Age-Related Changes in 1/f Neural Electrophysiological Noise

By Bradley Voytek, Mark A. Kramer, Johnny E. Case, Kyle Q. Lepage, Z. R. Tempesta, Robert T. Knight, Adam Gazzaley. Journal of Neuroscience, 2015

675 citations recorded by the source corpus.

Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15–53 years) and scalp EEG data from healthy younger (20–30 years) and older (60–70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often <−1 for electrophysiological data and has been shown to approach white noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging. SIGNIFICANCE STATEMENT Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world's population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/716beb57db28761ee0376fb5766033af27699fc6

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Citations in the corpus

Inferring Synaptic Excitation/Inhibition Balance from Field Potentials (2016, positive): “…of the PSD (“rotation”) have recently been observed in several empirical studies, linking it to changes in global awake and sleep states (He et al. 2010), age-related working memory decline (Voytek et al. 2015; Voytek & Knight 2015), and visuomotor task-related activation (Podvalny et al. 2015).”
Parameterizing neural power spectra into periodic and aperiodic components (2020, background): “1 | False oscillatory power changes and illusory oscillations. a , Here we took a real neural PSD (blue) and artificially introduced a change in the aperiodic exponent, similar to what is seen in healthy aging 18 .”
Scale-Free Neurodynamics as Functional Fingerprint of Brain Regions (2026, unknown): “Voytek et al. [17] further demonstrated that age-related changes in the spectral slope reflect meaningful alterations in cortical circuitry, while distinct developmental changes in aperiodic dynamics during infancy have also been reported [18,19].”
The development of aperiodic neural activity in the human brain (2024, unknown): “…hypothesized the following: (a) in association cortices, the aperiodic slope flattens with age into young adulthood; (b) in sensorimotor cortices, the aperiodic slope flattens with age into adolescence; (c) attentional state (task-based versus task-free) modulates age effects observed in (a) and…”
Aperiodic Dynamics of Cell Assemblies Recruited for L1 and L2 Processing of French Wh-Dependencies Highlight a Temporo-Parietal Engagement in Syntax (2026, unknown): “The exponent x has been argued to reflect the balance of postsynaptic and transmembrane activity for excitatory and inhibitory circuits [56–59], with a steeper slope indicating greater inhibitory signal [60].”
Non-Neural Sources Systematically Impact Aperiodic EEG Activity (2026, unknown): “While the offset has been linked to neuronal population spiking rates 3 , variations in the exponent are proposed to reflect differences in neural noise 4,5 , self-organized criticality 6 – 8 , or the most prominently 9 , the balance between inhibitory and excitatory synaptic activity 10,11 .”
Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “Studies have observed an age-related flattening of RS power spectra from infancy to adolescence (Tran, 2020; Schawor-onkow and Voytek, 2021; Karalunas, 2022; He et al., 2019; Waschke et al., 2017; Voytek, 2015; Merkin, 2023; Donoghue, 2025).”
Confirmation of a Useful Dark‐Room Resting‐State Procedure: Periodic and Aperiodic MEG Activity in Children (2026, unknown): “…children have shown that from childhood to late adolescence the RS 1/f power-spectrum slope flattens as a function of age until eventually stabilizing at an adult level (Schaworonkow and Voytek 2021; Karalunas et al. 2022; He et al. 2019; Tran et al. 2020; Waschke et al. 2017; Voytek et al. 2015).”
Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood (2025, unknown): “Longitudinal studies extending from childhood to adulthood have observed decreases in aperiodic slope with age (Hill et al., 2022; McSweeney et al., 2023; Merkin et al., 2023; Voytek et al., 2015).”
Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso (2025, unknown): “Given the emerging evidence, the AP-exponent shows strong variations in aging up to 70 years (Voytek et al., 2015) and childhood aged 4–12 years.”
Altered network connectivity and global efficiency in tourette syndrome: insights into sensorimotor integration (2025, unknown): “Increased neural noise is associated with reduced synchronization of neuronal activity (Voytek et al., 2015).”
Reevaluating the neural noise in dyslexia using biomarkers from electroencephalography and high-resolution magnetic resonance spectroscopy (2025, unknown): “…putatively characterized by changes in E/I balance, such as early development in infancy ( Schaworonkow and Voytek, 2021 ), healthy aging ( Voytek et al., 2015 ), and neurodevelopmental disorders like ADHD ( Ostlund et al., 2021 ), autism spectrum disorder ( Manyukhina et al., 2022 ), or…”
Heterochronous laminar maturation in the human prefrontal cortex (2025, unknown): “The aperiodic exponent decreases with a desynchronization of population-level firing 77 that results in faster temporal fluctuations in (i.e., a faster timescale of) activity 61,62 .”
Longitudinal trajectories of aperiodic EEG activity in early to middle childhood (2024, background): “The extant literature suggests there may be a shift in early childhood from increasing (steeper) to decreasing (flattening) slope (Hill et al., 2022; McSweeney et al., 2023; Voytek et al., 2015; Wilkinson et al., 2024).”
Exploring Background Aperiodic Electroencephalography (EEG) Activity in the Bucharest Early Intervention Project (2024, unknown): “He et al., 2019; Schaworonkow & Voytek, 2021; Voytek et al., 2015; Voytek & Knight, 2015), there is less longitudinal research focusing on longitudinal changes in aperiodic parameters across the life-span, with only McSweeney et al. (2021) examining changes through adolescence.”