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A neuromarker of sustained attention from whole-brain functional connectivity


Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

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Figure 1: Functional connectivity models predict sustained attention performance.
Figure 2: Functional connections predicting gradCPT performance and ADHD-RS scores.
Figure 3: Sustained Attention Network (SAN) models, defined with gradCPT subjects, significantly predict scores on the ADHD-Rating Scale (ADHD-RS) in an independent sample of children and adolescents from the ADHD-200 data set.
Figure 4: Connectivity models defined on ADHD-200 data predict gradCPT performance in an independent group of participants.


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M.D.R. and E.S.F. are supported by US National Science Foundation Graduate Research Fellowships. This work was also supported by US National Institutes of Health EB009666 to R.T.C. and T32 DA022975 to D.S. Data were provided by the ADHD-200 Consortium25, coordinated by M.P. Milham. Data collection at Peking University was supported by the following funding sources: The Commonwealth Sciences Foundation, Ministry of Health, China (200802073); The National Foundation, Ministry of Science and Technology, China (2007BAI17B03); The National Natural Sciences Foundation, China (30970802); The Funds for International Cooperation of the National Natural Science Foundation of China (81020108022); The National Natural Science Foundation of China (8100059); and the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning.

Author information




M.D.R., M.M.C., E.S.F. and R.T.C. conceived of and designed the study. E.S.F. developed the prediction methodology. M.D.R., E.S.F., X.S. and D.S. wrote the code. M.D.R. collected and preprocessed the gradCPT data. DS preprocessed the ADHD-200 data. M.D.R. ran the models and analyzed the output data with support and contributions from E.S.F. X.P., X.S. and D.S. contributed previously unpublished tools, including the specific functional brain parcellation used here and visualization software. M.D.R. wrote the paper with contributions from E.S.F. and M.M.C. All other authors commented on the paper.

Corresponding author

Correspondence to Monica D Rosenberg.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 The 236-region functional parcellation used to define network nodes in the ADHD-200 dataset.

Colored nodes were included in the atlas, whereas regions in faint gray were not. These regions, located mainly in the inferior portions of the cerebellum, brainstem, temporal poles and orbital frontal cortex, were excluded because some scans in the ADHD-200 dataset did not include full cortex and cerebellum coverage.

Supplementary Figure 2 Spearman’s (rank) correlations between predicted and observed d' values.

Models were trained on task data from n – 1 gradCPT subjects and tested on data from the left-out individual. Spearman’s correlation, rather than robust regression, was used at the edge selection step.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2 and Supplementary Tables 1–3 (PDF 502 kb)

Supplementary Methods Checklist (PDF 754 kb)

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Rosenberg, M., Finn, E., Scheinost, D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 19, 165–171 (2016).

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