The Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository

Intelligence and cortical morphometry: caveats in brain-behavior associations

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.

Differences in regional brain structure in toddlers with autism are related to future language outcomes

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.

Differences in educational opportunity predict white matter development

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

White matter trajectories over the lifespan

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years

Background

A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth.

Methods

To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology.

Results

Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC.

Conclusion

Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.

Typological thinking in human genomics research contributes to the production and prominence of scientific racism

Public genomic datasets like the 1000 Genomes project (1KGP), Human Genome Diversity Project (HGDP), and the Adolescent Brain Cognitive Development (ABCD) study are valuable public resources that facilitate scientific advancements in biology and enhance the scientific and economic impact of federally funded research projects. Regrettably, these datasets have often been developed and studied in ways that propagate outdated racialized and typological thinking, leading to fallacious reasoning among some readers that social and health disparities among the so-called races are due in part to innate biological differences between them. We highlight how this framing has set the stage for the racist exploitation of these datasets in two ways: First, we discuss the use of public biomedical datasets in studies that claim support for innate genetic differences in intelligence and other social outcomes between the groups identified as races. We further highlight recent instances of this which involve unauthorized access, use, and dissemination of public datasets. Second, we discuss the memification, use of simple figures meant for quick dissemination among lay audiences, of population genetic data to argue for a biological basis for purported human racial groups. We close with recommendations for scientists, to preempt the exploitation and misuse of their data, and for funding agencies, to better enforce violations of data use agreements.

Large-scale investigation of white matter structural differences in bilingual and monolingual children: An adolescent brain cognitive development data study

Emerging research has provided valuable insights into the structural characteristics of the bilingual brain from studies of bilingual adults; however, there is a dearth of evidence examining brain structural alterations in childhood associated with the bilingual experience. This study examined the associations between bilingualism and white matter organization in bilingual children compared to monolingual peers leveraging the large-scale data from the Adolescent Brain Cognitive Development (ABCD) Study. Then, 446 bilingual children (ages 9-10) were identified from the participants in the ABCD data and rigorously matched to a group of 446 monolingual peers. Multiple regression models for selected language and cognitive control white matter pathways were used to compare white matter fractional anisotropy (FA) values between bilinguals and monolinguals, controlling for demographic and environmental factors as covariates in the models. Results revealed significantly lower FA values in bilinguals compared to monolinguals across established dorsal and ventral language network pathways bilaterally (i.e., the superior longitudinal fasciculus and inferior frontal-occipital fasciculus) and right-hemispheric pathways in areas related to cognitive control and short-term memory (i.e., cingulum and parahippocampal cingulum). In contrast to the enhanced FA values observed in adult bilinguals relative to monolinguals, our findings of lower FA in bilingual children relative to monolinguals may suggest a protracted development of white matter pathways associated with language and cognitive control resulting from dual language learning in childhood. Further, these findings underscore the need for large-scale longitudinal investigation of white matter development in bilingual children to understand neuroplasticity associated with the bilingual experience during this period of heightened language learning.

Relationship between brain structural network integrity and emotional symptoms in youth with perinatally-acquired HIV

Perinatally acquired HIV infection (PHIV) currently affects approximately 1.7 million children worldwide. Youth with PHIV (YPHIV) are at increased risk for emotional and behavioral symptoms, yet few studies have examined relationships between these symptoms and brain structure. Previous neuroimaging studies in YPHIV report alterations within the salience network (SN), cognitive control network (CCN), and default mode network (DMN). These areas have been associated with social and emotional processing, emotion regulation, and executive function. We examined structural brain network integrity from MRI using morphometric similarity networks and graph theoretical measures of segregation (transitivity), resilience (assortativity), and integration (global efficiency). We examined brain network integrity of 40 YPHIV compared to 214 youths without HIV exposure or infection. Amongst YPHIV, we related structural brain network metrics to the Emotional Symptoms Index of the Behavioral Assessment System for Children, 2nd edition. We also examined the relationship of inflammatory biomarkers in YPHIV to brain network integrity. YPHIV had significantly lower global efficiency in the SN, DMN, and the whole brain network compared to controls. YPHIV also demonstrated lower assortativity or resilience (i.e., network robustness) compared to controls in the DMN and whole brain network. Further, higher emotional symptom score was associated with higher global efficiency in the SN and lower global efficiency in the DMN, signaling more emotional challenges. A significant association was also found between several inflammatory and cardiac markers with structural network integrity. These findings suggest an impact of HIV on developing brain networks, and potential dysfunction of the SN and DMN in relation to network efficiency.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

Brain structural covariance network features are robust markers of early heavy alcohol use

BACKGROUND AND AIMS

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

DESIGN AND SETTING

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

CASES

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

MEASUREMENTS

Graph theory metrics of segregation and integration were used to summarize SCN.

FINDINGS

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

CONCLUSION

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data

We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmap Reduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.

Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents

BACKGROUND

Depression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume.

METHODS

Participants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions.

RESULTS

Children and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes.

LIMITATIONS

Participants in these analyses were all of European ancestry.

CONCLUSIONS

Polygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies.

DECLARATIONS OF INTEREST

none.

DECLARATIONS OF INTEREST

None.

Automated temporalis muscle quantification and growth charts for children through adulthood

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Schizophrenia Polygenic Risk During Typical Development Reflects Multiscale Cortical Organization

Background

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development in otherwise typically developing children and adolescents may be revealed using polygenic risk scores for schizophrenia (PRS-SCZ).

Methods

We assessed PRS-SCZ and cortical morphometry in typically developing children and adolescents (3-21 years, 46.8% female) using whole-genome genotyping and T1-weighted magnetic resonance imaging (n = 390) from the PING (Pediatric Imaging, Neurocognition, and Genetics) cohort. We contextualized the findings using 1) age-matched transcriptomics, 2) histologically defined cytoarchitectural types and functionally defined networks, and 3) case-control differences of schizophrenia and other major psychiatric disorders derived from meta-analytic data of 6 ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) working groups, including a total of 12,876 patients and 15,670 control participants.

Results

Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ-related increases in vertexwise cortical thickness were mainly distributed in association cortical areas, particularly the ventral attention network, while relatively sparing koniocortical type cortex (i.e., primary sensory areas). The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders derived from the ENIGMA consortium. Age group models illustrate a possible trajectory from PRS-SCZ-associated cortical thickness increases in early childhood toward thinning in late adolescence, with the latter resembling the adult brain phenotype of schizophrenia.

Conclusions

Collectively, combining imaging genetics with multiscale mapping, our work provides novel insight into how genetic risk for schizophrenia affects the cortex early in life.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet

Introduction

Neurodevelopment and related mental disorders (NDDs) are one of the most frequent disabilities among young people. They have complex clinical phenotypes often associated with transnosographic dimensions, such as emotion dysregulation and executive dysfunction, that lead to adverse impacts in personal, social, academic, and occupational functioning. Strong overlap exists then across NDDs phenotypes that are challenging for diagnosis and therapeutic intervention. Recently, digital epidemiology uses the rapidly growing data streams from various devices to advance our understanding of health's and disorders' dynamics, both in individuals and the general population, once coupled with computational science. An alternative transdiagnostic approach using digital epidemiology may thus better help understanding brain functioning and hereby NDDs in the general population.

Objective

The EPIDIA4Kids study aims to propose and evaluate in children, a new transdiagnostic approach for brain functioning examination, combining AI-based multimodality biometry and clinical e-assessments on an unmodified tablet. We will examine this digital epidemiology approach in an ecological context through data-driven methods to characterize cognition, emotion, and behavior, and ultimately the potential of transdiagnostic models of NDDs for children in real-life practice.

Methods and analysis

The EPIDIA4Kids is an uncontrolled open-label study. 786 participants will be recruited and enrolled if eligible: they are (1) aged 7 to 12 years and (2) are French speaker/reader; (3) have no severe intellectual deficiencies. Legal representative and children will complete online demographic, psychosocial and health assessments. During the same visit, children will perform additionally a paper/pencil neuro-assessments followed by a 30-min gamified assessment on a touch-screen tablet. Multi-stream data including questionnaires, video, audio, digit-tracking, will be collected, and the resulting multimodality biometrics will be generated using machine- and deep-learning algorithms. The trial will start in March 2023 and is expected to end by December 2024.

Discussion

We hypothesize that the biometrics and digital biomarkers will be capable of detecting early onset symptoms of neurodevelopment compared to paper-based screening while as or more accessible in real-life practice.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.

Longitudinal Structural and Diffusion-Weighted Neuroimaging of Young Children Born Preterm

BACKGROUND

Children born preterm are at risk for diffuse injury to subcortical gray and white matter.

METHODS

We used a longitudinal cohort study to examine the development of subcortical gray matter and white matter volumes, and diffusivity measures of white matter tracts following preterm birth. Our participants were 47 children born preterm (24 to 32 weeks gestational age) and 28 children born at term. None of the children born preterm had significant neonatal brain injury. Children received structural and diffusion weighted magnetic resonance imaging scans at ages five, six, and seven years. We examined volumes of amygdala, hippocampus, caudate nucleus, putamen, thalamus, brainstem, cerebellar white matter, intracranial space, and ventricles, and volumes, fractional anisotropy, and mean diffusivity of anterior thalamic radiation, cingulum, corticospinal tract, corpus callosum, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, temporal and parietal superior longitudinal fasciculus, and uncinate fasciculus.

RESULTS

Children born preterm had smaller volumes of thalamus, brainstem, cerebellar white matter, cingulum, corticospinal tract, inferior frontal occipital fasciculus, uncinate fasciculus, and temporal superior longitudinal fasciculus, whereas their ventricles were larger compared with term-born controls. We found no significant effect of preterm birth on diffusivity measures. Despite developmental changes and growth, group differences were present and similarly strong at all three ages.

CONCLUSION

Even in the absence of significant neonatal brain injury, preterm birth has a persistent impact on early brain development. The lack of a significant term status by age interaction suggests a delayed developmental trajectory.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Impact of Socioeconomic Status on Amygdala and Hippocampus Subdivisions in Children and Adolescents

Socioeconomic status (SES) in childhood can impact behavioral and brain development. Past work has consistently focused on the amygdala and hippocampus, two brain areas critical for emotion and behavioral responding. While there are SES differences in amygdala and hippocampal volumes, there are many unanswered questions in this domain connected to neurobiological specificity, and for whom these effects may be more pronounced. We may be able to investigate some anatomical subdivisions of these brain areas, as well as if relations with SES vary by participant age and sex. No work to date has however completed these types of analyses. To overcome these limitations, here, we combined multiple, large neuroimaging datasets of children and adolescents with information about neurobiology and SES (N=2,765). We examined subdivisions of the amygdala and hippocampus and found multiple amygdala subdivisions, as well as the head of the hippocampus, were related to SES. Greater volumes in these areas were seen for higher-SES youth participants. Looking at age- and sex-specific subgroups, we tended to see stronger effects in older participants, for both boys and girls. Paralleling effects for the full sample, we see significant positive associations between SES and volumes for the accessory basal amygdala and head of the hippocampus. We more consistently found associations between SES and volumes of the hippocampus and amygdala in boys (compared to girls). We discuss these results in relation to conceptions of "sex-as-a-biological variable" and broad patterns of neurodevelopment across childhood and adolescence. These results fill in important gaps on the impact of SES on neurobiology critical for emotion, memory, and learning.

A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging.

The BDNF Val68Met polymorphism causes a sex specific alcohol preference over social interaction and also acute tolerance to the anxiolytic effects of alcohol, a phenotype driven by malfunction of BDNF in the ventral hippocampus of male mice

BACKGROUND

The brain-derived neurotrophic factor (BDNF) Valine 66 to Methionine human polymorphism results in impaired activity-dependent BDNF release and has been linked to psychiatric disorders including depression and anxiety. We previously showed that male knock-in mice carrying the mouse Methionine homolog (Met68BDNF) exhibit excessive and compulsive alcohol drinking behaviors as compared to the wild-type Val68BDNF mice.

OBJECTIVE

Here, we set out to determine the potential mechanism for the heightened and compulsive alcohol drinking phenotypes detected in Met68BDNF mice.

RESULTS

We found that male, but not female Met68BDNF mice exhibit social anxiety-like behaviors. We further show that male Met68BDNF mice exhibit a preference for alcohol over social interaction. In contrast, alcohol place preference without an alternative social reward, is similar in male Met68BDNF and Val68BDNF mice. Since the Met68BDNF mice show social anxiety phenotypes, we tested whether alcohol reliefs anxiety similarly in Met68BDNF and Val68BDNF mice and found that male, but not female Met68BDNF mice are insensitive to the acute anxiolytic action of alcohol. Finally, we show that this acute tolerance to alcohol-dependent anxiolysis can be restored by overexpressing wild-type Val68BDNF in the ventral hippocampus (vHC) of Met68BDNF mice.

CONCLUSIONS

Together, our results suggest that excessive alcohol drinking in the Met68BDNF may be attributed, in part, to heighted social anxiety and a lack of alcohol-dependent anxiolysis, a phenotype that is associated with malfunction of BDNF signaling in the vHC of male Met68BDNF mice.

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

Language, Social, and Face Regions Are Affected in Toddlers with Autism and Predictive of Language Outcome

Identifying prognostic early brain alterations is crucial for autism spectrum disorder (ASD). Leveraging structural MRI data from 166 ASD and 109 typical developing (TD) toddlers and controlling for brain size, we found that, compared to TD, ASD toddlers showed larger or thicker lateral temporal regions; smaller or thinner frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most of these differences were replicated in an independent cohort of 38 ASD and 37 TD toddlers. Moreover, the identified brain alterations were related to ASD symptom severity and cognitive impairments at intake, and, remarkably, they improved the accuracy for predicting later language outcome beyond intake clinical and demographic variables. In summary, brain regions involved in language, social, and face processing were altered in ASD toddlers. These early-age brain alterations may be the result of dysregulation in multiple neural processes and stages and are promising prognostic biomarkers for future language ability.

Neuroanatomical correlates of genetic risk for obesity in children

Obesity has a strong genetic component, with up to 20% of variance in body mass index (BMI) being accounted for by common polygenic variation. Most genetic polymorphisms associated with BMI are related to genes expressed in the central nervous system. At the same time, higher BMI is associated with neurocognitive changes. However, the direct link between genetics of obesity and neurobehavioral mechanisms related to weight gain is missing. Here, we use a large sample of participants (n > 4000) from the Adolescent Brain Cognitive Development cohort to investigate how genetic risk for obesity, expressed as polygenic risk score for BMI (BMI-PRS), is related to brain and behavioral measures in adolescents. In a series of analyses, we show that BMI-PRS is related to lower cortical volume and thickness in the frontal and temporal areas, relative to age-expected values. Relatedly, using structural equation modeling, we find that lower overall cortical volume is associated with higher impulsivity, which in turn is related to an increase in BMI 1 year later. In sum, our study shows that obesity might partially stem from genetic risk as expressed in brain changes in the frontal and temporal brain areas, and changes in impulsivity.

Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways Through Mechanistic Models of Development

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences.

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.

Volumetric development of hippocampal subfields and hippocampal white matter connectivity: Relationship with episodic memory

The hippocampus is a complex structure composed of distinct subfields. It has been central to understanding neural foundations of episodic memory. In the current cross-sectional study, using a large sample of 830, 3- to 21-year-olds from a unique, publicly available dataset we examined the following questions: (1) Is there elevated grey matter volume of the hippocampus and subfields in late compared to early development? (2) How does hippocampal volume compare with the rest of the cerebral cortex at different developmental stages? and (3) What is the relation between hippocampal volume and connectivity with episodic memory performance? We found hippocampal subfield volumes exhibited a nonlinear relation with age and showed a lag in volumetric change with age when compared to adjacent cortical regions (e.g., entorhinal cortex). We also observed a significant reduction in cortical volume across older cohorts, while hippocampal volume showed the opposite pattern. In addition to age-related differences in gray matter volume, dentate gyrus/cornu ammonis 3 volume was significantly related to episodic memory. We did not, however, find any associations with episodic memory performance and connectivity through the uncinate fasciculus, fornix, or cingulum. The results are discussed in the context of current research and theories of hippocampal development and its relation to episodic memory.

Key considerations for child and adolescent MRI data collection

Cognitive neuroimaging researchers' ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.

Diffusion tensor estimation with transformer neural networks

Diffusion tensor imaging (DTI) is a widely used method for studying brain white matter development and degeneration. However, standard DTI estimation methods depend on a large number of high-quality measurements. This would require long scan times and can be particularly difficult to achieve with certain patient populations such as neonates. Here, we propose a method that can accurately estimate the diffusion tensor from only six diffusion-weighted measurements. Our method achieves this by learning to exploit the relationships between the diffusion signals and tensors in neighboring voxels. Our model is based on transformer networks, which represent the state of the art in modeling the relationship between signals in a sequence. In particular, our model consists of two such networks. The first network estimates the diffusion tensor based on the diffusion signals in a neighborhood of voxels. The second network provides more accurate tensor estimations by learning the relationships between the diffusion signals as well as the tensors estimated by the first network in neighboring voxels. Our experiments with three datasets show that our proposed method achieves highly accurate estimations of the diffusion tensor and is significantly superior to three competing methods. Estimations produced by our method with six diffusion-weighted measurements are comparable with those of standard estimation methods with 30-88 diffusion-weighted measurements. Hence, our method promises shorter scan times and more reliable assessment of brain white matter, particularly in non-cooperative patients such as neonates and infants.

Educational attainment polygenic scores, socioeconomic factors, and cortical structure in children and adolescents

Genome‐wide polygenic scores for educational attainment (PGS‐EA) and socioeconomic factors, which are correlated with each other, have been consistently associated with academic achievement and general cognitive ability in children and adolescents. Yet, the independent associations of PGS‐EA and socioeconomic factors with specific underlying factors at the neural and neurocognitive levels are not well understood. The main goals of this study were to examine the unique contributions of PGS‐EA and parental education to cortical structure and neurocognitive skills in children and adolescents, and the associations among PGS‐EA, cortical structure, and neurocognitive skills. Participants were typically developing 3‐ to 21‐year‐olds (53% male; N = 391). High‐resolution, T1‐weighted magnetic resonance imaging data were acquired, and cortical thickness (CT) and surface area (SA) were measured. PGS‐EA were computed based on the EA3 genome‐wide association study of educational attainment. Participants completed executive function, vocabulary, and episodic memory tasks. Higher PGS‐EA and parental education were independently and significantly associated with greater total SA and vocabulary. Higher PGS‐EA was significantly associated with greater SA in the left medial orbitofrontal gyrus and inferior frontal gyrus, which was associated with higher executive function. Higher parental education was significantly associated with greater SA in the left parahippocampal gyrus after accounting for PGS‐EA and total brain volume. These findings suggest that education‐linked genetics may influence SA in frontal regions, leading to variability in executive function. Associations of parental education with cortical structure in children and adolescents remained significant after controlling for PGS‐EA, a source of genetic confounding.

Parental socioeconomic status is linked to cortical microstructure and language abilities in children and adolescents

Gradients in parental socioeconomic status (SES) are closely linked to important life outcomes in children and adolescents, such as cognitive abilities, school achievement, and mental health. Parental SES may also influence brain development, with several magnetic resonance imaging (MRI) studies reporting associations with youth brain morphometry. However, MRI signal intensity metrics have not been assessed, but could offer a microstructural correlate, thereby increasing our understanding of SES influences on neurobiology. We computed a parental SES score from family income, parental education and parental occupation, and assessed relations with cortical microstructure as measured by T1w/T2w ratio (n = 504, age = 3-21 years). We found negative age-stabile relations between parental SES and T1w/T2w ratio, indicating that youths from lower SES families have higher ratio in widespread frontal, temporal, medial parietal and occipital regions, possibly indicating a more developed cortex. Effect sizes were small, but larger than for conventional morphometric properties i.e. cortical surface area and thickness, which were not significantly associated with parental SES. Youths from lower SES families had poorer language related abilities, but microstructural differences did not mediate these relations. T1w/T2w ratio appears to be a sensitive imaging marker for further exploring the association between parental SES and child brain development.

Age-related changes in diffuse optical tomography sensitivity profiles from childhood to adulthood

SIGNIFICANCE

Diffuse optical tomography (DOT) uses near-infrared light spectroscopy to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of the brain location that generates the hemodynamic signal require accurate descriptions of the DOT sensitivity to the underlying cortex. DOT sensitivity profiles are different in infants compared with adults. However, the descriptions of DOT sensitivity profiles from early childhood to adulthood are lacking despite the continuous head and brain development.

AIM

We aim to investigate age-related differences in DOT sensitivity profiles in individuals aged from 2 to 34 years with narrow age ranges of 0.5 or 1 year.

APPROACH

We implemented existing photon migration simulation methods and computed source-detector channel DOT sensitivity using age-appropriate, realistic head models.

RESULTS

DOT sensitivity profiles change systematically as a function of source-detector separation distance for all age groups. Children displayed distinctive DOT sensitivity profiles compared to older individuals, and the differences were enhanced at larger separation distances.

CONCLUSIONS

The findings have important implications for the design of source-detector placement and image reconstruction. Age-appropriate realistic head models should be used to provide anatomical guidance for standalone DOT data. Using age-inappropriate head models will have more negative impacts on estimation accuracy in younger children.

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Mapping Complex Brain Torque Components and Their Genetic Architecture and Phenomic Associations in 24,112 Individuals

BACKGROUND

The functional significance and mechanisms determining the development and individual variability of structural brain asymmetry remain unclear. Here, we systematically analyzed all relevant components of the most prominent structural asymmetry, brain torque (BT), and their relationships with potential genetic and nongenetic modifiers in a sample comprising 24,112 individuals from six cohorts.

METHODS

BT features, including petalia, bending, dorsoventral shift, brain tissue distribution asymmetries, and cortical surface positional asymmetries, were directly modeled using a set of automatic three-dimensional brain shape analysis approaches. Age-, sex-, and handedness-related effects on BT were assessed. The genetic architecture and phenomic associations of BT were investigated using genome- and phenome-wide association scans.

RESULTS

Our results confirmed the population-level predominance of the typical counterclockwise torque and suggested a first attenuating, then enlarging dynamic across the life span (3-81 years) primarily for frontal, occipital, and perisylvian BT features. Sex/handedness, BT, and cognitive function of verbal-numerical reasoning were found to be interrelated statistically. We observed differential heritability of up to 56% for BT, especially in temporal language areas. Individual variations of BT were also associated with various phenotypic variables of neuroanatomy, cognition, lifestyle, sociodemographics, anthropometry, physical health, and adult and child mental health. Our genomic analyses identified a number of genetic associations at lenient significance levels, which need to be further validated using larger samples in the future.

CONCLUSIONS

This study provides a comprehensive description of BT and insights into biological and other factors that may contribute to the development and individual variations of BT.

Do aggregate, multimodal structural neuroimaging measures replicate regional developmental differences observed in highly cited cellular histological studies?

Influential investigations of postmortem human brain tissue showed regional differences in tissue properties at early phases of development, such as between prefrontal and primary sensory cortical regions. Large-scale neuroimaging studies enable characterization of age-related trajectories with much denser sampling of cortical regions, assessment ages, and demographic variables than postmortem tissue analyses, but no single imaging measure perfectly captures what is measured with histology. Using publicly available data from the Pediatric Imaging, Neurocognition, and Genetics (PING) study, including 951 participants with ages ranging from 3 to 21 years, we characterized cortical regional variability in developmental trajectories of multimodal brain imaging measures. Multivariate analyses integrated morphometric and microstructural cortical surface measures. To replicate foundational histological work showing delayed synapse elimination in middle frontal gyrus relative to primary sensory areas, we tested whether developmental trajectories differ between prefrontal and visual or auditory cortex. We extended this to a whole-cortex analysis of interregional differences, producing cortical parcellations with maximally different developmental trajectories. Consistent with the general conclusions of postmortem analyses, our imaging results suggest that prefrontal regions show a protracted period of greater developmental change; however, they also illustrate the challenges of drawing conclusions about the relative maturational phases of different brain regions.

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Multimodal, multivariate, nonlinear modeling, integrating morphometric and microstructural measures.

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Tested regional developmental differences previously found in highly influential cellular histological studies.

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Produced cortical parcellations with maximally different, multimodal, developmental trajectories.

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Findings converge with evidence from histological studies showing delayed prefrontal cortical development.

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Interregional differences vary by measure and illustrate complexities of defining which regions mature first.

Functional MRI research involving healthy children: Ethics, safety and recommended procedures

AIM

This specific review aims to expose clinicians, researchers and administrators in hospitals to the importance, procedures and safety of fMRI studies to promote the increased utilisation of such studies in different geographical places worldwide. The child's brain is developing rapidly, both structurally and functionally. These functional changes can only be detected using functional scans generated from an MRI machine and referred to as a functional MRI (fMRI). This method may be used clinically in complex medical and surgical conditions (e.g., epilepsy surgery), but these days are often used for research purposes. However, due to ethical and logistical considerations, fMRI in the paediatric population is not widely and equally used in different geographical places.

CONCLUSIONS

The benefits of using this method to define the functional changes occurring in the developing brain are discussed in this review, along with desensitisation methods recommended when working with this vulnerable population in research and even in a clinical setting.

Privacy-preserving quality control of neuroimaging datasets in federated environments

Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.

Factors in the neurodevelopment of negative urgency: Findings from a community-dwelling sample

This study investigated neuroanatomic, genetic, cognitive, sociodemographic and emotional underpinnings of the Negative Urgency subscale of the Urgency, Premeditation, Perseverance, Sensation-Seeking and Positive Urgency Impulsive Behavior Scale in a healthy developmental sample. The goal of the investigation is to contribute to the harmonisation of behavioural, brain and neurogenetic aspects of behavioural self-control. Three domains – (1) Demographic, developmental, psychiatric and cognitive ability; (2) Regional brain volumes (neurobiological); and (3) Genetic variability (single nucleotide polymorphisms) – were examined, and models with relevant predictor variables were selected. Least absolute shrinkage and selection operator and best subset regressions were used to identify sparse models predicting negative urgency scores, which revealed that variables related to emotional regulation and right cingulate volume, as well as single nucleotide polymorphisms in CADM2 and SLC6A4, were associated with negative urgency. Our results contribute to the construct and criterion validity of negative urgency and support the hypothesis that negative urgency is a result of a complex array of influences across domains whose integration furthers developmental psychopathology research.

MDReg‐Net: Multi‐resolution diffeomorphic image registration using fully convolutional networks with deep self‐supervision

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self‐supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image‐wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi‐resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self‐supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high‐resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state‐of‐the‐art image registration algorithms.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

On Genetic Correlation Estimation With Summary Statistics From Genome-Wide Association Studies

Cross-trait polygenic risk score (PRS) method has gained popularity for assessing genetic correlation of complex traits using summary statistics from biobank-scale genome-wide association studies (GWAS). However, empirical evidence has shown a common bias phenomenon that highly significant cross-trait PRS can only account for a very small amount of genetic variance (R ² can be < 1%) in independent testing GWAS. The aim of this paper is to investigate and address the bias phenomenon of cross-trait PRS in numerous GWAS applications. We show that the estimated genetic correlation can be asymptotically biased toward zero. A consistent cross-trait PRS estimator is then proposed to correct such asymptotic bias. In addition, we investigate whether or not SNP screening by GWAS p-values can lead to improved estimation and show the effect of overlapping samples among GWAS. We analyze GWAS summary statistics of reaction time and brain structural magnetic resonance imaging-based features measured in the Pediatric Imaging, Neurocognition, and Genetics study. We find that the raw cross-trait PRS estimators heavily underestimate the genetic similarity between cognitive function and human brain structures (mean R ² = 1.32%), whereas the bias-corrected estimators uncover the moderate degree of genetic overlap between these closely related heritable traits (mean R ² = 22.42%). Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed

Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Investigating brain structural maturation in children and adolescents born very preterm using the brain age framework

Very preterm (VP) birth is associated with an increased risk for later neurodevelopmental and behavioural challenges. Although the neurobiological underpinnings of such challenges continue to be explored, previous studies have reported brain volume and morphology alterations in children and adolescents born VP compared with full-term (FT)-born controls. How these alterations relate to the trajectory of brain maturation, with potential implications for later brain ageing, remains unclear. In this longitudinal study, we investigate the relationship between VP birth and brain development during childhood and adolescence. We construct a normative 'brain age' model to predict age over childhood and adolescence based on measures of brain cortical and subcortical volumes and cortical morphology from structural MRI of a dataset of typically developing children aged 3-21 years (n = 768). Using this model, we examined deviations from normative brain development in a separate dataset of children and adolescents born VP (<30 weeks' gestation) at two timepoints (ages 7 and 13 years) compared with FT-born controls (120 VP and 29 FT children at age 7 years; 140 VP and 47 FT children at age 13 years). Brain age delta (brain-predicted age minus chronological age) was, on average, higher in the VP group at both timepoints compared with controls, however this difference had a small to medium effect size and was not statistically significant. Variance in brain age delta was higher in the VP group compared with controls; this difference was significant at the 13-year timepoint. Within the VP group, there was little evidence of associations between brain age delta and perinatal risk factors or cognitive and motor outcomes. Under the brain age framework, our results may suggest that children and adolescents born VP have similar brain structural developmental trajectories to term-born peers between 7 and 13 years of age.

Neuroimaging brain growth charts: A road to mental health

Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.

Early Adversity and Development: Parsing Heterogeneity and Identifying Pathways of Risk and Resilience

Adversity early in life is common and is a major risk factor for the onset of psychopathology. Delineating the neurodevelopmental pathways by which early adversity affects mental health is critical for early risk identification and targeted treatment approaches. A rapidly growing cross-species literature has facilitated advances in identifying the mechanisms linking adversity with psychopathology, specific dimensions of adversity and timing-related factors that differentially relate to outcomes, and protective factors that buffer against the effects of adversity. Yet, vast complexity and heterogeneity in early environments and neurodevelopmental trajectories contribute to the challenges of understanding risk and resilience in the context of early adversity. In this overview, the author highlights progress in four major areas-mechanisms, heterogeneity, developmental timing, and protective factors; synthesizes key challenges; and provides recommendations for future research that can facilitate progress in the field. Translation across species and ongoing refinement of conceptual models have strong potential to inform prevention and intervention strategies that can reduce the immense burden of psychopathology associated with early adversity.

Infants' cortex undergoes microstructural growth coupled with myelination during development

Development of cortical tissue during infancy is critical for the emergence of typical brain functions in cortex. However, how cortical microstructure develops during infancy remains unknown. We measured the longitudinal development of cortex from birth  to six months of age  using multimodal quantitative imaging of cortical microstructure. Here we show that infants' cortex undergoes profound microstructural tissue growth during the first six months of human life. Comparison of postnatal to prenatal transcriptomic gene expression data demonstrates that myelination and synaptic processes are dominant contributors to this postnatal microstructural tissue growth. Using visual cortex as a model system, we find hierarchical microstructural growth: higher-level visual areas have less mature tissue at birth than earlier visual areas but grow at faster rates. This overturns the prominent view that visual areas that are most mature at birth develop fastest. Together, in vivo, longitudinal, and quantitative measurements, which we validated with ex vivo transcriptomic data, shed light on the rate, sequence, and biological mechanisms of developing cortical systems during early infancy. Importantly, our findings propose a hypothesis that cortical myelination is a key factor in cortical development during early infancy, which has important implications for diagnosis of neurodevelopmental disorders and delays in infants.