Ray L. Somorjai

Motor Area Activity During Mental Rotation Studied by Time-Resolved Single-Trial fMRI

The functional equivalence of overt movements and dynamic imagery is of fundamental importance in neuroscience. Here, we investigated the participation of the neocortical motor areas in a classic task of dynamic imagery, Shepard and Metzlers mental rotation task, by time-resolved single-trial functional Magnetic Resonance Imaging (fMRI). The subjects performed the mental-rotation task 16 times, each time with different object pairs. Functional images were acquired for each pair separately, and the onset times and widths of the activation peaks in each area of interest were compared to the response times. We found a bilateral involvement of the superior parietal lobule, lateral premotor area, and supplementary motor area in all subjects; we found, furthermore, that those areas likely participate in the very act of mental rotation. We also found an activation in the left primary motor cortex, which seemed to be associated with the right-hand button press at the end of the task period.

Near-optimal region selection for feature space reduction novel preprocessing methods for classifying MR spectra

We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is genetic algorithm‐guided. It is compared with our enhanced version of the standard forward selection algorithm. Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. The power of the methods is demonstrated on two biomedical examples: the discrimination between meningioma and astrocytoma in brain tissue biopsies, and the classification of colorectal biopsies into normal and tumour classes. Both preprocessing methods lead to classification accuracies over 97% for the two examples.

A Monte Carlo method for conformational analysis of saccharides.

A Metropolis Monte Carlo (MMC) algorithm was applied to explore conformational spaces spanned by the exocyclic dihedral angles of four disaccharides alpha-D-Man(1-->3)-alpha-D-Man(1-->O)Me (1), alpha-D-Man(1-->2)-alpha-D-Man(1-->O)Me (2), methyl beta-cellobioside (3), and methyl beta-maltoside (4). The simulation method uses the HSEA force field and randomly samples the conformational space with an automatic preference for low-energy states. In comparison to a systematic grid search, MMC offers a much more convenient and efficient protocol for the computation of ensemble average values of experimentally accessible NMR parameters such as NOE effects or 3J coupling constants. Energy barriers of a few kcal/mol were found to be surmounted easily when running the simulations with the temperature parameter set at room temperature, whereas passing significantly higher barriers required elevated temperature parameters. Ensemble average NOE values were calculated using the MMC technique and a conventional systematic grid search showing that the MMC method adequately samples the conformational spaces of 1-4. Theoretical NOEs derived for global or local minimum conformations are different from ensemble average values, and it is shown that averaged NOEs agree significantly better with experimental data. Ensemble average NOEs for 1 derived from MMC/HSEA, and previously reported MM2CARB and AMBER calculations all showed good agreement with experimental data, with MMC/HSEA giving the closest fit.

Magnetic resonance spectroscopy of the malignant prostate gland after radiotherapy: a histopathologic study of diagnostic validity

PURPOSE Accurate spatial representation of tumor clearance after conformal radiotherapy is an endpoint of clinical importance. Magnetic resonance spectroscopy (MRS) can diagnose malignancy in the untreated prostate gland through measurements of cellular metabolites. In this study we sought to describe spectral metabolic changes in prostatic tissue after radiotherapy and validate a multivariate analytic strategy (based on MRS) that could identify viable tumor. METHODS AND MATERIALS Transrectal ultrasound-guided prostate biopsies from 35 patients were obtained 18-36 months after external beam radiotherapy. One hundred sixteen tissue specimens were subjected to 1H MRS, submitted to histopathology, and analyzed for correlation with a multivariate strategy specifically developed for biomedical spectra. RESULTS The sensitivity and specificity of MRS in identifying a malignant biopsy were 88.9% and 92% respectively, with an overall classification accuracy of 91.4%. The diagnostic spectral regions identified by our algorithm included those due to choline, creatine, glutamine, and lipid. Citrate, an important discriminating resonance in the untreated prostate gland, was invisible in all spectra, regardless of histology. CONCLUSIONS Although the spectral features of prostate tissue markedly change after radiotherapy, MRS combined with multivariate methods of analysis can accurately identify histologically malignant biopsies. MRS shows promise as a modality that could integrate three-dimensional measures of tumor response.

Rapid Identification of Candida Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy

ABSTRACT Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.

Metabolomic biomarkers in a model of asthma exacerbation: urine nuclear magnetic resonance.

RATIONALE Airway obstruction in patients with asthma is associated with airway dysfunction and inflammation. Objective measurements including sputum analysis can guide therapy, but this is often not possible in typical clinical settings. Metabolomics is the study of molecules generated by metabolic pathways. We hypothesize that airway dysfunction and inflammation in an animal model of asthma would produce unique patterns of urine metabolites measured by multivariate statistical analysis of high-resolution proton nuclear magnetic resonance ((1)H NMR) spectroscopy data. OBJECTIVES To develop a noninvasive means of monitoring asthma status by metabolomics and urine sampling. METHODS Five groups of guinea pigs were studied: control, control treated with dexamethasone, sensitized (ovalbumin, administered intraperitoneally), sensitized and challenged (ovalbumin, administered intraperitoneally, plus ovalbumin aerosol), and sensitized-challenged with dexamethasone. Airway hyperreactivity (AHR) to histamine (administered intravenously) and inflammation were measured. Multivariate statistical analysis of NMR spectra based on a library of known urine metabolites was performed by partial least-squares discriminant analysis. In addition, the raw NMR spectra exported as xy-trace data underwent linear discriminant analysis. MEASUREMENTS AND MAIN RESULTS Challenged guinea pigs developed AHR and increased inflammation compared with sensitized or control animals. Dexamethasone significantly improved AHR. Using concentration differences in metabolites, partial least-squares discriminant analysis could discriminate challenged animals with 90% accuracy. Using only three or four regions of the NMR spectra, linear discriminant analysis-based classification demonstrated 80-90% separation of the animal groups. CONCLUSIONS Urine metabolites correlate with airway dysfunction in an asthma model. Urine NMR analysis is a promising, noninvasive technique for monitoring asthma in humans.

An ex vivo study exploring the diagnostic potential of 1H magnetic resonance spectroscopy in squamous cell carcinoma of the head and neck region.

Definitive diagnosis of head and neck cancer is generally made by histopathologic evaluation. Management and prognosis largely depend on accurate and timely diagnosis. We have explored the use of 1H magnetic resonance spectroscopy in search of a better or complementary diagnostic technique.

The use of 1H magnetic resonance spectroscopy in inflammatory bowel diseases: distinguishing ulcerative colitis from Crohn's disease

OBJECTIVES:The distinction between the two major forms of inflammatory bowel diseases (IBD), i.e., ulcerative colitis (UC) and Crohns disease is sometimes difficult and may lead to a diagnosis of indeterminate colitis. We have used 1H magnetic resonance spectroscopy (MRS) combined with multivariate methods of spectral data analysis to differentiate UC from Crohns disease and to evaluate normal-appearing mucosa in IBD.METHODS:Colon mucosal biopsies (45 UC and 31 Crohns disease) were submitted to 1H MRS, and multivariate analysis was applied to distinguish the two diseases. A second study was performed to test endoscopically and histologically normal biopsies from IBD patients. A classifier was developed by training on 101 spectra (76 inflamed IBD tissues and 25 normal control tissues). The spectra of 38 biopsies obtained from endoscopically and histologically normal areas of the colons of patients with IBD were put into the validation test set.RESULTS:The classification accuracy between UC and Crohns disease was 98.6%, with only one case of Crohns disease and no cases of UC misclassified. The diagnostic spectral regions identified by our algorithm included those for taurine, lysine, and lipid. In the second study, the classification accuracy between normal controls and IBD was 97.9%. Only 47.4% of the endoscopically and histologically normal IBD tissue spectra were classified as true normals; 34.2% showed “abnormal” magnetic resonance spectral profiles, and the remaining 18.4% could not be classified unambiguously.CONCLUSIONS:There is a strong potential for MRS to be used in the accurate diagnosis of indeterminate colitis; it may also be sensitive in detecting preclinical inflammatory changes in the colon.

Magnetic resonance spectroscopy detects biochemical changes in the brain associated with chronic low back pain: a preliminary report

Magnetic resonance (MR) spectroscopy is a noninvasive technique that can be used to detect and measure the concentration of metabolites and neurotransmitters in the brain and other organs. We used in vivo 1H MR spectroscopy in subjects with low back pain compared with control subjects to detect alterations in biochemistry in three brain regions associated with pain processing. A pattern recognition approach was used to determine whether it was possible to discriminate accurately subjects with low back pain from control subjects based on MR spectroscopy. MR spectra were obtained from the prefrontal cortex, anterior cingulate cortex, and thalamus of 32 subjects with low back pain and 33 control subjects without pain. Spectra were analyzed and compared between groups using a pattern recognition method (Statistical Classification Strategy). Using this approach, it was possible to discriminate between subjects with low back pain and control subjects with accuracies of 100%, 99%, and 97% using spectra obtained from the anterior cingulate cortex, thalamus, and prefrontal cortex, respectively. These results demonstrate that MR spectroscopy, in combination with an appropriate pattern recognition approach, is able to detect brain biochemical changes associated with chronic pain with a high degree of accuracy.

A fast, simple active contour algorithm for biomedical images

A new method for the application of active contours to biomedical images is described. The new approach, which involves extensive modification of the internal energy function and a different method of minimising the energy functional, yields rapid, excellent fits to MR images.