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Puberty is the defining biological process of adolescent development, yet its effects on fundamental properties of brain physiology such as cerebral blood flow (CBF) have never been investigated. Capitalizing on a sample of 922 youths ages 8–22 y imaged using arterial spin labeled MRI as part of the Philadelphia Neurodevelopmental Cohort, we studied normative developmental differences in cerebral perfusion in males and females, as well as specific associations between puberty and CBF. Males and females had conspicuously divergent nonlinear trajectories in CBF evolution with development as modeled by penalized splines. Seventeen brain regions, including hubs of the executive and default mode networks, showed a robust nonlinear age-by-sex interaction that surpassed Bonferroni correction. Notably, within these regions the decline in CBF was similar between males and females in early puberty and only diverged in midpuberty, with CBF actually increasing in females. Taken together, these results delineate sex-specific growth curves for CBF during youth and for the first time to our knowledge link such differential patterns of development to the effects of puberty.

The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8–21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development. •The PNC is a large-scale study of neurodevelopment, with 1445 subjects imaged.•Measures span multi-modal MRI, genomics, and testing of cognition and psychopathology.•The PNC will be a public resource to study normal and pathological brain development.

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8–21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics. All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep. TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images. Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development. •Derives metrics of data quality assurance in a developmental sample of over 1500 DTI scans•Temporal signal to noise ratio reliably differentiated high quality and low quality data.•Failure to remove low quality data impacts typical DTI measures (FA/MD).•Developmental effects on FA/MD are reduced when impact of data quality is not considered.

The human cortex is highly folded to allow for a massive expansion of surface area. Notably, the thickness of the cortex strongly depends on cortical topology, with gyral cortex sometimes twice as thick as sulcal cortex. We recently demonstrated that global differences in thickness between gyral and sulcal cortex continue to evolve throughout adolescence. However, human cortical development is spatially heterogeneous, and global comparisons lack power to detect localized differences in development or psychopathology. Here we extend previous work by proposing a new measure – local cortical coupling – that is sensitive to differences in the localized topological relationship between cortical thickness and sulcal depth. After estimation, subject-level coupling maps can be analyzed using standard neuroimaging analysis tools. Capitalizing on a large cross-sectional sample (n=932) of youth imaged as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that local coupling is spatially heterogeneous and exhibits nonlinear development-related trajectories. Moreover, we uncover sex differences in coupling that indicate divergent patterns of cortical topology. Developmental changes and sex differences in coupling support its potential as a neuroimaging phenotype for investigating neuropsychiatric disorders that are increasingly conceptualized as disorders of brain development. R code to estimate subject-level coupling maps from any two cortical surfaces generated by FreeSurfer is made publicly available along with this manuscript. •Cortical coupling describes the local relationship between any two cortical surfaces.•Analyses reveal robust relationships between cortical thickness and sulcal depth•CT/SD coupling undergoes dramatic change during adolescent development•Code for calculating coupling is publicly available

Rationale The common methionine (met) for valine (val) at codon 158 (val 158 met) polymorphism in the catechol- O -methyltransferase ( COMT ) gene has been associated with nicotine dependence, alterations in executive cognitive function, and abstinence-induced working memory deficits in smokers. Objectives We sought to replicate the association of the COMT val allele with abstinence-induced alterations in working memory-related activity in task-positive (executive control) and task-negative (default mode network) regions. Methods Forty smokers (20 val/val and 20 met/met) performed an N -back task while undergoing blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) on two separate occasions: following 72 h of confirmed abstinence and during smoking as usual. An independent sample of 48 smokers who completed the identical N -back task during fMRI in smoking vs. abstinence for another study was used as a validation sample. Results Contrary to expectations, genotype by session interactions on BOLD signal in executive control regions (dorsolateral prefrontal cortex and dorsal cingulate/medial prefrontal cortex) revealed significant abstinence-induced reductions in the met/met group, but not the val/val group. Results also revealed that val/val smokers may exhibit less suppression of activation in task-negative regions such as the posterior cingulate cortex during abstinence (vs. smoking). These patterns were confirmed in the validation sample and in the whole-brain analysis, though the regions differed from the a priori regions of interest (ROIs) (e.g., precuneus, insula). Conclusions The COMT val 158 met polymorphism was associated with abstinence-related working memory deficits in two independent samples of smokers. However, inconsistencies compared to prior findings and across methods (ROI vs. whole-brain analysis) highlight the challenges inherent in reproducing results of imaging genetic studies in addiction.

Brief abstinence from smoking impairs cognition, particularly executive function, and this has a role in relapse to smoking. This study examined whether working memory-related brain activity predicts subsequent smoking relapse above and beyond standard clinical and behavioral measures. Eighty treatment-seeking smokers completed two functional magnetic resonance imaging sessions (smoking satiety vs 24 h abstinence challenge) during performance of a visual N-back task. Brief counseling and a short-term quit attempt followed. Relapse during the first 7 days was biochemically confirmed by the presence of the nicotine metabolite cotinine. Mean percent blood oxygen level-dependent (BOLD) signal change was extracted from a priori regions of interest: bilateral dorsolateral prefrontal cortex (DLPFC), medial frontal/cingulate gyrus, posterior cingulate cortex (PCC), and ventromedial prefrontal cortex. Signal from these brain regions and additional clinical measures were used to model outcome status, which was then validated with resampling techniques. Relapse to smoking was predicted by increased withdrawal symptoms, decreased left DLPFC and increased PCC BOLD percent signal change (abstinence vs smoking satiety). Receiver operating characteristic analysis demonstrated 81% area under the curve using these predictors, a significant improvement over the model with clinical variables only. The combination of abstinence-induced decreases in left DLPFC activation and reduced suppression of PCC may be a prognostic marker for poor outcome, specifically early smoking relapse.

The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale study of child development that combines neuroimaging, diverse clinical and cognitive phenotypes, and genomics. Data from this rich resource is now publicly available through the Database of Genotypes and Phenotypes (dbGaP). Here we focus on the data from the PNC that is available through dbGaP and describe how users can access this data, which is evolving to be a significant resource for the broader neuroscience community for studies of normal and abnormal neurodevelopment. •The PNC is a large-scale study of neurodevelopment.•Data includes imaging, rich cognitive and clinical phenotyping, and genomics.•Investigators can access data through dbGaP.

Introduction Psychosocial stress is considered to be an important mechanism underlying smoking behavior and relapse. Thus, understanding the effects of acute nicotine withdrawal on responses to stress is important to intervene to prevent stress-induced relapse. The current study investigated the neural correlates of psychosocial stress during acute nicotine withdrawal in chronic smokers. Methods Thirty-nine treatment-seeking smokers were randomized to one of two conditions (abstinent 24 h ( n  = 21) or smoking as usual ( n  = 18)). They were then exposed to the Montreal Imaging Stress Task (MIST), a psychosocial stress task consisting of difficult mental arithmetic problems while receiving negative performance feedback while undergoing functional magnetic resonance imaging (fMRI). Results Subjective measures of stress increased following the MIST, compared to baseline. Whole brain between-group analysis identified significant activation clusters in four regions for the stress induction minus control contrast: inferior frontal gyrus (IFG), anterior/para cingulate cortex (ACC), precuneus, and supramarginal gyrus (SMG). In all regions, the deprived group showed significantly greater activation compared to the non-deprived group. No significant correlations were found between subjective stress and BOLD signal activation ( p s > 0.07). Conclusions This study provides new evidence that brain regions previously shown to be predictive of relapse, such as the precuneus and IFG, display heightened neural responses to stress during nicotine deprivation. These data identify the brain regions that may be associated with withdrawal-related stress responses. Increased stress-related activation during nicotine withdrawal may identify those most vulnerable to relapse and represent a target for novel pharmacological intervention.