Automated graph-based analysis and correction of cortical volume topology

Simplified intersubject averaging on the cortical surface using SUMA

Task and group comparisons in functional magnetic resonance imaging (fMRI) studies are often accomplished through the creation of intersubject average activation maps. Compared with traditional volume-based intersubject averages, averages made using computational models of the cortical surface have the potential to increase statistical power because they reduce intersubject variability in cortical folding patterns. We describe a two-step method for creating intersubject surface averages. In the first step cortical surface models are created for each subject and the locations of the anterior and posterior commissures (AC and PC) are aligned. In the second step each surface is standardized to contain the same number of nodes with identical indexing. An anatomical average from 28 subjects created using the AC-PC technique showed greater sulcal and gyral definition than the corresponding volume-based average. When applied to an fMRI dataset, the AC-PC method produced greater maximum, median, and mean t-statistics in the average activation map than did the volume average and gave a better approximation to the theoretical-ideal average calculated from individual subjects. The AC-PC method produced average activation maps equivalent to those produced with surface-averaging methods that use high-dimensional morphing. In comparison with morphing methods, the AC-PC technique does not require selection of a template brain and does not introduce deformations of sulcal and gyral patterns, allowing for group analysis within the original folded topology of each individual subject. The tools for performing AC-PC surface averaging are implemented and freely available in the SUMA software package.

Automated analysis of fluorescence microscopy images to identify protein-protein interactions

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

Automated Topology Correction for Human Brain Segmentation

We describe a new method to reconstruct human brain structures from 3D magnetic resonance brain images. Our method provides a fully automatic topology correction mechanism, thus avoiding tedious manual correction. Topological correctness is important because it is an essential prerequisite for brain atlas deformation and surface flattening. Our method uses an axis-aligned sweep through the volume to locate handles. Handles are detected by successively constructing and analyzing a directed graph. A multiple local region-growing process is used which simultaneously acts on the foreground and the background to isolate handles and tunnels. The sizes of handles and tunnels are measured, then handles are removed or tunnels filled based on their sizes. This process was used for 256 T1-weighted MR volumes.

A stochastic model for studying the laminar structure of cortex from MRI

The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts.

Cortical surface segmentation and mapping

Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.

CRUISE: Cortical reconstruction using implicit surface evolution

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

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.

Dynamic mapping of human cortical development during childhood through early adulthood

We report the dynamic anatomical sequence of human cortical gray matter development between the age of 4-21 years using quantitative four-dimensional maps and time-lapse sequences. Thirteen healthy children for whom anatomic brain MRI scans were obtained every 2 years, for 8-10 years, were studied. By using models of the cortical surface and sulcal landmarks and a statistical model for gray matter density, human cortical development could be visualized across the age range in a spatiotemporally detailed time-lapse sequence. The resulting time-lapse "movies" reveal that (i) higher-order association cortices mature only after lower-order somatosensory and visual cortices, the functions of which they integrate, are developed, and (ii) phylogenetically older brain areas mature earlier than newer ones. Direct comparison with normal cortical development may help understanding of some neurodevelopmental disorders such as childhood-onset schizophrenia or autism.

The generalized spherical homeomorphism theorem for digital images

The spherical homeomorphism conjecture, proposed by Shattuck and Leahy in 2001, serves as the backbone of their algorithm to correct the topology of magnetic resonance images of the human cerebral cortex. Using a canonical image-thickening technique and the authors' previously proven "spherical homeomorphism theorem for surfaces," we formulate and prove a spherical homeomorphism theorem which is valid for all digital images when utilizing the (26,6)-connectivity rule.

Temporal dynamics of brain anatomy

The brain changes profoundly in structure and function during development and as a result of diseases such as the dementias, schizophrenia, multiple sclerosis, and tumor growth. Strategies to measure, map, and visualize these brain changes are of immense value in basic and clinical neuroscience. Algorithms that map brain change with sufficient spatial and temporal sensitivity can also assess drugs that aim to decelerate or arrest these changes. In neuroscience studies, these tools can reveal subtle brain changes in adolescence and old age and link these changes with measurable differences in brain function and cognition. Early detection of brain change in patients at risk for dementia; tumor recurrence; or relapsing-remitting conditions, such as multiple sclerosis, is also vital for optimizing therapy. We review a variety of mathematical and computational approaches to detect structural brain change with unprecedented sensitivity, both spatially and temporally. The resulting four-dimensional (4-D) maps of brain anatomy are warehoused in population-based brain atlases. Here, statistical tools compare brain changes across subjects and across populations, adjusting for complex differences in brain structure. Brain changes in an individual can be compared with a normative database comprised of subjects matched for age, gender, and other demographic factors. These dynamic brain maps offer key biological markers for understanding disease progression and testing therapeutic response. The early detection of disease-related brain changes is also critical for possible pre-emptive intervention before the ravages of disease have set in.

Dynamic programming generation of boundaries of local coordinatized submanifolds in the neocortex: application to the planum temporale

Dynamic programming is used to define boundaries of cortical submanifolds with focus on the planum temporale (PT) of the superior temporal gyrus (STG), which has been implicated in a variety of neuropsychiatric disorders. To this end, automated methods are used to generate the PT manifold from 10 high-resolution MRI subvolumes ROI masks encompassing the STG. A procedure to define the subvolume ROI masks from original MRI brain scans is developed. Bayesian segmentation is then used to segment the subvolumes into cerebrospinal fluid, gray matter (GM), and white matter (WM). 3D isocontouring using the intensity value at which there is equal probability of GM and WM is used to reconstruct the triangulated graph representing the STG cortical surface, enabling principal curvature at each point on the graph to be computed. Dynamic programming is used to delineate the PT manifold by tracking principal curves from the retro-insular end of the Heschl's gyrus (HG) to the STG, along the posterior STG up to the start of the ramus and back to the retro-insular end of the HG. A coordinate system is then defined on the PT manifold. The origin is defined by the retro-insular end of the HG and the y-axis passes through the point on the posterior STG where the ramus begins. Automated labeling of GM in the STG is robust with L(1) distances between Bayesian and manual segmentation in the range 0.001-0.12 (n = 20). PT reconstruction is also robust with 90% of the vertices of the reconstructed PT within about 1 voxel (n = 20) from semiautomated contours. Finally, the reliability index (based on interrater intraclass correlation) for the surface area derived from repeated reconstructions is 0.96 for the left PT and 0.94 for the right PT, thus demonstrating the robustness of dynamic programming in defining a coordinate system on the PT. It provides a method with potential significance in the study of neuropsychiatric disorders.

The LONI Pipeline Processing Environment

The analysis of raw data in neuroimaging has become a computationally entrenched process with many intricate steps run on increasingly larger datasets. Many software packages exist that provide either complete analyses or specific steps in an analysis. These packages often possess diverse input and output requirements, utilize different file formats, run in particular environments, and have limited abilities with certain types of data. The combination of these packages to achieve more sensitive and accurate results has become a common tactic in brain mapping studies but requires much work to ensure valid interoperation between programs. The handling, organization, and storage of intermediate data can prove difficult as well. The LONI Pipeline Processing Environment is a simple, efficient, and distributed computing solution to these problems enabling software inclusion from different laboratories in different environments. It is used here to derive a T1-weighted MRI atlas of the human brain from 452 normal young adult subjects with fully automated processing. The LONI Pipeline Processing Environment's parallel processing efficiency using an integrated client/server dataflow model was 80.9% when running the atlas generation pipeline from a PC client (Acer TravelMate 340T) on 48 dedicated server processors (Silicon Graphics Inc. Origin 3000). The environment was 97.5% efficient when the same analysis was run on eight dedicated processors.

A proof of the spherical homeomorphism conjecture for surfaces

The human cerebral cortex is topologically equivalent to a sphere when it is viewed as closed at the brain stem. Due to noise and/or resolution issues, magnetic resonance imaging may see "handles" that need to be eliminated to reflect the true spherical topology. Shattuck and Leahy present an algorithm to correct such an image. The basis for their correction strategy is a conjecture, which they call the spherical homeomorphism conjecture, stating that the boundary between the foreground region and the background region is topologically spherical if certain associated foreground and background multigraphs are both graph-theoretic trees. In this paper, we prove the conjecture, and its converse, under the assumption that the foreground/background boundary is a surface.

BrainSuite: an automated cortical surface identification tool

We describe a new magnetic resonance (MR) image analysis tool that produces cortical surface representations with spherical topology from MR images of the human brain. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The tools include skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.