th  - 1988 - 51st ENC Conference

- - 92 l
CHARACTERIZATION OF NORMAL BRAIN TISSUE USING MRI
PARAMETERS AND A STATISTICAL ANALYSIS SYSTEM. Timothy J. Hyman*,
George C. Levy, Department of Chemistry, Syracuse University,
Syracuse, NY 13244, Robert J. Kurland, Jon D. Shoop, MRI Facility,
Geisinger Medical Center, Danville PA, 17822.
Doctors and scientis~ have been working toward techniques that
utilize Magnetic Resonance Imaging for characterizing tissue types
in the human body. Our study introduces a technique for the
characterization of normal brain tissue. We will present a
statistical analysissystem for discriminating 13 regions in the
brain for 49 volunteers. The statistical method implemented in our
technique is Bayes Maximum Likelihood which produced a
discrimination accuracy of 90.8% for three sets of age groups. The
method for calculating TI, T2 and proton density values implemented
single-echo sequences, along with multiple-echo and inversion
recovery sequences, in an effort to eliminate the affects of
diffusion and of the inaccurate 90" pulse. T2 values calculated from
single-echo images were found to have a higher accuracy of
reproducibility and discrimination than T2 values calculated from
multiple-echo images. The nine parameters showed excellent
reproducibility with percent standard deviations between 7% and 18%.
Finally, our study shows that a better discrimination is obtained
when using all nine parameters rather than using just three
traditional parameters or three averaged parameters.
93 TOWARD A COMPUTER ASSISTED ANALYSIS OF NOESY SPECTRA:
A MULTIVARIATE PATYERN RECOGNITION ANALYSIS OF
DNA AND RNA NOESY SPECTRA
Hans Grahn, Frank Delaglio, Ulf Edlundt, Mark W. Roggenbuck and Phil Borer*
NMR and Data Processing Laboratory, NIH Resource and CASE Center,
Syracuse University, NY 13244-1200.
Two dimensional NMR spectra of biomolecules present us with a wealth of data. However, if we wish to
access this information on a routine basis, automated methods for spectral assignment are essential, since
the spectra are so complex. A 2D NOESY spectrum of a relatively small DNA or RNA fragment can contain
several hundreds of cross-peaks. The situation is especially critical for RNA spectra, which typically include
several regions of severe overlap or minimal resolution. Therefore, even the most basic task of selecting the
peaks to be included in an initial analysis is difficult and time-consuming. For the same reasons, the actual
procedures of manual assignment are also difficult. In this study we show that several of the above issues
can be addressed by appealing to multivariate representations of the NOESY spectra. The analysis generates
projections of the multivariate space by calculating principal components, with rather remarkable
consequences. These projections directly identify relevant spectral bands. Subsequent multivariate analysis
can provide peak assignment information according to type of base or conformation, and can even supply
reliability estimates for proposed assignments, based on previously assigned spectra. The techniques are
illustrated for separation of different structural segments of an RNA duplex NOESY spectra of
(CACAUGUG) 2.
NMR Research Group, Department of Chemistry, Ume~ University, S-901 87 Ume~, Sweden.
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