Mindboggling morphometry of human brains.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

Automated extraction of nested sulcus features from human brain MRI data.

Extracting objects related to a fold in the cerebral cortex ("sulcus features") from human brain magnetic resonance imaging data has applications in morphometry, landmark-based registration, and anatomical labeling. In prior work, sulcus features such as surfaces, fundi and pits have been extracted separately. Here we define and extract nested sulcus features in a hierarchical manner from a cortical surface mesh having curvature or depth values. Our experimental results show that the nested features are comparable to features extracted separately using other methods, and that they are consistent across subjects and with manual label boundaries. Our open source feature extraction software will be made freely available as part of the Mindboggle project (http://www.mindboggle.info).

Identification of relevant properties for epitopes detection using a regression model.

A B-cell epitope is a part of an antigen that is recognized by a specific antibody or B-cell receptor. Detecting the immunogenic region of the antigen is useful in numerous immunodetection and immunotherapeutics applications. The aim of this paper is to find relevant properties to discriminate the location of potential epitopes from the rest of the protein surface. The most relevant properties, identified using two evaluation approaches, are the geometric properties, followed by the conservation score and some chemical properties, such as the proportion of glycine. The selected properties are used in a patch-based epitope localization method including a Single-Layer Perceptron for regression. The output of this Single-Layer Perceptron is used to construct a probability map on the antigen surface. The predictive performances of the method are assessed by computing the AUC using cross validation on two benchmark data sets and by computing the AUC and the precision for a third independent test set.

Fast surface-based travel depth estimation algorithm for macromolecule surface shape description.

Travel Depth, introduced by Coleman and Sharp in 2006, is a physical interpretation of molecular depth, a term frequently used to describe the shape of a molecular active site or binding site. Travel Depth can be seen as the physical distance a solvent molecule would have to travel from a point of the surface, i.e., the Solvent-Excluded Surface (SES), to its convex hull. Existing algorithms providing an estimation of the Travel Depth are based on a regular sampling of the molecule volume and the use of the Dijkstra's shortest path algorithm. Since Travel Depth is only defined on the molecular surface, this volume-based approach is characterized by a large computational complexity due to the processing of unnecessary samples lying inside or outside the molecule. In this paper, we propose a surface-based approach that restricts the processing to data defined on the SES. This algorithm significantly reduces the complexity of Travel Depth estimation and makes possible the analysis of large macromolecule surface shape description with high resolution. Experimental results show that compared to existing methods, the proposed algorithm achieves accurate estimations with considerably reduced processing times.

Molecular surface mesh generation by filtering electron density map.

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Regression applied to protein binding site prediction and comparison with classification.

BACKGROUND: The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools. RESULTS: We compared predictive performances of regression tools with performances of machine learning classifiers. Using leave-one-out cross-validation, we showed that regression tools provide better predictions than classification ones. Among regression tools, Multilayer Perceptron ranked highest in the quality of predictions. We compared also the predictive performance of our patches based method using Multilayer Perceptron with the performance of three other methods usable through a web server. Our method performed similarly to the other methods. CONCLUSION: Regression is more efficient than classification when applied to our binding site localization method. When it is possible, using regression instead of classification for other existing binding site predictors will probably improve results. Furthermore, the method presented in this work is flexible because the size of the predicted binding site is adjustable. This adaptability is useful when either false positive or negative rates have to be limited.