Print Email Facebook Twitter ASH Title ASH: an Automatic pipeline to generate realistic and individualized chronic Stroke volume conduction Head models Author Piastra, Maria Carla (Radboud University Medical Center) van der Cruijsen, J. (Erasmus MC) Piai, Vitória (Radboud University Medical Center) Jeukens, Floor E.M. (Student TU Delft) Manoochehri, M. (TU Delft Biomechatronics & Human-Machine Control) Schouten, A.C. (TU Delft Biomechatronics & Human-Machine Control; University of Twente) Selles, R.W. (Erasmus MC) Oostendorp, Thom (Radboud University Medical Center) Date 2021 Abstract Objective. Large structural brain changes, such as chronic stroke lesions, alter the current pathways throughout the patients' head and therefore have to be taken into account when performing transcranial direct current stimulation simulations. Approach. We implement, test and distribute the first MATLAB pipeline that automatically generates realistic and individualized volume conduction head models of chronic stroke patients, by combining the already existing software SimNIBS, for the mesh generation, and lesion identification with neighborhood data analysis, for the lesion identification. To highlight the impact of our pipeline, we investigated the sensitivity of the electric field distribution to the lesion location and lesion conductivity in 16 stroke patients' datasets. Main results. Our pipeline automatically generates 1 mm-resolution tetrahedral meshes including the lesion compartment in less than three hours. Moreover, for large lesions, we found a high sensitivity of the electric field distribution to the lesion conductivity value and location. Significance. This work facilitates optimizing electrode configurations with the goal to obtain more focal brain stimulations of the target volumes in rehabilitation for chronic stroke patients. Subject Automatic pipelineChronic strokeLesion conductivityMotor rehabilitationTdcsVolume conduction head model To reference this document use: http://resolver.tudelft.nl/uuid:bb9d127f-098a-464c-83a1-9c372a003ade DOI https://doi.org/10.1088/1741-2552/abf00b ISSN 1741-2560 Source Journal of Neural Engineering, 18 (4) Part of collection Institutional Repository Document type journal article Rights © 2021 Maria Carla Piastra, J. van der Cruijsen, Vitória Piai, Floor E.M. Jeukens, M. Manoochehri, A.C. Schouten, R.W. Selles, Thom Oostendorp Files PDF Carla_Piastra_2021_J._Neu ... 044001.pdf 1.56 MB Close viewer /islandora/object/uuid:bb9d127f-098a-464c-83a1-9c372a003ade/datastream/OBJ/view