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Integrating multimodal connectivity improves prediction of individual cognitive abilities

View ORCID ProfileElvisha Dhamala, View ORCID ProfileKeith W. Jamison, Abhishek Jaywant, Sarah Dennis, View ORCID ProfileAmy Kuceyeski
doi: https://doi.org/10.1101/2020.06.25.172387
Elvisha Dhamala
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA, 10065
2Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA, 10065
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  • For correspondence: eld2024@med.cornell.edu amk2012@med.cornell.edu
Keith W. Jamison
2Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA, 10065
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Abhishek Jaywant
3Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA, 10065
4Department of Rehabilitation Medicine, Weill Cornell Medicine, New York, NY, USA, 10065
5NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, NY, USA, 10065
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Sarah Dennis
6Sarah Lawrence College, Bronxville, NY, USA, 10708
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Amy Kuceyeski
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA, 10065
2Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA, 10065
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  • For correspondence: eld2024@med.cornell.edu amk2012@med.cornell.edu
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Summary

How white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information – at both a model input or output level – significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly better than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 29, 2020.
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Integrating multimodal connectivity improves prediction of individual cognitive abilities
Elvisha Dhamala, Keith W. Jamison, Abhishek Jaywant, Sarah Dennis, Amy Kuceyeski
bioRxiv 2020.06.25.172387; doi: https://doi.org/10.1101/2020.06.25.172387
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Integrating multimodal connectivity improves prediction of individual cognitive abilities
Elvisha Dhamala, Keith W. Jamison, Abhishek Jaywant, Sarah Dennis, Amy Kuceyeski
bioRxiv 2020.06.25.172387; doi: https://doi.org/10.1101/2020.06.25.172387

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