Diffusion tensor tractography allows visualization of the corticospinal tract （CST） in three dimensions. Transcranial magnetic stimulation offers a unique advantage in that it can distinguish between the corticospin...Diffusion tensor tractography allows visualization of the corticospinal tract （CST） in three dimensions. Transcranial magnetic stimulation offers a unique advantage in that it can distinguish between the corticospinal tract and the non-CST by analyzing the characteristics of a motor-evoked potential. A 15 year-old female showed right hemiparesis, due to intracerebral hemorrhage in the left corona radiata, and the posterior limb of the internal capsule. Diffusion tensor tractography revealed that the tracts of both hemispheres originated from the precentral gyrus, and descended through the known CST pathway. Specifically, the tract of the affected hemisphere descended through an isolated area in the leukomalactic lesion at the posterior limb level. In addition, the characteristics of the motor-evoked potential obtained from the right hand when stimulating the hot spot of the left motor cortex corresponded to a CST. In conclusion, we report on a patient with intracerebral hemorrhage who showed an isolated CST in a leukomalactic lesion. This result suggests the importance of saving the adjacent area or penumbra around a hematoma after an intracerebral hemorrhage.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
基金National Research Foundation Grant funded by the Korean Gov-ernment, No. KRF-2008-314-E00173
文摘Diffusion tensor tractography allows visualization of the corticospinal tract （CST） in three dimensions. Transcranial magnetic stimulation offers a unique advantage in that it can distinguish between the corticospinal tract and the non-CST by analyzing the characteristics of a motor-evoked potential. A 15 year-old female showed right hemiparesis, due to intracerebral hemorrhage in the left corona radiata, and the posterior limb of the internal capsule. Diffusion tensor tractography revealed that the tracts of both hemispheres originated from the precentral gyrus, and descended through the known CST pathway. Specifically, the tract of the affected hemisphere descended through an isolated area in the leukomalactic lesion at the posterior limb level. In addition, the characteristics of the motor-evoked potential obtained from the right hand when stimulating the hot spot of the left motor cortex corresponded to a CST. In conclusion, we report on a patient with intracerebral hemorrhage who showed an isolated CST in a leukomalactic lesion. This result suggests the importance of saving the adjacent area or penumbra around a hematoma after an intracerebral hemorrhage.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
