A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: Preliminary results

Brain language networks and cognitive outcomes in children with frontotemporal lobe epilepsy

Introduction

Pediatric frontal and temporal lobe epilepsies (FLE, TLE) have been associated with language impairments and structural and functional brain alterations. However, there is no clear consensus regarding the specific patterns of cerebral reorganization of language networks in these patients. The current study aims at characterizing the cerebral language networks in children with FLE or TLE, and the association between brain network characteristics and cognitive abilities.

Methods

Twenty (20) children with FLE or TLE aged between 6 and 18 years and 29 age- and sex-matched healthy controls underwent a neuropsychological evaluation and a simultaneous functional near-infrared spectroscopy and electroencephalography (fNIRS-EEG) recording at rest and during a receptive language task. EEG was used to identify potential subclinical seizures in patients. We removed these time intervals from the fNIRS signal to investigate language brain networks and not epileptogenic networks. Functional connectivity matrices on fNIRS oxy-hemoglobin concentration changes were computed using cross-correlations between all channels.

Results and discussion

Group comparisons of residual matrices (=individual task-based matrix minus individual resting-state matrix) revealed significantly reduced connectivity within the left and between hemispheres, increased connectivity within the right hemisphere and higher right hemispheric local efficiency for the epilepsy group compared to the control group. The epilepsy group had significantly lower cognitive performance in all domains compared to their healthy peers. Epilepsy patients' local network efficiency in the left hemisphere was negatively associated with the estimated IQ (p = 0.014), suggesting that brain reorganization in response to FLE and TLE does not allow for an optimal cognitive development.

Maturation of large-scale brain systems over the first month of life

The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.

Optogenetic stimulation of anterior insular cortex neurons in male rats reveals causal mechanisms underlying suppression of the default mode network by the salience network

The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the male rat brain. Optogenetic stimulation of Chronos-expressing AI neurons suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

(Un)common space in infant neuroimaging studies: A systematic review of infant templates

In neuroimaging, spatial normalization is an important step that maps an individual's brain onto a template brain permitting downstream statistical analyses. Yet, in infant neuroimaging, there remain several technical challenges that have prevented the establishment of a standardized template for spatial normalization. Thus, many different approaches are used in the literature. To quantify the popularity and variability of these approaches in infant neuroimaging studies, we performed a systematic review of infant magnetic resonance imaging (MRI) studies from 2000 to 2020. Here, we present results from 834 studies meeting inclusion criteria. Studies were classified into (a) processing data in single subject space, (b) using an off the shelf, or "off the shelf," template, (c) creating a study specific template, or (d) using a hybrid of these methods. We found that across the studies in the systematic review, single subject space was the most used (no common space). This was the most used common space for diffusion-weighted imaging and structural MRI studies while functional MRI studies preferred off the shelf atlases. We found a pattern such that more recently published studies are more commonly using off the shelf atlases. When considering special populations, preterm studies most used single subject space while, when no special populations were being analyzed, an off the shelf template was most common. The most used off the shelf templates were the UNC Infant Atlases (24%). Using a systematic review of infant neuroimaging studies, we highlight a lack of an established "standard" template brain in these studies.

Brain MR Atlas Construction Using Symmetric Deep Neural Inpainting

Modeling statistical properties of anatomical structures using magnetic resonance imaging is essential for revealing common information of a target population and unique properties of specific subjects. In brain imaging, a statistical brain atlas is often constructed using a number of healthy subjects. When tumors are present, however, it is difficult to either provide a common space for various subjects or align their imaging data due to the unpredictable distribution of lesions. Here we propose a deep learning-based image inpainting method to replace the tumor regions with normal tissue intensities using only a patient population. Our framework has three major innovations: 1) incompletely distributed datasets with random tumor locations can be used for training; 2) irregularly-shaped tumor regions are properly learned, identified, and corrected; and 3) a symmetry constraint between the two brain hemispheres is applied to regularize inpainted regions. Henceforth, regular atlas construction and image registration methods can be applied using inpainted data to obtain tissue deformation, thereby achieving group-specific statistical atlases and patient-to-atlas registration. Our framework was tested using the public database from the Multimodal Brain Tumor Segmentation challenge. Results showed increased similarity scores as well as reduced reconstruction errors compared with three existing image inpainting methods. Patient-to-atlas registration also yielded better results with improved normalized cross-correlation and mutual information and a reduced amount of deformation over the tumor regions.

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.

Construction and validation of a database of head models for functional imaging of the neonatal brain

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

White matter extension of the Melbourne Children's Regional Infant Brain atlas: M-CRIB-WM

Brain atlases providing standardised identification of neonatal brain regions are key in investigating neurological disorders of early childhood. Our previously developed Melbourne Children's Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 brain regions including cortical, subcortical, and cerebellar regions. The aim of this study was to extend M-CRIB atlas coverage to include 54 white matter (WM) regions. Participants were 10 healthy term-born neonates that were used to create the initial M-CRIB atlas. WM regions were manually segmented based on T₂ images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions imitate the Johns Hopkins University neonatal atlas, with minor anatomical modifications. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy. The resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions, and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB cortical, subcortical, and cerebellar parcellations in 10 individual neonatal MRI data sets. The novel M-CRIB-WM atlas, along with the M-CRIB cortical and subcortical atlases, provide neonatal whole brain MRI coverage in the first multi-subject manually parcellated neonatal atlas compatible with atlases commonly used at older time points. The M-CRIB-WM atlas is publicly available, providing a valuable tool that will help facilitate neuroimaging research into neonatal brain development in both healthy and diseased states.

A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

A representative template of the neonatal cerebellum

The cerebellum plays an important role in human brain development. To improve the spatial specificity of the analysis of human cerebellar magnetic resonance imaging (MRI) data, we present a new template of the neonatal human cerebellum and brainstem based on the anatomy of 20 full-term healthy neonates. The template is spatially unbiased, which means that the location of each structure is not biased by the anatomy of the individuals used to create the template. In comparison to current whole-brain templates, it allows for an improved voxel-by-voxel normalization for MRI analysis. To align the cerebellum to the template, it needs to be isolated from the surrounding tissue, a process for which an automated algorithm has been developed. Our methodology outperforms normalization to a whole-brain neonatal template, using either linear or nonlinear transformations. Our algorithm reduces the spatial variability of the infratentorial area, while simultaneously increasing the overlap of the cerebellum. The template and the related software are freely available as part of SUIT v3.3 SPM toolbox.

Assessment of neonatal brain volume and growth at different postmenstrual ages by conventional MRI

Data regarding neonatal brain volumes represent a basis for monitoring early brain development, and large sample of neonatal brain volume data has not been well described. This study was focused on neonatal brain volumes at different postmenstrual ages (PMA) and postnatal age (PNA).A cohort of 415 neonates with PMA 30 to 43 weeks were recruited for the determination of brain volumes. Intracranial cavity (ICC), total brain tissue (TBT), and cerebrospinal fluid (CSF) were evaluated on the basis of T1-weighted sagittal plane magnetic resonance images. Brain magnetic resonance imaging was assessed using maturation scoring system and multiple linear regression analysis was conducted to forecast the effect factors of brain volumes.TBT volume reached a peak growth at 39 to 40 weeks, ICC volume presented peak growth later at around 43 to 44 weeks, and CSF had a cliff fallen at 37 to 38 weeks PMA at scan. The maturation score increased along with PMA, and the TBT and CSF volumes were significantly different between higher and lower gestational age (GA) groups. The ICC and TBT volumes in higher GA group were larger than lower GA group. Most infants in higher GA group had higher TMS than those in lower GA group. Gender, PMA, PNA, and birth weight were predictors of TBT and ICC volumes.Our results showed that premature volumes of ICC and TBT enlarged with the increasing PMA, while volumes of CSF decreased at 37 weeks. Premature earlier to leave the uterus can lead to brain mature retard although they had the same GA compared with those later birth neonates.

Baby brain atlases

The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.

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.

Computational neuroanatomy of baby brains: A review

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old

Diffusion imaging is critical for detecting acute brain injury. However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this article, we explored clinical PACS and electronic healthcare records (EHR) to create age-specific ADC atlases for clinical radiology reference. Using the EHR and three rounds of multiexpert reviews, we found ADC maps from 201 children 0-6 years of age scanned between 2006 and 2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1-6 year age range (an age bin per year). Unbiased group-wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross-sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age-specific whole-brain and region-wise ADC values were compared to those from age-matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically acquired images can be used to construct normative age-specific atlases. These first of their kind age-specific normative ADC atlases quantitatively characterize changes of myelination-related water diffusion in the first 6 years of life. The quantified voxel-wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at https://www.nitrc.org/projects/mgh_adcatlases. Hum Brain Mapp 38:3052-3068, 2017. © 2017 Wiley Periodicals, Inc.

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.

A Neonatal Bimodal MR-CT Head Template

Neonatal MR templates are appropriate for brain structural analysis and spatial normalization. However, they do not provide the essential accurate details of cranial bones and fontanels-sutures. Distinctly, CT images provide the best contrast for bone definition and fontanels-sutures. In this paper, we present, for the first time, an approach to create a fully registered bimodal MR-CT head template for neonates with a gestational age of 39 to 42 weeks. Such a template is essential for structural and functional brain studies, which require precise geometry of the head including cranial bones and fontanels-sutures. Due to the special characteristics of the problem (which requires inter-subject inter-modality registration), a two-step intensity-based registration method is proposed to globally and locally align CT images with an available MR template. By applying groupwise registration, the new neonatal CT template is then created in full alignment with the MR template to build a bimodal MR-CT template. The mutual information value between the CT and the MR template is 1.17 which shows their perfect correspondence in the bimodal template. Moreover, the average mutual information value between normalized images and the CT template proposed in this study is 1.24±0.07. Comparing this value with the one reported in a previously published approach (0.63±0.07) demonstrates the better generalization properties of the new created template and the superiority of the proposed method for the creation of CT template in the standard space provided by MR neonatal head template. The neonatal bimodal MR-CT head template is freely downloadable from https://www.u-picardie.fr/labo/GRAMFC.

Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging

Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2* sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

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.

Learning Biomarker Models for Progression Estimation of Alzheimer's Disease

Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and--employing cognitive scores and image-based biomarkers--real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.

Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images

Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6 years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6 years of life.

A framework for quantification and visualization of segmentation accuracy and variability in 3D lateral ventricle ultrasound images of preterm neonates

PURPOSE

Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates. Three dimensional ultrasound (US) imaging systems have been developed to visualize 3D anatomical structure of preterm neonatal intracranial ventricular system with IVH and ventricular dilation. To allow quantitative analysis, the ventricle system is required to be segmented accurately and efficiently from 3D US images. Although semiautomatic segmentation algorithms have been developed, local segmentation accuracy and variability associated with these algorithms should be evaluated statistically before they can be applied in clinical settings. This work proposes a statistical framework to quantify the local accuracy and variability and performs statistical tests to identify locations where the semiautomatically segmented surfaces are significantly different from manually segmented surfaces.

METHODS

Three dimensional lateral ventricle US images of preterm neonates were each segmented six times manually and using a semiautomated segmentation algorithm. The local difference between manually and algorithmically segmented surfaces as well as the segmentation variability for each method was computed and superimposed on the ventricular surface of each subject. To summarize the segmentation performance for a whole group of subjects, the subject-specific local difference and standard deviation maps were registered onto a 3D template ventricular surface using a nonrigid registration algorithm. Pointwise, intersubject average accuracy and pooled variability for the whole group of subjects can be computed and visualized on the template surface, providing a summary of performance of the segmentation algorithm for the whole group of ventricles with highly variable geometry. In addition to pointwise statistical analysis performed on the template surface, statistical conclusion regarding the accuracy of the segmentation algorithm was made for subregions and the whole ventricle with the spatial correlation of pointwise accuracy taken into account.

RESULTS

Ten 3D US images were involved in this study. Pointwise local difference, ΔS, its absolute value |ΔS| as well as the standard deviations of the manual and algorithm segmentations were computed and superimposed on the each ventricle surface. Regions with lower segmentation accuracy and higher segmentation variability can be identified from these maps, and the localized information was applied to improve the accuracy of the algorithm. Intersubject average ΔS and |ΔS| as well as pooled standard deviations was computed on the template surface. Intersubject average ΔS and |ΔS| indicated that the algorithm underestimated regions in the neighborhood of the tips of anterior, inferior, and posterior horns. Intersubject pooled standard deviations indicated that manual segmentation had a higher segmentation variability than algorithm segmentation over the whole ventricle. Statistical analysis on the template surface showed that there was significant difference between algorithm and manual methods for segmenting the right lateral ventricle but not for the left lateral ventricle.

CONCLUSIONS

A framework was proposed for evaluating, visualizing, and summarizing the local accuracy and variability of a segmentation algorithm. This framework can be used for improving the accuracy of segmentation algorithms, as well as providing useful feedback to improve the manual segmentation performance. More importantly, this framework can be applied for longitudinal monitoring of local ventricular changes of neonates with IVH.

Stereotaxic Magnetic Resonance Imaging Brain Atlases for Infants from 3 to 12 Months

BACKGROUND

Accurate labeling of brain structures within an individual or group is a key issue in neuroimaging. Methods for labeling infant brains have depended on the labels done on adult brains or average magnetic resonance imaging (MRI) templates based on adult brains. However, the features of adult brains differ in several ways from infant brains, so the creation of a labeled stereotaxic atlas based on infants would be helpful. The current work builds on the recent creation of age-appropriate average MRI templates during the first year (3, 4.5, 6, 7.5, 9, and 12 months) by creating anatomical label sets for each template.

METHODS

We created stereotaxic atlases for the age-specific average MRI templates. Manual delineation of cortical and subcortical areas was done on the average templates based on infants during the first year. We also applied a procedure for automatic computation of macroanatomical atlases for individual infant participants using two manually segmented adult atlases (Hammers, LONI Probabilistic Brain Atlas-LPBA40). To evaluate our methods, we did manual delineation of several cortical areas on selected individuals from each age. Linear and nonlinear registration of the individual and average template was used to transform the average atlas into the individual participant's space, and the average-transformed atlas was compared to the individual manually delineated brain areas. We also applied these methods to an external data set - not used in the atlas creation - to test generalizability of the atlases.

RESULTS

Age-appropriate manual atlases were the best fit to the individual manually delineated regions, with more error seen at greater age discrepancy. There was a close fit between the manually delineated and the automatically labeled regions for individual participants and for the age-appropriate template-based atlas transformed into participant space. There was close correspondence between automatic labeling of individual brain regions and those from the age-appropriate template. These relationships held even when tested on an external set of images.

CONCLUSION

We have created age-appropriate labeled templates for use in the study of infant development at 6 ages (3, 4.5, 6, 7.5, 9, and 12 months). Comparison with manual methods was quite good. We developed three stereotaxic atlases (one manual, two automatic) for each infant age, which should allow more fine-grained analysis of brain structure for these populations than was previously possible with existing tools. The template-based atlases constructed in the current study are available online (http://jerlab.psych.sc.edu/NeurodevelopmentalMRIDatabase).

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.

The potential of infant fMRI research and the study of early life stress as a promising exemplar

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fMRI research with infants and toddlers has increased rapidly over the past decade.

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Infant fMRI has provided unique insight into early functional brain development.

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Complex methodological challenges associated with infant fMRI warrant careful consideration and ongoing research.

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Infant fMRI has potential to contribute to multiple fields of study.

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The study of early life stress is a prime example of a field that is ripe to benefit from this technique.

Functional magnetic resonance imaging (fMRI) research with infants and toddlers has increased rapidly over the past decade, and provided a unique window into early brain development. In the current report, we review the state of the literature, which has established the feasibility and utility of task-based fMRI and resting state functional connectivity MRI (rs-fcMRI) during early periods of brain maturation. These methodologies have been successfully applied beginning in the neonatal period to increase understanding of how the brain both responds to environmental stimuli, and becomes organized into large-scale functional systems that support complex behaviors. We discuss the methodological challenges posed by this promising area of research. We also highlight that despite these challenges, early work indicates a strong potential for these methods to influence multiple research domains. As an example, we focus on the study of early life stress and its influence on brain development and mental health outcomes. We illustrate the promise of these methodologies for building on, and making important contributions to, the existing literature in this field.

A Bayesian approach to the creation of a study-customized neonatal brain atlas

Atlas-based image analysis (ABA), in which an anatomical "parcellation map" is used for parcel-by-parcel image quantification, is widely used to analyze anatomical and functional changes related to brain development, aging, and various diseases. The parcellation maps are often created based on common MRI templates, which allow users to transform the template to target images, or vice versa, to perform parcel-by-parcel statistics, and report the scientific findings based on common anatomical parcels. The use of a study-specific template, which represents the anatomical features of the study population better than common templates, is preferable for accurate anatomical labeling; however, the creation of a parcellation map for a study-specific template is extremely labor intensive, and the definitions of anatomical boundaries are not necessarily compatible with those of the common template. In this study, we employed a volume-based template estimation (VTE) method to create a neonatal brain template customized to a study population, while keeping the anatomical parcellation identical to that of a common MRI atlas. The VTE was used to morph the standardized parcellation map of the JHU-neonate-SS atlas to capture the anatomical features of a study population. The resultant "study-customized" T1-weighted and diffusion tensor imaging (DTI) template, with three-dimensional anatomical parcellation that defined 122 brain regions, was compared with the JHU-neonate-SS atlas, in terms of the registration accuracy. A pronounced increase in the accuracy of cortical parcellation and superior tensor alignment were observed when the customized template was used. With the customized atlas-based analysis, the fractional anisotropy (FA) detected closely approximated the manual measurements. This tool provides a solution for achieving normalization-based measurements with increased accuracy, while reporting scientific findings in a consistent framework.

A 4D neonatal head model for diffuse optical imaging of pre-term to term infants

Diffuse optical tomography is most accurate when an individual's MRI data can be used as a spatial prior for image reconstruction and for visualization of the resulting images of changes in oxy- and deoxy-hemoglobin concentration. As this necessitates an MRI scan to be performed for each study, which undermines many of the advantages of diffuse optical methods, the use of registered atlases to model the individual's anatomy is becoming commonplace. Infant studies require carefully age-matched atlases because of the rapid growth and maturation of the infant brain. In this paper, we present a 4D neonatal head model which, for each week from 29 to 44 weeks post-menstrual age, includes: 1) a multi-layered tissue mask which identifies extra-cerebral layers, cerebrospinal fluid, gray matter, white matter, cerebellum and brainstem, 2) a high-density tetrahedral head mesh, 3) surface meshes for the scalp, gray-matter and white matter layers and 4) cranial landmarks and 10-5 locations on the scalp surface. This package, freely available online at www.ucl.ac.uk/medphys/research/4dneonatalmodel can be applied by users of near-infrared spectroscopy and diffuse optical tomography to optimize probe locations, optimize image reconstruction, register data to cortical locations and ultimately improve the accuracy and interpretation of diffuse optical techniques in newborn populations.

Age-specific average head template for typically developing 6-month-old infants

Due to the rapid anatomical changes that occur within the brain structure in early human development and the significant differences between infant brains and the widely used standard adult templates, it becomes increasingly important to utilize appropriate age- and population-specific average templates when analyzing infant neuroimaging data. In this study we created a new and highly detailed age-specific unbiased average head template in a standard MNI152-like infant coordinate system for healthy, typically developing 6-month-old infants by performing linear normalization, diffeomorphic normalization and iterative averaging processing on 60 subjects' structural images. The resulting age-specific average templates in a standard MNI152-like infant coordinate system demonstrate sharper anatomical detail and clarity compared to existing infant average templates and successfully retains the average head size of the 6-month-old infant. An example usage of the average infant templates transforms magnetoencephalography (MEG) estimated activity locations from MEG's subject-specific head coordinate space to the standard MNI152-like infant coordinate space. We also created a new atlas that reflects the true 6-month-old infant brain anatomy. Average templates and atlas are publicly available on our website (http://ilabs.washington.edu/6-m-templates-atlas).

Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

A tool to investigate symmetry properties of newborns brain: the newborns' symmetric brain atlas

It is well established that the two hemispheres of the human brain exhibit a certain degree of asymmetry. Postmortem studies of developing brains of pre- and postpartum infants have shown that already in this early stage of development Heschl gyrus, planum temporale and superior temporal sulcus (STS) exhibit pronounced asymmetry. Advances in acquisition and computational evaluation of high-resolution magnetic resonance images provide enhanced tools for noninvasive studies of brain asymmetry in newborns. Until now most atlases used for image processing contain themselves asymmetry and may thus introduce and/or increase asymmetry already contained in the original data of brain structural or functional images. So, it is preferable to avoid the application of these asymmetric atlases. Thus, in this paper we present our framework to create a symmetric brain atlas from a group of newborns aged between 39 and 42 weeks after gestation. The resulting atlas demonstrates no difference between its original and its flipped version as should be the case for an asymmetric atlas. Consequently, the resulting symmetric atlas can be used for applications such as analysis of development of brain asymmetry in the context of language development.

Usefulness of simultaneous EEG-NIRS recording in language studies

One of the most challenging tasks in neuroscience in language studies, is investigation of the brain's ability to integrate and process information. This task can only be successfully addressed by applying various assessment techniques integrated into a multimodal approach. Each of these techniques has its advantages and disadvantages, but help to elucidate certain aspects of the capacity of neural networks to process information. These methods provide information about changes in electrical, hemodynamic and metabolic activities. Ideally, they should be noninvasive in order to facilitate their use particularly in children. In the present review, we describe the advantages of simultaneous electroencephalographic (EEG) acquisition with near infrared spectroscopy (NIRS) to provide a better understanding of the mechanisms involved in cerebral activation. This coregistration is also useful to avoid misleading interpretation of NIRS, notably during the various phases of sleep. Development and implementation of the various tools required and assessment strategies are also discussed.

Advanced neonatal NeuroMRI

This article describes the potentials and challenges of quantitative analyses of human neonatal brain images using structural magnetic resonance (MR) imaging and diffusion tensor imaging. To maximize the potential of MR imaging for neonatal brain studies, the combination of both contrasts is highly beneficial. Based on the multicontrast data, a neonate brain atlas was created, which allows automated segmentation of neonate brain MR images. The accuracy, advantages, and potential pitfalls of this atlas-based segmentation approach are discussed. The accurate and reproducible MR imaging quantification achieved by this approach could be an initial step toward the successful clinical evaluation of the neonatal brain.

Association between sociability and diffusion tensor imaging in BALB/cJ mice

The purpose of this study was to use high-resolution diffusion tensor imaging (DTI) to investigate the association between DTI metrics and sociability in BALB/c inbred mice. The sociability of prepubescent (30-day-old) BALB/cJ mice was operationally defined as the time that the mice spent sniffing a stimulus mouse in a social choice test. High-resolution ex vivo DTI data on 12 BALB/cJ mouse brains were acquired using a 9.4-T vertical-bore magnet. Regression analysis was conducted to investigate the association between DTI metrics and sociability. Significant positive regression (p < 0.001) between social sniffing time and fractional anisotropy was found in 10 regions located in the thalamic nuclei, zona incerta/substantia nigra, visual/orbital/somatosensory cortices and entorhinal cortex. In addition, significant negative regression (p < 0.001) between social sniffing time and mean diffusivity was found in five areas located in the sensory cortex, motor cortex, external capsule and amygdaloid region. In all regions showing significant regression with either the mean diffusivity or fractional anisotropy, the tertiary eigenvalue correlated negatively with the social sniffing time. This study demonstrates the feasibility of using DTI to detect brain regions associated with sociability in a mouse model system.

Quantitative effect of the neonatal fontanel on synthetic near infrared spectroscopy measurements

Near infrared spectroscopy (NIRS) is a functional imaging technique allowing measurement of local cerebral oxygenation. This modality is particularly adapted to critically ill neonates, as it can be used at the bedside and is a suitable and noninvasive tool for carrying out longitudinal studies. However, NIRS is sensitive to the imaged medium and consequently to the optical properties of biological tissues in which photons propagate. In this study, the effect of the neonatal fontanel was investigated by predicting photon propagation using a probabilistic Monte Carlo approach. Two anatomical newborn head models were created from computed tomography and magnetic resonance images: (1) a realistic model including the fontanel tissue and (2) a model in which the fontanel was replaced by skull tissue. Quantitative change in absorption due to simulated activation was compared for the two models for specific regions of activation and optical arrays simulated in the temporal area. A correction factor was computed to quantify the effect of the fontanel and defined by the ratio between the true and recovered change. The results show that recovered changes in absorption were more precise when determined with the anatomical model including the fontanel. The results suggest that the fontanel should be taken into account in quantification of NIRS responses to avoid misinterpretation in experiments involving temporal areas, such as language or auditory studies.

A neonatal brain MR image template of 1 week newborn

PURPOSE

Magnetic resonance imaging (MRI) is often used to detect and treat neonatal cerebral disorders. However, neonatal MR image interpretation is limited by intra- and inter-observer variability. To reduce such variability, a template-based computer-aided diagnosis system is being developed, and several methods for creating templates were evaluated.

METHOD

Spatial normalization for each individual's MR images is used to accommodate the individual variation in brain shape. Because the conventional normalization uses as adult brain template, it can be difficult to analyze the neonatal brain, as there are large difference between the adult brain and the neonatal brain. This article investigates three approaches for defining a neonatal template for 1-week-old newborns for diagnosing neonatal cerebral disorders. The first approach uses an individual neonatal head as the template. The second approach applies skull stripping to the first approach, and the third approach produces a template by averaging brain MR images of 7 neonates. To validate the approaches, the normalization accuracy was evaluated using mutual information and anatomical landmarks.

RESULTS

The experimental results of 7 neonates (revised age 5.6 ± 17.6 days) showed that normalization accuracy was significantly higher with the third approach than with the conventional adult template and the other two approaches (P < 0.01).

CONCLUSION

Three approaches to neonatal brain template matching for spinal normalization of MRI scans were applied, demonstrating that a population average gave the best results.

Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age

Spatial normalization and segmentation of pediatric brain magnetic resonance images (MRI) data with adult templates may impose biases and limitations in pediatric neuroimaging work. To remedy this issue, we created a single database made up of a series of pediatric, age-specific MRI average brain templates. These average, age-specific templates were constructed from brain scans of individual children obtained from two sources: (1) the NIH MRI Study of Normal Brain Development and (2) MRIs from University of South Carolina's McCausland Brain Imaging Center. Participants included young children enrolled at ages ranging from 8 days through 4.3 years of age. A total of 13 age group cohorts spanning the developmental progression from birth through 4.3 years of age were used to construct age-specific MRI brain templates (2 weeks, 3, 4.5, 6, 7.5, 9, 12, 15, 18 months, 2, 2.5, 3, 4 years). Widely used processing programs (FSL, SPM, and ANTS) extracted the brain and constructed average templates separately for 1.5T and 3T MRI volumes. The resulting age-specific, average templates showed clear changes in head and brain size across ages and between males and females, as well as changes in regional brain structural characteristics (e.g., myelin development). This average brain template database is available via our website (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase) for use by other researchers. Use of these age-specific, average pediatric brain templates by the research community will enhance our ability to gain a clearer understanding of the early postnatal development of the human brain in health and in disease.

Functional connectivity MRI in infants: exploration of the functional organization of the developing brain

Advanced neuroimaging techniques have been increasingly applied to the study of preterm and term infants in an effort to further define the functional cerebral architecture of the developing brain. Despite improved understanding of the complex relationship between structure and function obtained through these investigations, significant questions remain regarding the nature, location, and timing of the maturational changes which occur during early development. Functional connectivity magnetic resonance imaging (fcMRI) utilizes spontaneous, low frequency (< 0.1 Hz), coherent fluctuations in blood oxygen level dependent (BOLD) signal to identify networks of functional cerebral connections. Due to the intrinsic characteristics of its image acquisition and analysis, fcMRI offers a novel neuroimaging approach well suited to investigation of infants. Recently, this methodology has been successfully applied to examine neonatal populations, defining normative patterns of large-scale neural network development in the maturing brain. The resting-state networks (RSNs) identified in these studies reflect the evolving cerebral structural architecture, presumably driven by varied genetic and environmental influences. Principal features of these investigations and their role in characterization of the tenets of neural network development during this critical developmental period are highlighted in this review. Despite these successes, optimal methods for fcMRI data acquisition and analysis for this population have not yet been defined. Further, appropriate schemes for interpretation and translation of fcMRI results remain unknown, a matter of increasing importance as functional neuroimaging findings are progressively applied in the clinical arena. Notwithstanding these concerns, fcMRI provides insight into the earliest forms of cerebral connectivity and therefore holds great promise for future neurodevelopmental investigations.