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OBJECTIVE To explore whether neurosonography can detect differences in cortical development and corpus callosal length in late-onset small fetuses subclassified into small-for-gestational age (SGA) or growth restricted (FGR). METHODS This was a prospective cohort study in singleton pregnancies, including normally grown fetuses (birth weight between the 10th and 90th centiles) and late-onset small fetuses (estimated fetal weight

It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defied distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classifcation of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning. V. A. Zimmer is supported by the grant FI-DGR 2013 (2013 FI B00159) from the Generalitat de Catalunya. This research was partially funded by the Spanish Ministry of Economy and Competitiveness (TIN2012-35874). This study was also supported by Instituto de Salud Carlos III (PI16/00861), integrated in the Plan Nacional de I+D+I and co-financed by ISCIII-Subdirección General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER) \Una manera de hacer Europa"; additionally, the research leading to these results has received funding from \la Caixa" Foundation.

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

Fetal ventriculomegaly (VM) is a condition with dilation of one or both lateral ventricles, 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 studies use a holistic approach (i.e., ventricle as a whole) based on diagnosis or ventricular volume, thus failing to reveal the spatially-heterogeneous association patterns between cortex and ventricle. To address this issue, we develop a novel method to identify spatially fine-scaled association maps between cortical development and VM by leveraging vertex-wise correlations between the growth patterns of both ventricular and cortical surfaces in terms of area expansion and curvature information. Our approach comprises multiple steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where their joint growth patterns are projected. More importantly, in the joint ventricle-cortex space, the vertices of associated regions from both cortical and ventricular surfaces would lie close to each other. In the final step, we perform 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 results show that the proposed approach is able to reveal clinically relevant and meaningful regional associations.

BACKGROUND AND PURPOSE: Fetuses with isolated nonsevere ventriculomegaly (INSVM) are at risk of presenting neurodevelopmental delay. However, the currently used clinical parameters are insufficient to select cases with high risk and determine whether subtle changes in brain development are present and might be a risk factor. The aim of this study was to perform a comprehensive evaluation of cortical development in INSVM by magnetic resonance (MR) imaging and assess its association with neonatal neurobehavior. MATERIALS AND METHODS: Thirty-two INSVM fetuses and 29 healthy controls between 26-28 weeks of gestation were evaluated using MR imaging. We compared sulci and fissure depth, cortical maturation grading of specific areas and sulci and volumes of different brain regions obtained from 3D brain reconstruction of cases and controls. Neonatal outcome was assessed by using the Neonatal Behavioral Assessment Scale at a mean of 4 ± 2 weeks after birth. RESULTS: Fetuses with INSVM showed less profound and underdeveloped sulcation, including the Sylvian fissure (mean depth: controls 16.8 ± 1.9 mm, versus INSVM 16.0 ± 1.6 mm; P = .01), and reduced global cortical grading (mean score: controls 42.9 ± 10.2 mm, versus INSVM: 37.8 ± 9.9 mm; P = .01). Fetuses with isolated nonsevere ventriculomegaly showed a mean global increase of gray matter volume (controls, 276.8 ± 46.0 ×10 mm3, versus INSVM 277.5 ± 49.3 ×10 mm3, P = .01), but decreased mean cortical volume in the frontal lobe (left: controls, 53.2 ± 8.8 ×10 mm3, versus INSVM 52.4 ± 5.4 ×10 mm3; P = < .01). Sulcal depth and brain volumes were significantly associated with the Neonatal Behavioral Assessment Scale severity (P = .005, Nagelkerke R2 = 0.732). CONCLUSIONS: INSVM fetuses showed differences in cortical development, including regions far from the lateral ventricles, that are associated with neonatal neurobehavior. These results suggest the possible use of these parameters to identify cases at higher risk of altered neurodevelopment. ispartof: AMERICAN JOURNAL OF NEURORADIOLOGY vol:40 issue:9 pages:1567-1574 ispartof: location:United States status: published

Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface-based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data-driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic 'growth patterns' of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age-specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.

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.

In the field of multi-atlas segmentation, patch-based approaches have shown promising results in the segmentation of biomedical images. In the most common approach, registration is used to warp the atlases to the target space and then the warped atlas labelmaps are fused into a consensus segmentation based on local appearance information encoded in form of patches. The registration step establishes spatial correspondence, which is important to obtain anatomical priors. Patch-based label fusion in the target space has shown to produce very accurate segmentations although at the expense of registering all atlases to each target image. Moreover, appearance (i.e., patches) and label information used by label fusion is extracted from the warped atlases, which are subject to interpolation errors. In this work, we revisit and extend the patch-based label fusion framework, exploring the role of extracting this information from the native space of both atlases and target images, thus avoiding interpolation artifacts, but at the same time, we do it in a way that it does not sacrifice the anatomical priors derived by registration. We further propose a common formulation for two widely-used label fusion strategies, i.e., similarity-based and a particular type of learning-based label fusion. The proposed framework is evaluated on subcortical structure segmentation in adult brains and tissue segmentation in fetal brain MRI. Our results indicate that using atlas patches in their native space yields superior performance than warping the atlases to the target image. The learning-based approach tends to outperform the similarity-based approach, with the particularity that using patches in native space lessens the computational requirements of learning. As conclusion, the combination of learning-based label fusion and native atlas patches yields the best performance with reduced test times than conventional similarity-based approaches.

Dividing the human cerebral cortex into structurally and functionally distinct regions is important in many neuroimaging studies. Although many parcellations have been created for adults, they are not applicable for fetal studies, due to dramatic differences in brain size, shape and folding between adults and fetuses, as well as dynamic growth of fetal brains. To address this issue, we propose a novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures. As microstructures determine the molecular organization and functional principles of the cortex, growth patterns enable an accurate definition of distinct regions in development, microstructure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices and the other is based on the correlation profiles of vertices' growth trajectories in relation to those of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better captures both their common and complementary information than by simply averaging them. Finally, based on this fused matrix, we perform spectral clustering to divide fetal cortical surfaces into distinct regions. We have applied our method on 25 normal fetuses from 26 to 29 gestational weeks and generated biologically meaningful parcellations.

Objectives To perform a comprehensive assessment of cortical development in fetuses with isolated nonsevere ventriculomegaly (INSVM) by neurosonography. Methods We prospectively included 40 fetuses with INSVM and 40 controls. INSVM was defined as atrial width between 10.0 and 14.9 mm without associated malformation, infection, or chromosomal abnormality. Cortical development was assessed by neurosonography at 26 and 30 weeks of gestation measuring depth of selected sulci and applying a maturation scale from 0 (no appearance) to 5 (maximally developed) of main sulci and areas. Results INSVM showed underdeveloped calcarine and parieto-occipital sulci. In addition, significant delayed maturation pattern was also observed in regions distant to ventricular system including Insula depth (controls 30.8 mm [SD 1.7] vs INSVM 31.7 mm [1.8]; P = .04), Sylvian fissure grading (>2 at 26 weeks: controls 87.5% vs INSVM 50%, P = .01), mesial area grading (>2 at 30 weeks: controls 95% vs INSVM 62.5%; P = .03), and cingulate sulcus grading (>2 at 30 weeks: controls 100% vs INSVM 80.5%; P = .01). Conclusions Fetuses with INSVM showed underdeveloped cortical maturation including also regions, where effect of ventricular dilatation is unlikely. These results suggest that in a proportion of fetuses with INSVM, ventricular dilation might be related with altered cortical architecture.

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., Highlights • Studies showed high risk of neurodevelopmental impairment in fetuses with ventriculomegaly. • Fetal ventriculomegaly may be related to other neurodevelopmental processes. • We studied the relationship of isolated non-severe ventriculomegaly with cortical folding. • Reduced cortical folding is observed in fetuses with ventriculomegaly. • Cortical regions with altered folding may constitute potential prognostic biomarkers.