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      Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development

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          As the brain matures, its responses become optimized. Behavioral measures show this through improved accuracy and decreased trial-to-trial variability. The question remains whether the supporting brain dynamics show a similar decrease in variability. We examined the relation between variability in single trial evoked electrical activity of the brain (measured with EEG) and performance of a face memory task in children (8–15 y) and young adults (20–33 y). Behaviorally, children showed slower, more variable response times (RT), and less accurate recognition than adults. However, brain signal variability increased with age, and showed strong negative correlations with intrasubject RT variability and positive correlations with accuracy. Thus, maturation appears to lead to a brain with greater functional variability, which is indicative of enhanced neural complexity. This variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses. Our results suggest that the moment-to-moment variability in brain activity may be a critical index of the cognitive capacity of the brain.

          Author Summary

          Intuitive notions of brain–behavior relationships would suggest that because children show more variability in behavior, their brains should also be more variable. We demonstrate that this is not the case. In measuring brain signal variability with EEG and behavior in a simple face recognition task, we found that brain signal variability increases in children from 8–15 y and is even higher in young adults. Importantly, we show that this increased brain variability correlates with reduced behavioral variability and more accurate performance. A brain that has more variability also has greater complexity and a greater capacity for information processing. The implication of our findings is that variability in brain signals, or what some would call noise, is actually a critical feature of brain function. For the brain to operate at an optimal level, a certain amount of internal noise is necessary. In a certain way it could be stated that a noisy brain is a healthy brain.

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          Most cited references 47

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          Approximate entropy as a measure of system complexity.

           S Pincus (1991)
          Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
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            Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs.

            Noise in dynamical systems is usually considered a nuisance. But in certain nonlinear systems, including electronic circuits and biological sensory apparatus, the presence of noise can in fact enhance the detection of weak signals. This phenomenon, called stochastic resonance, may find useful application in physical, technological and biomedical contexts.
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              Multiscale entropy analysis of complex physiologic time series.

              There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.

                Author and article information

                Role: Editor
                PLoS Comput Biol
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                July 2008
                July 2008
                4 July 2008
                : 4
                : 7
                Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada
                University College London, United Kingdom
                Author notes

                Conceived and designed the experiments: AM. Performed the experiments: RI. Analyzed the data: NK. Contributed reagents/materials/analysis tools: NK. Wrote the paper: AM NK RI.

                Mcintosh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 9
                Research Article
                Neuroscience/Theoretical Neuroscience

                Quantitative & Systems biology


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