Pattern, variability, and hemispheric differences of the subparietal sulcus on multiplanar reconstructed MR images.

PURPOSE: The aim of this study was to analyze variations in the morphological features of the subparietal sulcus (SPS) and to investigate interhemispheric and gender differences in these variations using multiplanar reconstructed (MPR) magnetic resonance (MR) images. METHODS: Two hundred subjects with normal cranial MR imaging, including high-resolution T1-weighted volumetric data, were enrolled in the study. The sagittal or oblique sagittal MPR images created from high-resolution T1-weighted data were analyzed for the following morphological features: the SPS patterns, the continuity of the SPS with the cingulate sulcus and parieto-occipital sulcus (POS), and the presence of upwardly directed SPS branches reaching to the hemispheric surface. Interindividual variability of the morphologic features, hemispheric asymmetry, and gender differences were investigated. RESULTS: Considerable variations were found in the morphological features of the SPS. The H-pattern, no connection with the cingulate sulcus or the POS, and one upwardly directed branch reaching the hemispheric surface were most commonly observed morphologic features of the SPS in 45.2, 41.8, and 48 % of the all hemispheres, respectively. Furthermore, the connection of the SPS only with the cingulate sulcus and the presence of two upwardly directed branches reaching the hemispheric surface showed the significant leftward asymmetry (P < 0.05). CONCLUSIONS: Our study demonstrated the extensive morphological variability of the SPS and the hemispheric asymmetry for some morphological features. Knowledge of these variations and their hemispheric asymmetry may be helpful for surgical approaches in neurosurgery and structure-function correlations in functional neuroimaging studies involving the posteromedial hemisphere.

Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T.

This study aims classification of phosphorus magnetic resonance spectroscopic imaging ((31)P-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy (31)P MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine. This study showed that machine learning could be successfully applied for classification of (31)P-MR spectra of brain tumors. Future studies will measure the performance of classification algorithms for (31)P-MRSI of brain tumors in a larger patient cohort.