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  Microstructural correlates of cognitive and motor functioning revealed via multimodal multivariate analysis

Alasmar, Z., Tremblay, S., Baumeister, T. R., Carbonell, F., Iturria-Medina, Y., Gauthier, C., et al. (2024). Microstructural correlates of cognitive and motor functioning revealed via multimodal multivariate analysis. bioRxiv. doi:10.1101/2024.06.05.597645.

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Alasmar, Zaki, Author
Tremblay, Stefanie, Author
Baumeister, Tobias Robert, Author
Carbonell, Felix, Author
Iturria-Medina, Yasser, Author
Gauthier, Claudine, Author
Steele, Christopher1, Author                 
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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 Abstract: Recent advances in cognitive neuroscience emphasise the importance of healthy white matter (WM) in optimal behavioural functioning. It is now widely accepted that brain connectivity via WM contributes to the emergence of behaviour. However, the association between the microstructure of WM fibres and behaviour is poorly understood. This is in part due to indirect and overlapping methods of assessing microstructure, and the use of overly simplistic approaches in assessing behaviour. Here, we used the Mahalanobis Distance (D2) to integrate 10 metrics of WM derived from multimodal neuroimaging that have strong ties to microstructure. The D2 metric was chosen because it accounts for metrics' covariance as it measures the voxelwise distance between every subject and the average; thus providing a robust multiparametric assessment of microstructure. To examine WM-behaviour associations, we used multivariate correlation to examine the voxelwise correlates of 2 cognitive and 2 motor tasks, which allowed us to compare within and across domains in WM. We observed that behaviour is organised in cognitive, motor, and integrative variables that are widespread in their associations with WM, from frontal to parietal regions. Our results highlight the complex nature of microstructure and behaviour, and show the need for multivariate modelling when examining brain-behaviour associations.

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Language(s): eng - English
 Dates: 2024-06-08
 Publication Status: Published online
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 Identifiers: DOI: 10.1101/2024.06.05.597645
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Title: bioRxiv
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