BACKGROUND: Magnetic resonance imaging (MRI) studies have consistently demonstrated disproportionately smaller corpus callosa in individuals with a history of prenatal alcohol exposure (PAE) but have not previously examined the feasibility of detecting this effect in infants. Tissue segmentation of the newborn brain is challenging because analysis techniques developed for the adult brain are not directly transferable, and segmentation for cerebral morphometry is difficult in neonates, due to the latter's incomplete myelination. This study is the first to use volumetric structural MRI to investigate PAE effects in newborns using manual tracing and to examine the cross-sectional area of the corpus callosum (CC).
METHODS: Forty-three nonsedated infants born to 32 Cape Coloured heavy drinkers and 11 controls recruited prospectively during pregnancy were scanned using a custom-designed birdcage coil for infants, which increases signal-to-noise ratio almost 2-fold compared to the standard head coil. Alcohol use was ascertained prospectively during pregnancy, and fetal alcohol spectrum disorders diagnosis was conducted by expert dysmorphologists. Data were acquired using a multi-echo FLASH protocol adapted for newborns, and a knowledge-based procedure was used to hand-segment the neonatal brains.
RESULTS: CC was disproportionately smaller in alcohol-exposed neonates than controls after controlling for intracranial volume. By contrast, CC area was unrelated to infant sex, gestational age, age at scan, or maternal smoking, marijuana, or methamphetamine use during pregnancy.
CONCLUSIONS: Given that midline craniofacial anomalies have been recognized as a hallmark of fetal alcohol syndrome in humans and animal models since this syndrome was first identified, the CC deficit identified here in newborns may support early identification of a range of midline structural impairments. Smaller CC during the newborn period may provide an early indicator of fetal alcohol-related cognitive deficits that have been linked to this critically important brain structure in childhood and adolescence.
BACKGROUND: Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available.
NEW METHOD: The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9-13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to 'gold standard' manual tracings.
RESULTS: Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55-0.91; mean Hausdorff distance 0.9 mm; range 0.8-2.7 mm).
COMPARISON WITH EXISTING METHODS: In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (Neuroimage 2014;95(1):217) in adults (across all lobules mean DSC=0.73, range 0.40-0.89).
CONCLUSIONS: CAPCA18 and the associated multi-subject atlases of the training subjects yield improved segmentation of cerebellar structures in children.
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.
We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.
The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.
This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.
Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by highly orchestrated cellular migration. A greater appreciation of the processes involved in human fetal corticogenesis is vital to gaining an understanding of how developmental disturbances originating in gestation could establish a variety of complex neuropathology manifesting in childhood, or even in adult life. Magnetic resonance imaging modalities offer a unique insight into anatomical structure, and increasingly infer information regarding underlying microstructure in the human brain. In this study we applied a combination of high-resolution structural and diffusion-weighted magnetic resonance imaging to a unique cohort of three post-mortem fetal brain specimens, aged between 19 and 22 post-conceptual weeks. Specifically, we sought to assess patterns of diffusion coherence associated with subcortical neuroproliferative structures: the pallial ventricular/subventricular zone and subpallial ganglionic eminence. Two distinct three-dimensional patterns of diffusion coherence were evident: a clear radial pattern originating in ventricular/subventricular zone, and a tangentio-radial patterns originating in ganglionic eminence. These patterns appeared to regress in a caudo-rostral and lateral-ventral to medial-dorsal direction across the short period of fetal development under study. Our findings demonstrate for the first time distinct patterns of diffusion coherence associated with known anatomical proliferative structures. The radial pattern associated with dorsopallial ventricular/subventricular zone and the tangentio-radial pattern associated with subpallial ganglionic eminence are consistent with reports of radial-glial mediated neuronal migration pathways identified during human corticogenesis, supported by our prior studies of comparative fetal diffusion MRI and histology. The ability to assess such pathways in the fetal brain using MR imaging offers a unique insight into three-dimensional trajectories beyond those visualized using traditional histological techniques. Our results suggest that ex-vivo fetal MRI is a potentially useful modality in understanding normal human development and various disease processes whose etiology may originate in aberrant fetal neuronal migration.
In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.
We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.
In this paper, we propose a pipeline for evaluating the performance of brain image registration methods. Our aim is to compare how well the algorithms align subtle functional/anatomical boundaries that are not easily detectable in T1- or T2-weighted magnetic resonance images (MRI). In order to achieve this, we use structural connectivity information derived from diffusion-weighted MRI data. We demonstrate the approach by looking into how two competing registration algorithms perform at aligning fine-grained parcellations of subcortical structures. The results show that the proposed evaluation framework can offer new insights into the performance of registration algorithms in brain regions with highly varied structural connectivity profiles.
Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
The present study examined the relationship between hand preference degree and direction, functional language lateralization in Broca's and Wernicke's areas, and structural measures of the arcuate fasciculus. Results revealed an effect of degree of hand preference on arcuate fasciculus structure, such that consistently-handed individuals, regardless of the direction of hand preference, demonstrated the most asymmetric arcuate fasciculus, with larger left versus right arcuate, as measured by DTI. Functional language lateralization in Wernicke's area, measured via fMRI, was related to arcuate fasciculus volume in consistent-left-handers only, and only in people who were not right hemisphere lateralized for language; given the small sample size for this finding, future investigation is warranted. Results suggest handedness degree may be an important variable to investigate in the context of neuroanatomical asymmetries.
Previously we introduced an automated high-dimensional non-linear registration framework, CVS, that combines volumetric and surface-based alignment to achieve robust and accurate correspondence in both cortical and sub-cortical regions (Postelnicu et al., 2009). In this paper we show that using CVS to compute cross-subject alignment from anatomical images, then applying the previously computed alignment to diffusion weighted MRI images, outperforms state-of-the-art techniques for computing cross-subject alignment directly from the DWI data itself. Specifically, we show that CVS outperforms the alignment component of TBSS in terms of degree-of-alignment of manually labeled tract models for the uncinate fasciculus, the inferior longitudinal fasciculus and the corticospinal tract. In addition, we compare linear alignment using FLIRT based on either fractional anisotropy or anatomical volumes across-subjects, and find a comparable effect. Together these results imply a clear advantage to aligning anatomy as opposed to lower resolution DWI data even when the final goal is diffusion analysis.
Patients with schizophrenia consistently show deficient performance on tasks requiring volitional saccades. We previously reported reduced fractional anisotropy in the white matter underlying right dorsal anterior cingulate cortex in schizophrenia, which, along with lower fractional anisotropy in the right frontal eye field and posterior parietal cortex, predicted longer latencies of volitional saccades. This suggests that reduced microstructural integrity of dorsal anterior cingulate cortex white matter disrupts connectivity in the right hemisphere-dominant network for spatial attention and volitional ocular motor control. To test this hypothesis, we examined functional connectivity of the cingulate eye field component of this network, which is located in dorsal anterior cingulate cortex, during a task comprising volitional prosaccades and antisaccades. In patients with schizophrenia, we expected to find reduced functional connectivity, specifically in the right hemisphere, which predicted prolonged saccadic latency. Twenty-seven medicated schizophrenia outpatients and 21 demographically matched healthy controls performed volitional saccades during functional magnetic resonance imaging. Based on task-related activation, seed regions in the right and left cingulate eye field were defined. In both groups, the right and left cingulate eye field showed positive correlations with the ocular motor network and negative correlations with the default network. Patients showed reduced positive functional connectivity of the cingulate eye field, specifically in the right hemisphere. Negative functional connectivity of the right cingulate eye field predicted faster saccades, but these relations differed by group, and were only present in controls. This pattern of relations suggests that the coordination of activity between ocular motor and default networks is important for efficient task performance and is disrupted in schizophrenia. Along with prior observations of reduced white matter microstructural integrity (fractional anisotropy) in schizophrenia, the present finding of reduced functional connectivity suggests that functional and structural abnormalities of the right cingulate eye field disrupt connectivity in the network for spatial attention and volitional ocular motor control. These abnormalities may contribute to deficits in overcoming prepotency in the service of directing eye gaze and attention to the parts of the environment that are the most behaviourally relevant.
Ex vivo magnetic resonance imaging yields high resolution images that reveal detailed cerebral anatomy and explicit cytoarchitecture in the cerebral cortex, subcortical structures, and white matter in the human brain. Our data illustrate neuroanatomical correlates of limbic circuitry with high resolution images at high field. In this report, we have studied ex vivo medial temporal lobe samples in high resolution structural MRI and high resolution diffusion MRI. Structural and diffusion MRIs were registered to each other and to histological sections stained for myelin for validation of the perforant pathway. We demonstrate probability maps and fiber tracking from diffusion tensor data that allows the direct visualization of the perforant pathway. Although it is not possible to validate the DTI data with invasive measures, results described here provide an additional line of evidence of the perforant pathway trajectory in the human brain and that the perforant pathway may cross the hippocampal sulcus.
In this paper, we propose a novel method for the registration of volumetric images of the brain that optimizes the alignment of both cortical and subcortical structures. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and diffused into the volume using the Navier operator of elasticity, resulting in a volumetric warp that aligns cortical folding patterns. This warp field is then refined with an intensity driven optical flow procedure that registers noncortical regions, while preserving the cortical alignment. The result is a combined surface and volume morph (CVS) that accurately registers both cortical and subcortical regions, establishing a single coordinate system suitable for the entire brain.
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectation-maximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.