Article | Published:

Closed-loop training of attention with real-time brain imaging

Nature Neuroscience volume 18, pages 470475 (2015) | Download Citation

Abstract

Lapses of attention can have negative consequences, including accidents and lost productivity. Here we used closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses. During a sustained attention task, the focus of attention was monitored in real time with multivariate pattern analysis of whole-brain neuroimaging data. When indicators of an attentional lapse were detected in the brain, we gave human participants feedback by making the task more difficult. Behavioral performance improved after one training session, relative to control participants who received feedback from other participants' brains. This improvement was largest when feedback carried information from a frontoparietal attention network. A neural consequence of training was that the basal ganglia and ventral temporal cortex came to represent attentional states more distinctively. These findings suggest that attentional failures do not reflect an upper limit on cognitive potential and that attention can be trained with appropriate feedback about neural signals.

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Acknowledgements

This work was supported by US National Institutes of Health grant R01EY021755, US National Science Foundation (NSF) grant BCS1229597, NSF fellowship DGE1148900 and the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of these funding agencies.

Author information

Affiliations

  1. Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.

    • Megan T deBettencourt
    • , Jonathan D Cohen
    • , Ray F Lee
    • , Kenneth A Norman
    •  & Nicholas B Turk-Browne
  2. Department of Psychology, Princeton University, Princeton, New Jersey, USA.

    • Jonathan D Cohen
    • , Kenneth A Norman
    •  & Nicholas B Turk-Browne

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Contributions

M.T.dB., J.D.C., K.A.N. and N.B.T.-B. designed the experiment, discussed the data and wrote the paper. M.T.dB. and R.F.L. developed data acquisition and analysis tools. M.T.dB. collected and analyzed the data. All authors read and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Nicholas B Turk-Browne.

Integrated supplementary information

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–5

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    Supplementary Methods Checklist

Videos

  1. 1.

    Example neurofeedback block.

    This video depicts the real-time data analysis and stimulus-updating procedure during a feedback block. In this block, the participant was instructed to attend to scenes and respond when a scene was indoors. The left window shows what the participant saw. The top-right window shows the real-time fMRI estimate of the participant's attentional state (classifier evidence for task-relevant minus task-irrelevant categories; here, scene minus face outputs). The bottom-right window shows the mixture proportions of the composite stimuli. The mixture was initialized at 50% face/50% scene, and then updated on the basis of a moving window of classifier evidence over the preceding three volumes using a transfer function.

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DOI

https://doi.org/10.1038/nn.3940

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