Early life lead exposure from private well water increases juvenile delinquency risk among US teens.

Early life exposure to environmental lead (Pb) has been linked to decreased IQ, behavior problems, lower lifetime earnings, and increased criminal activity. Beginning in the 1970s, limits on Pb in paint, gasoline, food cans, and regulated water utilities sharply curtailed US environmental Pb exposure. Nonetheless, hundreds of thousands of US children remain at risk. This study reports on how unregulated private well water is an underrecognized Pb exposure source that is associated with an increased risk of teenage juvenile delinquency. We build a longitudinal dataset linking blood Pb measurements for 13,580 children under age 6 to their drinking water source, individual- and neighborhood-level demographics, and reported juvenile delinquency records. We estimate how early life Pb exposure from private well water influences reported delinquency. On average, children in homes with unregulated private wells had 11% higher blood Pb than those with community water service. This higher blood Pb was significantly associated with reported delinquency. Compared to children with community water service, those relying on private wells had a 21% (95% CI: 5 to 40%) higher risk of being reported for any delinquency and a 38% (95% CI: 10 to 73%) increased risk of being reported for serious delinquency after age 14. These results suggest that there could be substantial but as-yet-unrecognized social benefits from intervention programs to prevent children's exposure to Pb from private wells, on which 13% of the US population relies.

Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme.

Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography.

White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.

Children drinking private well water have higher blood lead than those with city water.

Although the Flint, Michigan, water crisis renewed concerns about lead (Pb) in city drinking water, little attention has been paid to Pb in private wells, which provide drinking water for 13% of the US population. This study evaluates the risk of Pb exposure in children in households relying on private wells. It is based on a curated dataset of blood Pb records from 59,483 North Carolina children matched with household water source information. We analyze the dataset for statistical associations between children's blood Pb and household drinking water source. The analysis shows that children in homes relying on private wells have 25% increased odds (95% CI 6.2 to 48%, P < 0.01) of elevated blood Pb, compared with children in houses served by a community water system that is regulated under the Safe Drinking Water Act. This increased Pb exposure is likely a result of corrosion of household plumbing and well components, because homes relying on private wells rarely treat their water to prevent corrosion. In contrast, corrosion control is required in regulated community water systems. These findings highlight the need for targeted outreach to prevent Pb exposure for the 42.5 million Americans depending on private wells for their drinking water.

Development of structure-function coupling in human brain networks during youth.

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure-function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure-function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure-function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure-function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres.

Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.

The emerging infrastructure of US firearms injury data.

For every fatal shooting in the United States, detailed information from reports of coroners or medical examiners, police departments, and other sources is recorded in the National Violent Death Reporting System. There is no such system in place for nonfatal shootings, which far outnumber fatalities. Hospital data systems are in place that could, with some improvements, provide access to reliable local, state and national estimates of firearm injuries. Such estimates are possible because most firearms injuries are treated in hospitals, and hospitals routinely assign "external cause of injury" codes to all injury encounters. Federal health agencies supervise a number of data systems that centralize hospital data. Challenges currently being addressed are public access, timeliness, and accuracy of coding of intent. (Hospitals misclassify many firearm assaults as accidents.) Law enforcement agencies provide detailed data on shootings in criminal circumstances, including shootings that are not treated in a hospital. The FBI's Uniform Crime Reports (UCR) system aggregates data from agencies. The FBI instituted a radical reform of this system beginning in 2021, resulting in a sharp agency participation drop that prevents valid national estimates. The reform requires agencies to report incident-level data instead of summary counts, which is all that was required for the previous 90 years. There are ongoing efforts to increase participation in the new system and restore its former status as the leading source of national crime estimates. In the meantime, data on nonfatal gunshot cases are available from a number of police departments. We discuss additional reforms needed to generate timely, accurate, publicly accessible data from hospitals and police.

Childhood trauma history is linked to abnormal brain connectivity in major depression.

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Rates of longitudinal change in 18 F-flortaucipir PET vary by brain region, cognitive impairment, and age in atypical Alzheimer's disease.

INTRODUCTION: Longitudinal positron emission tomography (PET) studies of tau accumulation in Alzheimer's disease (AD) have noted reduced increases or frank decreases in tau signal. We investigated how such reductions related to analytical confounds and disease progression markers in atypical AD. METHODS: We assessed regional and interindividual variation in longitudinal change on 18 F-flortaucipir PET imaging in 24 amyloid beta (Aß)+ patients with atypical, early-onset amnestic or non-amnestic AD plus 62 Aß- and 132 Aß+ Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. RESULTS: In atypical AD, 18 F-flortaucipir uptake slowed or declined over time in areas with high baseline signal and older, more impaired individuals. ADNI participants had reduced longitudinal change in early Braak stage regions relative to late-stage areas. DISCUSSION: Results suggested radioligand uptake plateaus or declines in advanced neurodegeneration. Further research should investigate whether results generalize to other radioligands and whether they relate to changes of the radioligand binding site structure or accessibility.

Gunshot-victim cooperation with police investigations: Results from the Chicago Inmate Survey.

Just one in ten nonfatal shootings in Chicago lead to an arrest. Unlike in fatal cases, gunshot victims who survive can often provide information of use in the police investigation. Nonetheless, nonfatal shooting cases in Chicago and elsewhere have much lower arrest rates than fatal cases, in part because most victims do not cooperate. We use data from the Chicago Inmate Survey (CIS) to analyze the potential value that gunshot-victim cooperation could have for increasing arrest rates. Half of CIS respondents reported they had been shot before. Very few cooperated with police investigations of these shootings, although at least half of them could have provided useful information. In fact, victims were more likely to speak with the police when they did not have any information on their shooter. Respondents explained their choice not to cooperate by reference to "street codes" against snitching, mistrust of the police, and the desire to retaliate against the shooter personally. If more shooting victims could be persuaded to cooperate, the police could solve more cases and hence be more effective in curtailing gun violence.

Underground Gun Markets and the Flow of Illegal Guns into the Bronx and Brooklyn: A Mixed Methods Analysis.

New York City (NYC) has experienced large reductions in violent crime over the last two decades, but gun-related violence continues to pose a threat to public safety. Despite strong gun laws, high-risk individuals in NYC neighborhoods are unfortunately still able to access and misuse firearms. This research analyzes NYC's underground gun market by closely examining the flow of guns into the two boroughs where gun violence and crime gun recoveries are most prevalent: the Bronx and Brooklyn. A mixed methods approach is utilized that consists of an assessment of firearms trace data and in-depth interviews with individuals considered to be at high risk for involvement in gun violence. Findings suggest that guns recovered in the Bronx and Brooklyn were significantly more likely to originate in states with less restrictive gun laws and more likely to have changed ownership in unregulated transactions relative to guns recovered elsewhere in NYC. Interviews revealed three primary avenues for illegal guns reaching Bronx and Brooklyn neighborhoods: high-volume gun brokers, middlemen, and individuals who make episodic low-level acquisitions from straw purchasers in other states. No subjects identified theft as a meaningful source of crime guns.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Longitudinal progression of grey matter atrophy in non-amnestic Alzheimer's disease.

Recent models of Alzheimer's disease progression propose that disease may be transmitted between brain areas either via local diffusion or long-distance transport via white matter fibre pathways. However, it is unclear whether such models are applicable in non-amnestic Alzheimer's disease, which is associated with domain-specific cognitive deficits and relatively spared episodic memory. To date, the anatomical progression of disease in non-amnestic patients remains understudied. We used longitudinal imaging to differentiate earlier atrophy and later disease spread in three non-amnestic variants, including logopenic-variant primary progressive aphasia (n = 25), posterior cortical atrophy (n = 20), and frontal-variant Alzheimer's disease (n = 12), as well as 17 amnestic Alzheimer's disease patients. Patients were compared to 37 matched controls. All patients had autopsy (n = 7) or CSF (n = 67) evidence of Alzheimer's disease pathology. We first assessed atrophy in suspected sites of disease origin, adjusting for age, sex, and severity of cognitive impairment; we then performed exploratory whole-brain analysis to investigate longitudinal disease spread both within and outside these regions. Additionally, we asked whether each phenotype exhibited more rapid change in its associated disease foci than other phenotypes. Finally, we investigated whether atrophy was related to structural brain connectivity. Each non-amnestic phenotype displayed unique patterns of initial atrophy and subsequent neocortical change that correlated with cognitive decline. Longitudinal atrophy included areas both proximal to and distant from sites of initial atrophy, suggesting heterogeneous mechanisms of disease spread. Moreover, regional rates of neocortical change differed by phenotype. Logopenic-variant patients exhibited greater initial atrophy and more rapid longitudinal change in left lateral temporal areas than other groups. Frontal-variant patients had pronounced atrophy in left insula and middle frontal gyrus, combined with more rapid atrophy of left insula than other non-amnestic patients. In the medial temporal lobes, non-amnestic patients had less atrophy at their initial scan than amnestic patients, but longitudinal rate of change did not differ between patient groups. Medial temporal sparing in non-amnestic Alzheimer's disease may thus be due in part to later onset of medial temporal degeneration than in amnestic patients rather than different rates of atrophy over time. Finally, the magnitude of longitudinal atrophy was predicted by structural connectivity, measured in terms of node degree; this result provides indirect support for the role of long-distance fibre pathways in the spread of neurodegenerative disease. 10.1093/brain/awz091_video1 awz091media1 6041544065001.

Diminished Cortical Thickness Is Associated with Impulsive Choice in Adolescence.

Adolescence is characterized by both maturation of brain structure and increased risk of negative outcomes from behaviors associated with impulsive decision-making. One important index of impulsive choice is delay discounting (DD), which measures the tendency to prefer smaller rewards available soon over larger rewards delivered after a delay. However, it remains largely unknown how individual differences in structural brain development may be associated with impulsive choice during adolescence. Leveraging a unique large sample of 427 human youths (208 males and 219 females) imaged as part of the Philadelphia Neurodevelopmental Cohort, we examined associations between delay discounting and cortical thickness within structural covariance networks. These structural networks were derived using non-negative matrix factorization, an advanced multivariate technique for dimensionality reduction, and analyzed using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. We found that impulsive choice, as measured by greater discounting, was most strongly associated with diminished cortical thickness in structural brain networks that encompassed the ventromedial prefrontal cortex, orbitofrontal cortex, temporal pole, and temporoparietal junction. Furthermore, structural brain networks predicted DD above and beyond cognitive performance. Together, these results suggest that reduced cortical thickness in regions known to be involved in value-based decision-making is a marker of impulsive choice during the critical period of adolescence.SIGNIFICANCE STATEMENT Risky behaviors during adolescence, such as initiation of substance use or reckless driving, are a major source of morbidity and mortality. In this study, we present evidence from a large sample of youths that diminished cortical thickness in specific structural brain networks is associated with impulsive choice. Notably, the strongest association between impulsive choice and brain structure was seen in regions implicated in value-based decision-making; namely, the ventromedial prefrontal and orbitofrontal cortices. Moving forward, such neuroanatomical markers of impulsivity may aid in the development of personalized interventions targeted to reduce risk of negative outcomes resulting from impulsivity during adolescence.

The Last Link: from Gun Acquisition to Criminal Use.

Guns that are used in crime and recovered by the police typically have changed hands often since first retail sale and are quite old. While there is an extensive literature on "time to crime" for guns, defined as the elapsed time from first retail sale to known use in a crime, there is little information available on the duration of the "last link"-the elapsed time from the transaction that actually provided the offender with the gun in question. In this article, we use data from the new Chicago Inmate Survey (CIS) to estimate the duration of the last link. The median is just 2 months. Many of the gun-involved respondents to the CIS (42%) did not have any gun 6 months prior to their arrest for the current crime. The CIS respondents were almost all barred from purchasing a gun from a gun store because of their prior criminal record-as a result, their guns were obtained by illegal transactions with friends, relatives, and the underground market. We conclude that more effective enforcement of the laws governing gun transactions may have a quick and pervasive effect on gun use in crime.

Cross-sectional and longitudinal analysis of the active commuting behaviors of U.S. Department of the Interior employees.

BACKGROUND: Despite evolving evidence of the health and economic benefits of active transportation (AT) to work, few studies have examined the determinants of AT in large organizations with multiple worksites nor how trends in commuting change over time. METHODS: The data were obtained from the U.S. Department of the Interior Employee Commuting Census of 2010 (n = 23,230), and 2012-2016 (n = 21,725-25,974). The respondents were grouped into four commuting categories: non-active mode, walking, biking, and mixed-mode. Multinomial logistic regression analysis was utilized to examine the correlates of choosing AT to work for the 2010 data. Next, a repeated cross-sectional analysis was completed for all six years of data. RESULTS: In 2010, AT modes were only chosen by approximately 10% of respondents. Employees who lived farther from work and did not have a public transit station within 0.5 miles from home were generally less likely to choose AT. Respondents working in non-metro workplaces were less likely to bike or take mixed-modes to work, but more likely to walk. Men were more likely to choose AT modes, particularly biking. Respondents aged ≤30 yrs. were less likely to bike than those 31 to 40 yrs., but more likely than those ≥61 yrs. In 2010, the number of respondents that walked was higher, and biked and took mixed-modes was lower when compared to 2016, while the choice to take mixed-modes was higher in 2012 and 2013 when compared to 2016. Daily commuting distances in 2016 tended to be lower than 2010 and 2012, and higher than 2013. However, overall AT choice and commute distance remained reasonably stable over time. CONCLUSIONS: Respondents who lived close to their workplace and a public transportation station, worked in a metro location, were male and younger were more likely to choose AT modes to work. The results provide insight for the U.S. Department of the Interior and other large organizations to develop intervention strategies that support AT to work. Further research is warranted to understand the concurrent individual, social, and environmental barriers and facilitators for choosing AT to work.

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI.

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.