The NIH MRI study of normal brain development

The HEALthy Brain and Child Development Study (HBCD): NIH collaboration to understand the impacts of prenatal and early life experiences on brain development

The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individual's genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments.

The Dynamic Interplay Between Puberty and Structural Brain Development as a Predictor of Mental Health Difficulties in Adolescence: A Systematic Review

Puberty is a time of intense reorganization of brain structure and a high-risk period for the onset of mental health problems, with variations in pubertal timing and tempo intensifying this risk. We conducted 2 systematic reviews of articles published up to February 1, 2024, focusing on 1) the role of brain structure in the relationship between puberty and mental health, and 2) precision psychiatry research evaluating the utility of puberty in making individualized predictions of mental health outcomes in young people. The first review provides inconsistent evidence about whether and how pubertal and psychopathological processes may interact in relation to brain development. While most studies found an association between early puberty and mental health difficulties in adolescents, evidence on whether brain structure mediates this relationship is mixed. The pituitary gland was found to be associated with mental health status during this time, possibly through its central role in regulating puberty and its function in the hypothalamic-pituitary-gonadal and hypothalamic-pituitary-adrenal axes. In the second review, the design of studies that have explored puberty in predictive models did not allow for a quantification of its predictive power. However, when puberty was evaluated through physically observable characteristics rather than hormonal measures, it was more commonly identified as a predictor of depression, anxiety, and suicidality in adolescence. Social processes may be more relevant than biological ones to the link between puberty and mental health problems and represent an important target for educational strategies.

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

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.

Meta-regression of sulcal patterns, clinical and environmental factors on neurodevelopmental outcomes in participants with multiple CHD types

Congenital heart disease affects 1% of infants and is associated with impaired neurodevelopment. Right- or left-sided sulcal features correlate with executive function among people with Tetralogy of Fallot or single ventricle congenital heart disease. Studies of multiple congenital heart disease types are needed to understand regional differences. Further, sulcal pattern has not been studied in people with d-transposition of the great arteries. Therefore, we assessed the relationship between sulcal pattern and executive function, general memory, and processing speed in a meta-regression of 247 participants with three congenital heart disease types (114 single ventricle, 92 d-transposition of the great arteries, and 41 Tetralogy of Fallot) and 94 participants without congenital heart disease. Higher right hemisphere sulcal pattern similarity was associated with improved executive function (Pearson r = 0.19, false discovery rate-adjusted P = 0.005), general memory (r = 0.15, false discovery rate P = 0.02), and processing speed (r = 0.17, false discovery rate P = 0.01) scores. These positive associations remained significant in for the d-transposition of the great arteries and Tetralogy of Fallot cohorts only in multivariable linear regression (estimated change β = 0.7, false discovery rate P = 0.004; β = 4.1, false discovery rate P = 0.03; and β = 5.4, false discovery rate P = 0.003, respectively). Duration of deep hypothermic circulatory arrest was also associated with outcomes in the multivariate model and regression tree analysis. This suggests that sulcal pattern may provide an early biomarker for prediction of later neurocognitive challenges among people with congenital heart disease.

Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder

Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.

Hippocampal sclerosis and temporal lobe epilepsy following febrile status epilepticus: The FEBSTAT study

OBJECTIVE

This study was undertaken to determine whether hippocampal T2 hyperintensity predicts sequelae of febrile status epilepticus, including hippocampal atrophy, sclerosis, and mesial temporal lobe epilepsy.

METHODS

Acute magnetic resonance imaging (MRI) was obtained within a mean of 4.4 (SD = 5.5, median = 2.0) days after febrile status on >200 infants with follow-up MRI at approximately 1, 5, and 10 years. Hippocampal size, morphology, and T2 signal intensity were scored visually by neuroradiologists blinded to clinical details. Hippocampal volumetry provided quantitative measurement. Upon the occurrence of two or more unprovoked seizures, subjects were reassessed for epilepsy. Hippocampal volumes were normalized using total brain volumes.

RESULTS

Fourteen of 22 subjects with acute hippocampal T2 hyperintensity returned for follow-up MRI, and 10 developed definite hippocampal sclerosis, which persisted through the 10-year follow-up. Hippocampi appearing normal initially remained normal on visual inspection. However, in subjects with normal-appearing hippocampi, volumetrics indicated that male, but not female, hippocampi were smaller than controls, but increasing hippocampal asymmetry was not seen following febrile status. Forty-four subjects developed epilepsy; six developed mesial temporal lobe epilepsy and, of the six, two had definite, two had equivocal, and two had no hippocampal sclerosis. Only one subject developed mesial temporal epilepsy without initial hyperintensity, and that subject had hippocampal malrotation. Ten-year cumulative incidence of all types of epilepsy, including mesial temporal epilepsy, was highest in subjects with initial T2 hyperintensity and lowest in those with normal signal and no other brain abnormalities.

SIGNIFICANCE

Hippocampal T2 hyperintensity following febrile status epilepticus predicted hippocampal sclerosis and significant likelihood of mesial temporal lobe epilepsy. Normal hippocampal appearance in the acute postictal MRI was followed by maintained normal appearance, symmetric growth, and lower risk of epilepsy. Volumetric measurement detected mildly decreased hippocampal volume in males with febrile status.

Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Evaluating child helmet protection and testing standards: A study using PIPER child head models aged 1.5, 3, 6, and 18 years

The anatomy of children's heads is unique and distinct from adults, with smaller and softer skulls and unfused fontanels and sutures. Despite this, most current helmet testing standards for children use the same peak linear acceleration threshold as for adults. It is unclear whether this is reasonable and otherwise what thresholds should be. To answer these questions, helmet-protected head responses for different ages are needed which is however lacking today. In this study, we apply continuously scalable PIPER child head models of 1.5, 3, and 6 years old (YO), and an upgraded 18YO to study child helmet protection under extensive linear and oblique impacts. The results of this study reveal an age-dependence trend in both global kinematics and tissue response, with younger children experiencing higher levels of acceleration and velocity, as well as increased skull stress and brain strain. These findings indicate the need for better protection for younger children, suggesting that youth helmets should have a lower linear kinematic threshold, with a preliminary value of 150g for 1.5-year-old helmets. However, the results also show a different trend in rotational kinematics, indicating that the threshold of rotational velocity for a 1.5YO is similar to that for adults. The results also support the current use of small-sized adult headforms for testing child helmets before new child headforms are available.

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.

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.

Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

BACKGROUND

Sex differences pose a challenge and an opportunity in biomedical research. Understanding how sex chromosomes and hormones affect disease-causing mechanisms will shed light on the mechanisms underlying predominantly idiopathic sex-biased neurodevelopmental disorders such as ADHD, schizophrenia, and autism. Gene expression is a crucial conduit for the influence of sex on developmental processes; therefore, this study focused on sex differences in gene expression and the regulation of gene expression. The increasing interest in microRNAs (miRNAs), small, non-coding RNAs, for their contribution to normal and pathological neurodevelopment prompted us to test how miRNA expression differs between the sexes in the developing brain.

METHODS

High-throughput sequencing approaches were used to identify transcripts, including miRNAs, that showed significantly different expression between male and female brains on day 15.5 of development (E15.5).

RESULTS

Robust sex differences were identified for some genes and miRNAs, confirming the influence of biological sex on RNA. Many miRNAs that exhibit the greatest differences between males and females have established roles in neurodevelopment, implying that sex-biased expression may drive sex differences in developmental processes. In addition to highlighting sex differences for individual miRNAs, gene ontology analysis suggested several broad categories in which sex-biased RNAs might act to establish sex differences in the embryonic mouse brain. Finally, mining publicly available SNP data indicated that some sex-biased miRNAs reside near the genomic regions associated with neurodevelopmental disorders.

CONCLUSIONS

Together, these findings reinforce the importance of cataloguing sex differences in molecular biology research and highlight genes, miRNAs, and pathways of interest that may be important for sexual differentiation in the mouse and possibly the human brain.

Deviation From Normative Whole Brain and Deep Gray Matter Growth in Children With MOGAD, MS, and Monophasic Seronegative Demyelination

BACKGROUND AND OBJECTIVES

Pediatric-acquired demyelination of the CNS associated with antibodies directed against myelin oligodendrocyte glycoprotein (MOG; MOG antibody-associated disease [MOGAD]) occurs as a monophasic or relapsing disease and with variable but often extensive T2 lesions in the brain. The impact of MOGAD on brain growth during maturation is unknown. We quantified the effect of pediatric MOGAD on brain growth trajectories and compared this with the growth trajectories of age-matched and sex-matched healthy children and children with multiple sclerosis (MS, a chronic relapsing disease known to lead to failure of normal brain growth and to loss of brain volume) and monophasic seronegative demyelination.

METHODS

We included children enrolled at incident attack in the prospective longitudinal Canadian Pediatric Demyelinating Disease Study who were recruited at the 3 largest enrollment sites, underwent research brain MRI scans, and were tested for serum MOG-IgG. Children seropositive for MOG-IgG were diagnosed with MOGAD. MS was diagnosed per the 2017 McDonald criteria. Monophasic seronegative demyelination was confirmed in children with no clinical or MRI evidence of recurrent demyelination and negative results for MOG-IgG and aquaporin-4-IgG. Whole and regional brain volumes were computed through symmetric nonlinear registration to templates. We computed age-normalized and sex-normalized z scores for brain volume using a normative dataset of 813 brain MRI scans obtained from typically developing children and used mixed-effect models to assess potential deviation from brain growth trajectories.

RESULTS

We assessed brain volumes of 46 children with MOGAD, 26 with MS, and 51 with monophasic seronegative demyelinating syndrome. Children with MOGAD exhibited delayed (p < 0.001) age-expected and sex-expected growth of thalamus, caudate, and globus pallidus, normalized for the whole brain volume. Divergence from expected growth was particularly pronounced in the first year postonset and was detected even in children with monophasic MOGAD. Thalamic volume abnormalities were less pronounced in children with MOGAD compared with those in children with MS.

DISCUSSION

The onset of MOGAD during childhood adversely affects the expected trajectory of growth of deep gray matter structures, with accelerated changes in the months after an acute attack. Further studies are required to better determine the relative impact of monophasic vs relapsing MOGAD and whether relapsing MOGAD with attacks isolated to the optic nerves or spinal cord affects brain volume over time.

Personalization of human body models and beyond via image registration

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Transformer, one of the latest technological advances of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance to computer vision, it is natural to inquire about the status quo of Transformers in medical imaging and ask the question: can the Transformer models transform medical imaging? In this paper, we attempt to make a response to the inquiry. After a brief introduction of the fundamentals of Transformers, especially in comparison with convolutional neural networks (CNNs), and highlighting key defining properties that characterize the Transformers, we offer a comprehensive review of the state-of-the-art Transformer-based approaches for medical imaging and exhibit current research progresses made in the areas of medical image segmentation, recognition, detection, registration, reconstruction, enhancement, etc. In particular, what distinguishes our review lies in its organization based on the Transformer's key defining properties, which are mostly derived from comparing the Transformer and CNN, and its type of architecture, which specifies the manner in which the Transformer and CNN are combined, all helping the readers to best understand the rationale behind the reviewed approaches. We conclude with discussions of future perspectives.

Modulations of right hemisphere connectivity in young children relates to the perception of spoken words

The early school years shape a young brain's capability to comprehend and contextualize words within milliseconds of exposure. Parsing word sounds (phonological interpretation) and word recognition (enabling semantic interpretation) are integral to this process. Yet little is known about the causal mechanisms of cortical activity during these early developmental stages. In this study, we aimed to explore these causal mechanisms via dynamic causal modelling of event-related potentials (ERPs) acquired from 30 typically developing children (ages 6-8 years) as they completed a spoken word-picture matching task. Source reconstruction of high-density electroencephalography (128 channels) was used to ascertain differences in whole-brain cortical activity during semantically "congruent" and "incongruent" conditions. Source activations analyzed during the N400 ERP window identified significant regions-of-interest (p_FWE<.05) localized primarily in the right hemisphere when contrasting congruent and incongruent word-picture stimuli. Dynamic causal models (DCMs) were tested on source activations in the fusiform gyrus (rFusi), inferior parietal lobule (rIPL), inferior temporal gyrus (rITG) and superior frontal gyrus (rSFG). DCM results indicated that a fully connected bidirectional model with self-(inhibiting) connections over rFusi, rIPL and rSFG provided the highest model evidence, based on exceedance probabilities derived from Bayesian statistical inferences. Connectivity parameters of rITG and rSFG regions from the winning DCM were negatively correlated with behavioural measures of receptive vocabulary and phonological memory (p_FDR<.05), such that lower scores on these assessments corresponded with increased connectivity between temporal pole and anterior frontal regions. The findings suggest that children with lower language processing skills required increased recruitment of right hemisphere frontal/temporal areas during task performance.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

Impaired Brain Growth in Myelin Oligodendrocyte Glycoprotein Antibody-Associated Acute Disseminated Encephalomyelitis

BACKGROUND AND OBJECTIVES

Acute disseminated encephalomyelitis (ADEM) is the most common phenotype in pediatric myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease. A previous study demonstrated impaired brain growth in ADEM. However, the effect of MOG antibodies on brain growth remains unknown. Here, we performed brain volume analyses in MOG-positive and MOG-negative ADEM at onset and over time.

METHODS

In this observational cohort study, we included a total of 62 MRI scans from 24 patients with ADEM (54.2% female; median age 5 years), of which 16 (66.7%) were MOG positive. Patients were compared with healthy controls from the NIH pediatric MRI data repository and a matched local cohort. Mixed-effect models were applied to assess group differences and other relevant factors, including relapses.

RESULTS

At baseline and before any steroid treatment, patients with ADEM, irrespective of MOG antibody status, showed reduced brain volume compared with matched controls (median [interquartile range] 1,741.9 cm3 [1,645.1-1,805.2] vs 1,810.4 cm³ [1,786.5-1,836.2]). Longitudinal analysis revealed reduced brain growth for both MOG-positive and MOG-negative patients with ADEM. However, MOG-negative patients showed a stronger reduction (-138.3 cm³ [95% CI -193.6 to -82.9]) than MOG-positive patients (-50.0 cm³ [-126.5 to -5.2]), independent of age, sex, and treatment. Relapsing patients (all MOG positive) showed additional brain volume loss (-15.8 cm³ [-68.9 to 37.3]).

DISCUSSION

Patients with ADEM exhibit brain volume loss and failure of age-expected brain growth. Importantly, MOG-negative status was associated with a more pronounced brain volume loss compared with MOG-positive patients.

The combined use of DTI and MR elastography for monitoring microstructural changes in the developing brain of a neurodevelopmental disorder model: Poly (I:C)-induced maternal immune-activated rats

Early neuropathology mechanisms in neurodevelopmental disorders are partially understood because routine anatomical magnetic resonance imaging (MRI) cannot detect subtle brain microstructural changes in vivo during postnatal development. Therefore, we investigated the potential value of magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI) in a rat model of neurodevelopmental disorder induced by maternal immune activation. We studied 12 offspring of mothers injected with polyriboinosinic-polyribocytidylic acid (poly (I:C), 4 mg/kg) on gestational day 15, plus 8 controls. T2-weighted anatomical MR images, MRE (800 Hz) and DTI (30 gradient directions, b = 765.8 s/mm2, 5 images, b = 0 s/mm2) were collected when the rats were 4 and 10 weeks old, and results were compared with histological analysis performed at week 10. Ventricles were ~1.4 fold larger from week 4 in poly (I:C) rats than in controls. No other morphological abnormalities were detected in poly(I:C) rats. At week 4, larger ventricles were correlated with lower external capsule fractional anisotropy and internal capsule radial diffusion (Pearson, r = -0.53, 95% confidence intervals (CI) [-0.79 to -0.12], and r = -0.45, 95% CI [-0.74 to -0.01], respectively). The mean and radial diffusion of the corpus callosum, the mean and axial diffusion of the internal capsule and the radial diffusion properties in the external capsule increased with age for poly (I:C) rats only (Sidak's comparison, P<0.05). Cortical stiffness did not increase with age in poly (I:C) rats, in contrast with controls (Sidak's comparison, P = 0.005). These temporal variations probably reflected abnormal myelin content, decreased cell density and microglia activation observed at week 10 after histological assessment. To conclude, MRE and DTI allow monitoring of abnormal brain microstructural changes in poly (I:C) rats from week 4 after birth. This suggests that both imaging techniques have the potential to be used as complementary imaging tools to routine anatomical imaging to assist with the early diagnosis of neurodevelopmental disorders and provide new insights into neuropathology.

Sensory Inhibition and Speech Perception-in-Noise Performance in Children With Normal Hearing

PURPOSE

This study investigated whether sensory inhibition in children may be associated with speech perception-in-noise performance. Additionally, gating networks associated with sensory inhibition were identified via standardized low-resolution brain electromagnetic tomography (sLORETA), and the detectability of the cortical auditory evoked potential (CAEP) N1 response was enhanced using a 4- to 30-Hz bandpass filter.

METHOD

CAEP gating responses, reflective of inhibition, were evoked via click pairs and recorded using high-density electroencephalography in neurotypical 5- to 8-year-olds and 22- to 24-year-olds. Amplitude gating indices were calculated and correlated with speech perception in noise. Gating generators were estimated using sLORETA. A 4- to 30-Hz filter was applied to detect the N1 gating component.

RESULTS

Preliminary findings indicate children showed reduced gating, but there was a correlational trend between better speech perception and decreased N2 gating. Commensurate with decreased gating, children presented with incomplete compensatory gating networks. The 4- to 30-Hz filter identified the N1 response in a subset of children.

CONCLUSIONS

There was a tenuous relationship between children's speech perception and sensory inhibition. This may suggest that sensory inhibition is only implicated in atypically poor speech perception. Finally, the 4- to 30-Hz filter settings are critical in N1 detectability.

SIGNIFICANCE

Gating may help evaluate reduced sensory inhibition in children with clinically poor speech perception using the appropriate methodology. Cortical gating generators in typically developing children are also newly identified.

Metabolic modulation of synaptic failure and thalamocortical hypersynchronization with preserved consciousness in Glut1 deficiency

Individuals with glucose transporter type I deficiency (G1D) habitually experience nutrient-responsive epilepsy associated with decreased brain glucose. However, the mechanistic association between blood glucose concentration and brain excitability in the context of G1D remains to be elucidated. Electroencephalography (EEG) in G1D individuals revealed nutrition time-dependent seizure oscillations often associated with preserved volition despite electrographic generalization and uniform average oscillation duration and periodicity, suggesting increased facilitation of an underlying neural loop circuit. Nonlinear EEG ictal source localization analysis and simultaneous EEG/functional magnetic resonance imaging converged on the thalamus-sensorimotor cortex as one potential circuit, and ¹⁸F-deoxyglucose positron emission tomography (¹⁸F-DG-PET) illustrated decreased glucose accumulation in this circuit. This pattern, reflected in a decreased thalamic to striatal ¹⁸F signal ratio, can aid with the PET imaging diagnosis of the disorder, whereas the absence of noticeable ictal behavioral changes challenges the postulated requirement for normal thalamocortical activity during consciousness. In G1D mice, ¹⁸F-DG-PET and mass spectrometry also revealed decreased brain glucose and glycogen, but preserved tricarboxylic acid cycle intermediates, indicating no overall energy metabolism failure. In brain slices from these animals, synaptic inhibition of cortical pyramidal neurons and thalamic relay neurons was decreased, and neuronal disinhibition was mitigated by metabolic sources of carbon; tonic-clonic seizures were also suppressed by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor inhibition. These results pose G1D as a thalamocortical synaptic disinhibition disease associated with increased glucose-dependent neuronal excitability, possibly in relation to reduced glycogen. Together with findings in other metabolic defects, inhibitory neuron dysfunction is emerging as a modulable mechanism of hyperexcitability.

The dual neural effects of oxytocin in autistic youth: results from a randomized trial

Recent discoveries have highlighted the effects of oxytocin (OT) on social behavior and perception among autistic individuals. However, a gap persists in the literature regarding the potential effects of OT and the neural temporal dynamics due to OT administration. We explored the effect of OT on autistic individuals using magnetoencephalography (MEG), focusing on M100, M170, and M250, social perception-related components that tend to show atypical patterns in autistic individuals. Twenty-five autistic adolescents participated in this randomized, double-blind MEG study. Autistic individuals arrived at the lab twice and received an acute dose of intranasal OT or placebo in each session. During the scans, participants were asked to identify pictures of social and non-social stimuli. Additionally, 23 typically developing (TD) adolescents performed the same task in the MEG as a benchmark that allowed us to better characterize neural regions of interest and behavioral results for this age group in this task. A source-model beamformer analysis revealed that OT enhanced neural activity for social stimuli in frontal regions during M170. Additionally, in each of the preselected time windows, OT increased activation in the left hemisphere, regardless of the content of the presented stimuli. We suggest that OT increased the processing of social stimuli through two separate mechanisms. First, OT increased neural activity in a nonspecific manner, allowing increased allocation of attention toward the stimuli. Second, OT enhanced M170 activity in frontal regions only in response to social stimuli. These results reveal the temporal dynamics of the effects of OT on the early stages of social and non-social perception in autistic adolescents.

Trial registration: This study was a part of a project registered as clinical trial October 27th, 2021. ClinicalTrials.gov Identifier: NCT05096676.

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative relation," "relative relation," "maximal relation" and "minimal relation." These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Intracranial Volumes of Healthy Children in the First 3 Years of Life: An Analysis of 270 Magnetic Resonance Imaging Scans

BACKGROUND

There is a paucity of data on normal intracranial volumes for healthy children during the first few years of life, when cranial growth velocity is greatest. The aim of this study was to generate a normative predictive model of intracranial volumes based on brain magnetic resonance imaging from a large sample of healthy children to serve as a reference tool for future studies on craniosynostosis.

METHODS

Structural magnetic resonance imaging data for healthy children up to 3 years of age was acquired from the National Institutes of Health Pediatric MRI Data Repository. Intracranial volumes were calculated using T1-weighted scans with FreeSurfer (version 6.0.0). Mean intracranial volumes were calculated and best-fit logarithmic curves were generated. Results were compared to previously published intracranial volume curves.

RESULTS

Two-hundred seventy magnetic resonance imaging scans were available: 118 were collected in the first year of life, 97 were collected between years 1 and 2, and 55 were collected between years 2 and 3. A best-fit logarithmic growth curve was generated for male and female patients. The authors' regression models showed that male patients had significantly greater intracranial volumes than female patients after 1 month of age. Predicted intracranial volumes were also greater in male and female patients in the first 6 months of life as compared to previously published intracranial volume curves.

CONCLUSIONS

To the authors' knowledge, this is the largest series of demographically representative magnetic resonance imaging-based intracranial volumes for children aged 3 years and younger. The model generated in this study can be used by investigators as a reference for evaluating craniosynostosis patients.

An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field

Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.

Low household income and neurodevelopment from infancy through adolescence

Despite advancements in the study of brain maturation at different developmental epochs, no work has linked the significant neural changes occurring just after birth to the subtler refinements in the brain occurring in childhood and adolescence. We aimed to provide a comprehensive picture regarding foundational neurodevelopment and examine systematic differences by family income. Using a nationally representative longitudinal sample of 486 infants, children, and adolescents (age 5 months to 20 years) from the NIH MRI Study of Normal Brain Development and leveraging advances in statistical modeling, we mapped developmental trajectories for the four major cortical lobes and constructed charts that show the statistical distribution of gray matter and reveal the considerable variability in regional volumes and structural change, even among healthy, typically developing children. Further, the data reveal that significant structural differences in gray matter development for children living in or near poverty, first detected during childhood (age 2.5-6.5 years), evolve throughout adolescence.

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.

Global-Local Transformer for Brain Age Estimation

Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer.

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.

Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort

The ongoing Chinese Color Nest Project (CCNP) was established to create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design. In the initial stage, CCNP aims to recruit 1520 healthy individuals (6-90 years), which comprises three phases: developing (devCCNP: 6-18 years, N = 480), maturing (matCCNP: 20-60 years, N = 560) and aging (ageCCNP: 60-84 years, N = 480). In this paper, we present an overview of the devCCNP, including study design, participants, data collection and preliminary findings. The devCCNP has acquired data with three repeated measurements from 2013 to 2017 in Southwest University, Chongqing, China (CCNP-SWU, N = 201). It has been accumulating baseline data since July 2018 and the second wave data since September 2020 in Chinese Academy of Sciences, Beijing, China (CCNP-CAS, N = 168). Each participant in devCCNP was followed up for 2.5 years at 1.25-year intervals. The devCCNP obtained longitudinal neuroimaging, biophysical, social, behavioral and cognitive data via MRI, parent- and self-reported questionnaires, behavioral assessments, and computer tasks. Additionally, data were collected on children's learning, daily life and emotional states during the COVID-19 pandemic in 2020. We address data harmonization across the two sites and demonstrated its promise of characterizing the growth curves for the overall brain morphometry using multi-center longitudinal data. CCNP data will be shared via the National Science Data Bank and requests for further information on collaboration and data sharing are encouraged.

Neural correlates of orbital telorism

Orbital telorism, the interocular distance, is clinically informative and in extremes is considered a minor physical anomaly. While its extremes, hypo- and hypertelorism, have been linked to disorders often related to cognitive ability, little is known about the neural correlates of normal variation of telorism within the general population. We derived measures of orbital telorism from cranial magnetic resonance imaging (MRI) by calculating the distance between the eyeball center of gravity in two population-based datasets (N = 5,653, N = 29,824; mean age 64.66, 63.75 years). This measure was found to be related to grey matter tissue density within numerous regions of the brain, including, but surprisingly not limited to, the frontal regions, in both positive and negative directions. Additionally, telorism was related to several cognitive functions, such as Purdue pegboard test (Beta, P-value (CI95%) -.02, 1.63 × 10^-7 (-.03:-.01)) and fluid intelligence (.02, 4.75 × 10^-6 (.01:0.02)), with some relationships driven by individuals with a smaller orbital telorism. This is reflective of the higher prevalence of hypotelorism in developmental disorders, specifically those that accompany lower cognitive lower functioning. This study suggests, despite previous links only made in clinical extremes, that orbital telorism holds some relation to structural brain development and cognitive function in the general population. This relationship is likely driven by shared developmental periods.

Advanced Diffusion of the Pediatric Brain and Spine

This article discusses new diffusion-weighted imaging (DWI) sequences, diffusion tensor imaging (DTI), and fiber tractography (FT), as well as more advanced diffusion imaging in pediatric brain and spine. Underlying disorder and pathophysiology causing diffusion abnormalities are discussed. Multishot echo planar imaging (EPI) DWI and non-EPI DWI provide higher spatial resolution with less susceptibility artifact and distortion, which are replacing conventional single-shot EPI DWI. DTI and FT have established clinical significance in pediatric brain and spine. This article discusses advanced diffusion imaging, including diffusion kurtosis imaging, neurite orientation dispersion and density imaging, diffusion spectrum imaging, intravoxel incoherent motion, and oscillating-gradient spin-echo.

Normal childhood brain growth and a universal sex and anthropomorphic relationship to cerebrospinal fluid

OBJECTIVE

The study of brain size and growth has a long and contentious history, yet normal brain volume development has yet to be fully described. In particular, the normal brain growth and cerebrospinal fluid (CSF) accumulation relationship is critical to characterize because it is impacted in numerous conditions of early childhood in which brain growth and fluid accumulation are affected, such as infection, hemorrhage, hydrocephalus, and a broad range of congenital disorders. The authors of this study aim to describe normal brain volume growth, particularly in the setting of CSF accumulation.

METHODS

The authors analyzed 1067 magnetic resonance imaging scans from 505 healthy pediatric subjects from birth to age 18 years to quantify component and regional brain volumes. The volume trajectories were compared between the sexes and hemispheres using smoothing spline ANOVA. Population growth curves were developed using generalized additive models for location, scale, and shape.

RESULTS

Brain volume peaked at 10-12 years of age. Males exhibited larger age-adjusted total brain volumes than females, and body size normalization procedures did not eliminate this difference. The ratio of brain to CSF volume, however, revealed a universal age-dependent relationship independent of sex or body size.

CONCLUSIONS

These findings enable the application of normative growth curves in managing a broad range of childhood diseases in which cognitive development, brain growth, and fluid accumulation are interrelated.

The developmental brain age is associated with adversity, depression, and functional outcomes among adolescents

BACKGROUND

Most psychiatric disorders emerge in the second decade of life. In the present study we examined whether environmental adversity, developmental antecedents, major depressive disorder (MDD), and functional impairment correlate with deviation from normative brain development in adolescence.

METHODS

We trained a brain age prediction model using 189 structural MRI brain features in 1299 typically developing adolescents (age range 9-19 years old, M = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (M = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (M = 13.6, SD = 2.82). We tested associations between the brain-age-gap and adversity, early antecedents, depression, and functional impairment.

RESULTS

We accurately predicted chronological age in typically developing adolescents (mean absolute error (MAE) = 1.53 years). The model generalized to the validation set (MAE = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (MAE = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intra class correlation = 0.94). Experience of environmental advertises (β = 0.18, 95% CI [0.04, 0.31], p = 0.02), diagnosis of MDD (β = 0.61, 95% CI [0.23, 0.99], p = 0.01) and functional impairment (β = 0.16, 95% CI [0.05, 0.27], p = 0.01) were associated with a positive brain-age-gap.

CONCLUSIONS

Risk factors, diagnosis, and impact of mental illness are associated with an older appearing brain during development.

Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation

PURPOSE

For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the training data (i.e., raw data and annotation data) and network architectures. The improvement of annotation quality is a significant concern because it requires much labor for labeling organ regions. To address this problem, we focus on skip connection architectures and reveal which skip connections are effective for training FCNs using sparsely annotated brain images.

METHODS

We tested 2D FCN architectures with four different types of skip connections. The first was a U-Net architecture with horizontal skip connections that transfer feature maps at the same scale from the encoder to the decoder. The second was a U-Net++ architecture with dense convolution layers and dense horizontal skip connections. The third was a full-resolution residual network (FRRN) architecture with vertical skip connections that pass feature maps between each downsampled scale path and the full-resolution scale path. The last one was a hybrid architecture with a combination of horizontal and vertical skip connections. We validated the effect of skip connections on medical image segmentation from sparse annotation based on these four FCN architectures, which were trained under the same conditions.

RESULTS

For multiclass segmentation of the cerebrum, cerebellum, brainstem, and blood vessels from sparsely annotated MR images, we performed a comparative evaluation of segmentation performance among the above four FCN approaches: U-Net, U-Net++, FRRN, and hybrid architectures. The experimental results show that the horizontal skip connections in the U-Net architectures were effective for the segmentation of larger sized objects, whereas the vertical skip connections in the FRRN architecture improved the segmentation of smaller sized objects. The hybrid architecture with both horizontal and vertical skip connections achieved the best results of the four FCN architectures. We then performed an ablation study to explore which skip connections in the FRRN architecture contributed to the improved segmentation of blood vessels. In the ablation study, we compared the segmentation performance between architectures with a horizontal path (HP), an HP and vertical up paths (HP+VUPs), an HP and vertical down paths (HP+VDPs), and an HP and vertical up and down paths (FRRN). We found that the vertical up paths were effective in improving the segmentation of smaller sized objects.

CONCLUSIONS

This paper investigated which skip connection architectures were effective for multiclass brain segmentation from sparse annotation. Consequently, using vertical skip connections with horizontal skip connections allowed FCNs to improve segmentation performance.

Abnormal Right-Hemispheric Sulcal Patterns Correlate with Executive Function in Adolescents with Tetralogy of Fallot

Neurodevelopmental disabilities are the most common noncardiac conditions in patients with congenital heart disease (CHD). Executive function skills have been frequently observed to be decreased among children and adults with CHD compared with peers, but a neuroanatomical basis for the association is yet to be identified. In this study, we quantified sulcal pattern features from brain magnetic resonance imaging data obtained during adolescence among 41 participants with tetralogy of Fallot (ToF) and 49 control participants using a graph-based pattern analysis technique. Among patients with ToF, right-hemispheric sulcal pattern similarity to the control group was decreased (0.7514 vs. 0.7553, P = 0.01) and positively correlated with neuropsychological testing values including executive function (r = 0.48, P < 0.001). Together these findings suggest that sulcal pattern analysis may be a useful marker of neurodevelopmental risk in patients with CHD. Further studies may elucidate the mechanisms leading to different alterations in sulcal patterning.

Clinical adaptation of synthetic MRI-based whole brain volume segmentation in children at 3 T: comparison with modified SPM segmentation methods

PURPOSE

To validate the use of synthetic magnetic resonance imaging (SyMRI) volumetry by comparing with child-optimized SPM 12 volumetry in 3 T pediatric neuroimaging.

METHODS

In total, 106 children aged 4.7-18.7 years who underwent both synthetic and 3D T1-weighted imaging and had no abnormal imaging/neurologic findings were included for the SyMRI vs. SPM T1-only segmentation (SPM T1). Forty of the 106 children who underwent an additional 3D T2-weighted imaging were included for the SyMRI vs. SPM multispectral segmentation (SPM multi). SPM segmentation using an age-appropriate atlas and inverse-transforming template-space intracranial mask was compared with SyMRI segmentation. Volume differences between SyMRI and SPM T1 were plotted against age to evaluate the influence of age on volume difference.

RESULTS

Measurements derived from SyMRI and two SPM methods showed excellent agreements and strong correlations except for the CSF volume (CSFV) (intraclass correlation coefficients = 0.87-0.98; r = 0.78-0.96; relative volume difference other than CSFV = 6.8-18.5% [SyMRI vs. SPM T1] and 11.3-22.7% [SyMRI vs. SPM multi]). Dice coefficients of all brain tissues (except CSF) were in the range 0.78-0.91. The Bland-Altman plot and age-related volume difference change suggested that the volume differences between the two methods were influenced by the volume of each brain tissue and subject's age (p < 0.05).

CONCLUSION

SyMRI and SPM segmentation results were consistent except for CSFV, which supports routine clinical use of SyMRI-based volumetry in pediatric neuroimaging. However, caution should be taken in the interpretation of the CSF segmentation results.

A human craniofacial life-course: Cross-sectional morphological covariations during postnatal growth, adolescence, and aging

Covariations between anatomical structures are fundamental to craniofacial ontogeny, maturation, and aging and yet are rarely studied in such a cognate fashion. Here, we offer a comprehensive investigation of the human craniofacial complex using freely available software and MRI datasets representing 575 individuals from 0 to 79 years old. We employ both standard craniometrics methods as well as Procrustes-based analyses to capture and document cross-sectional trends. Findings suggest that anatomical structures behave primarily as modules, and manifest integrated patterns of shape change as they compete for space, particularly with relative expansions of the brain during early postnatal life and of the face during puberty. Sexual dimorphism was detected in infancy and intensified during adolescence with gender differences in the magnitude and pattern of morphological covariation as well as of aging. These findings partly support the spatial-packing hypothesis and reveal important insights into phenotypic adjustments to deep-rooted, and presumably genetically defined, trajectories of morphological size and shape change that characterize the normal human craniofacial life-course.

Strengths and challenges of longitudinal non-human primate neuroimaging

Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.

Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan

Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.

Developmental variation in testosterone:cortisol ratio alters cortical- and amygdala-based cognitive processes

Testosterone (T) and cortisol (C) are the end products of neuroendocrine axes that interact with the process of shaping brain structure and function. Relative levels of T:C (TC ratio) may alter prefrontal-amygdala functional connectivity in adulthood. What remains unclear is whether TC-related effects are rooted to childhood and adolescence. We used a healthy cohort of 4-22-year-olds to test for associations between TC ratios, brain structure (amygdala volume, cortical thickness (CTh), and their coordinated growth), as well as cognitive and behavioral development. We found greater TC ratios to be associated with the growth of specific brain structures: 1) parietal CTh; 2) covariance of the amygdala with CTh in visual and somatosensory areas. These brain parameters were in turn associated with lower verbal/executive function and higher spatial working memory. In sum, individual TC profiles may confer a particular brain phenotype and set of cognitive strengths and vulnerabilities, prior to adulthood.

Maturational trajectories of pericortical contrast in typical brain development

In the last few years, a significant amount of work has aimed to characterize maturational trajectories of cortical development. The role of pericortical microstructure putatively characterized as the gray-white matter contrast (GWC) at the pericortical gray-white matter boundary and its relationship to more traditional morphological measures of cortical morphometry has emerged as a means to examine finer grained neuroanatomical underpinnings of cortical changes. In this work, we characterize the GWC developmental trajectories in a representative sample (n = 394) of children and adolescents (~4 to ~22 years of age), with repeated scans (1-3 scans per subject, total scans n = 819). We tested whether linear, quadratic, or cubic trajectories of contrast development best described changes in GWC. A best-fit model was identified vertex-wise across the whole cortex via the Akaike Information Criterion (AIC). GWC across nearly the whole brain was found to significantly change with age. Cubic trajectories were likeliest for 63% of vertices, quadratic trajectories were likeliest for 20% of vertices, and linear trajectories were likeliest for 16% of vertices. A main effect of sex was observed in some regions, where males had a higher GWC than females. However, no sex by age interactions were found on GWC. In summary, our results suggest a progressive decrease in GWC at the pericortical boundary throughout childhood and adolescence. This work contributes to efforts seeking to characterize typical, healthy brain development and, by extension, can help elucidate aberrant developmental trajectories.

A multimodal approach to the study of children treated for posterior fossa tumor: A review of the literature and a pilot study

OBJECTIVE

The aims of the present study were: (1) to review the literature on long-lasting cognitive sequelae in children treated for Posterior Fossa Tumor and (2) to investigate anatomic functional relations in a case series of 7 children treated for PFT using magnetic resonance imaging (MRI) post-processing methods.

METHODS

We retrospectively analyzed MRIs of children who underwent complete surgical resection of PFT and performed extensive neuropsychological evaluation. Tumor, ventricular volumes, and VPS insertion site were drawn on T1 volumetric MRI scans and normalized to a pediatric template. Children showed worse performances on tasks tapping executive functions, memory, visuo-motor precision, and expressive language.

RESULTS

Volumes of interest related to these functions showed a maximum overlap on the left vermis and the lateral ventricle enlargement, except for impaired narrative fluency -which was associated with left lateral ventricle enlargement- and narrative memory -which was related to the right vermis and the enlarged fourth ventricle.

CONCLUSION

Results suggest that anatomic functional relations in children treated for PFT are related to a combination of different pathophysiological factors.

Development of sex differences in the human brain

Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate 'Brain Sex' using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during adolescence. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age. That is, the very large effect size of d = 1.2 which was already evident during childhood increased even further from age 11 onward, and eventually reached an effect size of d = 1.6 at age 17. Altogether these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during adolescence.

Developmental changes of the central sulcus morphology in young children

The human brain grows rapidly in early childhood, reaching 95% of its final volume by age 6. Understanding brain growth in childhood is important both to answer neuroscience questions about anatomical changes in development, and as a comparison metric for neurological disorders. Metrics for neuroanatomical development including cortical measures pertaining to the sulci can be instrumental in early diagnosis, monitoring, and intervention for neurological diseases. In this paper, we examine the development of the central sulcus in children aged 12-60 months from structural magnetic resonance images. The central sulcus is one of the earliest sulci to develop at the fetal stage and is implicated in diseases such as Attention Deficit Hyperactive Disorder and Williams syndrome. We investigate the relationship between the changes in the depth of the central sulcus with respect to age. In our results, we observed a pattern of depth present early on, that had been previously observed in adults. Results also reveal the presence of a rightward depth asymmetry at 12 months of age at a location related to orofacial movements. That asymmetry disappears gradually, mostly between 12 and 24 months, and we suggest that it is related to the development of language skills.