Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.

Justification: Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004-2009), which builds upon previous expertise from the INTERPRET project (2000-2002) has allowed such an evaluation to take place.

Visualisation and interpretation of Support Vector Regression models.

This paper introduces a technique to visualise the information content of the kernel matrix and a way to interpret the ingredients of the Support Vector Regression (SVR) model. Recently, the use of Support Vector Machines (SVM) for solving classification (SVC) and regression (SVR) problems has increased substantially in the field of chemistry and chemometrics. This is mainly due to its high generalisation performance and its ability to model non-linear relationships in a unique and global manner.

Representing structural databases in a self-organizing map.

This paper presents a way to accomodate large numbers of crystal structures, as present in e.g. the Cambridge Structural Database (CSD), in a self-organizing map.

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification.

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel.

Multivariate calibration with least-squares support vector machines.

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and chemometrics. The advantages of SVM-based methods over many other methods are that these lead to global models that are often unique, and nonlinear regression can be performed easily as an extension to linear regression.

A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy.

A new classification approach was developed to improve the noninvasive diagnosis of brain tumors. Within this approach, information is extracted from magnetic resonance imaging and spectroscopy data, from which the relative location and distribution of selected tumor classes in feature space can be calculated. This relative location and distribution is used to select the best information extraction procedure, to identify overlapping tumor classes, and to calculate probabilities of class membership.

Effect on the partial least-squares prediction of yarn properties combining raman and infrared measurements and applying wavelength selection.

The combination of Raman and infrared spectroscopy on the one hand and wavelength selection on the other hand is used to improve the partial least-squares (PLS) prediction of seven selected yarn properties. These properties are important for on-line quality control during production. From 71 yarn samples, the Raman and infrared spectra are measured and reference methods are used to determine the selected properties.

Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis.

Purpose: To explore the possibilities of combining multispectral magnetic resonance (MR) images of different patients within one data matrix.

Materials And Methods: Principal component and linear discriminant analysis was applied to multispectral MR images of 12 patients with different brain tumors. Each multispectral image consisted of T1-weighted, T2-weighted, proton-density-weighted, and gadolinium-enhanced T1-weighted MR images, and a calculated relative regional cerebral blood volume map.

Automated correction of unwanted phase jumps in reference signals which corrupt MRSI spectra after eddy current correction.

A commonly applied step in the postprocessing of gradient localized proton MR spectroscopy, is correction for eddy current effects using the water signal as a reference. However, this method can degrade some of the metabolite signals, in particular if applied on proton MR spectroscopic imaging data. This artifact arises from the water reference signal in the presence of a second signal which resonates close to the main water resonance.

Automatic correction for phase shifts, frequency shifts, and lineshape distortions across a series of single resonance lines in large spectral data sets.

A new model-free method is presented that automatically corrects for phase shifts, frequency shifts, and additional lineshape distortions of one single resonance peak across a series of in vivo NMR spectra. All separate phase and frequency variations are quickly and directly derived from the common lineshape in the data set using principal component analysis and linear regression. First, the new approach is evaluated on simulated data in order to quantitatively assess the phase and frequency shifts which can be removed by the proposed correction procedure.

Predicting nucleic acid torsion angle values using artificial neural networks.

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles, chi, zeta and alpha from torsion angles delta, epsilon, beta and gamma for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed.

Parallel processing of chemical information in a local area network--III. Using genetic algorithms for conformational analysis of biomacromolecules.

Multi-dimensional nuclear magnetic resonance experiments are an excellent means of revealing the three-dimensional structure of biomacromolecules in solution. However, the search space in the conformational analysis of biomacromolecules, using multi-dimensional NMR data, is huge and complex. This calls for global optimization techniques with good sampling properties.

Parallel processing of chemical information in a local area network--II. A parallel cross-validation procedure for artificial neural networks.

This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper.

Near-infrared imaging spectroscopy (NIRIS) and image rank analysis for remote identification of plastics in mixed waste.

An infrared camera with focal plane InSb array detector has been applied to the characterization of macroscopic samples of household waste over distances up to two meters. Per waste sample (singelized), a sequence of images was taken at six optical wavelength ranges in the near infrared region (1100 nm - 2500 nm). The obtained three-dimensional data stack served as individual fingerprint per sample.

Selectivity for temporal characteristics of sound and interaural time difference of auditory midbrain neurons in the grassfrog: a system theoretical approach.

The selectivity for temporal characteristics of sound and interaural time difference (ITD) was investigated in the torus semicircularis (TS) of the grassfrog. Stimuli were delivered by means of a closed sound system and consisted of binaurally presented Poisson distributed condensation clicks, and pseudo-random (RAN) or equidistant (EQU) click trains of which ITD was varied. With RAN and EQU trains, 86% of the TS units demonstrated a clear selectivity for ITD.

Measuring and modelling the response of auditory midbrain neurons in the grassfrog to temporally structured binaural stimuli.

The combined selectivity for amplitude modulation frequency (AMF) and interaural time difference (ITD) was investigated for single units in the auditory midbrain of the grassfrog. Stimuli were presented by means of a closed sound system. A large number of units was found to be selective for AMF (95%) or ITD (85%) and mostly, these selectivities were intricately coupled.

Sensitivity for interaural time and intensity difference of auditory midbrain neurons in the grassfrog.

The sensitivity for interaural time (ITD) and intensity (IID) difference was investigated for single units in the auditory midbrain of the grassfrog. A temporally structured stimulus was used which was presented by means of a closed sound system. At best frequency (BF) the majority of units was selective for ITD as indicated by an asymmetrically (73%) or symmetrically (7%) shaped ITD-rate histogram.

A combined sensitivity for frequency and interaural intensity difference in neurons in the auditory midbrain of the grassfrog.

The relation between spectral tuning and sensitivity for interaural intensity difference (IID) was studied for single units in the auditory midbrain of the grassfrog. The stimuli consisted of sequences of pure tones of different frequency and interaural intensity differences presented by means of a closed sound system. At best excitatory frequency, three types of binaural interaction were observed: E0 (one ear excitatory 23%), EE (both ears excitatory 9%) and EI (one ear excitatory, the other inhibitory 67%).

Detection and estimation of neural connectivity based on crosscorrelation analysis.

Crosscorrelation analysis of simultaneously recorded activity of pairs of neurons is a common tool to infer functional neural connectivity. The adequacy of crosscorrelation procedures to detect and estimate neural connectivity has been investigated by means of computer simulations of small networks composed of fairly realistic modelneurons. If the mean interval of neural firings is much larger than the duration of postsynaptic potentials, which will be the case in many central brain areas excitatory connections are easier to detect than inhibitory ones.