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.

Abstract Objective To determine the relationship between adverse childhood events (ACE), executive function, and parenting. Method Pregnant individuals with and without a history of substance use were recruited to participate in a longitudinal study on perinatal reward processing and parenting. Data were collected from 111 pregnant participants, from which 43 contributed data at six months postpartum. Childhood trauma was collected via the ACE scale, and executive function (EF) challenges were measured using the Metacognition Index of the Behavior Ratings Inventory of Executive Functions for Adults (BRIEF-A). Parenting stress was assessed using the Parenting Stress Inventory, Short Form. The parental distress (PD) and parent perception of their child’s dysregulation (DC) scales were calculated from the PSI at the 6-month timepoint (N = 43 dyads, mean infant age = 27.14 (2.45) weeks.). A structural equation modeling approach was utilized to assess the direct and indirect effects of ACEs and EF challenges on PD and DC. A path mediation model was used. All models were estimated using the R Statistical Software Lavaan package. Bootstrapped confidence intervals were calculated for all paths. Missing data were handled using full information maximum likelihood. Results We found a significant indirect effect of ACEs on DC through metacognition (β = 0.1, 95% CI [0.01, 0.18], p = 0.026) and a significant indirect effect of ACEs on PD through metacognition (β = 0.13, 95% CI [0.02, 0.23], p = 0.019). There were no significant direct effects of ACE on PD or DC. Conclusion This analysis supports the hypothesis that EF, specifically cognitive regulation, impacts PS in the context of a history of ACE.

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Diagnostic accuracy of autism spectrum disorder (ASD) is crucial to track and characterize ASD, as well as to guide appropriate interventions at the individual level. However, under-diagnosis, over-diagnosis, and misdiagnosis of ASD are still prevalent. We describe 232 children (MAge = 10.71 years; 19

This analysis supports the hypothesis that experiencing adverse events during childhood can impact parenting stress through a change in both adaptive and maladaptive cognitive emotion regulation strategies.

Objective: Cannabis use among individuals of reproductive age has increased with cannabis legalization and heightened stress during the COVID-19 pandemic. Our study provides data on preconception cannabis use and cannabis use disorder (CUD) during the pandemic and models the association between preconception cannabis use and depression and anxiety during pregnancy.Methods: Data on substance use and depression and anxiety symptoms were collected from questionnaires and the Structured Clinical Interview for DSM-5 (SCID-5) from pregnant individuals in Oregon in 2019-2022. Linear regression was used to model the association between the frequency of preconception cannabis use and scores on the Center for Epidemiological Studies of Depression-Revised (CESD-R) and Beck Anxiety Inventory (BAI).Results: The prevalence of preconception cannabis use was 27.8% among 227 study participants. CUD was diagnosed in 19% of cannabis users, or 5.3% of the overall sample. Daily cannabis use, compared to rare/never use, was associated with increases in CESD-R (13 = 6.22, p 0.029) and BAI (13 = 4.71, p 0.045) scores.Conclusions: Cannabis use and CUD are common among individuals of reproductive age. Given the association between preconception cannabis use and depression and anxiety during pregnancy, more attention is needed on screening and counseling of cannabis use among people of reproductive age.

Objective: The COVID-19 pandemic has led to escalations in substance use, including alcohol consumption. Of particular concern are the potential impacts during the postpartum period, a time of heightened vulnerability to stress and potential transmission of the negative sequelae of substance use to offspring. However, postpartum alcohol consumption during the COVID-19 pandemic has not been well characterized. Method: Postpartum drinking habits and COVID-19-related stress were repeatedly assessed (every two weeks for 12 weeks, and at one-, six-, and 12-months postpartum) from N = 378 individuals during the COVID-19 pandemic. Average alcohol use trajectories as well as heterogeneity in trajectories were characterized. COVID-19-related trauma symptoms and coping were examined in relation to alcohol use over time. Results: Average postpartum alcohol use included an initial quadratic increase from one-to-four-months post-partum, followed by a plateau between four-to-12-months. Higher (15.08%), moderate (26.90%), and lower consumption (57.90%) subgroups were identified. Endorsement of COVID-19-related trauma symptoms and using alcohol to cope with stress predicted higher consumption. Conclusions: Findings suggest a potential sensitive period in establishing postpartum alcohol use patterns from one-to-four-months postpartum. Findings further suggest that postpartum alcohol use is heterogenous and that individual response to major traumatic stressors, like the COVID-19 pandemic, may influence emerging patterns of postpartum alcohol use.

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework’s ability to reliably capture brain-behavior relationships across 3 cognitive scores – general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 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) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions 1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping 4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations 5,6 . Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Heightened psychological stress during pregnancy has repeatedly been associated with increased risk for development of behavior problems and psychiatric disorders in offspring. This review covers a rapidly growing body of research with the potential to advance a mechanistic understanding of these associations grounded in knowledge about maternal-placental-fetal stress biology and fetal brain development. Specifically, we highlight research employing magnetic resonance imaging to examine the infant brain soon after birth in relation to maternal psychological stress during pregnancy. This approach increases capacity to identify specific alterations in brain structure and function and to differentiate between effects of pre- versus postnatal exposures. We then focus on the extensive preclinical literature and emerging research in humans that have found that heightened maternal inflammation during pregnancy as a mechanism through which maternal stress influences the developing fetal brain. We place these findings in the context of recent work identifying psychotherapeutic interventions that have been found to be effective for reducing psychological stress among pregnant individuals and that also show promise for reducing inflammation. We argue that a focus on inflammation, among other mechanistic pathways, may lead to a productive and necessary integration of research focused on the effects of maternal psychological stress on offspring brain development and on prevention and intervention studies aimed at reducing maternal psychological stress during pregnancy. In addition to increasing capacity for common measurements and understanding potential mechanisms of action relevant to maternal mental health and fetal neurodevelopment, this focus may inform and broaden thinking about prevention and intervention strategies.

SUMMARYThe brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of precision functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision network maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), an overlapping template matching method for identifying integration zones, as well as an unsupervised community detection algorithm (Infomap). From these individualized maps we also generated probabilistic network maps and integration zones for two demographically-matched groups of n∼3000 each. We demonstrate high reproducibility between groups (Pearson’s r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Brain Atlas allowed us to derive a set of brain regions that are largely invariant in network topography across populations, which provides more reproducible statistical maps of executive function in brain-wide associations. We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Brain Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCC’s data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

AbstractThis study sought to advance understanding of the potential long‐term consequences of the COVID‐19 pandemic for child development by characterizing trajectories of maternal perinatal depression, a common and significant risk factor for adverse child outcomes. Data came from 393 women (86% White, 8% Latina; mean age = 33.51 years) recruited during pregnancy (n = 247; mean gestational age = 22.94 weeks) or during the first year postpartum (n = 146; mean child age = 4.50 months; 55% female). Rates of depression appear elevated, relative to published reports and to a pre‐pandemic comparison group (N = 155). This study also provides evidence for subgroups of individuals who differ in their depressive symptom trajectories over the perinatal period. Subgroup membership was related to differences in maternal social support, but not to child birth outcomes.

Magnetic resonance imaging (MRI) continues to drive many important neuroscientific advances. However, progress in uncovering reproducible associations between individual differences in brain structure/function and behavioral phenotypes (e.g., cognition, mental health) may have been undermined by typical neuroimaging sample sizes (median N=25). Leveraging the Adolescent Brain Cognitive Development (ABCD) Study (N=11,878), we estimated the effect sizes and reproducibility of these brain-wide associations studies (BWAS) as a function of sample size. The very largest, replicable brain-wide associations for univariate and multivariate methods were r=0.14 and r=0.34, respectively. In smaller samples, typical for brain-wide association studies (BWAS), irreproducible, inflated effect sizes were ubiquitous, no matter the method (univariate, multivariate). Until sample sizes started to approach consortium-levels, BWAS were underpowered and statistical errors assured. Multiple factors contribute to replication failures; here, we show that the pairing of small brain-behavioral phenotype effect sizes with sampling variability is a key element in wide-spread BWAS replication failure. Brain-behavioral phenotype associations stabilize and become more reproducible with sample sizes of N⪆2,000. While investigator-initiated brain-behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible human brain-wide association studies.

Background: Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms. Methods: Participants (N = 130) included adolescents aged 7-16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups. Results: Both hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = - 4.9, p < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = -4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent "severity" patterns of over or under connectivity were observed between subgroups and TD. Conclusion: The FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Objective: Preventive interventions for postpartum depression (PPD) are critical for women at elevated risk of PPD. Mindfulness based cognitive therapy - perinatal depression (MBCT-PD) is a preventive intervention that has been shown to reduce risk for PPD in women with a prior history of depression. The objective of this clinical trial is to examine two potential mechanisms of action of MBCT-PD, emotion regulation and cognitive control, using behavioral and neuroimaging methods. Method: This baseline protocol describes a randomized control trial (RCT) with two arms, MBCT-PD and treatment as usual (TAU). We plan on enrolling 74 females with a prior history of a major depressive episode, with 37 participants randomized to each arm. Participants in the MBCT-PD arm will receive MBCT-PD during pregnancy, and the TAU group will receive standard prenatal care. All participants will complete the Center for Epidemiological Studies Depression Scale - Revised (CESD-R), Emotion Regulation Questionnaire (ERQ), and classic Stroop task at multiple points from pregnancy through six months postpartum. Participants will also complete an fMRI scan at six weeks postpartum. Results: All primary outcomes are collected at six weeks postpartum. Primary behavioral outcomes include: depressive symptoms on the CESD-R, cognitive reappraisal on the ERQ, and Stroop task performance. In parallel, the primary neurobiological outcomes include whole-brain activation during fMRI tasks when participants 1) regulate emotional responding and 2) engage cognitive control. Conclusions: This results of this innovative RCT will help identify potential behavioral and neurobiological mechanisms of action of preventive interventions for PPD for in-depth examination in larger scale RCTs.