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Gamma-band synchronization in visual cortex predicts speed of change detection


Our capacity to process and respond behaviourally to multiple incoming stimuli is very limited. To optimize the use of this limited capacity, attentional mechanisms give priority to behaviourally relevant stimuli at the expense of irrelevant distractors. In visual areas, attended stimuli induce enhanced responses and an improved synchronization of rhythmic neuronal activity in the gamma frequency band (40–70 Hz)1,2,3,4,5,6,7,8,9,10,11. Both effects probably improve the neuronal signalling of attended stimuli within and among brain areas1,12,13,14,15,16. Attention also results in improved behavioural performance and shortened reaction times. However, it is not known how reaction times are related to either response strength or gamma-band synchronization in visual areas. Here we show that behavioural response times to a stimulus change can be predicted specifically by the degree of gamma-band synchronization among those neurons in monkey visual area V4 that are activated by the behaviourally relevant stimulus. When there are two visual stimuli and monkeys have to detect a change in one stimulus while ignoring the other, their reactions are fastest when the relevant stimulus induces strong gamma-band synchronization before and after the change in stimulus. This enhanced gamma-band synchronization is also followed by shorter neuronal response latencies on the fast trials. Conversely, the monkeys' reactions are slowest when gamma-band synchronization is high in response to the irrelevant distractor. Thus, enhanced neuronal gamma-band synchronization and shortened neuronal response latencies to an attended stimulus seem to have direct effects on visually triggered behaviour, reflecting an early neuronal correlate of efficient visuo-motor integration.

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Figure 1: Stimuli, behavioural model and examples of gamma-band synchronization and its modulation by attention.
Figure 2: Spike–field coherence from one pair of recording sites.
Figure 3: Neuronal activity parameters in trials with fast and slow change detection.
Figure 4: Trial-by-trial prediction of reaction times by neuronal activity.

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We thank J. H. Reynolds, A. E. Rorie and A. F. Rossi for help with experiments; and R. Oostenveld and M. Bauer for discussions. This research was supported by grants from the Human Frontier Science Program Organization (to P.F. and P.P.M.), The Netherlands Organization for Scientific Research and The Volkswagen Foundation (to P.F.), the NIH (to P.P.M.) and the Intramural Program of the NIH, NIMH. Author Contributions T.W. and P.F. contributed equally to this work. P.F. conceived and performed the experiment. T.W., P.F. and P.P.M. conceived and performed the data analysis. P.F., T.W. and R.D. wrote the paper.

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Correspondence to Thilo Womelsdorf.

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Supplementary information

Supplementary Methods

This file contains details about the neurophysiological recording techniques and LFP preprocessing; time-frequency spectral analysis based on multitapering; neural activity sorted according to reaction time; correlation analysis; and estimation of spike-field coherence of single-trials. (PDF 42 kb)

Supplementary Notes

This states that the correlation between reaction times and neuronal activity is consistent across the two monkeys and not explained by a general effect of time-in-trial. (PDF 13 kb)

Supplementary Table

Average correlation coefficients of power and coherence with reaction time in a post-change epoch, separately for the two monkeys and for the alpha/beta- and the gamma-frequency bands. (PDF 9 kb)

Supplementary Figure 1

Scatter plot of reaction times as a function of time after stimulus onset separately for the two monkeys. (PDF 288 kb)

Supplementary Figure 2

Histograms of z-scores for the correlations between reaction times and gamma-band (40 - 72 Hz) power, gamma-band spike-field coherence and firing rate across individual (pairs of) recording sites and separately for the two monkeys. (PDF 93 kb)

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Womelsdorf, T., Fries, P., Mitra, P. et al. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature 439, 733–736 (2006).

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