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Connectome-based Models Predict Separable Components of Attention in Novel Individuals.
J Cogn Neurosci 2018; 30(2):160-173JC

Abstract

Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executive control (detecting and resolving cognitive conflict) [Posner, M. I., & Petersen, S. E. The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42, 1990]. Participants performed the Attention Network Task (ANT), which measures these three factors, and rested during fMRI scanning. CPMs tested with leave-one-subject-out cross-validation successfully predicted novel individual's overall ANT accuracy, RT variability, and executive control scores from functional connectivity observed during ANT performance. CPMs also generalized to predict participants' alerting scores from their resting-state functional connectivity alone, demonstrating that connectivity patterns observed in the absence of an explicit task contain a signature of the ability to prepare for an upcoming stimulus. Suggesting that significant variance in ANT performance is also explained by an overall sustained attention factor, the sustained attention CPM, a model defined in prior work to predict sustained attentional abilities, predicted accuracy, RT variability, and executive control from task-based data and predicted RT variability from resting-state data. Our results suggest that, whereas executive control may be closely related to sustained attention, the infrastructure that supports alerting is distinct and can be measured at rest. In the future, CPM may be applied to elucidate additional independent components of attention and relationships between the functional brain networks that predict them.

Authors+Show Affiliations

Yale University.Yale University.Yale School of Medicine.Yale University. Yale School of Medicine.Yale University. Yale School of Medicine.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

29040013

Citation

Rosenberg, Monica D., et al. "Connectome-based Models Predict Separable Components of Attention in Novel Individuals." Journal of Cognitive Neuroscience, vol. 30, no. 2, 2018, pp. 160-173.
Rosenberg MD, Hsu WT, Scheinost D, et al. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. J Cogn Neurosci. 2018;30(2):160-173.
Rosenberg, M. D., Hsu, W. T., Scheinost, D., Todd Constable, R., & Chun, M. M. (2018). Connectome-based Models Predict Separable Components of Attention in Novel Individuals. Journal of Cognitive Neuroscience, 30(2), pp. 160-173. doi:10.1162/jocn_a_01197.
Rosenberg MD, et al. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. J Cogn Neurosci. 2018;30(2):160-173. PubMed PMID: 29040013.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Connectome-based Models Predict Separable Components of Attention in Novel Individuals. AU - Rosenberg,Monica D, AU - Hsu,Wei-Ting, AU - Scheinost,Dustin, AU - Todd Constable,R, AU - Chun,Marvin M, Y1 - 2017/10/17/ PY - 2017/10/19/pubmed PY - 2018/12/12/medline PY - 2017/10/18/entrez SP - 160 EP - 173 JF - Journal of cognitive neuroscience JO - J Cogn Neurosci VL - 30 IS - 2 N2 - Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executive control (detecting and resolving cognitive conflict) [Posner, M. I., & Petersen, S. E. The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42, 1990]. Participants performed the Attention Network Task (ANT), which measures these three factors, and rested during fMRI scanning. CPMs tested with leave-one-subject-out cross-validation successfully predicted novel individual's overall ANT accuracy, RT variability, and executive control scores from functional connectivity observed during ANT performance. CPMs also generalized to predict participants' alerting scores from their resting-state functional connectivity alone, demonstrating that connectivity patterns observed in the absence of an explicit task contain a signature of the ability to prepare for an upcoming stimulus. Suggesting that significant variance in ANT performance is also explained by an overall sustained attention factor, the sustained attention CPM, a model defined in prior work to predict sustained attentional abilities, predicted accuracy, RT variability, and executive control from task-based data and predicted RT variability from resting-state data. Our results suggest that, whereas executive control may be closely related to sustained attention, the infrastructure that supports alerting is distinct and can be measured at rest. In the future, CPM may be applied to elucidate additional independent components of attention and relationships between the functional brain networks that predict them. SN - 1530-8898 UR - https://www.unboundmedicine.com/medline/citation/29040013/Connectome_based_Models_Predict_Separable_Components_of_Attention_in_Novel_Individuals_ L2 - http://www.mitpressjournals.org/doi/full/10.1162/jocn_a_01197?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -