| Autor: |
Dalboni da Rocha JL; Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland. Josue.Dalboni@unige.ch., Schneider P; Department of Neurology, Section of Biomagnetism, University Hospital Heidelberg, Heidelberg, Germany.; Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany., Benner J; Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany., Santoro R; Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland., Atanasova T; Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland., Van De Ville D; Medical Image Processing Lab, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland., Golestani N; Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland. |
| Abstrakt: |
Auditory cortex volume and shape differences have been observed in the context of phonetic learning, musicianship and dyslexia. Heschl's gyrus, which includes primary auditory cortex, displays large anatomical variability across individuals and hemispheres. Given this variability, manual labelling is the gold standard for segmenting HG, but is time consuming and error prone. Our novel toolbox, called 'Toolbox for the Automated Segmentation of HG' or TASH, automatically segments HG in brain structural MRI data, and extracts measures including its volume, surface area and cortical thickness. TASH builds upon FreeSurfer, which provides an initial segmentation of auditory regions, and implements further steps to perform finer auditory cortex delineation. We validate TASH by showing significant relationships between HG volumes obtained using manual labelling and using TASH, in three independent datasets acquired on different scanners and field strengths, and by showing good qualitative segmentation. We also present two applications of TASH, demonstrating replication and extension of previously published findings of relationships between HG volumes and (a) phonetic learning, and (b) musicianship. In sum, TASH effectively segments HG in a fully automated and reproducible manner, opening up a wide range of applications in the domains of expertise, disease, genetics and brain plasticity. |
