Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants

Sulcal pits, the locally deepest points in sulci of the highly convoluted and variable cerebral cortex, are found to be spatially consistent across human adult individuals. It is suggested that sulcal pits are genetically controlled and have close relationships with functional areas. To date, the existing imaging studies of sulcal pits are mainly focused on adult brains, yet little is known about the spatial distribution and temporal development of sulcal pits in the first 2 years of life, which is the most dynamic and critical period of postnatal brain development. Studying sulcal pits during this period would greatly enrich our limited understandings of the origins and developmental trajectories of sulcal pits, and also provide important insights into many neurodevelopmental disorders associated with abnormal cortical foldings. In this paper, by using surface-based morphometry, for the first time, we systemically investigated the spatial distribution and temporal development of sulcal pits in major cortical sulci from 73 healthy infants, each with longitudinal 3T MR scans at term birth, 1 year, and 2 years of age. Our results suggest that the spatially consistent distributions of sulcal pits in major sulci across individuals have already existed at term birth and this spatial distribution pattern keeps relatively stable in the first 2 years of life, despite that the cerebral cortex expands dramatically and the sulcal depth increases considerably during this period. Specially, the depth of sulcal pits increases regionally heterogeneously, with more rapid growth in the high-order association cortex, including the prefrontal and temporal cortices, than the sensorimotor cortex in the first 2 years of life. Meanwhile, our results also suggest that there exist hemispheric asymmetries of the spatial distributions of sulcal pits in several cortical regions, such as the central, superior temporal and postcentral sulci, consistently from birth to 2 years of age, which likely has close relationship with the lateralization of brain functions of these regions. This study provides detailed insights into the spatial distribution and temporal development of deep sulcal landmarks in infants

Anatomo-functional correspondence in the superior temporal sulcus

The superior temporal sulcus (STS) is an intriguing region both for its complex anatomy and for the multiple functions that it hosts. Unfortunately, most studies explored either the functional organization or the anatomy of the STS only. Here, we link these two aspects by investigating anatomo-functional correspondences between the voice-sensitive cortex (Temporal Voice Areas) and the STS depth. To do so, anatomical and functional scans of 116 subjects were processed such as to generate individual surface maps on which both depth and functional voice activity can be analyzed. Individual depth profiles of manually drawn STS and functional profiles from a voice localizer (voice > non-voice) maps were extracted and compared to assess anatomo-functional correspondences. Three major results were obtained: first, the STS exhibits a highly significant rightward depth asymmetry in its middle part. Second, there is an anatomo-functional correspondence between the location of the voice-sensitive peak and the deepest point inside this asymmetrical region bilaterally. Finally, we showed that this correspondence was independent of the gender and, using a machine learning approach, that it existed at the individual level. These findings offer new perspectives for the understanding of anatomo-functional correspondences in this complex cortical region

Cortical thickness and surface area in neonates at high risk for schizophrenia

Schizophrenia is a neurodevelopmental disorder associated with subtle abnormal cortical thickness and cortical surface area. However, it is unclear whether these abnormalities exist in neonates associated with genetic risk for schizophrenia. To this end, this preliminary study was conducted to identify possible abnormalities of cortical thickness and surface area in the high-genetic-risk neonates. Structural magnetic resonance images were acquired from offspring of mothers (N = 21) who had schizophrenia (N = 12) or schizoaffective disorder (N = 9), and also matched healthy neonates of mothers who were free of psychiatric illness (N = 26). Neonatal cortical surfaces were reconstructed and parcellated as regions of interest (ROIs), and cortical thickness for each vertex was computed as the shortest distance between the inner and outer surfaces. Comparisons were made for the average cortical thickness and total surface area in each of 68 cortical ROIs. After false discovery rate (FDR) correction, it was found that the female high-genetic-risk neonates had significantly thinner cortical thickness in the right lateral occipital cortex than the female control neonates. Before FDR correction, the high-genetic-risk neonates had significantly thinner cortex in the left transverse temporal gyrus, left banks of superior temporal sulcus, left lingual gyrus, right paracentral cortex, right posterior cingulate cortex, right temporal pole, and right lateral occipital cortex, compared with the control neonates. Before FDR correction, in comparison with control neonates, male high-risk neonates had significantly thicker cortex in the left frontal pole, left cuneus cortex, and left lateral occipital cortex; while female high-risk neonates had significantly thinner cortex in the bilateral paracentral, bilateral lateral occipital, left transverse temporal, left pars opercularis, right cuneus, and right posterior cingulate cortices. The high-risk neonates also had significantly smaller cortical surface area in the right pars triangularis (before FDR correction), compared with control neonates. This preliminary study provides the first evidence that early development of cortical thickness and surface area might be abnormal in the neonates at genetic risk for schizophrenia

Development of cortical shape in the human brain from 6 to 24months of age via a novel measure of shape complexity

The quantification of local surface morphology in the human cortex is important for examining population differences as well as developmental changes in neurodegenerative or neurodevelopmental disorders. We propose a novel cortical shape measure, referred to as the ‘shape complexity index’ (SCI), that represents localized shape complexity as the difference between the observed distributions of local surface topology, as quantified by the shape index (SI) measure, to its best fitting simple topological model within a given neighborhood. We apply a relatively small, adaptive geodesic kernel to calculate the SCI. Due to the small size of the kernel, the proposed SCI measure captures fine differences of cortical shape. With this novel cortical feature, we aim to capture comparatively small local surface changes that capture a) the widening versus deepening of sulcal and gyral regions, as well as b) the emergence and development of secondary and tertiary sulci. Current cortical shape measures, such as the gyrification index (GI) or intrinsic curvature measures, investigate the cortical surface at a different scale and are less well suited to capture these particular cortical surface changes. In our experiments, the proposed SCI demonstrates higher complexity in the gyral/sulcal wall regions, lower complexity in wider gyral ridges and lowest complexity in wider sulcal fundus regions. In early postnatal brain development, our experiments show that SCI reveals a pattern of increased cortical shape complexity with age, as well as sexual dimorphisms in the insula, middle cingulate, parieto-occipital sulcal and Broca's regions. Overall, sex differences were greatest at 6 months of age and were reduced at 24 months, with the difference pattern switching from higher complexity in males at 6 months to higher complexity in females at 24months. This is the first study of longitudinal, cortical complexity maturation and sex differences, in the early postnatal period from 6 to 24 months of age with fine scale, cortical shape measures. These results provide information that complement previous studies of gyrification index in early brain development

Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies: Estimation of Cortical Morphological Maps

Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named Dynamically-Assembled Regression Forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to 5 time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm

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 years of life, 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 7 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

Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants

Sulcal pits, the locally deepest points in sulci of the highly convoluted and variable cerebral cortex, are found to be spatially consistent across human adult individuals. It is suggested that sulcal pits are genetically controlled and have close relationships with functional areas. To date, the existing imaging studies of sulcal pits are mainly focused on adult brains, yet little is known about the spatial distribution and temporal development of sulcal pits in the first 2 years of life, which is the most dynamic and critical period of postnatal brain development. Studying sulcal pits during this period would greatly enrich our limited understandings of the origins and developmental trajectories of sulcal pits, and also provide important insights into many neurodevelopmental disorders associated with abnormal cortical foldings. In this paper, by using surface-based morphometry, for the first time, we systemically investigated the spatial distribution and temporal development of sulcal pits in major cortical sulci from 73 healthy infants, each with longitudinal 3T MR scans at term birth, 1 year, and 2 years of age. Our results suggest that the spatially consistent distributions of sulcal pits in major sulci across individuals have already existed at term birth and this spatial distribution pattern keeps relatively stable in the first 2 years of life, despite that the cerebral cortex expands dramatically and the sulcal depth increases considerably during this period. Specially, the depth of sulcal pits increases regionally heterogeneously, with more rapid growth in the high-order association cortex, including the prefrontal and temporal cortices, than the sensorimotor cortex in the first 2 years of life. Meanwhile, our results also suggest that there exist hemispheric asymmetries of the spatial distributions of sulcal pits in several cortical regions, such as the central, superior temporal and postcentral sulci, consistently from birth to 2 years of age, which likely has close relationship with the lateralization of brain functions of these regions. This study provides detailed insights into the spatial distribution and temporal development of deep sulcal landmarks in infants

Computational Cortical Surface Analysis for Study of Early Brain Development

The study of morphological attributes of the cerebral cortex and their development is very important in understanding the dynamic and critical early brain development. Comparing with conventional studies in the image space, cortical surface-based analysis provides a better way to display, observe, and quantify the attributes of the cerebral cortex. The goal of this dissertation is to develop novel cortical surface-based methods for better studying the attributes of the cerebral cortex during early brain development. Specifically, this dissertation aims to develop methods for 1) estimating the development of morphological attributes of the cerebral cortex and 2) discovering the major cortical folding patterns. Estimation of the Development of Cortical Attributes. The early development of cortical attributes is highly correlated to the brain cognitive functionality and some neurodevelopmental disorders. Hence, accurately modeling the early development of cortical attributes is crucial for better understanding the mysterious normal and abnormal brain development. This task is very challenging, because infant cortical attributes change dramatically, complicatedly and regionally-heterogeneously during the first year of life. To address these problems, this dissertation proposes a Dynamically-Assembled Regression Forest (DARF). DARF first trains a single decision tree at each vertex on the cortical surface, and then groups nearby decision trees around each vertex as a vertex-specific forest to predict the cortical attribute. Since the vertex-specific forest can better capture regional details than the conventional regression forest trained for the whole brain, the prediction result is more precise. Moreover, because nearby forests share a large portion of decision trees, the prediction result is spatially smooth. On the other hand, missing cortical attribute maps in the longitudinal datasets often lead to insufficient data for unbiased analysis or training of accurate prediction models. To address this issue, a missing data estimation strategy based on DARF is further proposed. Experiments show that DARF outperforms the existing popular regression methods, and the proposed missing data estimation strategy based on DARF can effectively recover the missing cortical attribute maps. Discovery of Major Cortical Folding Patterns. The folding patterns of the cerebral cortex are highly variable across subjects. Exploring major cortical folding patterns in neonates is of great importance in neuroscience. Conventional geometric measurements of the cortex have limited capability in distinguishing major folding patterns. Although the recent sulcal pits-based analysis provides a better way for comparing sulcal patterns across individuals of adults or older children, whether and how sulcal pits are suitable for discovering major sulcal patterns in infants remain unknown. This dissertation adapts a sulcal pits extraction method from adults to infants, and validates the spatial consistency of sulcal pits in infants, so that they can be used as reliable landmarks for exploring major sulcal patterns. This dissertation further proposes a sulcal graph-based method for discovering major sulcal patterns, which is then applied to studying three primary cortical regions in 677 neonatal cortical surfaces. The experiments show that the proposed method is able to identify the previously unreported major sulcal patterns. Finally, this dissertation investigates and verifies that the sulcal pattern information could be utilized to help DARF for better estimating cortical attribute maps.Doctor of Philosoph