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  Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference

Chormai, P., Pu, Y., Hu, H., Fisher, S. E., Francks, C., & Kong, X.-Z. (2022). Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference. NeuroImage, 262: 119534. doi:10.1016/j.neuroimage.2022.119534.

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 Creators:
Chormai, Pattarawat1, 2, 3, Author
Pu, Yi4, Author           
Hu, Haoyu5, Author
Fisher, Simon E.1, 6, Author
Francks, Clyde1, 6, 7, 8, Author
Kong, Xiang-Zhen1, 5, 9, Author
Affiliations:
1Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society, ou_792549              
2Technische Universität Berlin, Berlin, Germany, ou_persistent22              
3Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany, ou_persistent22              
4Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421697              
5Zhejiang University, Hangzhou, China, ou_persistent22              
6Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              
7Imaging Genomics, MPI for Psycholinguistics, Max Planck Society, ou_2579692              
8Radboud University Medical Center, Nijmegen, The Netherlands, ou_persistent22              
9Zhejiang University School of Medicine, Hangzhou, China, ou_persistent22              

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Free keywords: brain asymmetry, handedness, lateralization, machine learning, UK Biobank
 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.

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Language(s): eng - English
 Dates: 2022-07-312022-04-052022-08-012022-08-022022-11-15
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2022.119534
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 262 Sequence Number: 119534 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166