TASH

• Verfasst von: Dalboni da Rocha, Josué Luiz [VerfasserIn]
• Verfasst von: Schneider, Peter [VerfasserIn]
• Verfasst von: Benner, Jan [VerfasserIn]
• Verfasst von: Santoro, Roberta [VerfasserIn]
• Verfasst von: Atanasova, Tanja [VerfasserIn]
• Verfasst von: Van De Ville, Dimitri [VerfasserIn]
• Verfasst von: Golestani, Narly [VerfasserIn]
• Titel: TASH
• Titelzusatz: Toolbox for the Automated Segmentation of Heschl’s gyrus
• Verf.angabe: Josué Luiz Dalboni da Rocha, Peter Schneider, Jan Benner, Roberta Santoro, Tanja Atanasova, Dimitri Van De Ville & Narly Golestani
• E-Jahr: 2020
• Jahr: 03 March 2020
• Fussnoten: Gesehen am 14.09.2020
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Macmillan Publishers Limited, part of Springer Nature, 2011
• Jahr Quelle: 2020
• Band/Heft Quelle: 10(2020) Artikel-Nummer 3887, 15 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-020-60609-y
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1731798962

Abstract

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.