The YOUth cohort study: MRI protocol and test-retest reliability in adults

Individual differences of white matter characteristic along the anterior insula-based fiber tract circuit for pain empathy in healthy women and women with primary dysmenorrhea

Pain empathy, defined as the ability of one person to understand another person's pain, shows large individual variations. The anterior insula is the core region of the pain empathy network. However, the relationship between white matter (WM) properties of the fiber tracts connecting the anterior insula with other cortical regions and an individual's ability to modulate pain empathy remains largely unclear. In this study, we outline an automatic seed-based fiber streamline (sFS) analysis method and multivariate pattern analysis (MVPA) to predict the levels of pain empathy in healthy women and women with primary dysmenorrhoea (PDM). Using the sFS method, the anterior insula-based fiber tract network was divided into five fiber cluster groups. In healthy women, interindividual differences in pain empathy were predicted only by the WM properties of the five fiber cluster groups, suggesting that interindividual differences in pain empathy may rely on the connectivity of the anterior insula-based fiber tract network. In women with PDM, pain empathy could be predicted by a single cluster group. The mean WM properties along the anterior insular-rostroventral area of the inferior parietal lobule further mediated the effect of pain on empathy in patients with PDM. Our results suggest that chronic periodic pain may lead to maladaptive plastic changes, which could further impair empathy by making women with PDM feel more pain when they see other people experiencing pain. Our study also addresses an important gap in the analysis of the microstructural characteristics of seed-based fiber tract network.

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction

BACKGROUND

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume.

MATERIAL AND METHODS

A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.

RESULTS

The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.

CONCLUSION

The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.

NO LEVEL ASSIGNED

This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Correlations between developmental trajectories of brain functional connectivity, neurocognitive functions, and clinical symptoms in patients with attention-deficit hyperactivity disorder

Several studies on attention-deficit hyperactivity disorder (ADHD) have suggested a developmental sequence of brain changes: subcortico-subcortical connectivity in children, evolving to subcortico-cortical in adolescence, and culminating in cortico-cortical connectivity in young adulthood. This study hypothesized that children with ADHD would exhibit decreased functional connectivity (FC) between the cortex and striatum compared to adults with ADHD, who may show increased FC in these regions. Seventy-six patients with ADHD (26 children, 26 adolescents, and 24 adults) and 74 healthy controls (25 children, 24 adolescents, and 25 adults) participated in the study. Resting state magnetic resonance images were acquired using a 3.0 T Philips Achieva scanner. The results indicated a gradual decrease in the number of subcategories representing intelligence quotient deficits in the ADHD group with age. In adulthood, the ADHD group exhibited lower working memory compared to the healthy control group. The number of regions showing decreased FC from the cortex to striatum between the ADHD and control groups reduced with age, while regions with increased FC from the default mode network and attention network in the ADHD group increased with age. In adolescents and adults, working memory was positively associated with brain activity in the postcentral gyrus and negatively correlated with ADHD clinical symptoms. In conclusion, the findings suggest that intelligence deficits in certain IQ subcategories may diminish as individuals with ADHD age. Additionally, the study indicates an increasing anticorrelation between cortical and subcortical regions with age in individuals with ADHD.

A radiomics-based brain network in T1 images: construction, attributes, and applications

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

A sex-stratified analysis of the genetic architecture of human brain anatomy

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared males and females - despite theoretical grounds for potential sex differences. By systematically screening for sex differences in the common genetic architecture of > 1000 neuroanatomical phenotypes in the UK Biobank, we establish a general concordance between males and females in heritability estimates, genetic correlations and variant-level effects. Notable exceptions include: higher mean h 2 in females for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and, a male-specific effect common variant mapping to RBFOX1 - a gene linked to multiple male-biased neuropsychiatric disorders. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research as biobanks continue to grow in size.

Early infant prefrontal gray matter volume is associated with concurrent and future infant emotionality

High levels of infant negative emotionality (NE) are associated with emotional and behavioral problems later in childhood. Identifying neural markers of high NE as well as low positive emotionality (PE) in infancy can provide neural markers to aid early identification of vulnerability, and inform interventions to help delay or even prevent psychiatric disorders before the manifestation of symptoms. Prefrontal cortical (PFC) subregions support the regulation of NE and PE, with each PFC subregion differentially specializing in distinct emotional regulation processes. Gray matter (GM) volume measures show good test-retest reliability, and thus have potential use as neural markers of NE and PE. Yet, while studies showed PFC GM structural abnormalities in adolescents and young adults with affective disorders, few studies examined how PFC subregional GM measures are associated with NE and PE in infancy. We aimed to identify relationships among GM in prefrontal cortical subregions at 3 months and caregiver report of infant NE and PE, covarying for infant age and gender and caregiver sociodemographic and clinical variables, in two independent samples at 3 months (Primary: n = 75; Replication sample: n = 40) and at 9 months (Primary: n = 44; Replication sample: n = 40). In the primary sample, greater 3-month medial superior frontal cortical volume was associated with higher infant 3-month NE (p < 0.05); greater 3-month ventrolateral prefrontal cortical volume predicted lower infant 9-month PE (p < 0.05), even after controlling for 3-month NE and PE. GM volume in other PFC subregions also predicted infant 3- and 9-month NE and PE, together with infant demographic factors, caregiver age, and/or caregiver affective instability and anxiety. These findings were replicated in the independent sample. To our knowledge, this is the first study to determine in primary and replication samples associations among infant PFC GM volumes and concurrent and prospective NE and PE, and identify promising, early markers of future psychopathology risk.

Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study

BACKGROUND

Major depressive disorder (MDD) may be associated with accelerated brain aging (higher brain age than chronological age). This report evaluated whether brain age is a clinically useful biomarker by checking its test-retest reliability using magnetic resonance imaging scans acquired 1 week apart and by evaluating the association of accelerated brain aging with symptom severity and antidepressant treatment outcomes.

METHODS

Brain age was estimated in participants of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study using T1-weighted structural magnetic resonance imaging (MDD n = 290; female n = 192; healthy control participants n = 39; female n = 24). Intraclass correlation coefficient was used for baseline-to-week-1 test-retest reliability. Association of baseline Δ brain age (brain age minus chronological age) with Hamilton Depression Rating Scale-17 and Concise Health Risk Tracking Self-Report domains (impulsivity, suicide propensity [measures: pessimism, helplessness, perceived lack of social support, and despair], and suicidal thoughts) were assessed at baseline (linear regression) and during 8-week-long treatment with either sertraline or placebo (repeated-measures mixed models).

RESULTS

Mean ± SD baseline chronological age, brain age, and Δ brain age were 37.1 ± 13.3, 40.6 ± 13.1, and 3.1 ± 6.1 years in MDD and 37.1 ± 14.7, 38.4 ± 12.9, and 0.6 ± 5.5 years in healthy control groups, respectively. Test-retest reliability was high (intraclass correlation coefficient = 0.98-1.00). Higher baseline Δ brain age in the MDD group was associated with higher baseline impulsivity and suicide propensity and predicted smaller baseline-to-week-8 reductions in Hamilton Depression Rating Scale-17, impulsivity, and suicide propensity with sertraline but not with placebo.

CONCLUSIONS

Brain age is a reliable and potentially clinically useful biomarker that can prognosticate antidepressant treatment outcomes.

The Role of School Environment in Brain Structure, Connectivity, and Mental Health in Children: A Multimodal Investigation

BACKGROUND

Much work has been dedicated to understanding the effects of adverse home environments on brain development. While the school social and learning environment plays a role in child development, little work has been done to investigate the impact of the school environment on the developing brain. The goal of the present study was to examine associations between the school environment, brain structure and connectivity, and mental health.

METHODS

In this preregistered study we investigated these questions in a large sample of adolescents (9-10 years of age) from the Adolescent Brain Cognitive Development (ABCD) Study. We examined the association between school environment and gray matter (n = 10,435) and white matter (n = 10,770) structure and functional connectivity (n = 9528). We then investigated multivariate relationships between school-associated brain measures and mental health.

RESULTS

School environment was associated with connectivity of the auditory and retrosplenial temporal network as well as of higher-order cognitive networks like the cingulo-opercular, default mode, ventral attention, and frontoparietal networks. Multivariate analyses revealed that connectivity of the cingulo-opercular and default mode networks was also associated with mental health.

CONCLUSIONS

Findings shed light on the neural mechanisms through which favorable school environments may contribute to positive mental health outcomes in children. Our findings have implications for interventions targeted at promoting positive youth functioning through improving school environments.

PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three datasets of volunteers from two clinical trials were included. Breast MR images were acquired on 3 T Siemens Biograph mMR, Prisma, and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique. Two whole-breast segmentation strategies, utilizing image registration and 3D U-Net, were developed. Manual segmentation was performed. A task-based analysis was performed: a previously developed MR-based BD measure, MagDensity, was calculated and assessed using automated and manual segmentation. The mean squared error (MSE) and intraclass correlation coefficient (ICC) between MagDensity were evaluated using the manual segmentation as a reference. The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures (Δ2-1), MSE, and ICC. The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation, with ICCs of 0.986 (95%CI: 0.974-0.993) and 0.983 (95%CI: 0.961-0.992), respectively. For test-retest analysis, MagDensity derived using the registration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993 (95%CI: 0.982-0.997) when compared to other segmentation methods. In conclusion, the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD. Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment, with the registration exhibiting superior performance for highly reproducible BD measurements.

Test-retest reliability of diffusion tensor imaging scalars in 5-year-olds

Diffusion tensor imaging (DTI) has provided great insights into the microstructural features of the developing brain. However, DTI images are prone to several artifacts and the reliability of DTI scalars is of paramount importance for interpreting and generalizing the findings of DTI studies, especially in the younger population. In this study, we investigated the intrascan test–retest repeatability of four DTI scalars: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in 5‐year‐old children (N = 67) with two different data preprocessing approaches: a volume censoring pipeline and an outlier replacement pipeline. We applied a region of interest (ROI) and a voxelwise analysis after careful quality control, tensor fitting and tract‐based spatial statistics. The data had three subsets and each subset included 31, 32, or 33 directions thus a total of 96 unique uniformly distributed diffusion encoding directions per subject. The repeatability of DTI scalars was evaluated with intraclass correlation coefficient (ICC(3,1)) and the variability between test and retest subsets. The results of both pipelines yielded good to excellent (ICC(3,1) > 0.75) reliability for most of the ROIs and an overall low variability (<10%). In the voxelwise analysis, FA and RD had higher ICC(3,1) values compared to AD and MD and the variability remained low (<12%) across all scalars. Our results suggest high intrascan repeatability in pediatric DTI and lend confidence to the use of the data in future cross‐sectional and longitudinal studies.

Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers

Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation.

Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results.

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD)

As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development Study^SM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n = 7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

Structural and Functional Hippocampal Correlations in Environmental Enrichment During the Adolescent to Adulthood Transition in Mice

Environmental enrichment is known to induce neuronal changes; however, the underlying structural and functional factors involved are not fully known and remain an active area of study. To investigate these factors, we assessed enriched environment (EE) and standard environment (SE) control mice over 30 days using structural and functional MRI methods. Naïve adult male mice (n = 30, ≈20 g, C57BL/B6J, postnatal day 60 initial scan) were divided into SE and EE groups and scanned before and after 30 days. Structural analyses included volumetry based on manual segmentation as well as diffusion tensor imaging (DTI). Functional analyses included seed-based analysis (SBA), independent component analysis (ICA), the amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF). Structural results indicated that environmental enrichment led to an increase in the volumes of cornu ammonis 1 (CA1) and dentate gyrus. Structural results indicated changes in radial diffusivity and mean diffusivity in the visual cortex and secondary somatosensory cortex after EE. Furthermore, SBA and ICA indicated an increase in resting-state functional MRI (rsfMRI) functional connectivity in the hippocampus. Using parallel structural and functional analyses, we have demonstrated coexistent structural and functional changes in the hippocampal subdivision CA1. Future research should map alterations temporally during environmental enrichment to investigate the initiation of these structural and functional changes.

Right inferior frontal gyrus and ventromedial prefrontal activation during response inhibition is implicated in the development of PTSD symptoms

Background

Inhibition is a critical executive control process and an established neurobiological phenotype of PTSD, yet to our knowledge, no prospective studies have examined this using a contextual cue task that enables measurement of behavioural response and neural activation patterns across proactive and reactive inhibition.

Objective

The current longitudinal study utilised functional magnetic resonance imaging (fMRI) to examine whether deficits in proactive and reactive inhibition predicted PTSD symptoms six months after trauma.

Method

Twenty-three (65% males) medical patients receiving emergency medical care from a level 1 trauma centre were enrolled in the study and invited for an MRI scan 1-2-months post-trauma. PTSD symptoms were measured using self-report at scan and 6-months post-trauma. A stop-signal anticipation task (SSAT) during an fMRI scan was used to test whether impaired behavioural proactive and reactive inhibition, and reduced activation in right inferior frontal gyrus (rIFG), ventromedial prefrontal cortex (vmPFC), and bilateral hippocampus, were related to PTSD symptoms. We predicted that lower activation levels of vmPFC and rIFG during reactive inhibition and lower activation of hippocampus and rIFG during proactive inhibition would relate to higher 6-month PTSD symptoms.

Results

No significant associations were found between behavioural measures and 6-month PTSD. Separate linear regression analyses showed that reduced rIFG activation (F_1,21= 9.97, R²= .32, p = .005) and reduced vmPFC activation (F_1,21= 5.19, R²= .20, p = .03) significantly predicted greater 6-month PTSD symptoms; this result held for rIFG activation controlling for demographic variables and baseline PTSD symptoms (β = -.45, p = .04) and Bonferroni correction.

Conclusion

Our findings suggest that impaired rIFG and, to a lesser extent, vmPFC activation during response inhibition may predict the development of PTSD symptoms following acute trauma exposure. Given the small sample size, future replication studies are needed.

HIGHLIGHTS

Impaired inhibition may be an important risk factor for the development of PTSD following trauma, with less right inferior frontal gyrus and ventromedial prefrontal cortex activation during response inhibition predicting PTSD development.

Adverse childhood experiences and fronto-subcortical structures in the developing brain

The impact of adverse childhood experiences (ACEs) differs between individuals and depends on the type and timing of the ACE. The aim of this study was to assess the relation between various recently occurred ACEs and morphology in the developing brain of children between 8 and 11 years of age. We measured subcortical volumes, cortical thickness, cortical surface area and fractional anisotropy in regions of interest in brain scans acquired in 1,184 children from the YOUth cohort. ACEs were based on parent-reports of recent experiences and included: financial problems; parental mental health problems; physical health problems in the family; substance abuse in the family; trouble with police, justice or child protective services; change in household composition; change in housing; bereavement; divorce or conflict in the family; exposure to violence in the family and bullying victimization. We ran separate linear models for each ACE and each brain measure. Results were adjusted for the false discovery rate across regions of interest. ACEs were reported for 83% of children in the past year. Children were on average exposed to two ACEs. Substance abuse in the household was associated with larger cortical surface area in the left superior frontal gyrus, t(781) = 3.724, p _FDR = 0.0077, right superior frontal gyrus, t(781) = 3.409, p _FDR = 0.0110, left pars triangularis, t(781) = 3.614, p _FDR = 0.0077, left rostral middle frontal gyrus, t(781) = 3.163, p _FDR = 0.0195 and right caudal anterior cingulate gyrus, t(781) = 2.918, p _FDR = 0.0348. Household exposure to violence (was associated with lower fractional anisotropy in the left and right cingulum bundle hippocampus region t(697) = -3.154, p _FDR = 0.0101 and t(697) = -3.401, p _FDR = 0.0085, respectively. Lower household incomes were more prevalent when parents reported exposure to violence and the mean parental education in years was lower when parents reported substance abuse in the family. No other significant associations with brain structures were found. Longer intervals between adversity and brain measurements and longitudinal measurements may reveal whether more evidence for the impact of ACEs on brain development will emerge later in life.

Self-regulation in the pre-adolescent brain

Self-regulation refers to the ability to monitor and modulate emotions, behavior, and cognition, which in turn allows us to achieve goals and adapt to ever changing circumstances. This trait develops from early infancy well into adulthood, and features both low-level executive functions such as reactive inhibition, as well as higher level executive functions such as proactive inhibition. Development of self-regulation is linked to brain maturation in adolescence and adulthood. However, how self-regulation in daily life relates to brain functioning in pre-adolescent children is not known. To this aim, we have analyzed data from 640 children aged 8–11, who performed a stop-signal anticipation task combined with functional magnetic resonance imaging, in addition to questionnaire data on self-regulation. We find that pre-adolescent boys and girls who display higher levels of self-regulation, are better able to employ proactive inhibitory control strategies, exhibit stronger frontal activation and more functional coupling between cortical and subcortical areas of the brain. Furthermore, we demonstrate that pre-adolescent children show significant activation in areas of the brain that were previously only associated with reactive and proactive inhibition in adults and adolescents. Thus, already in pre-adolescent children, frontal-striatal brain areas are active during self-regulatory behavior.

•

Children with higher levels of self-regulation employ more proactive inhibition.

•

During proactive inhibition, children aged 8–11 show activation in frontal-cortical areas.

•

Children higher in self-regulation exhibit more cortical-subcortical coupling.

•

Children aged 8–11 show similar brain activation as adults during inhibition.

De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur.

Test-Retest Reliability of Neural Correlates of Response Inhibition and Error Monitoring: An fMRI Study of a Stop-Signal Task

Response inhibition (RI) and error monitoring (EM) are important processes of adaptive goal-directed behavior, and neural correlates of these processes are being increasingly used as transdiagnostic biomarkers of risk for a range of neuropsychiatric disorders. Potential utility of these purported biomarkers relies on the assumption that individual differences in brain activation are reproducible over time; however, available data on test-retest reliability (TRR) of task-fMRI are very mixed. This study examined TRR of RI and EM-related activations using a stop signal task in young adults (n = 56, including 27 pairs of monozygotic (MZ) twins) in order to identify brain regions with high TRR and familial influences (as indicated by MZ twin correlations) and to examine factors potentially affecting reliability. We identified brain regions with good TRR of activations related to RI (inferior/middle frontal, superior parietal, and precentral gyri) and EM (insula, medial superior frontal and dorsolateral prefrontal cortex). No subcortical regions showed significant TRR. Regions with higher group-level activation showed higher TRR; increasing task duration improved TRR; within-session reliability was weakly related to the long-term TRR; motion negatively affected TRR, but this effect was abolished after the application of ICA-FIX, a data-driven noise removal method.

The YOUth study: Rationale, design, and study procedures

•

This article describes the rationale, design, and procedures of the YOUth cohort.

•

YOUth is set up to investigate what drives the development of social competence and self-regulation in children.

•

YOUth specifically investigates the role of neurocognitive development in child development.

•

YOUth has a flexible longitudinal design with repeated measurements throughout childhood, starting prenatally.

Behavioral development in children shows large inter-individual variation, and is driven by the interplay between biological, psychological, and environmental processes. However, there is still little insight into how these processes interact. The YOUth cohort specifically focuses on two core characteristics of behavioral development: social competence and self-regulation. Social competence refers to the ability to engage in meaningful interactions with others, whereas self-regulation is the ability to control one’s emotions, behavior, and impulses, to balance between reactivity and control of the reaction, and to adjust to the prevailing environment. YOUth is an accelerated population-based longitudinal cohort study with repeated measurements, centering on two groups: YOUth Baby & Child and YOUth Child & Adolescent. YOUth Baby & Child aims to include 3,000 pregnant women, their partners and children, wheras YOUth Child & Adolescent aims to include 2,000 children aged between 8 and 10 years old and their parents. All participants will be followed for at least 6 years, and potentially longer.

In this paper we describe in detail the design of this study, the population included, the determinants, intermediate neurocognitive measures and outcomes included in the study. Furthermore, we describe in detail the procedures of inclusion, informed consent, and study participation.

FAIR, safe and high-quality data: The data infrastructure and accessibility of the YOUth cohort study

The YOUth cohort study aims to be a trailblazer for open science. Being a large-scale, longitudinal cohort following children in their development from gestation until early adulthood, YOUth collects a vast amount of data through a variety of research techniques. Data are collected through multiple platforms, including facilities managed by Utrecht University and the University Medical Center Utrecht. In order to facilitate appropriate use of its data by research organizations and researchers, YOUth aims to produce high-quality, FAIR data while safeguarding the privacy of participants. This requires an extensive data infrastructure, set up by collaborative efforts of researchers, data managers, IT departments, and the Utrecht University Library. In the spirit of open science, YOUth will share its experience and expertise in setting up a high-quality research data infrastructure for sensitive cohort data. This paper describes the technical aspects of our data and data infrastructure, and the steps taken throughout the study to produce and safely store FAIR and high-quality data. Finally, we will reflect on the organizational aspects that are conducive to the success of setting up such an enterprise, and we consider the financial challenges posed by individual studies investing in sustainable science.