Reproducibility of human functional MRI (fMRI) studies is essential for clinical and neuroresearch applications of this new human brain mapping method. Based on a recently presented study on reproducibility of gradient-echo fMRI in the human visual cortex (Moser et al. Magn Reson Imaging 1996; 14:567-579), comparing the performance of three different threshold strategies for correlation analysis, we demonstrate that (a) fuzzy clustering is a robust, model-independent method to extract functional information in time and space; (b) intertrial reproducibility of cortical activation is significantly improved by the capability of fuzzy clustering to separate signal contributions from larger vessels, running perpendicular to the slice orientation, from activation apparently close to the primary visual cortex; and (c) for repeated single subject studies, SDs of <20% for signal enhancement in approximately 80% of the studies and SDs of <30% for activated area size in approximately 65% of the studies are obtained. This, however, depends also on signal-to-noise ratio, (motion) artifacts, and subject cooperation.

We introduce and apply a new classification strategy we call computerized consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100% specificity and 100% sensitivity on the training sets, and 100% specificity and 98% sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.

We study how classification accuracy can be improved when both different data preprocessing methods and computerized consensus diagnosis (CCD) are applied to 1H magnetic resonance (MR) spectra of astrocytomas, meningiomas, and epileptic brain tissue. The MR spectra (360 MHz, 37 degrees C) of tissue specimens (biopsies) from subjects with meningiomas (95; 26 cases), astrocytomas (74; 26 cases), and epilepsy (37; 8 cases) were preprocessed by several methods. Each data set was partitioned into training and validation sets. Robust classification was carried out via linear discriminant analysis (LDA), artificial neural nets (NN), and CCD, and the results were compared with histopathological diagnosis of the MR specimens. Normalization of the relevant spectral regions affects classification accuracy significantly. The spectra-based average three-class classification accuracies of LDA and NN increased from 81.7% (unnormalized data sets) to 89.9% (normalized). CCD increased the classification accuracy of the normalized sets to an average of 91.8%. CCD invariably decreases the fraction of unclassifiable spectra. The same trends prevail, with improved results, for case-based classification. Preprocessing the 1H MR spectra is essential for accurate and reliable classification of astrocytomas, meningiomas, and nontumorous epileptic brain tissue. CCD improves classification accuracy, with an attendant decrease in the fraction of unclassifiable spectra or cases.

A novel method of analyzing spectroscopic imaging data is presented. A fuzzy C-means clustering algorithm has been applied to the analysis of near-infrared spectroscopic imaging data acquired with the combination of a CCD camera and a liquid crystal tunable filter. The use of fuzzy C-means clustering dramatically increased the information obtained from near-IR spectroscopic images and allowed for the detection of small subregions of the image that contained novel and unanticipated spectral features, without the need for a priori knowledge of the chemical composition of the sample. Two illustrative samples were analyzed, one comprised of four different inks printed on label paper and the other containing indocyanine green and human blood patches. The regions containing the different constituents were clearly demarcated and their mean spectra determined. The mean spectra of the second sample were shown to match those obtained using a scanning near-IR spectrometer. In addition to probing the spatial and spectral characteristics of the samples, the fuzzy C-means clustering analysis also helped improve the signal-to-noise ratio of the spectra.

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A combination of near-infrared spectroscopy and discrete wavelength near-infrared imaging is used to noninvasively monitor the forearm during periods of restricted blood outflow (venous outflow restriction) and interrupted blood inflow (ischemia). Multivariate analysis of image and spectral data time courses was used to identify highly correlated spectral and regional domains, while fuzzy C-means clustering of image time courses was used to reveal finer regional heterogeneities in the response of stressed tissues. Localized near-infrared spectroscopy was used to investigate the magnitude of the bulk changes in the tissue optical properties and the variation in tissue oxygenation saturation during venous outflow restriction and complete forearm ischemia. The imaging and spectroscopic analyses revealed highly localized regional variations in tissue oxygen saturation during forearm ischemia as compared to the more diffuse and global response of the forearm during venous outflow restriction.

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