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Task-induced brain state manipulation improves prediction of individual traits

Author

Listed:
  • Abigail S. Greene

    (Yale School of Medicine)

  • Siyuan Gao

    (Yale School of Engineering and Applied Science)

  • Dustin Scheinost

    (Yale School of Medicine)

  • R. Todd Constable

    (Yale School of Medicine
    Yale School of Medicine
    Yale School of Medicine)

Abstract

Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to

Suggested Citation

  • Abigail S. Greene & Siyuan Gao & Dustin Scheinost & R. Todd Constable, 2018. "Task-induced brain state manipulation improves prediction of individual traits," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04920-3
    DOI: 10.1038/s41467-018-04920-3
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    Cited by:

    1. de Souza, Erick Almeida & Silva, Stéphanie Andrade & Vieira, Bruno Hebling & Salmon, Carlos Ernesto Garrido, 2023. "fMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance," Intelligence, Elsevier, vol. 97(C).
    2. Hilger, Kirsten & Spinath, Frank M. & Troche, Stefan & Schubert, Anna-Lena, 2022. "The biological basis of intelligence: Benchmark findings," Intelligence, Elsevier, vol. 93(C).
    3. Junjiao Feng & Liang Zhang & Chunhui Chen & Jintao Sheng & Zhifang Ye & Kanyin Feng & Jing Liu & Ying Cai & Bi Zhu & Zhaoxia Yu & Chuansheng Chen & Qi Dong & Gui Xue, 2022. "A cognitive neurogenetic approach to uncovering the structure of executive functions," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Vieira, Bruno Hebling & Pamplona, Gustavo Santo Pedro & Fachinello, Karim & Silva, Alice Kamensek & Foss, Maria Paula & Salmon, Carlos Ernesto Garrido, 2022. "On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting," Intelligence, Elsevier, vol. 93(C).
    5. Jianzhong Chen & Angela Tam & Valeria Kebets & Csaba Orban & Leon Qi Rong Ooi & Christopher L. Asplund & Scott Marek & Nico U. F. Dosenbach & Simon B. Eickhoff & Danilo Bzdok & Avram J. Holmes & B. T., 2022. "Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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