Sulcal thickness as a vulnerability indicator for schizophrenia.

BACKGROUND: People with schizophrenia may demonstrate cortical abnormalities, with gyri and sulci potentially being differentially affected. AIMS: To measure frontal and temporal sulcal cortical thickness, surface area and volume in the non-psychotic relatives of patients with schizophrenia as a potential vulnerability indicator for the disorder. METHOD: An automated parcellation method was used to measure the superior frontal, inferior frontal, cingulate, superior temporal and inferior temporal sulci in the relatives of patients (n=19) and controls (n=22). RESULTS: Compared with controls, relatives had reversed hemispheric asymmetry in their cingulate sulcal thickness and a bilateral reduction in their superior temporal sulcal thickness. CONCLUSIONS: Cingulate and superior temporal sulcal thickness abnormalities may reflect neural abnormalities associated with the genetic liability to schizophrenia. Cortical thinning in these regions suggests that liability genes affect the dendrites, synapses or myelination process during the neurodevelopment of the cortical mantle.

A geometric method for automatic extraction of sulcal fundi.

Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangular mesh representing the cortical surface. The input to our algorithm is a triangular mesh representation of an extracted cortical surface as computed by one of several available software packages for performing automated and semi-automated cortical surface extraction. Given this input we first compute a geometric depth measure for each triangle on the cortical surface mesh, and based on this information we extract sulcal regions by checking for connected regions exceeding a depth threshold. We then identify endpoints of each region and delineate the fundus by thinning the connected region while keeping the endpoints fixed. The curves, thus, defined are regularized using weighted splines on the surface mesh to yield high-quality representations of the sulcal fundi. We present the geometric framework and validate it with real data from human brains. Comparisons with expert-labeled sulcal fundi are part of this validation process.

Regionally specific cortical thinning and gray matter abnormalities in the healthy relatives of schizophrenia patients.

Accumulated evidence suggests that schizophrenia is associated with subtle gray matter deficits throughout the cerebral cortex and regional cortical thinning. Although findings are not entirely consistent, healthy relatives of schizophrenia patients also show abnormalities in cortical gray matter volume, suggesting that this may be one aspect of an unexpressed genetic liability to the disorder. Cortical thickness and surface area are additional indicators of cortical cytoarchitectural integrity. To investigate the nature of cortical abnormalities in the healthy relatives of patients, this study used magnetic resonance imaging to evaluate gray matter volume, surface area, and thickness of 13 regions using an automated parcellation methodology. Compared with controls (n = 22), relatives (n = 19) had decreased volume and surface area in the right cingulate gyrus, a bilateral decrease in cingulate thickness, and decreased surface area in the superior temporal lobe. In addition, relatives had a subtle increase in gray matter volume and surface area in the left hemisphere, bilaterally in the parahippocampal gyri, and in the left middle temporal lobe. The results of this study suggest that the cortical regions most affected by the unexpressed genetic liability to schizophrenia may be the cingulate and temporal regions--regions associated with higher level cognitive, affective, and memory functions.

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm.

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against "gold standard" manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.

Quantitative evaluation of three cortical surface flattening methods.

During the past decade, several computational approaches have been proposed for the task of mapping highly convoluted surfaces of the human brain to simpler geometric objects such as a sphere or a topological disc. We report the results of a quantitative comparison of FreeSurfer, CirclePack, and LSCM with respect to measurements of geometric distortion and computational speed. Our results indicate that FreeSurfer performs best with respect to a global measurement of metric distortion, whereas LSCM performs best with respect to angular distortion and best in all but one case with a local measurement of metric distortion. FreeSurfer provides more homogeneous distribution of metric distortion across the whole cortex than CirclePack and LSCM. LSCM is the most computationally efficient algorithm for generating spherical maps, while CirclePack is extremely fast for generating planar maps from patches.

A meta-algorithm for brain extraction in MRI.

Accurate identification of brain tissue and cerebrospinal fluid (CSF) in a whole-head MRI is a critical first step in many neuroimaging studies. Automating this procedure can eliminate intra- and interrater variance and greatly increase throughput for a labor-intensive step. Many available procedures perform differently across anatomy and under different acquisition protocols. We developed the Brain Extraction Meta-Algorithm (BEMA) to address these concerns. It executes many extraction algorithms and a registration procedure in parallel to combine the results in an intelligent fashion and obtain improved results over any of the individual algorithms. Using an atlas space, BEMA performs a voxelwise analysis of training data to determine the optimal Boolean combination of extraction algorithms to produce the most accurate result for a given voxel. This allows the provided extractors to be used differentially across anatomy, increasing both the accuracy and robustness of the procedure. We tested BEMA using modified forms of BrainSuite's Brain Surface Extractor (BSE), FSL's Brain Extraction Tool (BET), AFNI's 3dIntracranial, and FreeSurfer's MRI Watershed as well as FSL's FLIRT for the registration procedure. Training was performed on T1-weighted scans of 136 subjects from five separate data sets with different acquisition parameters on separate scanners. Testing was performed on 135 separate subjects from the same data sets. BEMA outperformed the individual algorithms, as well as interrater results from a subset of the scans, when compared for the mean Dice coefficient, a rating of the similarity of output masks to the manually defined gold standards.

Quantitative comparison of four brain extraction algorithms.

In a companion paper (Rehm et al., 2004), we introduced Minneapolis Consensus Strip (McStrip), a hybrid algorithm for brain/non-brain segmentation. In this paper, we compare the performance of McStrip and three brain extraction algorithms (BEAs) in widespread use within the neuroimaging community--Statistical Parametric Mapping v.2 (SPM2), Brain Extraction Tool (BET), and Brain Surface Extractor (BSE)--to the "gold standard" of manually stripped T1-weighted MRI brain volumes. Our comparison was based on quantitative boundary and volume metrics, reproducibility across repeat scans of a single subject, and assessments of performance consistency across datasets acquired on different scanners at different institutions. McStrip, a hybrid method incorporating warping to a template, intensity thresholding, and edge detection, consistently outperformed SPM2, BET, and BSE, all of which rely on a single algorithmic strategy.

Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes.

We describe an approach to brain extraction from T1-weighted MR volumes that uses a hierarchy of masks created by different models to form a consensus mask. The algorithm (McStrip) incorporates atlas-based extraction via nonlinear warping, intensity-threshold masking with connectivity constraints, and edge-based masking with morphological operations. Volume and boundary metrics were computed to evaluate the reproducibility and accuracy of McStrip against manual brain extraction on 38 scans from normal and ataxic subjects. McStrip masks were reproducible across six repeat scans of a normal subject and were significantly more accurate than the masks produced by any of the individual algorithmic components.

The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics.

This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing.