We present a spectral-based method for automatically labeling and refining major sulcal curves of a human cerebral cortex. Given a set of input (unlabeled) sulcal curves automatically extracted from a cortical surface and a collection of expert-provided examples (labeled sulcal curves), our objective is to identify the input major sulcal curves and assign their neuroanatomical labels, and then refines these curves based on the expert-provided example data, without employing any atlas-based registration scheme as preprocessing. In order to construct the example data, neuroanatomists manually labeled a set of 24 major sulcal curves (12 each for the left and right hemispheres) for each individual subject according to a precise protocol. We collected 30 sets of such curves from 30 subjects. Given the raw input sulcal curve set of a subject, we choose the most similar example curve to each input curve in the set to label and refine the latter according to the former. We adapt a spectral matching algorithm to choose the example curve by exploiting the sulcal curve features and their relationship. The high dimensionality of sulcal curve data in spectral matching is addressed by using their multi-resolution representations, which greatly reduces time and space complexities. Our method provides consistent labeling and refining results even under high variability of cortical sulci across the subjects. Through experiments we show that the results are comparable in accuracy to those done manually. Most output curves exhibited accuracy values higher than 80%, and the mean accuracy values of the curves in the left and the right hemispheres were 84.69% and 84.58%, respectively.

Analyzing cortical sulci is important for studying cortical morphology and brain functions. Although sulcal lines on cortical surfaces can be defined in various ways, it is critical in a neuroimaging study to define a sulcal line along the valley of a cortical surface with a high curvature within a sulcus. To extract the sulcal lines automatically, we present a new geometric algorithm based on the computation of anisotropic skeletons of sulcal regions. Because anisotropic skeletons are highly adaptive to the anisotropic nature of the surface shape, the resulting sulcal lines lie accurately on the valleys of the sulcal areas. Our sulcal lines remain unchanged under local shape variabilities in different human brains. Through experiments, we show that the errors of the sulcal lines for both synthetic data and real cortical surfaces were nearly as constant as the function of random noise. By measuring the changes in sulcal shape in Alzheimer's disease (AD) patients, we further investigated the effectiveness of the accuracy of our sulcal lines using a large sample of MRI data. This study involved 70 normal controls (n [men/women]: 29/41, age [mean+/-SD]: 71.7+/-4.9 years), and 100 AD subjects (37/63, 72.3+/-5.5). We observe significantly lower absolute average mean curvature and shallower sulcal depth in AD subjects, where the group difference becomes more significant if we measure the quantities along the sulcal lines rather than over the entire sulcal area. The most remarkable difference in the AD patients was the average sulcal depth (control: 11.70 and AD: 11.34).

The locally deepest regions of major sulci, the sulcal pits, are thought to be the first cortical folds to develop and are closely related to functional areas. We examined the spatial distribution of sulcal pits across the entire cortical region, and assessed the hemispheric asymmetry in their frequency and distribution in a large group of normal adult brains. We automatically extracted sulcal pits from magnetic resonance imaging data using surface-based methods and constructed a group map from 148 subjects. The spatial distribution of the sulcal pits was relatively invariant between individuals, showing high frequency and density in specific focal areas. The left and right sulcal pits were spatially covariant in the regions of the earliest developed sulci. The sulcal pits with great spatial invariance appear to be useful as stable anatomical landmarks. We showed the most significant asymmetry in the frequency and spatial variance of sulcal pits in the superior temporal sulcus, which might be related to the lateralization of language function to the left hemisphere, developing more consistently and strongly than for the right. Our analyses support previous empirical and theoretical studies, and provide additional insights concerning the anatomical and functional development of the brain.

BACKGROUND AND PURPOSE: Amnestic mild cognitive impairment (MCI) is known to be a preclinical stage of Alzheimer's disease (AD). Similarly, MCI associated with small-vessel disease (svMCI), might be a forme froste of subcortical vascular dementia (SVaD). Patterns of cortical thinning in addition to the ischemia rating on MRI may further elucidate the clinical characteristics and pathogenesis of SVaD and svMCI. We tried to determine if svMCI differs from SVaD in the distribution of cortical atrophy, which may help understand the hierarchy between svMCI and SVaD and possibly also how svMCI evolves into SVaD. METHODS: Twenty patients with SVaD, 34 patients with svMCI, 115 patients with AD, and 96 individuals with normal-cognition (NC) were imaged with magnetic resonance imaging (MRI) including 3-dimensional volumetric images for cortical thickness analysis across the entire brain. RESULTS: Compared to NC, svMCI patients showed cortical thinning in inferior frontal and orbitofrontal gyri, anterior cingulate, insula, superior temporal gyrus, and lingual gyrus, while cortical thinning in SVaD patients involved all these areas plus dorsolateral prefrontal and temporal cortices. CONCLUSION: Our findings suggest the presence of hierarchy between svMCI and SVaD, and that the cognitive decline from svMCI to SVaD is associated with lesions in dorsolateral prefrontal and temporal cortices.

Mapping the structure and function of the brain with non-invasive brain imaging techniques has become a world-wide enterpise in the last 20 years. The core concept that drives this rapid growth has been the use of a standardized 3D coordinate space for combining data from many subjects and/or time-points. This has allowed geographically-separated laboratories to reproduce experi- ments in precise detail, to share data or to perform meta- analysis in ways that go far beyond the traditional reviewing of summary results in journal publications. A further corollary of the brain mapping approach is the natural fostering of multi-center collaboration among distant sites. This article describes recent progress in trans-Pacific collaboration between Canadian and Asian laboratories in the study of neuroanatomical networks obtained from MRI data, both in the normal brain and in neurodegenerative disorders.

We investigated the changes of sulcal shape (average mean curvature in folded regions and sulcal depth) in mild cognitive impairment (MCI) and Alzheimer's disease (AD) using quantitative surface-based methods in a large sample of magnetic resonance imaging data. Moreover, we observed their relationships with cortical thickness and gyral white matter (WM) volume, while considering age effect. This study involved 85 normal controls (n [men/women]: 36/49, age [mean+/-SD]: 71.1+/-4.9 years), and 100 MCI (44/56, 71.8+/-6.5) and 145 AD subjects (53/92, 72.7+/-7.3). We found significantly lower average mean curvature (greater sulcal widening) and shallower sulcal depth with disease progression from controls to MCI and MCI to AD. The most remarkable change in MCI and AD was sulcal widening observed in the temporal lobe (average mean curvature, control [mean]: 0.564, MCI: 0.534 (5.3% decrease from control), AD: 0.486 (13.8% and 9.0% decrease from control and MCI respectively)). Of the four measurements, the sulcal widening measurement showed the highest sensitivity in revealing group differences between control and MCI, which might be useful for detecting early dementia. Significant reductions in cortical thickness and gyral WM volume also occurred in MCI and AD. Multiple regression analysis demonstrated that a wider and shallower sulcal shape was primarily associated with decreased cortical thickness and gyral WM volume in each group. Age-related trends for the sulcal shape were not strongly found when cortical thickness and gyral WM volume were considered.

Previous magnetic resonance imaging (MRI) studies have used volumetric methods to investigate cerebral atrophy and showed its linear pattern with the measure of dementia severity in Alzheimer's disease (AD). This study analyzed the phase- and region-specific changes in cortical thickness with dementia severity. In 43 normal controls and 60 AD patients with clinical dementia rating (CDR) (0.5, n=21; 1, n=28; 2, n=11), the cortical thickness was measured using automated surface-based analysis of MRI data. Statistical analyses were performed to investigate overall the hemispheric mean thicknesses as well as the topography of cortical atrophy based on vertices in the groups. No significant difference in cortical thickness was observed for the mild (from CDR=0.5 to 1) stage of dementia. In contrast, a significant reduction of cortical thickness occurred from CDR=1 to 2. Topographic analysis of cortical atrophy showed that the significant cortical thinning in CDR=0.5 relative to normal was found in most association cortices, with this being more extensive than previously reported. There were significant cortical atrophies between CDR=1 and 2 in the frontal, inferolateral temporal, inferior parietal lobule, medial occipital, and posterior-cingulated regions. Our results confirm and extend previous findings, suggesting that widespread cortical thinning occurs before the onset of dementia (from normal to CDR=0.5), and that once dementia starts, cortical atrophy in association cortices accelerates in moderate AD (from CDR=1 to 2).

We investigated the scale relationship between size and cortical structure of human brains in a large sample of magnetic resonance imaging data. Cortical structure was estimated with several measures (cortical volume, surface area, and thickness, sulcal depth, and absolute mean curvature in sulcal regions and sulcal walls) using three-dimensional surface-based methods in 148 normal subjects (n [men/women]: 83/65, age [mean +/- standard deviation]: 25.0 +/- 4.9 years). We found significantly larger scaling exponents than geometrically predicted for cortical surface area, absolute mean curvature in sulcal regions and in sulcal walls, and smaller ones for cortical volume and thickness. As brain size increases, the cortex thickens only slightly, but the degree of sulcal convolution increases dramatically, indicating that human cortices are not simply scaled versions of one another. Our results are consistent with previous hypotheses that greater local clustering of interneuronal connections would be required in a larger brain, and fiber tension between local cortical areas would induce cortical folds. We suggest that sex effects are explained by brain size effects in cortical structure at a macroscopic and lobar regional level, and that it is necessary to consider true relationships between cortical measures and brain size due to the limitations of linear stereotaxic normalization.

Amnestic mild cognitive impairment (aMCI) can be classified into single domain (S-aMCI) and multiple domain (M-aMCI) subtypes. However, there have been no studies that specifically investigate the structural differences that support this classification. In an attempt to compare regional cortical thickness in two subtypes of aMCI, we aimed to map the distribution of cortical thinning using a surface based cortical analysis of magnetic resonance imaging. The cortical thickness across the entire brain was measured in 9 patients with S-aMCI, 22 patients with M-aMCI, and 61 normal healthy subjects. Differences in the patterns of cortical thinning between S-aMCI and M-aMCI were assessed using ANCOVA on a vertex-by-vertex basis, and statistical maps of differences in cortical thickness between the groups were constructed using a surface model. Relative to controls, S-aMCI patients showed cortical thinning in the left medial temporal lobe, and M-aMCI patients showed cortical thinning in the left medial temporal lobe, precuneus, and anterior and inferior basal temporal, insular, and temporal association cortices. When the two MCI groups were directly compared, M-aMCI patients showed cortical thinning in left precuneus. Our studies suggest that M-aMCI is a transitional state between S-aMCI and Alzheimer's disease, and that the cortical thinning is evidence that the precuneus is responsible for the multiple cognitive impairments in M-aMCI.

We proposed pattern classification based on principal components of cortical thickness between schizophrenic patients and healthy controls, which was trained using a leave-one-out cross-validation. The cortical thickness was measured by calculating the Euclidean distance between linked vertices on the inner and outer cortical surfaces. Principal component analysis was applied to each lobe for practical computational issues and stability of principal components. And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia for establishing practical confidence. It was simulated with 50 randomly selected validation set for the generalization and the average accuracy of classification was reported. This study showed that some principal components might be more useful than others for classification, but not necessarily matching the ordering of the variance amounts they explained. In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal: (left(%)|right(%))=91.07|88.80, parietal: 91.40|91.53, temporal: 93.60|91.47, occipital: 88.80|91.60). And, discriminative power appeared more spatially diffused bilaterally in the several regions, especially precentral, postcentral, superior frontal and temporal, cingulate and parahippocampal gyri. Since our results of discriminative patterns derived from classifier were consistent with a previous morphological analysis of schizophrenia, it can be said that the cortical thickness is a reliable feature for pattern classification and the potential benefits of such diagnostic tools are enhanced by our finding.