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Abstract: Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit, saccade and fixation deficits in first-degree biological relatives, we conducted a set of meta-analytic investigations. Among 18 measures of EMD, memory-guided saccade accuracy and error rate, global smooth pursuit dysfunction, intrusive saccades during fixation, antisaccade error rate and smooth pursuit closed-loop gain emerged as best differentiating relatives from controls (standardized mean differences ranged from .46 to .66), with no significant differences among these measures. Anticipatory saccades, but no other smooth pursuit component measures were also increased in relatives. Visually-guided reflexive saccades were largely normal. Moderator analyses examining design characteristics revealed few variables affecting the magnitude of the meta-analytically observed effects. Moderate effect sizes of relatives v. controls in selective aspects of EMD supports their endophenotype potential. Future work should focus on facilitating endophenotype utility through attention to heterogeneity of EMD performance, relationships among forms of EMD, and application in molecular genetics studies. [Copyright &y& Elsevier]

• The Penn Neurocognitive Battery was updated using computerized adaptive testing. • We explored the psychometric properties of this updated battery via convergent and criterion validity. • The updated battery shows psychometric properties acceptable for use in research. The Penn Computerized Neurocognitive Battery is an efficient tool for assessing brain-behavior domains, and its efficiency was augmented via computerized adaptive testing (CAT). This battery requires validation in a separate sample to establish psychometric properties. In a mixed community/clinical sample of N = 307 18-to-35-year-olds, we tested the relationships of the CAT tests with the full-form tests. We compared discriminability among recruitment groups (psychosis, mood, control) and examined how their scores relate to demographics. CAT-Full relationships were evaluated based on a minimum inter-test correlation of 0.70 or an inter-test correlation within at least 0.10 of the full-form correlation with a previous administration of the full battery. Differences in criterion relationships were tested via mixed models. Most tests (15/17) met the minimum criteria for replacing the full-form with the updated CAT version (mean r = 0.67; range = 0.53–0.80) when compared to relationships of the full-forms with previous administrations of the full-forms (mean r = 0.68; range = 0.50–0.85). Most (16/17) CAT-based relationships with diagnostics and other validity criteria were indistinguishable (interaction p > 0.05) from their full-form counterparts. The updated CNB shows psychometric properties acceptable for research. The full-forms of some tests should be retained due to insufficient time savings to justify the loss in precision. [ABSTRACT FROM AUTHOR]

Increasingly, the effects of copy number variation (CNV) in the genome on brain function and behaviors are recognized as means to elucidate pathophysiology of psychiatric disorders. Such studies require large samples and we characterized the neurocognitive profile of two cohorts of individuals with 22q11.2 deletion syndrome (22q11DS), the most common CNV associated with schizophrenia, in an effort to harmonize phenotyping in multi-site global collaborations. The Penn Computerized Neurocognitive Battery (PCNB) was administered to individuals with 22q11DS in Philadelphia (PHL; n=155, aged 12-40) and Tel Aviv (TLV; n=59, aged 12-36). We examined effect sizes of performance differences between the cohorts and confirmed the factor structure of PCNB performance efficiency in the combined sample based on data from a large comparison community sample. The cohorts performed comparably with notable deficits in executive function, episodic memory and social cognition domains that were previously associated with abnormal neuroimaging findings in 22q11DS. In mixed model analysis, while there was a main effect for site for accuracy (number of correct response) and speed (time to correct response) independently, there were no main site effects for standardized efficiency (average of accuracy and speed). The fit of a structural model was excellent indicating that PCNB tests were related to the targeted cognitive domains. Thus, our results provide preliminary support for the use of the PCNB as an efficient tool for neurocognitive assessment in international 22q11DS collaborations. [ABSTRACT FROM AUTHOR]

Background 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. Methods 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. Results 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. Conclusion 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. [Copyright &y& Elsevier]

Abstract: The ventral striatum (VS) is a critical brain region for reinforcement learning and motivation. Intrinsically motivated subjects performing challenging cognitive tasks engage reinforcement circuitry including VS even in the absence of external feedback or incentives. However, little is known about how such VS responses develop with age, relate to task performance, and are influenced by task difficulty. Here we used fMRI to examine VS activation to correct and incorrect responses during a standard n-back working memory task in a large sample (n=304) of healthy children, adolescents and young adults aged 8–22. We found that bilateral VS activates more strongly to correct than incorrect responses, and that the VS response scales with the difficulty of the working memory task. Furthermore, VS response was correlated with discrimination performance during the task, and the magnitude of VS response peaked in mid-adolescence. These findings provide evidence for scalable intrinsic reinforcement signals during standard cognitive tasks, and suggest a novel link between motivation and cognition during adolescent development. [Copyright &y& Elsevier]