In order to support efficient processing, data must be formatted according to standards that are prevalent in the field and widely supported among actively developed analysis tools.The Brain Imaging Data Structure (BIDS) (Gorgolewski et al., 2016) is an open standard designed for computational accessibility, operator legibility, and a wide and easily extendable scope of modalities -and is consequently used by numerous analysis and processing tools as the preferred input format in many fields of neuroscience.HeuDiConv (Heuristic DICOM Converter) enables flexible and efficient conversion of spatially reconstructed neuroimaging data from the DICOM format (quasi-ubiquitous in biomedical image acquisition systems, particularly in clinical settings) to BIDS, as well as other file layouts.HeuDiConv provides a multi-stage operator input workflow (discovery, manual tuning, conversion) where a manual tuning step is optional and the entire conversion can thus be seamlessly integrated into a data processing pipeline.HeuDiConv is written in Python, and supports the DICOM specification for input

Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms ( ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ , 95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.

AbstractObjectives:Data from neurocognitive assessments may not be accurate in the context of factors impacting validity, such as disengagement, unmotivated responding, or intentional underperformance. Performance validity tests (PVTs) were developed to address these phenomena and assess underperformance on neurocognitive tests. However, PVTs can be burdensome, rely on cutoff scores that reduce information, do not examine potential variations in task engagement across a battery, and are typically not well-suited to acquisition of large cognitive datasets. Here we describe the development of novel performance validity measures that could address some of these limitations by leveraging psychometric concepts using data embedded within the Penn Computerized Neurocognitive Battery (PennCNB).Methods:We first developed these validity measures using simulations of invalid response patterns with parameters drawn from real data. Next, we examined their application in two large, independent samples: 1) children and adolescents from the Philadelphia Neurodevelopmental Cohort (n = 9498); and 2) adult servicemembers from the Marine Resiliency Study-II (n = 1444).Results:Our performance validity metrics detected patterns of invalid responding in simulated data, even at subtle levels. Furthermore, a combination of these metrics significantly predicted previously established validity rules for these tests in both developmental and adult datasets. Moreover, most clinical diagnostic groups did not show reduced validity estimates.Conclusions:These results provide proof-of-concept evidence for multivariate, data-driven performance validity metrics. These metrics offer a novel method for determining the performance validity for individual neurocognitive tests that is scalable, applicable across different tests, less burdensome, and dimensional. However, more research is needed into their application.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for non-invasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Introduction There is an obvious need for efficient measurement of neuropsychiatric phenomena. A proven method-computerized adaptive testing (CAT)-is not feasible for all tests, necessitating alternatives for increasing test efficiency. Methods We combined/compared two methods for abbreviating rapid tests using two tests unamenable to CAT (a Continuous Performance Test [CPT] and n-back test [NBACK]). N=9,498 (mean age 14.2 years; 52% female) were administered the tests, and abbreviation was accomplished using methods answering two questions: what happens to measurement error as items are removed, and what happens to correlations with validity criteria as items are removed. The first was investigated using quasi-CAT simulation, while the second was investigated using bootstrapped confidence intervals around full-form-short-form comparisons. Results Results for the two methods overlapped, suggesting that the CPT could be abbreviated to 57% of original and NBACK could be abbreviated to 87% of original with the max-acceptable loss of precision and min-acceptable relationships with validity criteria. Conclusions This method combination shows promise for use in other test types, and the divergent results for the CPT/NBACK demonstrate the methods' abilities to detect when a test should not be shortened. The methods should be used in combination because they emphasize complementary measurement qualities: precision/validity..

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv , for heuristic-driven curation of data into BIDS, and flaudit , which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap." Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Subclinical psychosis experiences are common in children and adolescents, with more severe and persisting symptoms associated with greater risk of psychotic disorder transition. We examined trajectories of early psychosis spectrum (PS) symptoms to inform investigations of biobehavioral and environmental risk/protective factors associated with psychosis development in community youth.

Abstract Background Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of ‘risk calculators’ for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples. Methods We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting ‘psychosis risk’ enriched community samples. We assessed 632 participants age 8–21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines. Results Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70. Conclusions Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap”. Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap’s dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap (MBAG) is treated as a corrected deviation from age, model accuracy statistics such as R 2 will be artificially inflated. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality. Highlights The brain age gap is an out-of-sample residual, and as such varies as a function of age. A recently proposed modification of the brain age gap, designed to mitigate the dependence on age, results in inflated model accuracy statistics if used incorrectly. Given these limitations, we suggest that new methods should be developed to quantify deviation from normal developmental and aging trajectories.

Background: Depression and anxiety are common in adolescence, but adolescents’ reports of their symptoms are often at odds with their parents’ report. The extent to which reporting difference is a function of gender and context (i.e., clinical or community) has yet to be established. Since discrepancies have been found to be predictive of poor long-term health outcomes, it is critical to determine the extent of the problem in a community sample, and that disagreement itself be measured with the upmost precision. Methods: Lifetime symptoms of depression, generalized anxiety and social anxiety were modeled with a bifactor structure, and various quality assessments were performed. Adolescent and caregiver reports of the adolescent’s symptoms came from the GOASSESS on the Philadelphia Neurodevelopmental Cohort (4,812 adolescent-caregiver pairs; adolescents aged 11-17 years; 52.2% female; 57.1% White, 31.7% Black). Results: Controlling for lifetime internalizing severity, females still endorse crying more frequently than males, which indicates item bias (Chi^2=134.13, pBon<.05). The caregivers' reports for the adolescents also evidence this pattern, but to a lesser extent (Chi^2=29.49, pBon<.05). Caregivers tended to underestimate the severity of adolescents’ internalizing symptoms, and when they disagreed with adolescents on a specific symptom, it was often twice as likely that the adolescent endorsed the symptom and the caregiver denied it than the reverse. This effect was markedly more pronounced for female than male adolescents. Conclusions: Researchers should build on this work by exploring potential sources of the gender differences in disagreement, and by using measures that assess recent symptomatology using Likert scales.

The Parieto-Frontal Integration Theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional MRI measures of amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic and cerebellar regions showed comparable effects, hence PFIT needs expansion into an Extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining low resting energy state.

Measures of medical cannabis (MC) use are lacking. This study details the development and psychometric evaluation of The Medical Cannabis Expectancy Questionnaire (MCEQ), a novel measure of positive and negative expectations associated with using combustible, vaporizable, and edible MC. 333 adult MC users completed a 30-minute online survey in Spring 2017 (64.0% female, 82.3% White, mean age 32.77[±10.19] years). Participants reported on demographics, product preference, MCEs, frequency of MC use, quality of life, and negative cannabis use consequences. Psychometric analyses included evaluations of latent factor structure, measurement invariance, between-groups differences in MCEs, and test-criterion relationships with MC outcomes. The 27-item MCEQ evidenced a 2-factor structure (positive/negative). MCEs were scalar invariant by product type, sex, and reasons for MC use (medical only vs medical/recreational). Participants held more positive MCEs for combustibles than for vaporizables or edibles and more negative MCEs for combustibles and edibles than for vaporizables. MCEs did not differ by sex. Participants who also used cannabis recreationally reported stronger positive MCEs for all MC products. MCEs also differed by product preference. Additionally, preference for and more positive MCEs associated with using a specific product were associated with more frequent use of that product. Positive MCEs for all products also were associated with increased quality of life, but these relationships failed to reach statistical significance after accounting for covariates. Finally, negative MCEs for combustibles and edibles were associated with more negative consequences. The MCEQ is the first psychometrically promising measure of MC expectancies, and it uniquely distinguishes among expectations associated with using combustible, vaporizable, and edible MC. As MC use continues to proliferate, having measures dedicated to MC (versus recreational cannabis) may better inform research and clinical efforts. Further, differentiating between product types is important given established differences among them (e.g., duration of effect onset).

IntroductionMany e-cigarette users find the variety of e-cigarette flavors appealing. We examined whether preferences for e-liquid flavors and the total number of flavors preferred differed between samples of adolescent and adult e-cigarette users. We also examined whether these preferences were associated with e-cigarette use frequency for adolescents or adults, respectively.Materials and methodsThe analytic samples comprised 1) 396 adolescent, past-month e-cigarette users from 5 Connecticut high schools who completed an anonymous, school-based survey in Fall 2014 (56.1% male; 16.18 [1.18] years; 42.2% past-month smokers), and 2) 590 adult, past-month e-cigarette users who completed an anonymous, MTurk survey in Fall 2014 (53.7% male; 34.25 [9.89] years; 51.2% past-month smokers).ResultsCompared to adults, a larger proportion of adolescents preferred fruit, alcohol, and "other"-flavored e-liquids, whereas adults disproportionately preferred tobacco, menthol, mint, coffee, and spice-flavored e-liquids (p-values < .05). Adults also preferred a greater total number of flavors compared to adolescents and used e-cigarettes more frequently (p-values < .001). Flavor preferences uniquely were associated with frequency of e-cigarette use within the adolescent sample; the total number of flavors preferred was associated with more days of e-cigarette use (ηp2 = 0.04), as were preferences for fruit (ηp2 = 0.02), dessert (ηp2 = 0.02), and alcohol-flavored (ηp2 = 0.02) e-liquids.ConclusionsFlavor preferences differed between adolescent and adult samples. While youth reported less frequent e-cigarette use overall, their preferences for specific flavors and the total number of flavors preferred were associated with more days of e-cigarette use, indicating that flavor preferences may play an important role in adolescent e-cigarette use.

INTRODUCTION:Drinking at an early age (AO) and quickly progressing to drinking to intoxication (Delay=Age of Intoxication[AI]-AO) confer risk for alcohol use and alcohol-related problems. However, inconsistencies exist in the literature, which may reflect the use of different definitions of AO and AI. We evaluated whether 1) defining AO as age at first sip of alcohol (AO sip) versus age at which at least one standard drink was consumed (AO drink); and 2) defining AI as age at first "drunk" (AI drunk) versus age at first binge episode (≥5 standard drinks consumed; AI binge) resulted in different self-reported ages or differentially predicted drinking outcomes.METHODS:248 high school students (53.6% male; 16.50[1.19] years; 71.4% White) completed anonymous surveys assessing alcohol use.RESULTS:Participants reported a younger AO (sip) than AO (drink) and a younger AI (drunk) than AI (binge), resulting in significantly different Delay values for the four AO-AI pairings. Univariate general linear models indicated that AO-Delay pairings accounted for more variance in maximum drinks and alcohol-related problems than did the individual AO and AI variables. Pairings comprising AO (drink) and Delay (drink-binge) and AO (sip) and Delay (sip-binge), respectively, uniquely accounted for variance in both maximum drinks and problems.CONCLUSIONS:Clearly defining AO and AI using objective definitions that reflect specific amounts of alcohol (e.g., first sip; first standard drink; first binge) appears to outperform subjective definitions of alcohol use (e.g., first drunk).