Academic Journal
Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data
| العنوان: | Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data |
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| المؤلفون: | Feis, D L, Brodersen, K H, von Cramon, D Y, Luders, E, Tittgemeyer, M |
| المصدر: | Feis, D L; Brodersen, K H; von Cramon, D Y; Luders, E; Tittgemeyer, M (2013). Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data. NeuroImage, 70:250-257. |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2013 |
| المجموعة: | University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
| مصطلحات موضوعية: | Institute of Biomedical Engineering, 170 Ethics, 610 Medicine & health |
| الوصف: | The female brain contains a larger proportion of gray matter tissue, while the male brain comprises more white matter. Findings like these have sparked increasing interest in studying dimorphism of the human brain: the general effect of gender on aspects of brain architecture. To date, the vast majority of imaging studies is based on unimodal MR images and typically limited to a small set of either gray or white matter regions-of-interest. The morphological content of magnetic resonance (MR) images, however, strongly depends on the underlying contrast mechanism. Consequently, in order to fully capture gender-specific morphological differences in distinct brain tissues, it might prove crucial to consider multiple imaging modalities simultaneously. This study introduces a novel approach to perform such multimodal classification incorporating the relative strengths of each modality-specific physical aperture to tissue properties. To illustrate our approach, we analyzed multimodal MR images (T(1)-, T(2)-, and diffusion-weighted) from 121 subjects (67 females) using a linear support vector machine with a mass-univariate feature selection procedure. We demonstrate that the combination of different imaging modalities yields a significantly higher balanced classification accuracy (96%) than any one modality by itself (83%-88%). Our results do not only confirm previous morphometric findings; crucially, they also shed new light on the most discriminative features in gray-matter volume and microstructure in cortical and subcortical areas. Specifically, we find that gender disparities are primarily distributed along brain networks thought to be involved in social cognition, reward-based learning, decision-making, and visual-spatial skills. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1053-8119 |
| Relation: | https://www.zora.uzh.ch/90347; info:pmid/23298750; urn:issn:1053-8119 |
| DOI: | 10.1016/j.neuroimage.2012.12.068 |
| الاتاحة: | https://www.zora.uzh.ch/id/eprint/90347/ https://www.zora.uzh.ch/90347 https://doi.org/10.1016/j.neuroimage.2012.12.068 |
| Rights: | info:eu-repo/semantics/closedAccess |
| رقم الانضمام: | edsbas.11DDA237 |
| قاعدة البيانات: | BASE |
| تدمد: | 10538119 |
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| DOI: | 10.1016/j.neuroimage.2012.12.068 |