validated these brain-wide association study (BWAS) findings in a controlled, precision imaging drug trial (PIDT) with highly-sampled (165-210 minutes) healthy adults receiving high-dose methylphenidate (Ritalin, 40 mg). In both studies, stimulants were associated with altered FC in action and motor regions, matching patterns of norepinephrine transporter expression. Connectivity was also changed in the salience (SAL) and parietal memory networks (PMN), which are important for reward-motivated learning and closely linked to dopamine, but not the brain's attention systems (e.g. dorsal attention network, DAN). Stimulant-related differences in FC closely matched the rs-fMRI pattern of getting enough sleep, as well as EEG- and respiration-derived brain maps of arousal. Taking stimulants rescued the effects of sleep deprivation on brain connectivity and school grades. The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, explaining improved task effort and persistence without direct effects on attention networks.

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity (FC) gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical areas across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fits the underlying FC in individuals during the first 3 years, adult area parcellations still have utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the area parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

Executive functioning in children has been linked to intrinsic brain network organization assessed during the resting state, as well as to brain network organization during the performance of cognitive tasks. Prior work has established that task-based brain networks are stronger predictors of behavior than resting state networks, yet it is unclear if tasks only strengthen relationships that exist weakly at rest or if tasks also evoke unique relationships. A lack of discernment regarding how tasks and the resting state commonly and uniquely support executive functions precludes a holistic understanding of the neurobiological basis of executive functions. This project investigated differences in brain network organization and relationships with executive function ability between the resting state and two executive function tasks, a stop signal task and an emotional n-back task, using the Adolescent Brain and Cognitive Development Study dataset. Both executive function tasks evoked a more integrated network organization than the resting state, and executive function ability was related to different aspects of brain network organization during the resting state and during the tasks. Further, task-related shifts in brain network organization evoked several new relationships with executive function that were not detectable during the resting state and strengthened a relationship with executive function that existed weakly during the resting state. Overall, this study establishes a distinction between common and unique features of intrinsic and task-evoked brain function that facilitate executive function in children.

Discovering mechanisms underlying mental illness requires disentangling genetic and environmental factors influencing mental health. Researchers have started investigating the brain’s role as a potential intermediate biomarker linking genes and environment to mental health; however, understanding how much genetics shapes adolescent brain function remains elusive. Using data from the ABCD study (5,247 unrelated, 330 dizygotic, 248 monozygotic twin subjects), we estimated the heritability of functional connectivity and topography using both SNP and twin data. SNP-based heritability was calculated using genetic correlation and the recently developed AdjHE-RE estimator. We found low SNP heritability for brain functional connectivity (median = 2e-10% Gordon, 5.8e-7% probabilistic) and topography (max = 2%). Twin estimates using ACE models replicated prior findings from the literature (median = 8.9e-6% Gordon, 6% probabilistic, 27% topography). This suggest that additive genetic effects are minimally associated with functional brain features in adolescents highlighting the importance of considering both genetic and environmental factors when studying the development of functional brain networks and their relevance to mental health. ### Competing Interest Statement The authors have declared no competing interest.

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios.

In-scanner head motion introduces systematic bias to resting-state fMRI functional connectivity (FC) not completely removed by denoising algorithms. Researchers studying traits associated with motion (e.g. psychiatric disorders) need to know if their trait-FC relationships are impacted by residual motion to avoid reporting false positive results. We devised Split Half Analysis of Motion Associated Networks (SHAMAN) to assign a motion impact score to specific trait-FC relationships. SHAMAN distinguishes between motion causing overestimation or underestimation of trait-FC effects. We assessed 45 traits from n = 7270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) had significant underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced significant overestimation to 2% (1/45) of traits but did not decrease the number of traits with significant motion underestimation scores.

Sensory processing disorder (SPD) is a neurodevelopmental condition characterized by impaired sensory discrimination and responsivity. Although the causes and neural correlates of SPD remain poorly understood, prenatal influences should be considered, as the prenatal environment is strongly implicated in the progression of neurodevelopmental disorders. One factor hypothesized to promote SPD is perinatal Western-style diet (WSD) exposure. This study explored the effects of perinatal WSD exposure on the proposed neural correlates of SPD in Japanese macaques. Functional connectivity between sensory and emotional processing areas was assessed at 4 months of age using resting-state functional magnetic resonance imaging (rs-fMRI). A machine learning model successfully predicted perinatal diet group based on functional connectivity strengths, indicating that differences in sensory connectivity exist between diet groups. Intra-somatomotor, visual-auditory, somatomotor-auditory, somatomotor-visual, and intra-visual network connections demonstrated the greatest differences between groups, with primary motor cortex connectivity being the most impacted. Connections to the amygdala were not major contributors to accurate model performance, but amygdala connectivity, especially to the somatomotor network, may still be a weak driver of model performance. These findings suggest that a proposed predictor of SPD, perinatal WSD exposure, impacts the functional connectivity of sensory processing areas relevant in SPD during early infancy.

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision functional magnetic resonance imaging (fMRI) data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant-specific fMRI.

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold-standard brain segmentations. These segmentations are limited by the difficulty of infant brain segmentations, which require extensive neuroanatomical knowledge and are time-consuming in nature. Addressing this, we constructed the Baby Open Brains (BOBs) Dataset, an open source resource of manually curated and expert reviewed infant brain segmentations. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1-9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

Socially guided visual attention, such as gaze following and joint attention, represents the building block of higher-level social cognition in primates, although their neurodevelopmental processes are still poorly understood. Atypical development of these social skills has served as early marker of autism spectrum disorder and Williams syndrome. In this study, we trace the developmental trajectories of four neural networks underlying visual and attentional social engagement in the translational rhesus monkey model. Resting-state fMRI (rs-fMRI) data and gaze following skills were collected in infant rhesus macaques from birth through 6 months of age. Developmental trajectories from subjects with both resting-state fMRI and eye-tracking data were used to explore brain-behavior relationships. Our findings indicate robust increases in functional connectivity (FC) between primary visual areas (primary visual cortex [V1] - extrastriate area 3 [V3] and V3 - middle temporal area [MT], MT and anterior superior temporal sulcus area [AST], as well as between anterior temporal area [TE]) and amygdala (AMY) as infants mature. Significant FC decreases were found in more rostral areas of the pathways, such as between temporal area occipital part - TE in the ventral object pathway, V3 - lateral intraparietal (LIP) of the dorsal visual attention pathway and V3 - temporo-parietal area of the ventral attention pathway. No changes in FC were found between cortical areas LIP-FEF and temporo-parietal area - Area 12 of the dorsal and ventral attention pathways or between Anterior Superior Temporal sulcus area (AST)-AMY and AMY-insula. Developmental trajectory of gaze following revealed a period of dynamic changes with gradual increases from 1 to 2 months, followed by slight decreases from 3 to 6 months. Exploratory association findings across the 6-month period showed that infants with higher gaze following had lower FC between primary visual areas V1-V3, but higher FC in the dorsal attention areas V3-LIP, both in the right hemisphere. Together, the first 6 months of life in rhesus macaques represent a critical period for the emergence of gaze following skills associated with maturational changes in FC of socially guided attention pathways.

Background There is an imminent need to identify neural markers during preadolescence that are linked to developing depression during adolescence, especially among youth at elevated familial risk. However, longitudinal studies remain scarce and exhibit mixed findings. Here we aimed to elucidate functional connectivity (FC) patterns among preadolescents that interact with familial depression risk to predict depression two years later. Methods 9-10 year-olds in the Adolescent Brain Cognitive Development (ABCD) Study were classified as healthy (i.e., no lifetime psychiatric diagnoses) at high familial risk for depression (HR; n=559) or at low familial risk for psychopathology (LR; n=1,203). Whole-brain seed-to-voxel resting-state FC patterns with the amygdala, putamen, nucleus accumbens, and caudate were calculated. Multi-level, mixed-effects regression analyses were conducted to test whether FC at ages 9-10 interacted with familial risk to predict depression symptoms at ages 11-12. Results HR youth demonstrated stronger associations between preadolescent FC and adolescent depression symptoms (ps<0.001) as compared to LR youth (ps>0.001), primarily among amygdala/striatal FC with visual and sensory/somatomotor networks. Conclusions Preadolescent amygdala and striatal FC may be useful biomarkers of adolescent-onset depression, particularly for youth with family histories of depression. This research may point to neurobiologically-informed approaches to prevention and intervention for depression in adolescents.