| Title: | Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference |
| Journal: | NeuroImage |
| Published: | 2 Aug 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35931311/ |
| DOI: | https://doi.org/10.1016/j.neuroimage.2022.119534 |
Publication 10418
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Abstract
Lateralization is a fundamental characteristic of many behaviors and the organization of the brain, and atypical lateralization has been suggested to be linked to various brain-related disorders such as autism and schizophrenia. Right-handedness is one of the most prominent markers of human behavioural lateralization, yet its neurobiological basis remains to be determined. Here, we present a large-scale analysis of handedness, as measured by self-reported direction of hand preference, and its variability related to brain structural and functional organization in the UK Biobank (N = 36,024). A multivariate machine learning approach with multi-modalities of brain imaging data was adopted, to reveal how well brain imaging features could predict individual's handedness (i.e., right-handedness vs. non-right-handedness) and further identify the top brain signatures that contributed to the prediction. Overall, the results showed a good prediction performance, with an area under the receiver operating characteristic curve (AUROC) score of up to 0.72, driven largely by resting-state functional measures. Virtual lesion analysis and large-scale decoding analysis suggested that the brain networks with the highest importance in the prediction showed functional relevance to hand movement and several higher-level cognitive functions including language, arithmetic, and social interaction. Genetic analyses of contributions of common DNA polymorphisms to the imaging-derived handedness prediction score showed a significant heritability (h2=7.55%, p <0.001) that was similar to and slightly higher than that for the behavioural measure itself (h2=6.74%, p <0.001). The genetic correlation between the two was high (rg=0.71), suggesting that the imaging-derived score could be used as a surrogate in genetic studies where the behavioural measure is not available. This large-scale study using multimodal brain imaging and multivariate machine learning has shed new light on the neural correlates of human handedness.</p>
6 Authors
- Pattarawat Chormai
- Yi Pu
- Haoyu Hu
- Simon E Fisher
- Clyde Francks
- Xiang-Zhen Kong
1 Application
| Application ID | Title |
|---|---|
| 16066 | Genetics of brain asymmetry and language-related disorders |
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