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

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  • معلومة اضافية
    • بيانات النشر:
      Elsevier, 2022.
    • الموضوع:
      2022
    • Collection:
      LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
    • نبذة مختصرة :
      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
    • File Description:
      electronic resource
    • ISSN:
      1095-9572
    • Relation:
      http://www.sciencedirect.com/science/article/pii/S1053811922006498; https://doaj.org/toc/1095-9572
    • الرقم المعرف:
      10.1016/j.neuroimage.2022.119534
    • الرقم المعرف:
      edsdoj.9e827347f5fb44929bcf22da8fc3aef3