The Department of Genetic Medicine at Weill Cornell leads a dynamic and innovative translational research program, advancing diverse fields such as Genetic Therapy and Personalized Medicine.
Our translational research program aims to leverage our expertise in genetic therapies and personalized medicine to develop clinical solutions that target the molecular causes of human diseases.
The Department of Genetic Medicine advances treatments and diagnostics through diverse clinical trials, including drug testing and research to better understand diseases.
The Department of Genetic Medicine at Weill Cornell leads a dynamic and innovative translational research program, advancing diverse fields such as Genetic Therapy and Personalized Medicine.
Our translational research program aims to leverage our expertise in genetic therapies and personalized medicine to develop clinical solutions that target the molecular causes of human diseases.
The Department of Genetic Medicine advances treatments and diagnostics through diverse clinical trials, including drug testing and research to better understand diseases.
An important problem of the analysis of functional magnetic resonance imaging (fMRI) experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, recently the propagation-separation (PS) approach has been proposed. PS is a structure adaptive smoothing method that adapts to different shapes of activation areas. In this paper, we demonstrate how this method results in a more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning.