Functional modulation of power-law distribution in visual perception

Masanori Shimono, Takashi Owaki, Kaoru Amano, Keiichi Kitajo, and Tsunehiro Takeda

Phys. Rev. E 75, 051902 – Published 4 May 2007

Abstract

Neuronal activities have recently been reported to exhibit power-law scaling behavior. However, it has not been demonstrated that the power-law component can play an important role in human perceptual functions. Here, we demonstrate that the power spectrum of magnetoencephalograph recordings of brain activity varies in coordination with perception of subthreshold visual stimuli. We observed that perceptual performance could be better explained by modulation of the power-law component than by modulation of the peak power in particular narrow frequency ranges. The results suggest that the brain operates in a state of self-organized criticality, modulating the power spectral exponent of its activity to optimize its internal state for response to external stimuli.

Received 1 September 2006

DOI:https://doi.org/10.1103/PhysRevE.75.051902

Authors & Affiliations

Masanori Shimono¹, Takashi Owaki¹, Kaoru Amano², Keiichi Kitajo³, and Tsunehiro Takeda¹

¹Laboratory for Biological Complex Systems, Department of Complexity Science and Engineering, Graduate School of Frontier Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan
²Sensory and Motor Research Group, Human and Information Science Laboratory, NTT Communication Science Laboratories, 3-1, Morinosato Wakamiya Atsugi-shi, Kanagawa 243-0198, Japan
³Laboratory for Dynamics of Emergent Intelligence, RIKEN, Brain Science Institute, 2-1, Hirosawa, Wako, Saitama 351-0198, Japan

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Issue

Vol. 75, Iss. 5 — May 2007

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Images

Figure 1
(a) A representative wavelet power spectrum of a magnetic field on the surface of a human head, measured with MEG (black solid line). The subject was presented with weak flickering visual stimuli. The power spectrum approximately obeys a power law (dashed line). Dotted lines demarcate the five frequency bands $δ (1.5 - 3.5 Hz)$ , $θ (3.5 - 6.5 Hz)$ , $α (7.0 - 13 Hz)$ , $β (15 - 30 Hz)$ , $γ (\geq 30 Hz)$ . (b) Residual component after subtraction of the power-law component (PLC) from the raw wavelet power spectrum.Reuse & Permissions
Figure 2
(Color) (a) Time course of the stimulus presented to identify the detection threshold. (b) Relationship between stimulus amplitude and probability of correctly detecting the stimulus obtained from one subject. Error bars correspond to SD. A probability of 0.75 (dotted line) defined the detection threshold (arrow). (c) Time course of the stimulus for selection of sensors, which show activity depending on the stimulus position. (d) The difference between magnetic fields activated by a stimulus presented in the lower left and lower right visual fields. Positive (negative) areas are those in which neural activity was stronger (weaker) when the stimulation was in the left visual field than when it was in the right visual field.Reuse & Permissions
Figure 3
(Color) (a) Difference in mean WPS between correct and incorrect trials. (b) The mean difference for data set across all subjects in the residual WPS between the correct and incorrect trials. The residual WPS are obtained by subtracting PLC from the raw power spectrum for each trial. (c) Statistically significant difference in mean WPS in (a). Areas with significant differences are shown in white (two-tailed $t$ test with Bonferroni correction; $p < 0.005$ ). (d) Statistically significant difference in the residual WPS in (b) $(p < 0.005)$ . (e) Time courses of the averaged spectral exponents on correct (blue) and incorrect trials (red). Binary color map below time courses shows the results of a two-tailed $t$ test for spectral exponents $(p < 0.005)$ .Reuse & Permissions
Figure 4
(Color) Products of WPS modulations of frequency bands (a) $δ$ and $θ$ , (b) $β$ and $γ$ , (c) $δ$ and $γ$ , and (d) $θ$ and $β$ in time windows $A$ , $B$ , and $C$ . Error bars correspond to $S D$ . (e) Pairs of frequency components shown in (a)–(d). Statistical test is performed in each time-frequency window by comparing the averaged WPS for correct trials and that for incorrect trials (two-tailed $t$ test, $*; p < 0.05, * *; p < 0.01$ ).Reuse & Permissions
Figure 5
(Color) The contour map for the average of the product between $M (f_{1}, t)$ and $M (f_{2}, t)$ in time window $C$ . Here, $f_{1}$ and $f_{2}$ are two frequencies. Yellow and red areas indicate positive values, and blue areas indicate negative values, of the average product.Reuse & Permissions

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