Brain magnetic resonance images (MRI) of 104 healthy children and adolescents, age 4-18, showed significant effects of age and gender on brain morphometry. Males had larger cerebral (9%) and cerebellar (8%) volumes (P < 0.0001 and P = 0.008, respectively), which remained significant even after correction for height and weight. After adjusting for cerebral size, the putamen and globus pallidus remained larger in males, while relative caudate size was larger in females. Neither cerebral nor cerebellar volume changed significantly across this age range. Lateral ventricular volume increased significantly in males (trend for females), with males showing an increase in slope after age 11. In males only, caudate and putamen decrease with age (P = 0.007 and 0.05, respectively). The left lateral ventricles and putamen were significantly greater than the right (P = 0.01 and 0.001, respectively). In contrast, the cerebral hemispheres and caudate showed a highly consistent right-greater-than-left asymmetry (P < 0.0001 for both). All volumes demonstrated a high degree of variability. These findings highlight gender-specific maturational changes of the developing brain and the need for large gender-matched samples in pediatric neuropsychiatric studies.

The standard Gaussian function is proposed for the hemodynamic modulation function (HDMF) of functional magnetic resonance imaging (fMRI) time-series. Unlike previously proposed parametric models, the Gaussian model accounts independently for the delay and dispersion of the hemodynamic responses and provides a more flexible and mathematically convenient model. A suboptimal noniterative scheme to estimate the hemodynamic parameters is presented. The ability of the Gaussian function to represent the HDMF of brain activation is compared with Poisson and Gamma models. The proposed model seems valid because the lag and dispersion values of hemodynamic responses rendered by the Gaussian model are in the ranges of their previously reported values in recent optical and fMR imaging studies. An extension of multiple regression analysis to incorporate the HDMF is presented. The detected activity patterns exhibit improvements with hemodynamic correction. The proposed model and efficient parameter estimation scheme facilitated the investigation of variability of hemodynamic parameters of human brain activation. The hemodynamic parameters estimated over different brain regions and across different stimuli showed significant differences. Measurement of hemodynamic parameters over the brain during sensory or cognitive stimulation may reveal vital information on physiological events accompanying neuronal activation and functional variability of the human brain, and should lead to the investigation of more accurate and complex models.

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Bioinformatics techniques to protein secondary structure (PSS) prediction are mostly single-stage approaches in the sense that they predict secondary structures of proteins by taking into account only the contextual information in amino acid sequences. In this paper, we propose two-stage Multi-class Support Vector Machine (MSVM) approach where a MSVM predictor is introduced to the output of the first stage MSVM to capture the sequential relationship among secondary structure elements for the prediction. By using position specific scoring matrices, generated by PSI-BLAST, the two-stage MSVM approach achieves Q 3 accuracies of 78.0% and 76.3% on the RS126 dataset of 126 nonhomologous globular proteins and the CB396 dataset of 396 nonhomologous proteins, respectively, which are better than the highest scores published on both datasets to date.