Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data

Neurosurgical and pharmacological management of dystonia

Dystonia characterizes a group of neurological movement disorders characterized by abnormal muscle movements, often with repetitive or sustained contraction resulting in abnormal posturing. Different types of dystonia present based on the affected body regions and play a prominent role in determining the potential efficacy of a given intervention. For most patients afflicted with these disorders, an exact cause is rarely identified, so treatment mainly focuses on symptomatic alleviation. Pharmacological agents, such as oral anticholinergic administration and botulinum toxin injection, play a major role in the initial treatment of patients. In more severe and/or refractory cases, focal areas for neurosurgical intervention are identified and targeted to improve quality of life. Deep brain stimulation (DBS) targets these anatomical locations to minimize dystonia symptoms. Surgical ablation procedures and peripheral denervation surgeries also offer potential treatment to patients who do not respond to DBS. These management options grant providers and patients the ability to weigh the benefits and risks for each individual patient profile. This review article explores these pharmacological and neurosurgical management modalities for dystonia, providing a comprehensive assessment of each of their benefits and shortcomings.

Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

A review of PET attenuation correction methods for PET-MR

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

MRI-based brain age prediction model for children under 3 years old using deep residual network

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

Neuroimaging-based analysis of DBS outcomes in pediatric dystonia: Insights from the GEPESTIM registry

INTRODUCTION

Deep brain stimulation (DBS) is an established treatment in patients of various ages with pharmaco-resistant neurological disorders. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures, and on electrode connectivity to a specific distribution pattern within brain networks. Such information is usually collected using group-level analysis, which relies on the availability of normative imaging resources (atlases and connectomes). Analysis of DBS data in children with debilitating neurological disorders such as dystonia would benefit from such resources, especially given the developmental differences in neuroimaging data between adults and children. We assembled pediatric normative neuroimaging resources from open-access datasets in order to comply with age-related anatomical and functional differences in pediatric DBS populations. We illustrated their utility in a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources.

METHODS

An average pediatric brain template (the MNI brain template 4.5-18.5 years) was implemented and used to localize the DBS electrodes in 20 patients from the GEPESTIM registry cohort. A pediatric subcortical atlas, analogous to the DISTAL atlas known in DBS research, was also employed to highlight the anatomical structures of interest. A local pallidal sweetspot was modeled, and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcomes. Additionally, a pediatric functional connectome of 100 neurotypical subjects from the Consortium for Reliability and Reproducibility was built to allow network-based analyses and decipher a connectivity fingerprint responsible for the clinical improvements in our cohort.

RESULTS

We successfully implemented a pediatric neuroimaging dataset that will be made available for public use as a tool for DBS analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). The functional connectivity fingerprint of DBS outcomes was determined to be a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003).

CONCLUSIONS

Local sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcomes in dystonia using pediatric neuroimaging surrogate data. Implementation of this pediatric neuroimaging dataset might help to improve the practice and pave the road towards a personalized DBS-neuroimaging analyses in pediatric patients.

Deep Learning Classification of Reading Disability with Regional Brain Volume Features

Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contributes to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases.

Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab

Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.

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

AIM

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

CONCLUSIONS

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

Clinical Speech fMRI in Children and Adolescents : Development of an Optimal Protocol and Analysis Algorithm

Purpose

In patients with drug-resistant focal epilepsy, surgical resection is often the only treatment option to achieve long-term seizure control. Prior to brain surgery involving potential language areas, identification of hemispheric language dominance is crucial. Our group developed and validated a functional magnetic resonance imaging (fMRI) battery of four pediatric language tasks. The present study aimed at optimizing fMRI data acquisition and analysis using these tasks.

Methods

We retrospectively analyzed speech fMRI examinations of 114 neuropediatric patients (age range 5.8–17.8 years) who were examined prior to possible epilepsy surgery. In order to evaluate hemispheric language dominance, 1–4 language tasks (vowel identification task VIT, word-chain task WCT, beep-story task BST, synonym task SYT) were measured.

Results

Language dominance was classified using fMRI activation in the 13 validly lateralizing ROIs (VLR) in frontal, temporal and parietal lobes and cerebellum of the recent validation study from our group: 47/114 patients were classified as left-dominant, 34/114 as bilateral and 6/114 as right-dominant. In an attempt to enlarge the set of VLR, we then compared for each task agreement of these ROI activations with the classified language dominance. We found four additional task-specific ROIs showing concordant activation and activation in ≥ 10 sessions, which we termed validly lateralizing (VLR_new). The new VLRs were: for VIT the temporal language area and for SYT the middle frontal gyrus, the intraparietal sulcus and cerebellum. Finally, in order to find the optimal sequence of measuring the different tasks, we analyzed the success rates of single tasks and all possible task combinations. The sequence 1) VIT 2) WCT 3) BST 4) SYT was identified as the optimal sequence, yielding the highest chance to obtain reliable results even when the fMRI examination has to be stopped, e.g., due to lack of cooperation.

Conclusion

Our suggested task order together with the enlarged set of VLR_new may contribute to optimize pediatric speech fMRI in a clinical setting.

Supplementary Information

The online version of this article (10.1007/s00062-021-01097-z) contains supplementary material, which is available to authorized users.

The Neurophysiological Processing of Music in Children: A Systematic Review With Narrative Synthesis and Considerations for Clinical Practice in Music Therapy

Introduction: Evidence supporting the use of music interventions to maximize arousal and awareness in adults presenting with a disorder of consciousness continues to grow. However, the brain of a child is not simply a small adult brain, and therefore adult theories are not directly translatable to the pediatric population. The present study aims to synthesize brain imaging data about the neural processing of music in children aged 0-18 years, to form a theoretical basis for music interventions with children presenting with a disorder of consciousness following acquired brain injury. Methods: We conducted a systematic review with narrative synthesis utilizing an adaptation of the methodology developed by Popay and colleagues. Following the development of the narrative that answered the central question "what does brain imaging data reveal about the receptive processing of music in children?", discussion was centered around the clinical implications of music therapy with children following acquired brain injury. Results: The narrative synthesis included 46 studies that utilized EEG, MEG, fMRI, and fNIRS scanning techniques in children aged 0-18 years. From birth, musical stimuli elicit distinct but immature electrical responses, with components of the auditory evoked response having longer latencies and variable amplitudes compared to their adult counterparts. Hemodynamic responses are observed throughout cortical and subcortical structures however cortical immaturity impacts musical processing and the localization of function in infants and young children. The processing of complex musical stimuli continues to mature into late adolescence. Conclusion: While the ability to process fundamental musical elements is present from birth, infants and children process music more slowly and utilize different cortical areas compared to adults. Brain injury in childhood occurs in a period of rapid development and the ability to process music following brain injury will likely depend on pre-morbid musical processing. Further, a significant brain injury may disrupt the developmental trajectory of complex music processing. However, complex music processing may emerge earlier than comparative language processing, and occur throughout a more global circuitry.

The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas

BACKGROUND

Spatial normalization plays an essential role in multi-subject MRI and functional MRI (fMRI) experiments by facilitating a common space in which group analyses are performed. Although many prominent adult templates are available, their use for pediatric data is problematic. Generalized templates for pediatric populations are limited or constructed using older methods that result in less ideal normalization.

OBJECTIVE

The Haskins pediatric templates and atlases aim to provide superior registration and more precise accuracy in labeling of anatomical and functional regions essential for all fMRI studies involving pediatric populations.

MATERIALS AND METHODS

The Haskins pediatric templates and atlases were generated with nonlinear methods using structural MRI from 72 children (age range 7-14 years, median 10 years), allowing for a detailed template with corresponding parcellations of labeled atlas regions. The accuracy of these templates and atlases was assessed using multiple metrics of deformation distance and overlap.

RESULTS

When comparing the deformation distances from normalizing pediatric data between this template and both the adult templates and other pediatric templates, we found significantly less deformation distance for the Haskins pediatric template (P<0.0001). Further, the correct atlas classification was higher using the Haskins pediatric template in 74% of regions (P<0.0001).

CONCLUSION

The Haskins pediatric template results in more accurate correspondence across subjects because of lower deformation distances. This correspondence also provides better accuracy in atlas locations to benefit structural and functional imaging analyses of pediatric populations.

Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life

Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.

Hippocampal Shape Maturation in Childhood and Adolescence

The hippocampus is a subcortical structure critical for learning and memory, and a thorough understanding of its neurodevelopment is important for studying these processes in health and disease. However, few studies have quantified the typical developmental trajectory of the structure in childhood and adolescence. This study examined the cross-sectional age-related changes and sex differences in hippocampal shape in a multisite, multistudy cohort of 1676 typically developing children (age 1-22 years) using a novel intrinsic brain mapping method based on Laplace-Beltrami embedding of surfaces. Significant age-related expansion was observed bilaterally and nonlinear growth was observed primarily in the right head and tail of the hippocampus. Sex differences were also observed bilaterally along the lateral and medial aspects of the surface, with females exhibiting relatively larger surface expansion than males. Additionally, the superior posterior lateral surface of the left hippocampus exhibited an age-sex interaction with females expanding faster than males. Shape analysis provides enhanced sensitivity to regional changes in hippocampal morphology over traditional volumetric approaches and allows for the localization of developmental effects. Our results further support evidence that hippocampal structures follow distinct maturational trajectories that may coincide with the development of learning and memory skills during critical periods of development.

Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition

OBJECTIVES

This study introduced a tailored MP2RAGE-based brain acquisition for a comprehensive assessment of the normal maturing brain.

METHODS

Seventy normal patients (35 girls and 35 boys) from 1 to 16 years of age were recruited within a prospective monocentric study conducted from a single University Hospital. Brain MRI examinations were performed at 1.5 T using a 20-channel head coil and an optimized 3D MP2RAGE sequence with a total acquisition time of 6:36 min. Automated 38 region segmentation was performed using the MorphoBox (template registration, bias field correction, brain extraction, and tissue classification) which underwent a major adaptation of three age-group T1-weighted templates. Volumetry and T1 relaxometry reference ranges were established using a logarithmic model and a modified Gompertz growth respectively.

RESULTS

Detailed automated brain segmentation and T1 mapping were successful in all patients. Using these data, an age-dependent model of normal brain maturation with respect to changes in volume and T1 relaxometry was established. After an initial rapid increase until 24 months of life, the total intracranial volume was found to converge towards 1400 mL during adolescence. The expected volumes of white matter (WM) and cortical gray matter (GM) showed a similar trend with age. After an initial major decrease, T1 relaxation times were observed to decrease progressively in all brain structures. The T1 drop in the first year of life was more pronounced in WM (from 1000-1100 to 650-700 ms) than in GM structures.

CONCLUSION

The 3D MP2RAGE sequence allowed to establish brain volume and T1 relaxation time normative ranges in pediatrics.

KEY POINTS

• The 3D MP2RAGE sequence provided a reliable quantitative assessment of brain volumes and T1 relaxation times during childhood. • An age-dependent model of normal brain maturation was established. • The normative ranges enable an objective comparison to a normal cohort, which can be useful to further understand, describe, and identify neurodevelopmental disorders in children.

Maternal cerebellar gray matter volume is associated with daughters' psychotic experience

AIM

A substantial portion of children and adolescents show subthreshold psychotic symptoms called psychotic experience (PE). Because PE shares its biological and environmental risk factors with psychotic spectrum disorders, parental neuroanatomical variation could reflect a heritable biological underpinning of PE that may predict an offspring's PE.

METHODS

A total of 94 participants from 35 families without a diagnosis of major neuropsychiatric disorders were examined, including 14 mother-daughter, 17 mother-son, 12 father-daughter, and 16 father-son dyads. An offspring's PE was assessed with the Atypicality subscale of the Behavior Assessment System for Children - 2nd Edition, Self-Report of Personality form (BASCaty). We examined correlations between voxel-by-voxel parental gray matter volume and their offspring's BASCaty score.

RESULTS

Maternal cerebellar gray matter volume using voxel-based morphometry was positively correlated with their daughters' BASCaty scores. The findings were significant in a more robust approach using cerebellum-specific normalization known. We did not find significant correlation between paternal gray matter volume and BASCaty scores or between offspring gray matter volumes and their BASCaty scores.

CONCLUSION

Expanding upon parent-of-origin effects in psychosis, maternal neuroanatomical variation was associated with daughters' PE. The nature of this sex-specific intergenerational effect is unknown, but maternally transmitted genes may relate cerebellum development to PE pathogenesis.

Structural brain alterations and their association with cognitive function and symptoms in Attention-deficit/Hyperactivity Disorder families

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 to 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a ADHD/typically developing difference with subthreshold cases being intermediate between ADHD and typically developing controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to individuals with ADHD, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

The Maudsley Early Onset Schizophrenia Study: The effect of age of onset and illness duration on fronto-parietal gray matter

Objective

In Early Onset Schizophrenia (EOS; onset before the 18th birthday) late brain maturational changes may interact with disease mechanisms leading to a wave of back to front structural changes during adolescence. To further explore this effect we examined the relationship between age of onset and duration of illness on brain morphology in adolescents with EOS.

Subjects and methods

Structural brain magnetic resonance imaging scans were obtained from 40 adolescents with EOS. We used Voxel Based Morphometry and multiple regressions analyses, implemented in SPM, to examine the relationship between gray matter volume with age of onset and illness duration.

Results

Age of onset showed a positive correlation with regional gray matter volume in the right superior parietal lobule (Brodmann Area 7). Duration of illness was inversely related to regional gray matter volume in the left inferior frontal gyrus (BA 11/47).

Conclusions

Parietal gray matter loss may contribute to the onset of schizophrenia while orbitofrontal gray matter loss is associated with illness duration.

The effect of chronic neuroglycopenia on resting state networks in GLUT1 syndrome across the lifespan

Glucose transporter type I deficiency syndrome (GLUT1DS) is an encephalopathic disorder due to a chronic insufficient transport of glucose into the brain. PET studies in GLUT1DS documented a widespread cortico‐thalamic hypometabolism and a signal increase in the basal ganglia, regardless of age and clinical phenotype. Herein, we captured the pattern of functional connectivity of distinct striatal, cortical, and cerebellar regions in GLUT1DS (10 children, eight adults) and in healthy controls (HC, 19 children, 17 adults) during rest. Additionally, we explored for regional connectivity differences in GLUT1 children versus adults and according to the clinical presentation. Compared to HC, GLUT1DS exhibited increase connectivity within the basal ganglia circuitries and between the striatal regions with the frontal cortex and cerebellum. The excessive connectivity was predominant in patients with movement disorders and in children compared to adults, suggesting a correlation with the clinical phenotype and age at fMRI study. Our findings highlight the primary role of the striatum in the GLUT1DS pathophysiology and confirm the dependency of symptoms to the patients' chronological age. Despite the reduced chronic glucose uptake, GLUT1DS exhibit increased connectivity changes in regions highly sensible to glycopenia. Our results may portrait the effect of neuroprotective brain strategy to overcome the chronic poor energy supply during vulnerable ages.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ± 7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, "BRAHMA". A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.

Regional Gray Matter Volume Differences Between Adolescents With ADHD and Typically Developing Controls: Further Evidence for Anterior Cingulate Involvement

OBJECTIVE

The present study investigated structural brain differences between adolescents with ADHD and matched control participants.

METHOD

Voxel-based morphometry (VBM) using the DARTEL approach was performed to assess regional gray matter (GM) volumes. Additionally, individual performance on tests of attention was recorded to correlate ADHD related cognitive impairments with regional gray matter abnormalities.

RESULTS

We found significantly smaller GM volume in subjects with ADHD compared to their matched controls within the anterior cingulate cortex (ACC), the occipital cortex, bilateral hippocampus/amygdala and in widespread cerebellar regions. Further, reductions of the ACC gray matter volume were found to correlate with scores of selective inattention.

CONCLUSION

These findings underline that structural alterations in a widespread cortico-subcortical network seem to underlie the observable attention problems in patients with ADHD.

Gray Matter Increase in Motor Cortex in Pediatric ADHD: A Voxel-Based Morphometry Study

OBJECTIVE

Several studies report that ADHD is associated with reduced gray matter (GM), whereas others report no differences in GM volume between ADHD patients and controls, and some even report more GM volume in individuals with ADHD. These conflicting findings suggest that reduced GM is not a universal finding in ADHD, and that more research is needed to delineate with greater accuracy the range of GM alterations.

METHOD

The present study aimed to identify GM alterations in ADHD using pediatric templates. 19 drug-naïve ADHD patients and 18 controls, all aged 7 to 14 years, were scanned using magnetic resonance imaging.

RESULTS

Relative to the controls, the ADHD patients had more GM, predominantly in the precentral and supplementary motor areas. Moreover, there were positive correlations between GM volume in these areas and ADHD scale scores.

CONCLUSION

The clinical and pathophysiological significance of increased GM in the motor areas remains to be elucidated by additional research.

Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders

Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.

Sensorimotor network alterations in children and youth with prenatal alcohol exposure

Children with prenatal alcohol exposure (PAE) often have impaired sensorimotor function. While altered brain structure has been noted in sensorimotor areas, the functional brain alterations remain unclear. This study aims to investigate sensorimotor brain networks in children and youth with PAE using resting-state functional magnetic resonance imaging (rs-fMRI). A parcellation-based network analysis was performed to identify brain networks related to hand/lower limb and face/upper limb function in 59 children and youth with PAE and 50 typically developing controls. Participants with PAE and controls had similar organization of the hand and face areas within the primary sensorimotor cortex, but participants with PAE had altered functional connectivity (FC) between the sensorimotor regions and the rest of the brain. The sensorimotor regions in the PAE group showed less connectivity to certain hubs of the default mode network and more connectivity to areas of the salience network. Overall, our results show that despite similar patterns of organization in the sensorimotor network, subjects with PAE have increased FC between this network and other brain areas, perhaps suggesting overcompensation. These alterations in the sensorimotor network lay the foundation for future studies to evaluate interventions and treatments to improve motor function in children with PAE.

Maternal depressive symptoms during pregnancy are associated with amygdala hyperresponsivity in children

Depression during pregnancy is highly prevalent and has a multitude of potential risks of the offspring. Among confirmed consequences is a higher risk of psychopathology. However, it is unknown how maternal depression may impact the child's brain to mediate this vulnerability. Here we studied amygdala functioning, using task-based functional MRI, in children aged 6-9 years as a function of prenatal maternal depressive symptoms selected from a prospective population-based sample (The Generation R Study). We show that children exposed to clinically relevant maternal depressive symptoms during pregnancy (N = 19) have increased amygdala responses to negative emotional faces compared to control children (N = 20) [F(1,36) 7.02, p = 0.022]. Strikingly, postnatal maternal depressive symptoms, obtained at 3 years after birth, did not explain this relation. Our findings are in line with a model in which prenatal depressive symptoms of the mother are associated with amygdala hyperresponsivity in her offspring, which may represent a risk factor for later-life psychopathology.

Quantitative Synthetic MRI in Children: Normative Intracranial Tissue Segmentation Values during Development

BACKGROUND AND PURPOSE

Synthetic MR imaging is a new technique to create absolute R1 relaxivity (1/T1), R2 relaxivity (1/T2), and proton-density maps using a single multiple-spin-echo saturation recovery sequence. These relaxivity maps allow rapid automated intracranial segmentation of tissue types. To assess its utility in children, we created a normative data base of intracranial volume and brain parenchymal, GM, WM, CSF, and myelin volumes in a pediatric population with normal brain MRI findings using synthetic MR imaging.

MATERIALS AND METHODS

All multiple-spin-echo saturation recovery sequences containing brain MR imaging examinations performed during 34 months were retrospectively reviewed. Abnormal examination findings were excluded following a detailed radiographic and clinical chart review. The remaining normal examination findings were then quantitatively analyzed with synthetic MR imaging. Intracranial, brain parenchymal, GM, WM, CSF, and myelin volumes were plotted versus age. Qualitative assessment of segmentation accuracy was performed. Selected abnormal examination findings were compared with these normative curves.

RESULTS

One hundred twenty-two MRI examinations with normal findings were included of individuals ranging from 0.1 to 21.5 years of age (median, 11.8 years). Resulting normative data plots compared favorably with previously published data obtained using more onerous techniques. Differentiation from pathologic states was possible using quantitative values in select cases.

CONCLUSIONS

A pediatric data base of normal intracranial tissue volumes using a single sequence and rapid software analysis has been compiled and correlates with previously published data. This provides a framework for clinical interpretation of quantitative synthetic MR images during development. Improved age-based segmentation algorithms in young children are needed.

Microstructural abnormalities in white and gray matter in obese adolescents with and without type 2 diabetes

Aims/hypotheses

In adults, type 2 diabetes and obesity have been associated with structural brain changes, even in the absence of dementia. Some evidence suggested similar changes in adolescents with type 2 diabetes but comparisons with a non-obese control group have been lacking. The aim of the current study was to examine differences in microstructure of gray and white matter between adolescents with type 2 diabetes, obese adolescents and healthy weight adolescents.

Methods

Magnetic resonance imaging data were collected from 15 adolescents with type 2 diabetes, 21 obese adolescents and 22 healthy weight controls. Volumetric differences in the gray matter between the three groups were examined using voxel based morphology, while tract based spatial statistics was used to examine differences in the microstructure of the white matter.

Results

Adolescents with type 2 diabetes and obese adolescents had reduced gray matter volume in the right hippocampus, left putamen and caudate, bilateral amygdala and left thalamus compared to healthy weight controls. Type 2 diabetes was also associated with significant regional changes in fractional anisotropy within the corpus callosum, fornix, left inferior fronto-occipital fasciculus, left uncinate, left internal and external capsule. Fractional anisotropy reductions within these tracts were explained by increased radial diffusivity, which may suggest demyelination of white matter tracts. Mean diffusivity and axial diffusivity did not differ between the groups.

Conclusion/interpretation

Our data shows that adolescent obesity alone results in reduced gray matter volume and that adolescent type 2 diabetes is associated with both white and gray matter abnormalities.

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Type 2 diabetes and obesity in adolescents is associated with reduced gray matter volume.

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Type 2 diabetes was associated with significant regional changes in FA.

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FA reductions within these tracts were explained by increased RD.

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Mean diffusivity and axial diffusivity did not differ between the groups.

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Toward the automatic quantification of in utero brain development in 3D structural MRI: A review

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.

Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies.

Comparing Brain Morphometry Across Multiple Childhood Psychiatric Disorders

OBJECTIVE

In both children and adults, psychiatric illness is associated with structural brain alterations, particularly in the prefrontal cortex (PFC). However, most studies compare gray matter volume (GMV) in healthy volunteers (HVs) to one psychiatric group. We compared GMV among youth with anxiety disorders, bipolar disorder (BD), disruptive mood dysregulation disorder (DMDD), attention-deficit/hyperactivity disorder (ADHD), and HVs.

METHOD

3-Tesla T1-weighted magnetic resonance imaging scans were acquired in 184 youths (39 anxious, 20 BD, 52 DMDD, 20 ADHD, and 53 HV). Voxel-based morphometry analyses were conducted. One-way analysis of variance tested GMV differences with whole-brain familywise error (p < .05) correction; secondary, exploratory whole-brain analyses used a threshold of p < .001, ≥200 voxels. Given recent frameworks advocating dimensional approaches in psychopathology research, we also tested GMV associations with continuous anxiety, irritability, and inattention symptoms.

RESULTS

Specificity emerged in the left dorsolateral PFC (dlPFC), which differed among youth with BD, anxiety, and HVs; GMV was increased in youth with anxiety, but decreased in BD, relative to HVs. Secondary analyses revealed BD-specific GMV decreases in the right lateral PFC, right dlPFC, and dorsomedial PFC, and also anxiety-specific GMV increases in the left dlPFC, right ventrolateral PFC, frontal pole, and right parahippocampal gyrus/lingual gyrus. Both BD and DMDD showed decreased GMV relative to HVs in the right dlPFC/superior frontal gyrus. GMV was not associated with dimensional measures of anxiety, irritability, or ADHD symptoms.

CONCLUSION

Both disorder-specific and shared GMV differences manifest in pediatric psychopathology. Some differences were specific to anxiety disorders, others specific to BD, and others shared between BD and DMDD. Further developmental research might map commonalities and differences of structure and function in diverse pediatric psychopathologies.

A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)

Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain's Integrated Registration and Segmentation Tool) and FreeSurfer. These segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific segmentation technique will produce more accurate segmentation results. In this study, we describe a new automated segmentation technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual segmentation as the ground truth, utilizing spatial overlap (Dice's coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90% and ICC scores ≥0.77 when compared with manual segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR  < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.

Altered neural circuits accompany lower performance during narrative comprehension in children with reading difficulties: an fMRI study

Narrative comprehension is a linguistic ability that is foundational for future reading ability. The aim of the current study was to examine the neural circuitry of children with reading difficulties (RD) compared to typical readers during a narrative-comprehension task. We hypothesized that due to deficient executive functions, which support narrative comprehension abilities, children with RD would display altered activation and functional connectivity, as well as lower performance on a narrative-comprehension task. Children with RD and typical readers were scanned during a narrative-comprehension task and administered reading behavioral tests. Children with RD scored significantly lower on the narrative-comprehension task than did typical readers. Composite activation maps showed more diffused activation during narrative comprehension in the RD group. Maps comparing the two reading groups showed more activation in the frontal lobes (regions responsible for executive functions), and functional connectivity showed higher global efficiency in children with RD than in typical readers. Global efficiency was negatively correlated with phonological awareness and reading and executive function scores in the entire study group. Children with RD may suffer from narrative-comprehension difficulties due to diffused activation of language areas, as was observed during a narrative-comprehension task. Greater effort in this task may be reflected by the engagement of brain regions related to executive functions and higher functional connectivity or attributed to difficulties in phonological processing and reading and executive functions. Therefore, the accommodation given to children with RD of reading aloud may need to be revised due to the observed difficulty in this domain.

Diffusion Tensor Imaging Findings in Post-Concussion Syndrome Patients after Mild Traumatic Brain Injury: A Systematic Review

Objectives

To review the evidence for the use of diffusion tensor imaging (DTI) parameters in the human brain as a diagnostic tool for and predictor of post-concussion syndrome (PCS) after a mild traumatic brain injury (mTBI).

Design

Systematic review.

Data sources

All relevant studies in AMED, Embase, MEDLINE, Ovid, PubMed, Scopus, and Web of Science through 20 May, 2016.

Study selection

Studies that analyze traditional DTI measures [fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)] and the severity of PCS symptoms or the development of PCS in humans after an mTBI.

Data extraction

Population studied, patient source, mTBI diagnosis method, PCS diagnosis method, DTI values measured, significant findings, and correlation between DTI findings and PCS.

Data synthesis

Ten studies investigated correlations between DTI values and PCS symptom severity or between DTI values and the development of PCS in mTBI patients. Decreased FA and increased MD and RD were associated with the development and severity of PCS. AD was not found to change significantly. Brain regions found to have significant changes in DTI parameters varied from study to study, although the corpus callosum was most frequently cited as having abnormal DTI parameters in PCS patients.

Conclusion

DTI abnormalities correlate with PCS incidence and symptom severity, as well as indicate an increased risk of developing PCS after mTBI. Abnormal DTI findings should prompt investigation of the syndrome to ensure optimal symptom management at the earliest stages. Currently, there is no consensus in the literature about the use of one DTI parameter in a specific region of the brain as a biomarker for PCS because no definite trends for DTI parameters in PCS subjects have been identified. Further research is required to establish a standard biomarker for PCS.

Functional neuroanatomy of arithmetic and word reading and its relationship to age

Arithmetic and written language are uniquely human skills acquired during early schooling and used daily. While prior studies have independently characterized the neural bases for arithmetic and reading, here we examine both skills in a single study to capture their shared and unique cognitive mechanisms, as well as the role of age/experience in modulating their neural representations. We used functional MRI in 7- to 29-year-olds who performed single-digit subtraction, single-digit addition, and single-word reading. Using a factorial design, we examined the main effects of Task (subtraction, addition, reading) and Age (as a continuous variable), and their interactions. A main effect of Task revealed preferential activation for subtraction in bilateral intraparietal sulci and supramarginal gyri, right insula, inferior frontal gyrus, and cingulate. The right middle temporal gyrus and left superior temporal gyrus were preferentially active for both addition and reading, and left fusiform gyrus was preferentially active for reading. A main effect of Age revealed increased activity in older participants in right angular gyrus, superior temporal sulcus, and putamen, and less activity in left supplementary motor area, suggesting a left frontal to right temporo-parietal shift of activity with increasing age/experience across all tasks. Interactions for Task by Age were found in right hippocampus and left middle frontal gyrus, with older age invoking greater activity for addition and at the same time less activity for subtraction and reading. Together, in a study conducted in the same participants using similar task and acquisition parameters, the results reveal the neural substrates of these educationally relevant cognitive skills in typical participants in the context of age/experience.

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.

Parcellation of the Healthy Neonatal Brain into 107 Regions Using Atlas Propagation through Intermediate Time Points in Childhood

Neuroimage analysis pipelines rely on parcellated atlases generated from healthy individuals to provide anatomic context to structural and diffusion MRI data. Atlases constructed using adult data introduce bias into studies of early brain development. We aimed to create a neonatal brain atlas of healthy subjects that can be applied to multi-modal MRI data. Structural and diffusion 3T MRI scans were acquired soon after birth from 33 typically developing neonates born at term (mean postmenstrual age at birth 39⁺⁵ weeks, range 37⁺²–41⁺⁶). An adult brain atlas (SRI24/TZO) was propagated to the neonatal data using temporal registration via childhood templates with dense temporal samples (NIH Pediatric Database), with the final atlas (Edinburgh Neonatal Atlas, ENA33) constructed using the Symmetric Group Normalization (SyGN) method. After this step, the computed final transformations were applied to T2-weighted data, and fractional anisotropy, mean diffusivity, and tissue segmentations to provide a multi-modal atlas with 107 anatomical regions; a symmetric version was also created to facilitate studies of laterality. Volumes of each region of interest were measured to provide reference data from normal subjects. Because this atlas is generated from step-wise propagation of adult labels through intermediate time points in childhood, it may serve as a useful starting point for modeling brain growth during development.

Chinese Character and English Word processing in children's ventral occipitotemporal cortex: fMRI evidence for script invariance

Learning to read is thought to involve the recruitment of left hemisphere ventral occipitotemporal cortex (OTC) by a process of "neuronal recycling", whereby object processing mechanisms are co-opted for reading. Under the same theoretical framework, it has been proposed that the visual word form area (VWFA) within OTC processes orthographic stimuli independent of culture and writing systems, suggesting that it is universally involved in written language. However, this "script invariance" has yet to be demonstrated in monolingual readers of two different writing systems studied under the same experimental conditions. Here, using functional magnetic resonance imaging (fMRI), we examined activity in response to English Words and Chinese Characters in 1st graders in the United States and China, respectively. We examined each group separately and found the readers of English as well as the readers of Chinese to activate the left ventral OTC for their respective native writing systems (using both a whole-brain and a bilateral OTC-restricted analysis). Critically, a conjunction analysis of the two groups revealed significant overlap between them for native writing system processing, located in the VWFA and therefore supporting the hypothesis of script invariance. In the second part of the study, we further examined the left OTC region responsive to each group's native writing system and found that it responded equally to Object stimuli (line drawings) in the Chinese-reading children. In English-reading children, the OTC responded much more to Objects than to English Words. Together, these results support the script invariant role of the VWFA and also support the idea that the areas recruited for character or word processing are rooted in object processing mechanisms of the left OTC.

Right is not always wrong: DTI and fMRI evidence for the reliance of reading comprehension on language-comprehension networks in the right hemisphere

The Simple View theory suggests that reading comprehension relies on automatic recognition of words combined with language comprehension. The goal of the current study was to examine the structural and functional connectivity in networks supporting reading comprehension and their relationship with language comprehension within 7-9 year old children using Diffusion Tensor Imaging (DTI) and fMRI during a Sentence Picture Matching task. Fractional Anisotropy (FA) values in the left and right Inferior Longitudinal Fasciculus (ILF) and Superior Longitudinal Fasciculus (SLF), known language-related tracts, were correlated from DTI data with scores from the Woodcock-Johnson III (WJ-III) Passage Comprehension sub-test. Brodmann areas most proximal to white-matter regions with significant correlation to Passage Comprehension scores were chosen as Regions-of-Interest (ROIs) and used as seeds in a functional connectivity analysis using the Sentence Picture Matching task. The correlation between percentile scores for the WJ-III Passage Comprehension subtest and the FA values in the right and left ILF and SLF indicated positive correlation in language-related ROIs, with greater distribution in the right hemisphere, which in turn showed strong connectivity in the fMRI data from the Sentence Picture Matching task. These results support the participation of the right hemisphere in reading comprehension and may provide physiologic support for a distinction between different types of reading comprehension deficits vs difficulties in technical reading.

Relationship between ventricular size, white matter injury, and neurocognition in children with stable, treated hydrocephalus

OBJECT

Larger-than-normal ventricles can persist in children following hydrocephalus treatment, even if they are asymptomatic and clinically well. This study aims to answer the following question: do large ventricles result in brain injuries that are detectable on diffusion tensor imaging (DTI) and/or in measurable neurocognitive deficits in children with stable, treated hydrocephalus that are not seen in children with small ventricles?

METHODS

For this prospective study, we recruited 23 children (age range 8-18 years) with hydrocephalus due to aqueductal stenosis or tectal glioma who were asymptomatic following hydrocephalus treatment that had been performed at least 2 years earlier. All patients underwent detailed DTI and a full battery of neuropsychological tests. Correlation analysis was performed to assess the relationship between DTI parameters, neurocognitive tests, and ventricular size. The false-discovery rate method was used to adjust for multiple comparisons.

RESULTS

The median age of these 23 children at the time of assessment was 15.0 years (interquartile range [IQR] 12.1-17.6 years), and the median age at the first hydrocephalus treatment was 5.8 years (IQR 2.2 months-12.8 years). At the time of assessment, 17 children had undergone endoscopic third ventriculostomy and 6 children had received a shunt. After adjusting for multiple comparisons, there were no significant correlations between any neurocognitive test and ventricular volume, any DTI parameter and ventricular volume, or any DTI parameter and neurocognitive test.

CONCLUSIONS

Our data do not show an association between large ventricular size and additional white matter injury or worse neurocognitive deficits in asymptomatic children with stable, treated hydrocephalus caused by a discrete blockage of the cerebral aqueduct. Further investigations using larger patient samples are needed to validate these results.

Resting-state oscillatory dynamics in sensorimotor cortex in benign epilepsy with centro-temporal spikes and typical brain development

Benign Epilepsy with Centro-Temporal Spikes (BECTS) is a common childhood epilepsy associated with deficits in several neurocognitive domains. Neurophysiological studies in BECTS often focus on centro-temporal spikes, but these correlate poorly with morphology and cognitive impairments. To better understand the neural profile of BECTS, we studied background brain oscillations, thought to be integrally involved in neural network communication, in sensorimotor areas. We used independent component analysis of temporally correlated sources on magnetoencephalography recordings to assess sensorimotor resting-state network activity in BECTS patients and typically developing controls. We also investigated the variability of oscillatory characteristics within focal primary motor cortex (M1), localized with a separate finger abduction task. We hypothesized that background oscillations would differ between patients and controls in the sensorimotor network but not elsewhere, especially in the beta band (13-30 Hz) because of its role in network communication and motor processing. The results support our hypothesis: in the sensorimotor network, patients had a greater variability in oscillatory amplitude compared to controls, whereas there was no difference in the visual network. Network measures did not correlate with age. The coefficient of variation of resting M1 peak frequency correlated negatively with age in the beta band only, and was greater than average for a number of patients. Our results point toward a "disorganized" functional sensorimotor network in BECTS, supporting a neurodevelopmental delay in sensorimotor cortex. Our findings further suggest that investigating the variability of oscillatory peak frequency may be a useful tool to investigate deficits of disorganization in neurodevelopmental disorders.

Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age

This study created and tested a database of adult, age-specific MRI brain and head templates. The participants included healthy adults from 20 through 89 years of age. The templates were done in five-year, 10-year, and multi-year intervals from 20 through 89 years, and consist of average T1W for the head and brain, and segmenting priors for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). It was found that age-appropriate templates provided less biased tissue classification estimates than age-inappropriate reference data and reference data based on young adult templates. This database is available for use by other investigators and clinicians for their MRI studies, as well as other types of neuroimaging and electrophysiological research.¹

Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures

Manual segmentation of anatomy in brain MRI data taken to be the closest to the "gold standard" in quality is often used in automated registration-based segmentation paradigms for transfer of template labels onto the unlabeled MRI images. This study presents a library of template data with 16 subcortical structures in the central brain area which were manually labeled for MRI data from 22 children (8 male, [Formula: see text]). The lateral ventricle, thalamus, caudate, putamen, hippocampus, cerebellum, third vevntricle, fourth ventricle, brainstem, and corpuscallosum were segmented by two expert raters. Cross-validation experiments with randomized template subset selection were conducted to test for their ability to accurately segment MRI data under an automated segmentation pipeline. A high value of the dice similarity coefficient ([Formula: see text], [Formula: see text], [Formula: see text]) and small Hausdorff distance ([Formula: see text], [Formula: see text], [Formula: see text]) of the automated segmentation against the manual labels was obtained on this template library data. Additionally, comparison with segmentation obtained from adult templates showed significant improvement in accuracy with the use of an age-matched library in this cohort. A manually delineated pediatric template library such as the one described here could provide a useful benchmark for testing segmentation algorithms.

Isolated assessment of translation or rotation severely underestimates the effects of subject motion in fMRI data

Subject motion has long since been known to be a major confound in functional MRI studies of the human brain. For resting-state functional MRI in particular, data corruption due to motion artefacts has been shown to be most relevant. However, despite 6 parameters (3 for translations and 3 for rotations) being required to fully describe the head's motion trajectory between timepoints, not all are routinely used to assess subject motion. Using structural (n = 964) as well as functional MRI (n = 200) data from public repositories, a series of experiments was performed to assess the impact of using a reduced parameter set (translation_only and rotation_only) versus using the complete parameter set. It could be shown that the usage of 65 mm as an indicator of the average cortical distance is a valid approximation in adults, although care must be taken when comparing children and adults using the same measure. The effect of using slightly smaller or larger values is minimal. Further, both translation_only and rotation_only severely underestimate the full extent of subject motion; consequently, both translation_only and rotation_only discard substantially fewer datapoints when used for quality control purposes (“motion scrubbing”). Finally, both translation_only and rotation_only severely underperform in predicting the full extent of the signal changes and the overall variance explained by motion in functional MRI data. These results suggest that a comprehensive measure, taking into account all available parameters, should be used to characterize subject motion in fMRI.

Beyond classical inheritance: the influence of maternal genotype upon child's brain morphology and behavior

Genetic variance has been associated with variations in brain morphology, cognition, behavior, and disease risk. One well studied example of how common genetic variance is associated with brain morphology is the serotonin transporter gene polymorphism within the promoter region (5-HTTLPR). Because serotonin is a key neurotrophic factor during brain development, genetically determined variations in serotonin activity during maturation, in particular during early prenatal development, may underlie the observed association. However, the intrauterine microenvironment is not only determined by the child's, but also the mother's genotype. Therefore, we hypothesized that maternal 5-HTTLPR genotype influences the child's brain development beyond direct inheritance. To test this hypothesis, we investigated 76 children who were all heterozygous for the 5-HTTLPR (sl) and who had mothers who were either homozygous for the long (ll) or the short allele (ss). Using MRI, we assessed brain morphology as a function of maternal genotype. Gray matter density of the somatosensory cortex was found to be greater in children of ss mothers compared with children of ll mothers. Behavioral assessment showed that fine motor task performance was altered in children of ll mothers and the degree of this behavioral effect correlated with somatosensory cortex density across individuals. Our findings provide initial evidence that maternal genotype can affect the child's phenotype beyond effects of classical inheritance. Our observation appears to be explained by intrauterine environmental differences or by differences in maternal behavior.

MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis

Currently, it is unclear whether pediatric multiple sclerosis (PMS) is a pathoetiologically homogeneous disease phenotype due to clinical and epidemiological differences between early and late onset PMS (EOPMS and LOPMS). Consequently, the question was raised whether diagnostic guidelines need to be complemented by specific EOPMS markers. To search for such markers, we analyzed cerebral MRI images acquired with standard protocols using computer-based classification techniques. Specifically, we applied classification algorithms to gray (GM) and white matter (WM) tissue probability parameters of small brain regions derived from T2-weighted MRI images of EOPMS patients (onset <12 years), LOPMS patients (onset ≥12 years), and healthy controls (HC). This was done for PMS subgroups matched for disease duration and participant age independently. As expected, maximal diagnostic information for distinguishing PMS patients and HC was found in a periventricular WM area containing lesions (87.1% accuracy, p < 2.2 × 10⁻⁵). MRI-based biomarkers specific for EOPMS were identified in prefrontal cortex. Specifically, a coordinate in middle frontal gyrus contained maximal diagnostic information (77.3%, p = 1.8 × 10⁻⁴). Taken together, we were able to identify biomarkers reflecting pathognomonic processes specific for MS patients with very early onset. Especially GM involvement in the separation between PMS subgroups suggests that conventional MRI contains a richer set of diagnostically informative features than previously assumed.

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EOPMS can be diagnosed accurately with computer-based classification and T2w-MRI.

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Separation of EOPMS and HC confirmed the pivotal role of WM lesions for diagnosis.

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Separation of EOPMS and LOPMS showed that GM variations are also informative.

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Thus, conventional MRI contains a richer set of biomarkers than assumed so far.

Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population

Automated morphometric approaches are used to detect epileptogenic structural abnormalities in 3D MR images in adults, using the variance of a control population to obtain z-score maps in an individual patient. Due to the substantial changes the developing human brain undergoes, performing such analyses in children is challenging. This study investigated six features derived from high-resolution T1 datasets in four groups: normal children (1.5T or 3T data), normal clinical scans (3T data), and patients with structural brain lesions (3T data), with each n = 10. Normative control data were obtained from the NIH study on normal brain development (n = 401). We show that control group size substantially influences the captured variance, directly impacting the patient's z-scores. Interestingly, matching on gender does not seem to be beneficial, which was unexpected. Using data obtained at higher field scanners produces slightly different base rates of suprathreshold voxels, as does using clinically derived normal studies, suggesting a subtle but systematic effect of both factors. Two approaches for controlling suprathreshold voxels in a multidimensional approach (combining features and requiring a minimum cluster size) were shown to be substantial and effective in reducing this number. Finally, specific strengths and limitations of such an approach could be demonstrated in individual cases.