Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development

By Anthony Randal McIntosh, Natasa Kovacevic, Roxane J. Itier. PLoS Comput. Biol., 2008

439 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d3603f312c4f3f52c47e6478bf8eb33f15be3898

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Citations in the corpus

Emerging concepts for the dynamical organization of resting-state activity in the brain (2010, positive): “Some human developmental studies have suggested profound correlations between the emergence of ‘brain noise’ and cognitive stability 63,64 , whereas in normal aging this intrinsic variability shows regional decreases that may impact behaviour 65 . fMRI studies have demonstrated that the state of…”
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, methodology): “example, SR is mentioned in several recent PLoS articles [1–4],”
Dynamic reconfiguration of human brain networks during learning (2010, background): “Such a hypothesis is bolstered by recent complementary evidence suggesting that variability in brain signals also supports mental effort in a variety of cognitive operations (50), presumably by aiding the brain in switching between different network configurations as it masters a new task.”
Age-Related Changes in 1/f Neural Electrophysiological Noise (2015, background): “, 2001) or related information theoretic measures (McIntosh et al., 2008).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, background): “Notably, brain activity variability has been linked to task performance, development and disease and it has been suggested that signal variability is not “noise” but crucial for spontaneous or stimulated reorganizations of the brain (Garrett et al., 2010; McIntosh et al., 2008).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Over development, rs-fcMRI networks show increased within-subject variability (Hutchison and Morton, 2015; Marusak et al., 2016; Qin et al., 2015), consistent with EEG studies showing that signal complexity increases over development (McIntosh et al., 2008; Vakorin et al., 2011).”
Behavioral Phenotype Associations With Resting State EEG Signal Complexity and Power Spectral Density in Fragile X Syndrome (2026, unknown): “Greater complexity has been associated with brain maturation and better cognitive performances (Lippe et al. 2009; McIntosh et al. 2008; Vakorin et al. 2011).”