Coordinate-based versus structural approaches to brain image analysis

An integrated finite element method and machine learning algorithm for brain morphology prediction

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

"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.

Robust estimation of sulcal morphology

While it is well established that cortical morphology differs in relation to a variety of inter-individual factors, it is often characterized using estimates of volume, thickness, surface area, or gyrification. Here we developed a computational approach for estimating sulcal width and depth that relies on cortical surface reconstructions output by FreeSurfer. While other approaches for estimating sulcal morphology exist, studies often require the use of multiple brain morphology programs that have been shown to differ in their approaches to localize sulcal landmarks, yielding morphological estimates based on inconsistent boundaries. To demonstrate the approach, sulcal morphology was estimated in three large sample of adults across the lifespan, in relation to aging. A fourth sample is additionally used to estimate test–retest reliability of the approach. This toolbox is now made freely available as supplemental to this paper: https://cmadan.github.io/calcSulc/.

Primatologist: A modular segmentation pipeline for macaque brain morphometry

Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Primatologist, for multi-region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation-maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre-processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2-weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non-linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated.

Atypical sulcal anatomy in young children with autism spectrum disorder

Autism spectrum disorder is associated with an altered early brain development. However, the specific cortical structure abnormalities underlying this disorder remain largely unknown. Nonetheless, atypical cortical folding provides lingering evidence of early disruptions in neurodevelopmental processes and identifying changes in the geometry of cortical sulci is of primary interest for characterizing these structural abnormalities in autism and their evolution over the first stages of brain development. Here, we applied state-of-the-art sulcus-based morphometry methods to a large highly-selective cohort of 73 young male children of age spanning from 18 to 108 months. Moreover, such large cohort was selected through extensive behavioral assessments and stringent inclusion criteria for the group of 59 children with autism. After manual labeling of 59 different sulci in each hemisphere, we computed multiple shape descriptors for each single sulcus element, hereby separating the folding measurement into distinct factors such as the length and depth of the sulcus. We demonstrated that the central, intraparietal and frontal medial sulci showed a significant and consistent pattern of abnormalities across our different geometrical indices. We also found that autistic and control children exhibited strikingly different relationships between age and structural changes in brain morphology. Lastly, the different measures of sulcus shapes were correlated with the CARS and ADOS scores that are specific to the autistic pathology and indices of symptom severity. Inherently, these structural abnormalities are confined to regions that are functionally relevant with respect to cognitive disorders in ASD. In contrast to those previously reported in adults, it is very unlikely that these abnormalities originate from general compensatory mechanisms unrelated to the primary pathology. Rather, they most probably reflect an early disruption on developmental trajectory that could be part of the primary pathology.

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A new single-site cohort of 73 young children (1.5–11 years) with autism and controls

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State-of-the-art methodology used to compare geometrical attributes of sulci

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Combination of automatic extraction of descriptors with manual identification of sulci

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Clearly evidence localized sulcal shape abnormalities in the autism group

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Different relationships between age and structural changes in brain morphology

Longitudinal change detection: inference on the diffusion tensor along white matter pathways

Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies.

A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex

Sharing of analysis techniques and tools is among the main driving forces of modern neuroscience. We describe a library of tools developed to quantify global and regional differences in cortical anatomy in high resolution structural MR images. This library is distributed as a plug-in application for popular structural analysis software, BrainVisa (BV). It contains tools to measure global and regional gyrification, gray matter thickness and sulcal and gyral white matter spans. We provide a description of each tool and examples for several case studies to demonstrate their use. These examples show how the BV library was used to study cortical folding process during antenatal development and recapitulation of this process during cerebral aging. Further, the BV library was used to perform translation research in humans and non-human primates on the genetics of cerebral gyrification. This library, including source code and self-contained binaries for popular computer platforms, is available from the NIH-Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) resource ( http://www.nitrc.org/projects/brainvisa_ext ).

On the genetic architecture of cortical folding and brain volume in primates

Understanding the evolutionary forces that produced the human brain is a central problem in neuroscience and human biology. Comparisons across primate species show that both brain volume and gyrification (the degree of folding in the cerebral cortex) have progressively increased during primate evolution and there is a strong positive correlation between these two traits across primate species. The human brain is exceptional among primates in both total volume and gyrification, and therefore understanding the genetic mechanisms influencing variation in these traits will improve our understanding of a landmark feature of our species. Here we show that individual variation in gyrification is significantly heritable in both humans and an Old World monkey (baboons, Papio hamadryas). Furthermore, contrary to expectations based on the positive phenotypic correlation across species, the genetic correlation between cerebral volume and gyrification within both humans and baboons is estimated as negative. These results suggest that the positive relationship between cerebral volume and cortical folding across species cannot be explained by one set of selective pressures or genetic changes. Our data suggest that one set of selective pressures favored the progressive increase in brain volume documented in the primate fossil record, and that a second independent selective process, possibly related to parturition and neonatal brain size, may have favored brains with progressively greater cortical folding. Without a second separate selective pressure, natural selection favoring increased brain volume would be expected to produce less folded, more lissencephalic brains. These results provide initial evidence for the heritability of gyrification, and possibly a new perspective on the evolutionary mechanisms underlying long-term changes in the nonhuman primate and human brain.

Connectivity-based parcellation of the cortical mantle using q-ball diffusion imaging

This paper exploits the idea that each individual brain region has a specific connection profile to create parcellations of the cortical mantle using MR diffusion imaging. The parcellation is performed in two steps. First, the cortical mantle is split at a macroscopic level into 36 large gyri using a sulcus recognition system. Then, for each voxel of the cortex, a connection profile is computed using a probabilistic tractography framework. The tractography is performed from q fields using regularized particle trajectories. Fiber ODF are inferred from the q-balls using a sharpening process focusing the weight around the q-ball local maxima. A sophisticated mask of propagation computed from a T1-weighted image perfectly aligned with the diffusion data prevents the particles from crossing the cortical folds. During propagation, the particles father child particles in order to improve the sampling of the long fascicles. For each voxel, intersection of the particle trajectories with the gyri lead to a connectivity profile made up of only 36 connection strengths. These profiles are clustered on a gyrus by gyrus basis using a K-means approach including spatial regularization. The reproducibility of the results is studied for three subjects using spatial normalization.

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.

Development of structural MR brain imaging protocols to study genetics and maturation

Structural imaging research offers excellent translational benefits when non-human primate (NHP) models are employed. In this paper, we will discuss the development of anatomical MR imaging protocols for two important applications of structural imaging in NHPs: studies of genetic variability in brain morphology and longitudinal imaging of fetal brain maturation trends. In contrast with imaging studies of adult humans, structural imaging in the NHPs is challenging due to a comparatively small brain size (2- to 200-fold smaller volume, depending on the species). This difference in size is further accentuated in NHP studies of brain development in which fetal brain volumes are 10-50% of their adult size. The sizes of cortical gyri and sulci scale allometrically with brain size. Thus, achieving spatial sampling that is comparable to that of high-quality human studies (approximately 1.0 mm(3)) requires a brain-size-adjusted reduction in the sampling volumes of from 500-to-150 microm(3). Imaging at this spatial resolution while maintaining sufficient contrast and signal to noise ratio necessitates the development of specialized MRI protocols. Here we discuss our strategy to optimize the protocol parameters for two commonly available structural imaging sequences: MPRAGE and TrueFisp. In addition, computational tools developed for the analysis of human structural images were applied to the NHP studies. These included removal of non-brain tissues, correction for RF inhomogeneity, spatial normalization, building of optimized target brain and analysis of cerebral gyrification and individual cortical variability. Finally, recent findings in the genetics of cerebral gyrification and tracking of maturation trends in the fetal, newborn and adult brain are described.

Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging

We explored relationships between decline in cognitive processing speed (CPS) and change in frontal lobe MRI/MRS-based indices of cerebral integrity in 38 healthy adults (age 57-90 years). CPS was assessed using a battery of four timed neuropsychological tests: Grooved Pegboard, Coding, Symbol Digit Modalities Test and Category Fluency (Fruits and Furniture). The neuropsychological tests were factor analyzed to extract two components of CPS: psychomotor (PM) and psychophysical (PP). MRI-based indices of cerebral integrity included three cortical measurements per hemisphere (GM thickness, intergyral and sulcal spans) and two subcortical indices (fractional anisotropy (FA), measured using track-based spatial statistics (TBSS), and the volume of hyperintense WM (HWM)). MRS indices included levels of choline-containing compounds (GPC+PC), phosphocreatine plus creatine (PCr+Cr), and N-acetylaspartate (NAA), measured bilaterally in the frontal WM bundles. A substantial fraction of the variance in the PM-CPS (58%) was attributed to atrophic changes in frontal WM, observed as increases in sulcal span, declines in FA values and reductions in concentrations of NAA and choline-containing compounds. A smaller proportion (20%) of variance in the PP-CPS could be explained by bilateral increases in frontal sulcal span and increases in HWM volumes.

Genetics of primary cerebral gyrification: Heritability of length, depth and area of primary sulci in an extended pedigree of Papio baboons

Genetic control over morphological variability of primary sulci and gyri is of great interest in the evolutionary, developmental and clinical neurosciences. Primary structures emerge early in development and their morphology is thought to be related to neuronal differentiation, development of functional connections and cortical lateralization. We measured the proportional contributions of genetics and environment to regional variability, testing two theories regarding regional modulation of genetic influences by ontogenic and phenotypic factors. Our measures were surface area, and average length and depth of eleven primary cortical sulci from high-resolution MR images in 180 pedigreed baboons. Average heritability values for sulcal area, depth and length (h(2)(Area)=.38+/-.22; h(2)(Depth)=.42+/-.23; h(2)(Length)=.34+/-.22) indicated that regional cortical anatomy is under genetic control. The regional pattern of genetic contributions was complex and, contrary to previously proposed theories, did not depend upon sulcal depth, or upon the sequence in which structures appear during development. Our results imply that heritability of sulcal phenotypes may be regionally modulated by arcuate U-fiber systems. However, further research is necessary to unravel the complexity of genetic contributions to cortical morphology.

Can structural MRI indices of cerebral integrity track cognitive trends in executive control function during normal maturation and adulthood?

We explored the relationship between structural neuroimaging-based indices of cerebral integrity and executive control function (ECF) in two groups of healthy subjects: A maturing group (33 subjects; 19-29 years) and a senescing group (38 adults; 30-90 years). ECF was assessed using the Executive Interview (EXIT) battery. Cortical indices of cerebral integrity included GM thickness, intergyral span, and sulcal span, each measured for five cortical regions per hemisphere. Subcortical indices included fractional anisotropy (FA), measured using track-based-spatial-statistics (TBSS), and the volume of T2-hyperintense WM (HWM). In the maturing group, no significant relationships between neuroanatomical changes and ECF were found; however, there were hints that late-term maturation of cerebral WM influenced variability in ECF. In the senescing group, the decline in ECF corresponded to atrophic changes in cerebral WM (sulcal and intergyral span) primarily in the superior frontal and anterior cingulate regions. A large fraction of the variability in ECF (62%) can be explained by variability in the structural indices from these two regions.

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.

Relationship among neuroimaging indices of cerebral health during normal aging

Sensitive measures of brain aging show great promise for gauging factors that affect aging and degenerative processes, such as risk genes or therapy. Here we examined age-related trends for three indices of cerebral health: gyral gray matter (GM) thickness, dilation of sulcal spaces with CSF, and the volume of T2-hyperintense white matter (HWM) lesions. The study involved 31 healthy adults age 57-82 years old. Measurements of average GM thickness, average sulcal span and HWM volume were performed using high-resolution 3D T1- and T2-weighted brain MR images. Age-related trends for the three cerebral health indices were consistent with previously published work though the analysis of their covariance led to a previously unreported relationship. Simultaneous multiple regression found that dilation of cortical sulci were primarily (t = 2.59, P < 0.01) related to the increases in HWM volume and secondarily related (t = -2.51, P < 0.01) to the reductions of the cortical GM thickness. The are-corrected correlation between reduction in GM thickness and increases in HWM volume, was not significant (P = 0.34). These findings are of interest in designing quantitative measures of brain aging for monitoring individual patients and in large-scale clinical trials.

Fast reproducible identification and large-scale databasing of individual functional cognitive networks

BACKGROUND

Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.

RESULTS

81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.

CONCLUSION

This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.

An interactive medical image segmentation system based on the optimal management of regions of interest using topological medical knowledge

This paper presents an original interactive system for efficient medical image segmentation in computer aided diagnosis. The main originality concerns the method used to manage, according to an a priori topological-based structural model, regions of interest (ROIs) within which computations can be constrained. The goal is then to avoid the processing of irrelevant image points, therefore improving and accelerating segmentations. In the case of a hierarchical modeling procedure, our ROI management method enables, for delineating a given medical structure, to optimally determine image points of interest by taking previously segmented structures into account. We propose a mathematical formulation of the method as well as a possible implementation within an interactive system. We also detail an experience report focussing on the segmentation of several abdominal structures from a CT image. It illustrates the behavior and the potential of our method.

Age-related morphology trends of cortical sulci

The age-related trends of the width and the depth of major cortical sulci were studied in normal adults. Ninety healthy subjects (47 males, 43 females) age 20-82 years were evaluated. Measurements of average sulcal width and depth in 14 prominent sulcal structures per hemisphere were performed with high-resolution anatomical MRI. The average sulcal width increased at a rate of about 0.7 mm/decade, while the average sulcal depth decreased at a rate of about 0.4 mm/decade. Sulcal age-related trends were found to be highly influenced by gender in the superior temporal, collateral, and cingulate sulci (P < 0.05), with males showing more pronounced age-related change in sulcal width than females. Sulcal structures located in multimodal cortical areas showed more profound age-related changes than sulcal structures in unimodal cortical areas (P < 0.05).

Program for Assisted Labeling of Sulcal Regions (PALS): description and reliability

With the improvements in techniques for generating surface models from magnetic resonance (MR) images, it has recently become feasible to study the morphological characteristics of the human brain cortex in vivo. Studies of the entire surface are important for measuring global features, but analysis of specific cortical regions of interest provides a more detailed understanding of structure. We have previously developed a method for automatically segmenting regions of interest from the cortical surface using a watershed transform. Each segmented region corresponds to a cortical sulcus and is thus termed a "sulcal region." In this work, we describe two important augmentations of this methodology. First, we describe a user interface that allows for the efficient labeling of the segmented sulcal regions called the Program for Assisted Labeling of Sulcal Regions (PALS). An additional augmentation allows for even finer divisions on the cortex with a methodology that employs the fast marching technique to track a curve on the cortical surface that is then used to separate segmented regions. After regions of interest have been identified, we compute both the cortical surface area and gray matter volume. Reliability experiments are performed to assess both the long-term stability and short-term repeatability of the proposed techniques. These experiments indicate the proposed methodology gives both highly stable and repeatable results.

Object-based morphometry of the cerebral cortex

Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.

Deformable registration of cortical structures via hybrid volumetric and surface warping

Registration of cortical structures across individuals is a very important step for quantitative analysis of the human brain cortex. This paper presents a method for deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. In the first step, a feature-based volumetric registration algorithm is used to warp a model cortical surface to the individual's space. This step greatly reduces the variation between the model and individual, thus providing a good initialization for the next step of surface warping. In the second step, a surface registration method, based on matching geometric attributes, warps the model surface to the individual. Point correspondences are also established at this step. The attribute vector, as the morphological signature of surface, was designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on both synthesized and real brain data demonstrate the performance of the proposed method in the registration of cortical structures across individuals.

A generic framework for the parcellation of the cortical surface into gyri using geodesic Voronoï diagrams

In this paper we propose a generic automatic approach for the parcellation of the cortical surface into labeled gyri. These gyri are defined from a set of pairs of sulci selected by the user. The selected sulci are first automatically identified in the data, then projected onto the cortical surface. The parcellation stems from two nested Voronoï diagrams computed geodesically to the cortical surface. The first diagram provides the zones of influence of the sulci. The boundary between the two zones of influence of each selected pair of sulci stands for a gyrus seed. A second diagram yields the gyrus parcellation. The distance underlying the Voronoï diagram allows the method to interpolate the gyrus boundaries where the limiting sulci are interrupted. The method is illustrated with 12 different hemispheres.

A framework to study the cortical folding patterns

This paper describes a decade-long research program focused on the variability of the cortical folding patterns. The program has developed a framework of using artificial neuroanatomists that are trained to identify sulci from a database. The framework relies on a renormalization of the brain warping problem, which consists in matching the cortices at the scale of the folds. Another component of the program is the search for the alphabet of the folding patterns, namely, a list of indivisible elementary sulci. The search relies on the study of the cortical folding process using antenatal imaging and on backward simulations of morphogenesis aimed at revealing traces of the embryologic dimples in the mature cortical surface. The importance of sulcal-based morphometry is illustrated by a simple study of the correlates of handedness on asymmetry indices. The study shows for instance that the central sulcus is larger in the dominant hemisphere.