Sex, gender diversity, and brain structure in early adolescence.

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves.

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.

Associations between perinatal risk and physical health in pre-adolescence in the Adolescent Brain Cognitive Development (ABCD) Study®: the unexpected relationship with sleep disruption.

BACKGROUND: To investigate relationships among different physical health problems in a large, sociodemographically diverse sample of 9-to-10-year-old children and determine the extent to which perinatal health factors are associated with childhood physical health problems. METHODS: A cross-sectional study was conducted utilizing the Adolescent Brain Cognitive Development℠ (ABCD) Study (n = 7613, ages 9-to-10-years-old) to determine the associations among multiple physical health factors (e.g., prenatal complications, current physical health problems). Logistic regression models controlling for age, sex, pubertal development, household income, caregiver education, race, and ethnicity evaluated relationships between perinatal factors and childhood physical health problems. RESULTS: There were significant associations between perinatal and current physical health measures. Specifically, those who had experienced perinatal complications were more likely to have medical problems by 9-to-10 years old. Importantly, sleep disturbance co-occurred with several physical health problems across domains and developmental periods. CONCLUSION: Several perinatal health factors were associated with childhood health outcomes, highlighting the importance of understanding and potentially improving physical health in youth. Understanding the clustering of physical health problems in youth is essential to better identify which physical health problems may share underlying mechanisms. IMPACT: Using a multivariable approach, we investigated the associations between various perinatal and current health problems amongst youth. Our study highlights current health problems, such as sleep problems at 9-to-10 years old, that are associated with a cluster of factors occurring across development (e.g., low birth weight, prenatal substance exposure, pregnancy complications, current weight status, lifetime head injury). Perinatal health problems are at large, non-modifiable (in this retrospective context), however, by identifying which are associated with current health problems, we can identify potential targets for intervention and prevention efforts.

Air pollution and age-dependent changes in emotional behavior across early adolescence in the U.S.

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study investigates if annual average PM2.5 and NO2 exposure at ages 9-10 years moderates age-related changes in internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at the baseline, 1-year follow-up, and 2-year follow-up visits, for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Against our hypothesis, there was no evidence that greater air pollution exposure was related to more behavioral problems with age over time.

Profiling intra- and inter-individual differences in brain development across early adolescence.

As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders.

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Microstructural properties within the amygdala and affiliated white matter tracts across adolescence.

The amygdala is a heterogenous set of nuclei with widespread cortical connections that continues to develop postnatally with vital implications for emotional regulation. Using high-resolution anatomical and multi-shell diffusion MRI in conjunction with novel amygdala segmentation, cutting-edge tractography, and Neurite Orientation Dispersion and Density (NODDI) methods, the goal of the current study was to characterize age associations with microstructural properties of amygdala subnuclei and amygdala-related white matter connections across adolescence (N = 61, 26 males; ages of 8-22 years). We found age-related increases in the Neurite Density Index (NDI) in the lateral nucleus (LA), dorsal and intermediate divisions of the basolateral nucleus (BLDI), and ventral division of the basolateral nucleus and paralaminar nucleus (BLVPL). Additionally, there were age-related increases in the NDI of the anterior commissure, ventral amygdalofugal pathway, cingulum, and uncinate fasciculus, with the strongest age associations in the frontal and temporal regions of these white matter tracts. This is the first study to utilize NODDI to show neurite density of basolateral amygdala subnuclei to relate to age across adolescence. Moreover, age-related differences were also notable in white matter microstructural properties along the anterior commissure and ventral amydalofugal tracts, suggesting increased bilateral amygdalae to diencephalon structural connectivity. As these basolateral regions and the ventral amygdalofugal pathways have been involved in associative emotional conditioning, future research is needed to determine if age-related and/or individual differences in the development of these microstructural properties link to socio-emotional functioning and/or risk for psychopathology.

Inter-individual variability in structural brain development from late childhood to young adulthood.

A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0-26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual- and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations.

Child physical activity as a modifier of the relationship between prenatal exposure to maternal overweight/obesity and neurocognitive outcomes in offspring.

BACKGROUND/OBJECTIVES: With rising obesity rates among pregnant women, more children are exposed in utero to maternal obesity. In prior epidemiological studies, exposure to maternal obesity was associated with lower intelligence quotient (IQ) scores and worse cognitive abilities in offspring. Further studies have shown that offspring exposed to maternal obesity, exhibit differences in the white matter microstructure properties, fractional anisotropy (FA) and mean diffusivity (MD). In contrast, physical activity was shown to improve cognition and white matter microstructure during childhood. We examined if child physical activity levels modify the relationship between prenatal exposure to maternal obesity with IQ and white matter microstructure in offspring. SUBJECTS/METHODS: One hundred children (59% girls) age 7-11 years underwent brain magnetic resonance imaging and IQ testing. Maternal pre-pregnancy BMI was abstracted from electronic medical records. White matter was assessed using diffusion tensor imaging with the measures, global FA, MD. The 3-day physical activity recall was used to measure moderate-to-vigorous physical activity and vigorous physical activity (VPA). Linear regression was used to test for interactions between prenatal exposure to maternal overweight/obesity and child PA levels on child IQ and global FA/MD. RESULTS: The relationship between prenatal exposure to maternal overweight/obesity and child IQ and global FA varied by child VPA levels. Children exposed to mothers with overweight/obesity who engaged in more VPA had higher IQ scores and global FA compared to exposed children who engaged in less VPA. Associations were independent of child age, sex, BMI Z-score and socioeconomic status. Children born to normal-weight mothers did not differ in either IQ or global FA by time in VPA. CONCLUSIONS: Our findings support findings in rodent models and suggest that VPA during childhood modifies the relationship between prenatal exposure to maternal obesity and child IQ and white matter microstructure.

Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness.

Before we can assess and interpret how developmental changes in human brain structure relate to cognition, affect, and motivation, and how these processes are perturbed in clinical or at-risk populations, we must first precisely understand typical brain development and how changes in different structural components relate to each other. We conducted a multisample magnetic resonance imaging study to investigate the development of cortical volume, surface area, and thickness, as well as their inter-relationships, from late childhood to early adulthood (7-29 years) using four separate longitudinal samples including 388 participants and 854 total scans. These independent datasets were processed and quality-controlled using the same methods, but analyzed separately to study the replicability of the results across sample and image-acquisition characteristics. The results consistently showed widespread and regionally variable nonlinear decreases in cortical volume and thickness and comparably smaller steady decreases in surface area. Further, the dominant contributor to cortical volume reductions during adolescence was thinning. Finally, complex regional and topological patterns of associations between changes in surface area and thickness were observed. Positive relationships were seen in sulcal regions in prefrontal and temporal cortices, while negative relationships were seen mainly in gyral regions in more posterior cortices. Collectively, these results help resolve previous inconsistencies regarding the structural development of the cerebral cortex from childhood to adulthood, and provide novel insight into how changes in the different dimensions of the cortex in this period of life are inter-related.SIGNIFICANCE STATEMENT Different measures of brain anatomy develop differently across adolescence. Their precise trajectories and how they relate to each other throughout development are important to know if we are to fully understand both typical development and disorders involving aberrant brain development. However, our understanding of such trajectories and relationships is still incomplete. To provide accurate characterizations of how different measures of cortical structure develop, we performed an MRI investigation across four independent datasets. The most profound anatomical change in the cortex during adolescence was thinning, with the largest decreases observed in the parietal lobe. There were complex regional patterns of associations between changes in surface area and thickness, with positive relationships seen in sulcal regions in prefrontal and temporal cortices, and negative relationships seen mainly in gyral regions in more posterior cortices.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes.

The developmental patterns of subcortical brain volumes in males and females observed in previous studies have been inconsistent. To help resolve these discrepancies, we examined developmental trajectories using three independent longitudinal samples of participants in the age-span of 8-22 years (total 216 participants and 467 scans). These datasets, including Pittsburgh (PIT; University of Pittsburgh, USA), NeuroCognitive Development (NCD; University of Oslo, Norway), and Orygen Adolescent Development Study (OADS; The University of Melbourne, Australia), span three countries and were analyzed together and in parallel using mixed-effects modeling with both generalized additive models and general linear models. For all regions and across all samples, males were found to have significantly larger volumes as compared to females, and significant sex differences were seen in age trajectories over time. However, direct comparison of sample trajectories and sex differences identified within samples were not consistent. The trajectories for the amygdala, putamen, and nucleus accumbens were most consistent between the three samples. Our results suggest that even after using similar preprocessing and analytic techniques, additional factors, such as image acquisition or sample composition may contribute to some of the discrepancies in sex specific patterns in subcortical brain changes across adolescence, and highlight region-specific variations in congruency of developmental trajectories.

Puberty and structural brain development in humans.

Adolescence is a transitional period of physical and behavioral development between childhood and adulthood. Puberty is a distinct period of sexual maturation that occurs during adolescence. Since the advent of magnetic resonance imaging (MRI), human studies have largely examined neurodevelopment in the context of age. A breadth of animal findings suggest that sex hormones continue to influence the brain beyond the prenatal period, with both organizational and activational effects occurring during puberty. Given the animal evidence, human MRI research has also set out to determine how puberty may influence otherwise known patterns of age-related neurodevelopment. Here we review structural-based MRI studies and show that pubertal maturation is a key variable to consider in elucidating sex- and individual- based differences in patterns of human brain development. We also highlight the continuing challenges faced, as well as future considerations, for this vital avenue of research.

Lower total and regional grey matter brain volumes in youth with perinatally-acquired HIV infection: Associations with HIV disease severity, substance use, and cognition.

Despite improved survival due to combination antiretroviral therapy (cART), youth with perinatally-acquired HIV (PHIV) show cognitive deficits and developmental delay at increased rates. HIV affects the brain during critical periods of development, and the brain may be a persistent reservoir for HIV due to suboptimal blood brain barrier penetration of cART. We conducted structural magnetic resonance imaging (sMRI) and cognitive testing in 40 PHIV youth (mean age=16.7years) recruited from the NIH Pediatric HIV/AIDS Cohort Study (PHACS) who are part of the first generation of PHIV youth surviving into adulthood. Historical and current HIV disease severity and substance use measures were also collected. Total and regional cortical grey matter brain volumes were compared to a group of 334 typically-developing, HIV-unexposed and uninfected youth (frequency-matched for age and sex) from the Pediatric Imaging, Neurocognition, and Genetics (PING) study (mean age=16.1years). PHIV youth had smaller (2.8-5.1%) total and regional grey matter volumes than HIV-unexposed and uninfected youth, with smallest volumes seen among PHIV youth with higher past peak viral load (VL) and recent unsuppressed VL. In PHIV youth, worse cognitive performance correlated with smaller volumes. This pattern of smaller grey matter volumes suggests that PHIV infection may influence brain development and underlie cognitive dysfunction seen in this population. Among PHIV youth, smaller volumes were also linked to substance use (alcohol use: 9.0-13.4%; marijuana use: 10.1-16.0%). In this study, collection of substance use information was limited to the PHIV cohort; future studies should also collect substance use information in controls to further address interactions between HIV and substance use on brain volume.

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena.

Structural brain development between childhood and adulthood: Convergence across four longitudinal samples.

Longitudinal studies including brain measures acquired through magnetic resonance imaging (MRI) have enabled population models of human brain development, crucial for our understanding of typical development as well as neurodevelopmental disorders. Brain development in the first two decades generally involves early cortical grey matter volume (CGMV) increases followed by decreases, and monotonic increases in cerebral white matter volume (CWMV). However, inconsistencies regarding the precise developmental trajectories call into question the comparability of samples. This issue can be addressed by conducting a comprehensive study across multiple datasets from diverse populations. Here, we present replicable models for gross structural brain development between childhood and adulthood (ages 8-30years) by repeating analyses in four separate longitudinal samples (391 participants; 852 scans). In addition, we address how accounting for global measures of cranial/brain size affect these developmental trajectories. First, we found evidence for continued development of both intracranial volume (ICV) and whole brain volume (WBV) through adolescence, albeit following distinct trajectories. Second, our results indicate that CGMV is at its highest in childhood, decreasing steadily through the second decade with deceleration in the third decade, while CWMV increases until mid-to-late adolescence before decelerating. Importantly, we show that accounting for cranial/brain size affects models of regional brain development, particularly with respect to sex differences. Our results increase confidence in our knowledge of the pattern of brain changes during adolescence, reduce concerns about discrepancies across samples, and suggest some best practices for statistical control of cranial volume and brain size in future studies.

Atypical parietal lobe activity to subliminal faces in youth with a family history of alcoholism.

BACKGROUND: Adults with alcohol use disorders (AUDs) show different behavioral and neurological functioning during emotional processing tasks from healthy controls. Adults with a family history (FHP) of AUD also show different activation in limbic brain areas, such as the amygdala. However, it is unclear if this pattern exists during adolescence before any episodes of heavy alcohol use. OBJECTIVES: We hypothesized that the amygdalar response to subliminally-presented fearful faces would be reduced in FHP adolescents compared to peers who were family history negative (FHN) for AUD. METHOD: An adapted Masked Faces paradigm was used to examine blood oxygen level-dependent response to subliminal fearful vs. neutral faces in 14 FHP (6 females, 8 males) and 15 FHN (6 females, 9 males) youth, ages 11-15 years. Both FHP and FHN youth had no history of heavy alcohol consumption. RESULTS: A significant difference was seen between groups in the left superior parietal lobule FHN youth showed deactivation to fearful and neutral masked faces compared to baseline, whereas FHP youth showed deactivation only to fearful masked faces. No significant differences in amygdalar activation were seen between groups. CONCLUSION: The left superior parietal lobule is part of the fronto-parietal network, which has been implicated in attentional control. Lack of reduced neural activity to neutral faces among FHP youth may represent differences in suppressing attention networks to less salient emotional stimuli, or perhaps, a higher threshold of saliency for emotional stimuli among at-risk youth.

The role of testosterone and estradiol in brain volume changes across adolescence: a longitudinal structural MRI study.

It has been postulated that pubertal hormones may drive some neuroanatomical changes during adolescence, and may do so differently in girls and boys. Here, we use growth curve modeling to directly assess how sex hormones [testosterone (T) and estradiol (E2)] relate to changes in subcortical brain volumes utilizing a longitudinal design. 126 adolescents (63 girls), ages 10 to 14, were imaged and restudied ∼2 years later. We show, for the first time, that best-fit growth models are distinctly different when using hormones as compared to a physical proxy of pubertal maturation (Tanner Stage) or age, to predict brain development. Like Tanner Stage, T and E2 predicted white matter and right amygdala growth across adolescence in both sexes, independent of age. Tanner Stage also explained decreases in both gray matter and caudate volumes, whereas E2 explained only gray matter decreases and T explained only caudate volume decreases. No pubertal measures were related to hippocampus development. Although specificity was seen, sex hormones had strikingly similar relationships with white matter, gray matter, right amygdala, and bilateral caudate volumes, with larger changes in brain volume seen at early pubertal maturation (as indexed by lower hormone levels), followed by less robust, or even reversals in growth, by late puberty. These novel longitudinal findings on the relationship between hormones and brain volume change represent crucial first steps toward understanding which aspects of puberty influence neurodevelopment.

Outdoor air pollution and brain development in childhood and adolescence.

Exposure to outdoor air pollution has been linked to adverse health effects, including potential widespread impacts on the CNS. Ongoing brain development may render children and adolescents especially vulnerable to neurotoxic effects of air pollution. While mechanisms remain unclear, promising advances in human neuroimaging can help elucidate both sensitive periods and neurobiological consequences of exposure to air pollution. Herein we review the potential influences of air pollution exposure on neurodevelopment, drawing from animal toxicology and human neuroimaging studies. Due to ongoing cellular and system-level changes during childhood and adolescence, the developing brain may be more sensitive to pollutants' neurotoxic effects, as a function of both timing and duration, with relevance to cognition and mental health. Building on these foundations, the emerging field of environmental neuroscience is poised to further decipher which air toxicants are most harmful and to whom.

High Resolution Multi-delay Arterial Spin Labeling with Transformer based Denoising for Pediatric Perfusion MRI.

Multi-delay arterial spin labeling (MDASL) can quantitatively measure cerebral blood flow (CBF) and arterial transit time (ATT), which is particularly suitable for pediatric perfusion imaging. Here we present a high resolution (iso-2mm) MDASL protocol and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR of perfusion images, as well as the SNR, bias and repeatability of the fitted CBF and ATT maps. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as directly applying a pretrained Transformer model on a larger dataset. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted CBF and ATT maps. The proposed method also improved test-retest repeatability of whole-brain perfusion measurements. This may facilitate the use of MDASL in neurodevelopmental studies to characterize typical and aberrant brain development.