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Tom Eichele, Stefan Debener, Vince D. Calhoun, Karsten Specht, Andreas K. Engel, Kenneth Hugdahl, D. Yves von Cramon, and Markus Ullsperger

Prediction of human errors by maladaptive changes in event-related brain networks
PNAS published April 21, 2008, 10.1073/pnas.0708965105

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Abstract 1 of 1NEUROSCIENCE
Prediction of human errors by maladaptive changes in event-related brain networks

Tom Eichele^*,, Stefan Debener, Vince D. Calhoun^,¶,||, Karsten Specht^*,**, Andreas K. Engel, Kenneth Hugdahl^*,**, D. Yves von Cramon, and Markus Ullsperger^,

*Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway; Medical Research Council Institute of Hearing Research, Southampton SO14 OYG, United Kingdom; MIND Institute, Albuquerque, NM 87131; ^¶Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131; ^||Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520; **Haukeland University Hospital, 5021 Bergen, Norway; Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg–Eppendorf, 20246 Hamburg, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; and Max Planck Institute for Neurological Research, 50931 Cologne, Germany

Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved March 4, 2008 (received for review September 21, 2007)

Abstract

Humans engaged in monotonous tasks are susceptible to occasionalerrors that may lead to serious consequences, but little isknown about brain activity patterns preceding errors. Usingfunctional MRI and applying independent component analysis followedby deconvolution of hemodynamic responses, we studied errorpreceding brain activity on a trial-by-trial basis. We founda set of brain regions in which the temporal evolution of activationpredicted performance errors. These maladaptive brain activitychanges started to evolve 30 sec before the error. In particular,a coincident decrease of deactivation in default mode regionsof the brain, together with a decline of activation in regionsassociated with maintaining task effort, raised the probabilityof future errors. Our findings provide insights into the brainnetwork dynamics preceding human performance errors and suggestthat monitoring of the identified precursor states may helpin avoiding human errors in critical real-world situations.

deconvolution | performance monitoring | default mode | frontal lobe

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Footnotes

Author contributions: S.D., A.K.E., D.Y.v.C., and M.U. designedresearch; S.D. and M.U. performed research; T.E. and V.D.C.contributed new reagents/analytic tools; T.E., S.D., V.D.C.,K.S., and M.U. analyzed data; and T.E., S.D., V.D.C., K.S.,A.K.E., K.H., D.Y.v.C., and M.U. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

To whom correspondence should be addressed. E-mail: tom.eichele{at}psybp.uib.no

© 2008 by The National Academy of Sciences of the USA

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