Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data
| Autor: | D. Yves von Cramon, Eileen Luders, Kay Henning Brodersen, Delia-Lisa Feis, Marc Tittgemeyer |
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| Přispěvatelé: | University of Zurich |
| Rok vydání: | 2013 |
| Předmět: |
2805 Cognitive Neuroscience
Adult Male Cognitive Neuroscience 610 Medicine & health Feature selection 170 Ethics White matter Young Adult Discriminative model medicine Humans 10237 Institute of Biomedical Engineering Brain Mapping Sex Characteristics Modality (human–computer interaction) medicine.diagnostic_test Brain Contrast (statistics) Magnetic resonance imaging Human brain Diffusion Magnetic Resonance Imaging medicine.anatomical_structure Neurology 2808 Neurology Female Psychology Neuroscience Diffusion MRI |
| Zdroj: | NeuroImage. 70:250-257 |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2012.12.068 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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