Automatic inference of sulcus patterns using 3D moment invariants

Multivariate Concavity Amplitude Index (MCAI) for characterizing Heschl's gyrus shape

Heschl's gyrus (HG), which includes primary auditory cortex, is highly variable in its shape (i.e. gyrification patterns), between hemispheres and across individuals. Differences in HG shape have been observed in the context of phonetic learning skill and expertise, and of professional musicianship, among others. Two of the most common configurations of HG include single HG, where a single transverse temporal gyrus is present, and common stem duplications (CSD), where a sulcus intermedius (SI) arises from the lateral aspect of HG. Here we describe a new toolbox, called 'Multivariate Concavity Amplitude Index' (MCAI), which automatically assesses the shape of HG. MCAI works on the output of TASH, our first toolbox which automatically segments HG, and computes continuous indices of concavity, which arise when sulci are present, along the outer perimeter of an inflated representation of HG, in a directional manner. Thus, MCAI provides a multivariate measure of shape, which is reproducible and sensitive to small variations in shape. We applied MCAI to structural magnetic resonance imaging (MRI) data of N=181 participants, including professional and amateur musicians and from non-musicians. Former studies have shown large variations in HG shape in the former groups. We validated MCAI by showing high correlations between the dominant (i.e. highest) lateral concavity values and continuous visual assessments of the degree of lateral gyrification of the first gyrus. As an application of MCAI, we also replicated previous visually obtained findings showing a higher likelihood of bilateral CSDs in musicians. MCAI opens a wide range of applications in evaluating HG shape in the context of individual differences, expertise, disorder and genetics.

"Plis de passage" Deserve a Role in Models of the Cortical Folding Process

Cortical folding is a hallmark of brain topography whose variability across individuals remains a puzzle. In this paper, we call for an effort to improve our understanding of the pli de passage phenomenon, namely annectant gyri buried in the depth of the main sulci. We suggest that plis de passage could become an interesting benchmark for models of the cortical folding process. As an illustration, we speculate on the link between modern biological models of cortical folding and the development of the Pli de Passage Frontal Moyen (PPFM) in the middle of the central sulcus. For this purpose, we have detected nine interrupted central sulci in the Human Connectome Project dataset, which are used to explore the organization of the hand sensorimotor areas in this rare configuration of the PPFM.

An evaluation of automated tracing for orbitofrontal cortex sulcogyral pattern typing

BACKGROUND

Characterization of stereotyped orbitofrontal cortex (OFC) sulcogyral patterns formed by the medial and lateral orbitofrontal sulci (MOS and LOS) can be used to characterize individual variability; however, in practice, issues exist for reliability and reproducibility of anatomical classifications, as current methods rely on manual tracing.

NEW METHOD

We assessed whether an automated tracing procedure would be useful for characterizing OFC sulcogyral patterns. 100 subjects from a published collection of manual OFC tracings and characterizations of patients with bipolar disorder, schizophrenia, and typical controls were used to evaluate an automated tracing procedure implemented using the BrainVISA Morphologist Pipeline.

RESULTS

Automated tracings of caudal and rostral segments of the medial (MOSc/MOSr) and lateral (LOSc/LOSr) orbitofrontal sulci, as well as the intermediate (IOS) and transverse orbitofrontal sulci (TOS) were found to accurately identify OFC sulci, accurately portray sulci continuity, and reliably inform manual sulcogyral pattern characterization.

COMPARISON WITH EXISTING METHOD

Automated tracings produced visibly similar tracings of OFC sulci and removed subjective influence from locating sulci. The semi-automated pipeline of automated tracing and manual sulcogyral pattern characterization can eliminate the need for direct input during the most time-consuming process of the manual pipeline.

CONCLUSIONS

The results suggest that automated OFC sulci tracing methods using BrainVISA Morphologist are feasible and useful in a semi-automated pipeline to characterize OFC sulcogyral patterns. Automated OFC sulci tracing methods will improve reliability and reproducibility of sulcogyral characterizations and can allow for characterizations of sulcal patterns types in larger sample sizes, previously unattainable using traditional manual tracing procedures.

Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications

The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i.e., the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.

Discovering cortical sulcal folding patterns in neonates using large-scale dataset

The folding of the human cerebral cortex is highly complex and variable across individuals, but certain common major patterns of cortical folding do exist. Mining such common patterns of cortical folding is of great importance in understanding the inter-individual variability of cortical folding and their relationship with cognitive functions and brain disorders. As primary cortical folds are mainly genetically influenced and are well established at term birth, neonates with minimal exposure to the complicated postnatal environmental influences are ideal candidates for mining the major patterns of cortical folding. In this paper, we propose a sulcal-pit-based method to discover the major sulcal patterns of cortical folding. In our method, first, the sulcal pattern is characterized by the spatial distribution of sulcal pits, which are the locally deepest points in cortical sulci. Since deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suited for representing and characterizing the sulcal patterns. Then, the similarity between the distributions of sulcal pits is measured from the spatial, geometrical, and topological points of view. Next, a comprehensive similarity matrix is constructed for the whole dataset by adaptively fusing these measurements together, thus capturing both their common and complementary information. Finally, leveraging the similarity matrix, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains, and revealed multiple distinct and meaningful sulcal patterns in the central sulcus, superior temporal sulcus, and cingulate sulcus.

Sulcal pits and patterns in developing human brains

Spatial distribution and specific geometric and topological patterning of early sulcal folds have been hypothesized to be under stronger genetic control and are more associated with optimal organization of cortical functional areas and their white matter connections, compared to later developing sulci. Several previous studies of sulcal pit (putative first sulcal fold) distribution and sulcal pattern analyses using graph structures have provided evidence of the importance of sulcal pits and patterns as remarkable anatomical features closely related to human brain function, suggesting additional insights concerning the anatomical and functional development of the human brain. Recently, early sulcal folding patterns have been observed in healthy fetuses and fetuses with brain abnormalities such as polymicrogyria and agenesis of corpus callosum. Graph-based quantitative sulcal pattern analysis has shown high sensitivity in detecting emerging subtle abnormalities in cerebral cortical growth in early fetal stages that are difficult to detect via qualitative visual assessment or using traditional cortical measures such as gyrification index and curvature. It has proven effective for characterizing genetically influenced early cortical folding development. Future studies will be aimed at better understanding a comprehensive map of spatio-temporal dynamics of fetal cortical folding in a large longitudinal cohort in order to examine individual clinical fetal MRIs and predict postnatal neurodevelopmental outcomes from early fetal life.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.

Exploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information

The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding. Second, for each feature, we build a dissimilarity matrix for measuring the difference of cortical folding between any pair of subjects. Then, we convert these dissimilarity matrices as similarity matrices, and nonlinearly fuse them into a single matrix via a similarity network fusion method. Finally, we apply a hierarchical affinity propagation clustering approach to group subjects into several clusters based on the fused similarity matrix. The proposed framework is generic and can be applied to any cortical region, or even the whole cortical surface. Experiments are carried out on a large dataset with 600+ term-born neonates to mine the principal folding patterns of three representative gyral regions.

Discovering Cortical Folding Patterns in Neonatal Cortical Surfaces Using Large-Scale Dataset

The cortical folding of the human brain is highly complex and variable across individuals. Mining the major patterns of cortical folding from modern large-scale neuroimaging datasets is of great importance in advancing techniques for neuroimaging analysis and understanding the inter-individual variations of cortical folding and its relationship with cognitive function and disorders. As the primary cortical folding is genetically influenced and has been established at term birth, neonates with the minimal exposure to the complicated postnatal environmental influence are the ideal candidates for understanding the major patterns of cortical folding. In this paper, for the first time, we propose a novel method for discovering the major patterns of cortical folding in a large-scale dataset of neonatal brain MR images (N = 677). In our method, first, cortical folding is characterized by the distribution of sulcal pits, which are the locally deepest points in cortical sulci. Because deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suitable for representing and characterizing cortical folding. Then, the similarities between sulcal pit distributions of any two subjects are measured from spatial, geometrical, and topological points of view. Next, these different measurements are adaptively fused together using a similarity network fusion technique, to preserve their common information and also catch their complementary information. Finally, leveraging the fused similarity measurements, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains (the largest neonatal dataset to our knowledge) in the central sulcus, superior temporal sulcus, and cingulate sulcus, and revealed multiple distinct and meaningful folding patterns in each region.

Intensity and sulci landmark combined brain atlas construction for Chinese pediatric population

Constructing an atlas from a population of brain images is of vital importance to medical image analysis. Especially in neuroscience study, creating a brain atlas is useful for intra- and inter-population comparison. Research on brain atlas construction has attracted great attention in recent years, but the research on pediatric population is still limited, mainly due to the limited availability and the relatively low quality of pediatric magnetic resonance brain images. This article is targeted at creating a high quality representative brain atlas for Chinese pediatric population. To achieve this goal, we have designed a set of preprocessing procedures to improve the image quality and developed an intensity and sulci landmark combined groupwise registration method to align the population of images for atlas construction. As demonstrated in experiments, the newly constructed atlas can better represent the size and shape of brains of Chinese pediatric population, and show better performance in Chinese pediatric brain image analysis compared with other standard atlases.

Consistent sulcal parcellation of longitudinal cortical surfaces

Automated accurate and consistent sulcal parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains, since longitudinal cortical changes are normally very subtle, especially in aging brains. However, applying the existing methods (which were typically developed for cortical sulcal parcellation of a single cortical surface) independently to longitudinal cortical surfaces might generate longitudinally-inconsistent results. To overcome this limitation, this paper presents a novel energy function based method for accurate and consistent sulcal parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal parcellation results. The energy function is efficiently minimized by a graph cut method. The proposed method has been successfully applied to sulcal parcellation of both real and simulated longitudinal inner cortical surfaces of human brain MR images. Both qualitative and quantitative evaluation results demonstrate the validity of the proposed method.

An automated pipeline for cortical sulcal fundi extraction

In this paper, we propose a novel automated pipeline for extraction of sulcal fundi from triangulated cortical surfaces. This method consists of four consecutive steps. Firstly, we adopt a finite difference method to estimate principal curvatures, principal directions and curvature derivatives, along the principal directions, for each vertex. Then, we detect the sulcal fundi segment in each triangle of the cortical surface based on curvatures and curvature derivatives. Afterwards, we link the sulcal fundi segments into continuous curves. Finally, we connect breaking sulcal fundi and smooth bumping sulcal fundi by using the fast marching method on the cortical surface. The proposed method can find the accurate sulcal fundi using curvatures and curvature derivatives without any manual interaction. The method was applied to 10 normal brain MR images on inner cortical surfaces. We quantitatively evaluated the accuracy of the sulcal fundi extraction method using manually labeled sulcal fundi by experts. The average difference between automatically extracted major sulcal fundi and the expert labeled results is consistently around 1.0mm on 10 subject images, indicating the good performance of the proposed method.

Feature-based morphometry: discovering group-related anatomical patterns

This paper presents feature-based morphometry (FBM), a new fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1).

Automatic cortical sulcal parcellation based on surface principal direction flow field tracking

The human cerebral cortex is a highly convoluted structure composed of sulci and gyri, corresponding to the valleys and ridges of the cortical surface respectively. Automatic parcellation of the cortical surface into sulcal regions is of great importance in structural and functional mapping of the human brain. In this paper, a novel method is proposed for automatic cortical sulcal parcellation based on the geometric characteristics of cortical surface including its principal curvatures and principal directions. This method is composed of two major steps: 1) employing the hidden Markov random field model (HMRF) and the expectation maximization (EM) algorithm on the maximum principal curvatures of the cortical surface for sulcal region segmentation, and 2) using a principal direction flow field tracking method on the cortical surface for sulcal basin segmentation. The flow field is obtained by diffusing the principal direction field on the cortical surface mesh. A unique feature of this method is that the automatic sulcal parcellation process is quite robust and efficient, and is independent of any external guidance such as atlas-based warping. The method has been successfully applied to the inner cortical surfaces of twelve healthy human brain MR images. Both quantitative and qualitative evaluation results demonstrate the validity and efficiency of the proposed method.

Constructing a dictionary of human brain folding patterns

Brain imaging provides a wealth of information that computers can explore at a massive scale. Categorizing the patterns of the human cortex has been a challenging issue for neuroscience. In this paper, we propose a data mining approach leading to the construction of the first computerized dictionary of cortical folding patterns, from a database of 62 brains. The cortical folds are extracted using BrainVisa open software. The standard sulci are manually identified among the folds. 32 sets of sulci covering the cortex are selected. Clustering techniques are further applied to identify in each set the different patterns observed in the population. After affine global normalization, the geometric distance between sulci of two subjects is calculated using the Iterative Closest Point (ICP) algorithm. The dimension of the resulting distance matrix is reduced using Isomap algorithm. Finally, a dedicated hierarchical clustering algorithm is used to extract out the main patterns. This algorithm provides a score which evaluates the strengths of the patterns found. The score is used to rank the patterns for setting up a dictionary to characterize the variability of cortical anatomy.