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Individual differences in cognitive abilities emerge early during development, and children with poorer cognition are at increased risk for adverse outcomes as they enter adolescence. Caregiving plays an important role in supporting cognitive development, yet it remains unclear how specific types of caregiving behaviors may shape cognition, highlighting the need for large-scale studies. In the present study, we characterized replicable yet specific associations between caregiving behaviors and cognition in two large sub-samples of children ages 9-10 years old from the Adolescent Brain Cognitive Development Study® (ABCD). Across both discovery and replication sub-samples, we found that child reports of caregiver monitoring (supervision or regular knowledge of the child's whereabouts) were positively associated with general cognition abilities, after covarying for age, sex, household income, neighborhood deprivation, and parental education. This association was specific to the type of caregiving behavior (caregiver monitoring, but not caregiver warmth), and was most strongly associated with a broad domain of general cognition (but not executive function or learning/memory). Additionally, we found that caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development. Together, these findings underscore the influence of differences in caregiving behavior in shaping youth cognition.

Key Points: Question: Can a nonlinear association between cognitive abilities and psychiatric symptom severity explain conflicting reports from samples evaluating disparate symptom severities? Findings: In a study of 5175 children across the US, the association between cognitive abilities and psychiatric symptoms varied by the range and class of symptoms being queried. Linear, polynomial, and generalized additive models revealed significant yet opposite associations between internalizing symptoms and cognitive performance at low vs high symptom severities. Meaning: The findings suggest that accounting for prominent nonlinearities in the association between cognition and psychiatric symptoms may be able to better capture between-child heterogeneity and resolve ostensibly conflicting results in studies assaying dissimilar symptom severities. This study assesses associations between cognition and mental health by symptom severity. Importance: Mental illnesses are a leading cause of disability globally, and functional disability is often in part caused by cognitive impairments across psychiatric disorders. However, studies have consistently reported seemingly opposite findings regarding the association between cognition and psychiatric symptoms. Objective: To determine if the association between general cognition and mental health symptoms diverges at different symptom severities in children. Design, Setting, and Participants: A total of 5175 children with complete data at 2 time points assessed 2 years apart (aged 9 to 11 years at the first assessment) from the ongoing Adolescent Brain and Cognitive Development (ABCD) study were evaluated for a general cognition factor and mental health symptoms from September 2016 to August 2020 at 21 sites across the US. Polynomial and generalized additive models afforded derivation of continuous associations between cognition and psychiatric symptoms across different ranges of symptom severity. Data were analyzed from December 2022 to April 2024. Main Outcomes and Measures: Aggregate cognitive test scores (general cognition) were primarily evaluated in relation to total and subscale-specific symptoms reported from the Child Behavioral Checklist. Results: The sample included 5175 children (2713 male [52.4%] and 2462 female [47.6%]; mean [SD] age, 10.9 [1.18] years). Previously reported mixed findings regarding the association between general cognition and symptoms may consist of several underlying, opposed associations that depend on the class and severity of symptoms. Linear models recovered differing associations between general cognition and mental health symptoms, depending on the range of symptom severities queried. Nonlinear models confirm that internalizing symptoms were significantly positively associated with cognition at low symptom burdens higher cognition = more symptoms) and significantly negatively associated with cognition at high symptom burdens. Conclusions and Relevance: The association between mental health symptoms and general cognition in this study was nonlinear. Internalizing symptoms were both positively and negatively associated with general cognition at a significant level, depending on the range of symptom severities queried in the analysis sample. These results appear to reconcile mixed findings in prior studies, which implicitly assume that symptom severity tracks linearly with cognitive ability across the entire spectrum of mental health. As the association between cognition and symptoms may be opposite in low vs high symptom severity samples, these results reveal the necessity of clinical enrichment in studies of cognitive impairment. [ABSTRACT FROM AUTHOR]

Attention is thought to be the gateway between information and learning, yet there is much we do not understand about how students pay attention in the classroom. Leveraging ideas from cognitive neuroscience and psychology, we explore a framework for understanding attention in the classroom, organized along two key dimensions: internal/external attention and on-topic/off-topic attention. This framework helps us to build new theories for why active-learning strategies are effective teaching tools and how synchronized brain activity across students in a classroom may support learning. These ideas suggest new ways of thinking about how attention functions in the classroom and how different approaches to the same active-learning strategy may vary in how effectively they direct students' attention. We hypothesize that some teaching approaches are more effective than others because they leverage natural fluctuations in students' attention. We conclude by discussing implications for teaching and opportunities for future research.

Background: Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD. Methods: Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity. Results: Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8–13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems. Conclusions: These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD. [ABSTRACT FROM AUTHOR]

Background: A key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex., Aims: We aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks., Method: We leveraged person-specific atlases of functional brain networks defined using nonnegative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants' sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression., Results: Sex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants' PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons., Conclusions: Sex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence., Competing Interests: Declaration of Interest: R.T.S. and A.A-B. have received consulting income from Octave Bioscience and compensation for scientific reviewing from the American Medical Association. J.S. and A.A-B. are co-founders and equity holders in Centile Bioscience, and J.S. is on the board of directors for Centile Bioscience. All other authors declare no conflicts of interest.

Importance: Functional brain networks are associated with both behavior and genetic factors. To uncover clinically translatable mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development., Objective: To determine the relationship between transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence., Design: The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing longitudinal cohort study of 21 collection sites across the United States. Here, we conduct a cross-sectional analysis of ABCD baseline data, collected 2017-2018., Setting: The ABCD Study ® is a multi-site community-based study., Participants: The sample is largely recruited through school systems. Exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure., Exposures: Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis., Main Outcomes and Measures: (1) P-factor derived from bifactor models of youth- and parent-reported mental health assessments. (2) Person-specific functional brain network topography derived from functional magnetic resonance imaging (fMRI) scans., Results: Total participants included 11,873 youths ages 9-10 years old; 5,678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable ( N =7,459, 57.06% of vertices h 2 p FDR <0.05, mean h 2 =0.35). PRS-F1 was associated with p-factor ( N =5,815, r =0.12, 95% CI [0.09-0.15], p<0.001). Interindividual differences in functional network topography were associated with p-factor ( N =7,459, mean r =0.12), PRS-F1 ( N =3,982, mean r =0.05), and PRS-F2 ( N =3,982, mean r =0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were highly correlated ( r =0.7, p =0.003)., Conclusions and Relevance: Polygenic risk for transdiagnostic adulthood psychopathology is associated with both p-factor and heritable PFN topography during early adolescence. These results advance our understanding of the developmental drivers of psychopathology.

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.

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

Background |: Symptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents., Methods |: We used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity., Results |: Multivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; p perm = 0.001) and older adolescents (HCP-D; p perm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth., Conclusion |: Individual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD., Competing Interests: Financial Disclosures The authors report no biomedical financial interests or potential conflicts of interest.