A framework for in vivo quantification of regional brain folding in premature neonates

Atypical characteristic changes of surface morphology and structural covariance network in developmental dyslexia

BACKGROUND

Developmental dyslexia (DD) is a neurodevelopmental disorder that is characterized by difficulties with all aspects of information acquisition in the written word, including slow and inaccurate word recognition. The neural basis behind DD has not been fully elucidated.

METHOD

The study included 22 typically developing (TD) children, 16 children with isolated spelling disorder (SpD), and 20 children with DD. The cortical thickness, folding index, and mean curvature of Broca's area, including the triangular part of the left inferior frontal gyrus (IFGtriang) and the opercular part of the left inferior frontal gyrus, were assessed to explore the differences of surface morphology among the TD, SpD, and DD groups. Furthermore, the structural covariance network (SCN) of the triangular part of the left inferior frontal gyrus was analyzed to explore the changes of structural connectivity in the SpD and DD groups.

RESULTS

The DD group showed higher curvature and cortical folding of the left IFGtriang than the TD group and SpD group. In addition, compared with the TD group and the SpD group, the structural connectivity between the left IFGtriang and the left middle-frontal gyrus and the right mid-orbital frontal gyrus was increased in the DD group, and the structural connectivity between the left IFGtriang and the right precuneus and anterior cingulate was decreased in the DD group.

CONCLUSION

DD had atypical structural connectivity in brain regions related to visual attention, memory and which might impact the information input and integration needed for reading and spelling.

Effects of stress-dependent growth on evolution of sulcal direction and curvature in models of cortical folding

The majority of human brain folding occurs during the third trimester of gestation. Although many studies have investigated the physical mechanisms of brain folding, a comprehensive understanding of this complex process has not yet been achieved. In mechanical terms, the "differential growth hypothesis" suggests that the formation of folds results from a difference in expansion rates between cortical and subcortical layers, which eventually leads to mechanical instability akin to buckling. It has also been observed that axons, a substantial component of subcortical tissue, can elongate or shrink under tensile or compressive stress, respectively. Previous work has proposed that this cell-scale behavior in aggregate can produce stress-dependent growth in the subcortical layers. The current study investigates the potential role of stress-dependent growth on cortical surface morphology, in particular the variations in folding direction and curvature over the course of development. Evolution of sulcal direction and mid-cortical surface curvature were calculated from finite element simulations of three-dimensional folding in four different initial geometries: (i) sphere; (ii) axisymmetric oblate spheroid; (iii) axisymmetric prolate spheroid; and (iv) triaxial spheroid. The results were compared to mid-cortical surface reconstructions from four preterm human infants, imaged and analyzed at four time points during the period of brain folding. Results indicate that models incorporating subcortical stress-dependent growth predict folding patterns that more closely resemble those in the developing human brain.

Statement of Significance

Cortical folding is a critical process in human brain development. Aberrant folding is associated with disorders such as autism and schizophrenia, yet our understanding of the physical mechanism of folding remains limited. Ultimately mechanical forces must shape the brain. An important question is whether mechanical forces simply deform tissue elastically, or whether stresses in the tissue modulate growth. Evidence from this paper, consisting of quantitative comparisons between patterns of folding in the developing human brain and corresponding patterns in simulations, supports a key role for stress-dependent growth in cortical folding.

Maternal psychological distress during the COVID-19 pandemic and structural changes of the human fetal brain

Background

Elevated maternal psychological distress during pregnancy is linked to adverse outcomes in offspring. The potential effects of intensified levels of maternal distress during the COVID-19 pandemic on the developing fetal brain are currently unknown.

Methods

We prospectively enrolled 202 pregnant women: 65 without known COVID-19 exposures during the pandemic who underwent 92 fetal MRI scans, and 137 pre-pandemic controls who had 182 MRI scans. Multi-plane, multi-phase single shot fast spin echo T2-weighted images were acquired on a GE 1.5 T MRI Scanner. Volumes of six brain tissue types were calculated. Cortical folding measures, including brain surface area, local gyrification index, and sulcal depth were determined. At each MRI scan, maternal distress was assessed using validated stress, anxiety, and depression scales. Generalized estimating equations were utilized to compare maternal distress measures, brain volume and cortical folding differences between pandemic and pre-pandemic cohorts.

Results

Stress and depression scores are significantly higher in the pandemic cohort, compared to the pre-pandemic cohort. Fetal white matter, hippocampal, and cerebellar volumes are decreased in the pandemic cohort. Cortical surface area and local gyrification index are also decreased in all four lobes, while sulcal depth is lower in the frontal, parietal, and occipital lobes in the pandemic cohort, indicating delayed brain gyrification.

Conclusions

We report impaired fetal brain growth and delayed cerebral cortical gyrification in COVID-19 pandemic era pregnancies, in the setting of heightened maternal psychological distress. The potential long-term neurodevelopmental consequences of altered fetal brain development in COVID-era pregnancies merit further study.

Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).

Association Between Socioeconomic Status and In Utero Fetal Brain Development

IMPORTANCE

Children raised in settings with lower parental socioeconomic status are at increased risk for neuropsychological disorders. However, to date, the association between socioeconomic status and fetal brain development remains poorly understood.

OBJECTIVE

To determine the association between parental socioeconomic status and in vivo fetal brain growth and cerebral cortical development using advanced, 3-dimensional fetal magnetic resonance imaging.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study of fetal brain development enrolled 144 healthy pregnant women from 2 low-risk community obstetrical hospitals from 2012 through 2019 in the District of Columbia. Included women had a prenatal history without complications that included recommended screening laboratory and ultrasound studies. Exclusion criteria were multiple gestation pregnancy, known or suspected congenital infection, dysmorphic features of the fetus, and documented chromosomal abnormalities. T2-weighted fetal brain magnetic resonance images were acquired. Each pregnant woman was scanned at up to 2 points in the fetal period. Data were analyzed from June through November 2020.

EXPOSURES

Parental education level and occupation status were documented.

MAIN OUTCOMES AND MEASURES

Regional fetal brain tissue volume (for cortical gray matter, white matter, cerebellum, deep gray matter, and brainstem) and cerebral cortical features (ie, lobe volume, local gyrification index, and sulcal depth) in the frontal, parietal, temporal, and occipital lobes were calculated.

RESULTS

Fetal brain magnetic resonance imaging studies were performed among 144 pregnant women (median [interquartile range] age, 32.5 [27.0-36.1] years) with gestational age from 24.0 to 39.4 weeks; 75 fetuses (52.1%) were male, and 69 fetuses (47.9%) were female. Higher parental education level was associated with significantly increased volume in the fetal white matter (mothers: β, 2.86; 95% CI, 1.26 to 4.45; P = .001; fathers: β, 2.39; 95% CI, 0.97 to 3.81; P = .001), deep gray matter (mothers: β, 0.16; 95% CI, 0.002 to 0.32; P = .048; fathers: β, 0.16; 95% CI, 0.02 to 0.31; P = .02), and brainstem (mothers: β, 0.06; 95% CI, 0.02 to 0.10; P = .01; fathers: β, 0.04; 95% CI, 0.004 to 0.08; P = .03). Higher maternal occupation status was associated with significantly increased volume in the fetal white matter (β, 2.07; 95% CI, 0.88 to 3.26; P = .001), cerebellum (β, 0.17; 95% CI, 0.04 to 0.29; P = .01), and brainstem (β, 0.03; 95% CI, 0.001 to 0.07; P = .04), and higher paternal occupation status was associated with significantly increased white matter volume (β, 1.98; 95% CI, 0.71 to 3.25; P < .01). However, higher socioeconomic status was associated with significantly decreased fetal cortical gray matter volume (mothers: β, -0.11; 95% CI, -0.18 to -0.03; P = .01; fathers: β, -0.10; 95% CI, -0.18 to -0.03; P = .01). Higher parental socioeconomic status was associated with increased volumes of 3 brain lobes of white matter: frontal lobe (mothers: β, 0.07; 95% CI, 0.02 to 0.13; P = .01; fathers: β, 0.06; 95% CI, 0.01 to 0.11; P = .03), parietal lobe (mothers: β, 0.07; 95% CI, 0.03 to 0.11; P < .001; fathers: β, 0.06; 95% CI, 0.03 to 0.10; P = .001), and temporal lobe (mothers: β, 0.04; 95% CI, 0.02 to 0.07; P < .001; fathers: β, 0.04; 95% CI, 0.02 to 0.07; P < .001), and maternal SES score was associated with significantly decreased volume in the occipital lobe (β, 0.02; 95% CI, 0.002 to 0.04; P = .03). Higher parental socioeconomic status was associated with decreased cortical local gyrification index (for example, for the frontal lobe, mothers: β, -1.1; 95% CI, -1.9 to -0.3; P = .01; fathers: β, -0.8; 95% CI, -1.6 to -0.1; P = .03) and sulcal depth, except for the frontal lobe (for example, for the parietal lobe, mothers: β, -9.5; 95% CI, -13.8 to -5.3; P < .001; fathers: β, -8.7; 95% CI, -13.0 to -4.4; P < .001).

CONCLUSIONS AND RELEVANCE

This cohort study found an association between parental socioeconomic status and altered in vivo fetal neurodevelopment. While being born and raised in a lower socioeconomic status setting is associated with poorer neuropsychological, educational, and socioeconomic outcomes in children, these findings suggest that altered prenatal programming may be associated with these outcomes and that future targeted prenatal interventions may be needed.

Quantification of Fetal Gyrogenesis in the Third Trimester. A Novel Algorithm for Evaluating Fetal Sulci Development

BACKGROUND AND PURPOSE

The fetal brain changes significantly throughout gestation. From a smooth (lissencephalic) cortex, it transforms into its convolved (gyrencephalic) state. Despite its importance, the diagnosis of delay in brain gyrogenesis is a challenge for many sonographers. This study presents a novel semiautomatic image processing algorithm for simple quantification of sagittal sulci maturation in the third trimester.

METHODS

Mid-sagittal fetal brain ultrasound images were obtained during routine third trimester scans. Fetal brain sulci length measurements were performed using a novel semiautomatic image processing algorithm followed by manual measurements. Correlations between the total length of the sulci, gestational age, and fetal biometry were examined.

RESULTS

The study included 64 patients. A significant positive linear correlation was found between total sulci length and gestational age (r = .658 for automated measurement, r = .7 for manual measurement, P < .0001). A similar relationship was found comparing total sulci length and fetal head circumference (r = .694 for automated measurement, r = .74 for manual measurement; P < .0001). A significant correlation was observed between automated and manual measurements (r = .947).

CONCLUSIONS

We found that fetal gyrogenesis is linear throughout the third trimester of pregnancy. The use of a computer algorithm to measure fetal sulci can be used as a simple prenatal screening test for delayed gyral maturation of the fetal brain.

A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly

Fetal ventriculomegaly (VM) is a condition in which one or both lateral ventricles are enlarged, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing works use a single scalar value such as diagnosis or lateral ventricular volume to characterize VM and study its relationship with alterations in cortical folding, thus failing to reveal the spatially-heterogeneous associations. In this work, we propose a novel approach to identify fine-grained associations between cortical folding and ventricular enlargement by leveraging the vertex-wise correlations between their growth patterns in terms of area expansion and curvature. Our approach comprises three steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. The joint Laplacian is built based on multiple cortical features. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where its nodes are projected according to the joint ventricle-cortex growth patterns. In this low-dimensional joint ventricle-cortex space, associated growth patterns lie close to each other. In the final step, we perform hierarchical clustering in the joint embedded space to identify associated sub-regions between cortex and ventricle. Using a dataset of 25 healthy fetuses and 23 fetuses with isolated non-severe VM within the age range of 26-29 gestational weeks, our approach reveals clinically relevant and heterogeneous regional associations. Cortical regions forming these associations are further validated using statistical analysis, revealing regions with altered folding that are significantly associated with ventricular dilation.

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.

The dynamics of cortical folding waves and prematurity-related deviations revealed by spatial and spectral analysis of gyrification

In the human brain, the appearance of cortical sulci is a complex process that takes place mostly during the second half of pregnancy, with a relatively stable temporal sequence across individuals. Since deviant gyrification patterns have been observed in many neurodevelopmental disorders, mapping cortical development in vivo from the early stages on is an essential step to uncover new markers for diagnosis or prognosis. Recently this has been made possible by MRI combined with post-processing tools, but the reported results are still fragmented. Here we aimed to characterize the typical folding progression ex utero from the pre- to the post-term period, by considering 58 healthy preterm and full-term newborns and infants imaged between 27 and 62 weeks of post-menstrual age. Using a method of spectral analysis of gyrification (SPANGY), we detailed the spatial-frequency structure of cortical patterns in a quantitative way. The modeling of developmental trajectories revealed three successive waves that might correspond to primary, secondary and tertiary folding. Some deviations were further detected in 10 premature infants without apparent neurological impairment and imaged at term equivalent age, suggesting that our approach is sensitive enough to highlight the subtle impact of preterm birth and extra-uterine life on folding.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly

Neuroimaging of brain diseases plays a crucial role in understanding brain abnormalities and early diagnosis. Of great importance is the study of brain abnormalities in utero and the assessment of deviations in case of maldevelopment. In this work, brain magnetic resonance images from 23 isolated non-severe ventriculomegaly (INSVM) fetuses and 25 healthy controls between 26 and 29 gestational weeks were used to identify INSVM-related cortical folding deviations from normative development. Since these alterations may reflect abnormal neurodevelopment, our working hypothesis is that markers of cortical folding can provide cues to improve the prediction of later neurodevelopmental problems in INSVM subjects. We analyzed the relationship of ventricular enlargement with cortical folding alterations in a regional basis using several curvature-based measures describing the folding of each cortical region. Statistical analysis (global and hemispheric) and sparse linear regression approaches were then used to find the cortical regions whose folding is associated with ventricular dilation. Results from both approaches were in great accordance, showing a significant cortical folding decrease in the insula, posterior part of the temporal lobe and occipital lobe. Moreover, compared to the global analysis, stronger ipsilateral associations of ventricular enlargement with reduced cortical folding were encountered by the hemispheric analysis. Our findings confirm and extend previous studies by identifying various cortical regions and emphasizing ipsilateral effects of ventricular enlargement in altered folding. This suggests that INSVM is an indicator of altered cortical development, and moreover, cortical regions with reduced folding constitute potential prognostic biomarkers to be used in follow-up studies to decipher the outcome of INSVM fetuses.

Complementary cortical gray and white matter developmental patterns in healthy, preterm neonates

Preterm birth is associated with brain injury and altered cognitive development. However, the consequences of extrauterine development are not clearly distinguished from perinatal brain injury. Therefore, we characterized cortical growth patterns from 30 to 46 postmenstrual weeks (PMW) in 27 preterm neonates (25-32 PMW at birth) without detectable brain injury on magnetic resonance imaging. We introduce surface-based morphometric descriptors that quantify radial (thickness) and tangential (area) change rates. Within a tensor-based morphometry framework, we use a temporally weighted formulation of regression to simultaneously model local age-related changes in cortical gray matter (GM) and underlying white matter (WM) mapped onto the cortical surface. The spatiotemporal pattern of GM and WM development corresponded to the expected gyrification time course of primary sulcal deepening and branching. In primary gyri, surface area and thickness rates were below average along sulcal pits and above average on gyral banks and crests in both GM and WM. Above average surface area rates in GM corresponded to emergence of secondary and tertiary folds. These findings map the development of neonatal cortical morphometry in the context of extrauterine brain development using a novel approach. Future studies may compare this developmental trajectory to preterm populations with brain injury. Hum Brain Mapp 38:4322-4336, 2017. © 2017 Wiley Periodicals, Inc.

Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes

Congenital brain lesions result in a wide range of cerebral tissue alterations observed in children with cerebral palsy (CP) that are associated with a range of functional impairments. The relationship between injury severity and functional outcomes, however, remains poorly understood. This research investigates the differences in cortical shape between children with congenital brain lesions and typically developing children (TDC) and investigates the correlations between cortical shape and functional outcome in a large cohort of patients diagnosed with unilateral CP. Using 139 structural magnetic resonance images, including 95 patients with clinically diagnosed CP and 44 TDC, cortical segmentations were obtained using a modified expectation maximization algorithm. Three shape characteristics (cortical thickness, curvature, and sulcal depth) were computed within a number of cortical regions. Significant differences in these shape measures compared to the TDC were observed on both the injured hemisphere of children with CP (P < 0.004), as well as on the apparently uninjured hemisphere, illustrating potential compensatory mechanisms in these children. Furthermore, these shape measures were significantly correlated with several functional outcomes, including motor, cognition, vision, and communication (P < 0.012), with three out of these four models performing well on test set validation. This study highlights that cortical neuroplastic effects may be quantified using MR imaging, allowing morphological changes to be studied longitudinally, including any influence of treatment. Ultimately, such approaches could be used for the long term prediction of outcomes and the tailoring of treatment to individuals. Hum Brain Mapp 37:3588-3603, 2016. © 2016 Wiley Periodicals, Inc.

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T₂-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T₂-weighted images of preterm infants acquired at 40 weeks PMA, axial T₁-weighted images of ageing adults acquired at an average age of 70 years, and T₁-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

Patch-based augmentation of Expectation-Maximization for brain MRI tissue segmentation at arbitrary age after premature birth

Accurate automated tissue segmentation of premature neonatal magnetic resonance images is a crucial task for quantification of brain injury and its impact on early postnatal growth and later cognitive development. In such studies it is common for scans to be acquired shortly after birth or later during the hospital stay and therefore occur at arbitrary gestational ages during a period of rapid developmental change. It is important to be able to segment any of these scans with comparable accuracy. Previous work on brain tissue segmentation in premature neonates has focused on segmentation at specific ages. Here we look at solving the more general problem using adaptations of age specific atlas based methods and evaluate this using a unique manually traced database of high resolution images spanning 20 gestational weeks of development. We examine the complimentary strengths of age specific atlas-based Expectation-Maximization approaches and patch-based methods for this problem and explore the development of two new hybrid techniques, patch-based augmentation of Expectation-Maximization with weighted fusion and a spatial variability constrained patch search. The former approach seeks to combine the advantages of both atlas- and patch-based methods by learning from the performance of the two techniques across the brain anatomy at different developmental ages, while the latter technique aims to use anatomical variability maps learnt from atlas training data to locally constrain the patch-based search range. The proposed approaches were evaluated using leave-one-out cross-validation. Compared with the conventional age specific atlas-based segmentation and direct patch based segmentation, both new approaches demonstrate improved accuracy in the automated labeling of cortical gray matter, white matter, ventricles and sulcal cortical-spinal fluid regions, while maintaining comparable results in deep gray matter.

Neuroprotective potential of erythropoietin in neonates; design of a randomized trial

Background

In 2013, nearly four million babies were born in the U.S., among whom 447,875 were born preterm. Approximately 30,000 of these infants were born before 28 weeks of gestation. These infants, termed Extremely Low Gestational Age Neonates (ELGANs), experience high morbidity and mortality despite modern therapies: approximately 20 % of ELGANs admitted to an NICU die before discharge, 20 % of survivors have severe, and 20 % moderate neurodevelopmental impairment (NDI). New approaches are needed to improve neonatal outcomes. Recombinant erythropoietin (Epo) is a promising neuroprotective agent that is widely available, affordable, and has been used safely in neonates to stimulate erythropoiesis. There are extensive preclinical data to support its use as a neuroprotective intervention: Epo promotes normal brain maturation by increasing neurogenesis, angiogenesis, and by protecting oligodendrocytes. Epo also decreases acute brain injury following hypoxia ischemia by decreasing inflammation, oxidative and excitotoxic injury, resulting in decreased apoptosis. Despite the availability of both preclinical and safety data there has not been a definitive clinical evaluation of the benefit of Epo, and a large phase III trial is necessary to provide evidence to support potential changes in practice guidelines.

Findings

We first review the preclinical data motivating further clinical trials, and then describe in detail the design of the PENUT study (Preterm Epo Neuroprotection). PENUT is a phase III study evaluating the effect of neonatal Epo treatment on the combined outcome of death or severe NDI among ELGANS. 940 subjects will be randomized to determine: 1) whether Epo decreases the combined outcome of death or NDI at 22–26 months corrected age; 2) the safety of high dose Epo administration to ELGANs; 3) whether Epo treatment decreases serial measures of circulating inflammatory mediators, and improves biomarkers of brain injury; and 4) whether Epo treatment improves brain structure at 36 weeks postmenstrual age as measured by MRI.

Conclusions

Epo neuroprotection is an exciting new approach to preterm neuroprotection, and if efficacious, will provide a much-needed therapy for this group of vulnerable infants.

Comparison of cortical folding measures for evaluation of developing human brain

We evaluated 22 measures of cortical folding, 20 derived from local curvature (curvature-based measures) and two based on other features (sulcal depth and gyrification index), for their capacity to distinguish between normal and aberrant cortical development. Cortical surfaces were reconstructed from 12 term-born control and 63 prematurely-born infants. Preterm infants underwent 2-4 MR imaging sessions between 27 and 42weeks postmenstrual age (PMA). Term infants underwent a single MR imaging session during the first postnatal week. Preterm infants were divided into two groups. One group (38 infants) had no/minimal abnormalities on qualitative assessment of conventional MR images. The second group (25 infants) consisted of infants with injury on conventional MRI at term equivalent PMA. For both preterm infant groups, all folding measures increased or decreased monotonically with increasing PMA, but only sulcal depth and gyrification index differentiated preterm infants with brain injury from those without. We also compared scans obtained at term equivalent PMA (36-42weeks) for all three groups. No curvature-based measured distinguished between the groups, whereas sulcal depth distinguished term control from injured preterm infants and gyrification index distinguished all three groups. When incorporating total cerebral volume into the statistical model, sulcal depth no longer distinguished between the groups, though gyrification index distinguished between all three groups and positive shape index distinguished between the term control and uninjured preterm groups. We also analyzed folding measures averaged over brain lobes separately. These results demonstrated similar patterns to those obtained from the whole brain analyses. Overall, though the curvature-based measures changed during this period of rapid cerebral development, they were not sensitive for detecting the differences in folding associated with brain injury and/or preterm birth. In contrast, gyrification index was effective in differentiating these groups.

Regional growth and atlasing of the developing human brain

Detailed morphometric analysis of the neonatal brain is required to characterise brain development and define neuroimaging biomarkers related to impaired brain growth. Accurate automatic segmentation of neonatal brain MRI is a prerequisite to analyse large datasets. We have previously presented an accurate and robust automatic segmentation technique for parcellating the neonatal brain into multiple cortical and subcortical regions. In this study, we further extend our segmentation method to detect cortical sulci and provide a detailed delineation of the cortical ribbon. These detailed segmentations are used to build a 4-dimensional spatio-temporal structural atlas of the brain for 82 cortical and subcortical structures throughout this developmental period. We employ the algorithm to segment an extensive database of 420 MR images of the developing brain, from 27 to 45 weeks post-menstrual age at imaging. Regional volumetric and cortical surface measurements are derived and used to investigate brain growth and development during this critical period and to assess the impact of immaturity at birth. Whole brain volume, the absolute volume of all structures studied, cortical curvature and cortical surface area increased with increasing age at scan. Relative volumes of cortical grey matter, cerebellum and cerebrospinal fluid increased with age at scan, while relative volumes of white matter, ventricles, brainstem and basal ganglia and thalami decreased. Preterm infants at term had smaller whole brain volumes, reduced regional white matter and cortical and subcortical grey matter volumes, and reduced cortical surface area compared with term born controls, while ventricular volume was greater in the preterm group. Increasing prematurity at birth was associated with a reduction in total and regional white matter, cortical and subcortical grey matter volume, an increase in ventricular volume, and reduced cortical surface area.

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A novel methodology is proposed for delineating the cortical ribbon.

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Regional brain growth is assessed in the developing preterm brain.

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We investigate the effect of prematurity on brain growth and cortical development.

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A spatio-temporal neonatal atlas is constructed with 82 brain structures.

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.

Development of Cortical Morphology Evaluated with Longitudinal MR Brain Images of Preterm Infants

INTRODUCTION

The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA).

METHODS

In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA.

RESULTS

The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development.

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester

BACKGROUND AND PURPOSE

Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester.

MATERIALS AND METHODS

We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure.

RESULTS

The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks).

CONCLUSIONS

Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.

Are Developmental Trajectories of Cortical Folding Comparable Between Cross-sectional Datasets of Fetuses and Preterm Newborns?

Magnetic resonance imaging has proved to be suitable and efficient for in vivo investigation of the early process of brain gyrification in fetuses and preterm newborns but the question remains as to whether cortical-related measurements derived from both cases are comparable or not. Indeed, the developmental folding trajectories drawn up from both populations have not been compared so far, neither from cross-sectional nor from longitudinal datasets. The present study aimed to compare features of cortical folding between healthy fetuses and early imaged preterm newborns on a cross-sectional basis, over a developmental period critical for the folding process (21-36 weeks of gestational age [GA]). A particular attention was carried out to reduce the methodological biases between the 2 populations. To provide an accurate group comparison, several global parameters characterizing the cortical morphometry were derived. In both groups, those metrics provided good proxies for the dramatic brain growth and cortical folding over this developmental period. Except for the cortical volume and the rate of sulci appearance, they depicted different trajectories in both groups suggesting that the transition from into ex utero has a visible impact on cortical morphology that is at least dependent on the GA at birth in preterm newborns.

Constructing 4D infant cortical surface atlases based on dynamic developmental trajectories of the cortex

Cortical surface atlases play an increasingly important role for analysis, visualization, and comparison of results across different neuroimaging studies. As the first two years of life is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex, longitudinal (4D) cortical surface atlases for the infant brains during this period is highly desired yet still lacking for early brain development studies. In this paper, we construct the first longitudinal (4D) cortical surface atlases for the dynamic developing infant cortical structures at 1, 3, 6, 9, 12, 18 and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. To ensure longitudinal consistency and unbiasedness of the 4D infant cortical surface atlases, we first compute the within-subject mean cortical folding geometries by groupwise registration of longitudinal surfaces of each infant. Then we establish intersubject cortical correspondences by groupwise registration of the within-subject mean cortical folding geometries of all infants. More importantly, for the first time, we further parcellate the 4D infant surface atlases into developmentally and functionally distinctive regions based solely on the dynamic developmental trajectories of the cortical thickness, by using the spectral clustering method. Specifically, to deal with the problem that each infant has different number of scans, we first compute the within-subject affinity matrix of vertices' cortical thickness trajectories of each infant, and then we use the averaged affinity matrix of all infants for parcellation. Our constructed 4D infant cortical surface atlases with developmental trajectories based parcellation will greatly facilitate the surface-based analysis of dynamic brain development in infants.

Quantitative MRI for studying neonatal brain development

Quantitative MRI techniques based on morphology and tissue microstructure dependent contrast provide a unique window on brain development in the neonatal period. The dramatic changes in morphology and MRI contrast that occur during this period have the potential to be used to identify normal and abnormal developmental trajectories that predict neurodevelopmental outcome in at risk populations. Here, we review these technologies focussing on two broad categories: gross morphological analysis and tissue microstructure assessment. With respect to morphology, we examine the role of image registration and atlas-based techniques, highlighting the challenges posed by the scale of the anatomical changes and the high incidence of radiologically abnormal scans in the premature infant population. With respect to microstructure, we examine the potential and remaining challenges for using quantitative MRI to dissociate processes of cell proliferation, neuronal maturation, and myelination by combining different signal contrasts. Recent progress from our group in this area is highlighted.

Novel surface-smoothing based local gyrification index

The quantication of cortical surface folding is important in identifying and classifying many neurodegenerative diseases. Much work has been done to identify regional and global brain folding, and in this paper we review some of these methods, as well as propose a new method that has advantages over the existing state of art. Using our novel proposed method, we mapped the local gyrification index on the cortical surface for subjects with mild Alzheimer's dementia (n=20) , very mild dementia (n=23) and age-matched healthy subjects (n=52). In our experiments we find a consistent pattern of gyrification changes in the dementia subjects, with regions generally affected early on in the progression of Alzheimer pathology, including medial temporal lobe, and cingulate gyrus, having decreased gyrification. At the same time we observe increased gyrification in dementia subjects, in frontal, anterior temporal and posteriorly located regions. We speculate that in neurodegenerative diseases including Alzheimer Disease, the folding of the entire cortical mantle undergoes dynamic changes as regional atrophy begins and expands, with both decreases and increases in gyrification.

Shape and curvedness analysis of brain morphology using human fetal magnetic resonance images in utero

The 3-D morphological change has gained increasing significance in recent investigations on human fetal brains. This study uses a pair of new indices, the shape index (SI) and curvedness index (CVD), to quantify 3-D morphological changes in developing brains from 22 to 33 weeks of gestation. The SI was used to automatically locate the gyral nodes and sulcal pits, and the CVD was used to measure the degree of deviation of cortical shapes from a flat plane. The CVD values of classified regions were compared with two traditional biomarkers: cerebral volume and cortical surface area. Because the fetal brains dramatically deform with age, the age effect was controlled during the comparison between morphological changes and volume and surface area. The results show that cerebral volume, the cortical surface area, and the CVD values of gyral nodes and sulcal pits increased with gestational age. However, with age controlled, the CVD values of gyral nodes and sulcal pits did not correlate with cerebral volume, but the CVD of gyral nodes increased slightly with the cortical surface area. These findings suggest that the SI, in conjunction with the CVD, provides developmental information distinct from the brain volumetry. This approach provides additional insight into 3-D cortical morphology in the assessment of fetal brain development.

Common lesions in the newborn brain

With the wider use of magnetic resonance imaging, the recognition of different patterns of injury has become established. These patterns vary in relation to the level of immaturity of the infant. This review will outline the major patterns of abnormality that are found in the term born and preterm infant.

Spatial and temporal variations of cortical growth during gyrogenesis in the developing ferret brain

Spatial and temporal variations in cortical growth were studied in the neonatal ferret to illuminate the mechanisms of folding of the cerebral cortex. Cortical surface representations were created from magnetic resonance images acquired between postnatal day 4 and 35. Global measures of shape (e.g., surface area, normalized curvature, and sulcal depth) were calculated. In 2 ferrets, relative cortical growth was calculated between surfaces created from in vivo images acquired at P14, P21, and P28. The isocortical surface area transitions from a slower (12.7 mm(2)/day per hemisphere) to a higher rate of growth (36.7 mm(2)/day per hemisphere) approximately 13 days after birth, which coincides with the time of transition from neuronal proliferation to cellular morphological differentiation. Relative cortical growth increases as a function of relative geodesic distance from the origin of the transverse neurogenetic gradient and is related to the change in fractional diffusion anisotropy over the same time period. The methods presented here can be applied to study cortical growth during development in other animal models or human infants. Our results provide a quantitative spatial and temporal description of folding in cerebral cortex of the developing ferret brain, which will be important to understand the underlying mechanisms that drive folding.

Geometric computation of human gyrification indexes from magnetic resonance images

Human brains are highly convoluted surfaces with multiple folds. To characterize the complexity of these folds and their relationship with neurological and psychiatric conditions, different techniques have been developed to quantify the folding patterns, also known as the surface complexity or gyrification of the brain. In this study, the authors propose a new geometric approach to measure the gyrification of human brains from magnetic resonance images. This approach is based on intrinsic 3D measurements that relate the local brain surface area to the corresponding area of a tightly wrapped sheet. The authors also present an adaptation of this technique in which the geodesic depth is incorporated into the gyrification computation. These gyrification measures are efficiently and accurately computed by solving geometric partial differential equations. The presentation of the geometric framework is complemented with experimental results for brain complexity in typically developing children and adolescents. Using this novel approach, the authors provide evidence for a gradual decrease in brain surface complexity throughout childhood and adolescence. These developmental differences occur earlier in the occipital lobe and move anterior as children progress into young adulthood.

Regional quantification of developing human cortical shape with a three-dimensional surface-based magnetic resonance imaging analysis in utero

Although regional differences in cerebral volume have been revealed in developing human brains, little is known regarding the regionalization of cortical shape. This study documented the regional and quantitative shape difference of cortical surfaces for in utero normal fetal brains over a time period essential for the formation of primary cortical folding (22-33 weeks). Each brain surface with complete three-dimensional morphology was manually extracted from the reconstructed image, which combined surface information from three orthogonal magnetic resonance images in utero. An innovative parcellation was used to dissect the fetal brains into frontal, parietal, temporal and occipital lobes, and to avoid the determination of non-existent and immature sulci for young fetuses. Distinct cortical shapes were encoded by the shape index automatically. The results of this study show faster shape changes in the occipital lobe than in other regions. Both regional and global shape patterns show that the gyral surface smoothens, whereas the sulcal surface becomes more angular, with gestational age. In addition, the smoothing of gyri is related mainly to the changes in shape of gyral crowns. This study presents the regional differences in early gyrification from the novel aspect of shape. The results of shape pattern analysis for normal fetuses may act as a reference in assessments of prenatal brain pathology and in extensive comparisons between various life stages.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Early folding patterns and asymmetries of the normal human brain detected from in utero MRI

Early cortical folding and the emergence of structural brain asymmetries have been previously analyzed by neuropathology as well as qualitative analysis of magnetic resonance imaging (MRI) of fetuses and preterm neonates. In this study, we present a dedicated image analysis framework and its application for the detection of folding patterns during the critical period for the formation of many primary sulci (20-28 gestational weeks). Using structural information from in utero MRI, we perform morphometric analysis of cortical plate surface development and modeling of early folding in the normal fetal brain. First, we identify regions of the fetal brain surface that undergo significant folding changes during this developmental period and provide precise temporal staging of these changes for each region of interest. Then, we highlight the emergence of interhemispheric structural asymmetries that may be related to future functional specialization of cortical areas. Our findings complement previous descriptions of early sulcogenesis based on neuropathology and qualitative evaluation of 2D in utero MRI by accurate spatial and temporal mapping of the emergence of individual sulci as well as structural brain asymmetries. The study provides the missing starting point for their developmental trajectories and extends our understanding of normal cortical folding.

In-vivo measurement of cortical morphology: means and meanings

PURPOSE OF REVIEW

Study of the variability of the cortical mantle thickness is now a key issue in neuroimaging. Here we describe a more recent trend aiming at the study of the variability of the cortical folding morphology.

RECENT FINDINGS

Computerized three-dimensional versions of gyrification index and other morphometric features dedicated to the folding patterns are modified in psychiatric syndromes and neurologic disorders. These observations provide new insights into the mechanisms involved in abnormal development or abnormal aging.

SUMMARY

Quantification of the folding morphology will contribute to the global endeavor aiming at building biomarkers from neuroimaging data, with a specific focus on developmental diseases.

Cortical curvature analysis in MRI-negative temporal lobe epilepsy: a surrogate marker for malformations of cortical development

PURPOSE

To investigate cerebral cortical surface morphology in a magnetic resonance (MRI)-negative temporal lobe epilepsy (TLE) cohort, and to differentiate between the effects on cortical morphology of cerebral volume loss associated with TLE, and abnormalities suggestive of malformations of cortical development (MCDs).

METHODS

MRI data was gathered for 29 MRI-negative patients and 40 neurologically normal controls. Automated methods of surface reconstruction were applied to all MRI data for the purposes of localized analysis of cortical curvature. As an adjunct to this analysis, measures of whole-brain gray and white matter volumes, as well as cortical thickness, were also generated to determine the degree of whole-brain volume loss in TLE, and its impact on cortical morphology.

RESULTS

Automated analysis of the average cortical surface of the patient group revealed an area of abnormal cortical curvature in the basal left temporal lobe. The presence of whole-brain volume loss in TLE was confirmed and found not to contribute to the cortical curvature abnormality in the temporal lobe. These results support the hypothesis that cortical curvature abnormalities in TLE may be indicative of a subtle MCD.

DISCUSSION

Subtle MCDs such as abnormal indices of curvature may be associated with partial epilepsy. Analysis of these parameters may increase the diagnostic yield from MRI.

Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development

This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to "complexity". The paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results.

Surface area accounts for the relation of gray matter volume to reading-related skills and history of dyslexia

It is unknown whether the abnormalities in brain structure and function observed in dyslexic readers are congenital or arise later in development. Analyzing the 2 components of gray matter volume separately may help in differentiating these possibilities. Gray matter volume is the product of cortical surface area, determined during prenatal brain development, and cortical thickness, determined during postnatal development. For this study, 16 adults with a history of phonological dyslexia and 16 age- and gender-matched controls underwent magnetic resonance imaging and an extensive battery of tests of reading-related skills. Cortical surface area and gray matter volume measures of the whole brain, the inferior frontal gyrus, and the fusiform gyrus were similarly related to phonological skills and a history of dyslexia. There was no relationship between cortical thickness and phonological skills or history of dyslexia. Because cortical surface area reflects cortical folding patterns determined prenatally, this suggests that brain differences in dyslexia are rooted in early cortical development and are not due to compensatory changes that occur during postnatal development and would be expected to influence cortical thickness. This study demonstrates the importance of examining the separate components of gray matter volume when studying developmental abnormalities.

Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation

In recent years, postprocessing of fast multislice magnetic resonance imaging (MRI) to correct fetal motion has provided the first true 3-D MR images of the developing human brain in utero. Early approaches have used reconstruction based algorithms, employing a two-step iterative process, where slices from the acquired data are realigned to an approximate 3-D reconstruction of the fetal brain, which is then refined further using the improved slice alignment. This two step slice-to-volume process, although powerful, is computationally expensive in needing a 3-D reconstruction, and is limited in its ability to recover subvoxel alignment. Here, we describe an alternative approach which we term slice intersection motion correction (SIMC), that seeks to directly co-align multiple slice stacks by considering the matching structure along all intersecting slice pairs in all orthogonally planned slices that are acquired in clinical imaging studies. A collective update scheme for all slices is then derived, to simultaneously drive slices into a consistent match along their lines of intersection. We then describe a 3-D reconstruction algorithm that, using the final motion corrected slice locations, suppresses through-plane partial volume effects to provide a single high isotropic resolution 3-D image. The method is tested on simulated data with known motions and is applied to retrospectively reconstruct 3-D images from a range of clinically acquired imaging studies. The quantitative evaluation of the registration accuracy for the simulated data sets demonstrated a significant improvement over previous approaches. An initial application of the technique to studying clinical pathology is included, where the proposed method recovered up to 15 mm of translation and 30 degrees of rotation for individual slices, and produced full 3-D reconstructions containing clinically useful additional information not visible in the original 2-D slices.

MR imaging of the fetal brain

Fetal MRI is clinically performed to evaluate the brain in cases where an abnormality is detected by prenatal sonography. These most commonly include ventriculomegaly, abnormalities of the corpus callosum, and abnormalities of the posterior fossa. Fetal MRI is also increasingly performed to evaluate fetuses who have normal brain findings on prenatal sonogram but who are at increased risk for neurodevelopmental abnormalities, such as complicated monochorionic twin pregnancies. This paper will briefly discuss the common clinical conditions imaged by fetal MRI as well as recent advances in fetal MRI research.

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.

Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C)

Multiple system atrophy of the cerebellar type (MSA-C) is a degenerative neurological disease of the central nervous system. This study aims to demonstrate that the morphological changes of cerebellar structure, specifically, the cerebellum white matter (CBWM) and cerebellum gray matter (CBGM) from T1-weighted magnetic resonance (MR) images, can be quantified by three-dimensional (3D) fractal dimension (FD) analysis, which is a measure of complexity. Twenty-three MSA-C patients and twenty-one normal subjects participated in this study. The results of this study show that MSA-C patients presented significantly lower FD values compared to the control group, and that morphological change in the CBWM dominates the cerebellar degeneration. In addition, the FD analysis method is superior to conventional volumetric methods in quantifying the structural changes of WM and GM because it exhibits smaller variances and less gender effect. Since a decrease of cerebellar FD value indicates degeneration of the cerebellar structure, this study further suggests that the morphological changes of cerebellar structures (CBGM and CBWM) can be characterized by FD analysis.