Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

Associations between cannabis use and cognitive functioning in cross-sectional studies of adolescents and young adults are small and may be of questionable clinical importance for most individuals. Furthermore, abstinence of longer than 72 hours diminishes cognitive deficits associated with cannabis use. Although other outcomes (eg, psychosis) were not examined in the included studies, results indicate that previous studies of cannabis in youth may have overstated the magnitude and persistence of cognitive deficits associated with use. Reported deficits may reflect residual effects from acute use or withdrawal. Future studies should examine individual differences in susceptibility to cannabis-associated cognitive dysfunction.

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Cognitive activity resulted in increased flow of blood to the cerebral hemispheres. The increase was greater to the left hemisphere for a verbal task and greater to the right hemisphere for a spatial task. The direction and degree of hemispheric flow asymmetry were influenced by sex and handedness, females having a higher rate of blood flow per unit weight of brain, and females and left-handers having a greater percentage of fast-clearing tissue, presumably gray matter.

IMPORTANCELow socioeconomic status (L-SES) and the experience of traumatic stressful events (TSEs) are environmental factors implicated in behavioral deficits, abnormalities in brain development, and accelerated maturation. However, the relative contribution of these environmental factors is understudied.OBJECTIVE To compare the association of L-SES and TSEs with psychopathology, puberty, neurocognition, and multimodal neuroimaging parameters in brain maturation. DESIGN, SETTING, AND PARTICIPANTS ThePhiladelphiaNeurodevelopmentalCohortisacommunitybased study examining psychopathology, neurocognition, and neuroimaging among participants recruited through the Children's Hospital of Philadelphia pediatric network. Participants are youths aged 8 to 21 years at enrollment with stable health and fluency in English. The sample of 9498 participants was racially (5298 European ancestry [55.8%], 3124 African ancestry [32.9%], and 1076 other [11.4%]) and economically diverse. A randomly selected subsample (n = 1601) underwent multimodalneuroimaging.DatawerecollectedfromNovember5,2009,throughDecember30,2011, and analyzed from February 1 through November 7, 2018.MAIN OUTCOMES AND MEASURES The following domains were examined: (1) clinical, including psychopathology, assessed with a structured interview based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children, and puberty, assessed with the Tanner scale; (2) neurocognition, assessed by the Penn Computerized Neurocognitive Battery; and(3) multimodal magnetic resonance imaging parameters of brain structure and function.RESULTS A total of 9498 participants were included in the analysis (4906 [51.7%] female; mean [SD] age, 14.2 [3.7] years). Clinically, L-SES and TSEs were associated with greater severity of psychiatric symptoms across the psychopathology domains of anxiety/depression, fear, externalizing behavior, and the psychosis spectrum. Low SES showed small effect sizes (highest for externalizing behavior, 0.306 SD; 95% CI, 0.269 to 0.342), whereas TSEs had large effect sizes, with the highest in females for anxiety/depression (1.228 SD; 95% CI, 1.156 to 1.300) and in males for the psychosis spectrum (1.099 SD; 95% CI, 1.032 to 1.166). Both were associated with early puberty. Cognitively, L-SES had moderate effect sizes on poorer performance, the greatest being on complex cognition (−0.500 SD 95% CI, −0.536 to −0.464), whereas TSEs were associated with slightly better memory (0.129 SD; 95% CI, 0.084 to 0.174) and poorer complex reasoning (−0.109 SD; 95% CI, −0.154 to −0.064). Environmental factors had common and distinct associations with brain structure and function. Structurally, both were associated with lower volume, but L-SES had correspondingly lower gray matter density, whereas TSEs were associated with higher gray matter density. Functionally, both were associated with lower regional cerebral blood flow and coherence and with accelerated brain maturation.CONCLUSIONS AND RELEVANCE Low SES and TSEs are associated with common and unique dif...

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

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