Scale-free brain activity: past, present and future

By Biyu J. He. Trends in Cognitive Sciences, 2014

748 citations recorded by the source corpus.

Brain activity observed at many spatiotemporal scales exhibits a 1/f-like power spectrum, including neuronal membrane potentials, neural field potentials, noninvasive electroencephalography, magnetoencephalography and functional magnetic resonance imaging signals. A 1/f-like power spectrum is indicative of arrhythmic brain activity that does not contain a predominant temporal scale (hence, “scale-free”). This characteristic of scale-free brain activity distinguishes it from brain oscillations. While scale-free brain activity and brain oscillations coexist, our understanding of the former remains very limited. Recent research has shed light on the spatiotemporal organization, functional significance and potential generative mechanisms of scale-free brain activity, as well as its developmental and clinical relevance. A deeper understanding of this prevalent brain signal should provide new insights and analytical tools for cognitive neuroscience.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ad93a855a5393558cec4e7104b229845b7dec835

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In Cognitive Neuroscience

Citations in the corpus

Colloquium: Criticality and dynamical scaling in living systems (2017, background): “Understanding the genesis and functionality of spontaneous cortical activity – which accounts for about 20% of the total oxygen consumption of a person at rest– is key to shedding light onto how the cortex processes information and computes (Arieli et al., 1996; Deco et al., 2011, 2013a; He, 2014).”
EEG microstates as a tool for studying the temporal dynamics of whole‐brain neuronal networks: A review (2017, background): “…power-law properties appear ubiquitous in a large number of natural phenomena including EEG and ECoG signals nd local field potentials, the question whether 1/f scaling property is evidence for the exist nce of neuronal critical states still remains controversial (Bedard et al., 2006; He, 2014).”
Parameterizing neural power spectra into periodic and aperiodic components (2020, methodology): “…such as frequency 11,12 , power 9,10 and potentially bandwidth, as well as the aperiodic parameters of broadband offset 13–15 and exponent 8,16–19 , can and do change in behaviorally and physiologically meaningful ways, with some emerging evidence suggesting that they interact with one…”
The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine (2019, neutral): “redundancy) (He, 2014), which is consistent with decreased randomness of EEG signals during propofol anesthesia (Wang et al.”
Neural population coding: combining insights from microscopic and mass signals (2015, background): “…of the joint measurements arises from the complementary nature of single-neuron ﬁring and Measurements of neural mass activity with high temporal resolution (LFP, MEG, EEG, ECoG) provide rich signals varying on multiple timescales [110] and exhibiting reasonable spatial specificity [110].”
The development of theta and alpha neural oscillations from ages 3 to 24 years (2021, background): “Furthermore, the aperiodic offset may reflect overall neuronal population firing rates and scale-free brain activity that is dissociable from ongoing oscillations (He, 2014; Ray and Maunsell, 2011).”
Scale-Free Neurodynamics as Functional Fingerprint of Brain Regions (2026, unknown): “The PSD in both the low-frequency (0.5–4 Hz) and high-frequency (33–80 Hz) ranges exhibited a scale-free trend (with different slopes), consistent with previous reports, particularly in cortical areas [15,31,32].”
Disentangling Shared and Private Neural Dynamics with SPIRE: A Latent Modeling Framework for Deep Brain Stimulation (2025, unknown): “…warps and bilinear mixing with 1 /f noise and AR(1) latents; D2 extends D1 with a slow time-varying inter-regional lag (sinusoidal) to model phase-dependent communication and latency variability (Bedard et al., 2006; He, 2014; Dupr´e la Tour et al., 2017; Fries, 2005; 2015; Lakatos et al., 2008).”
Non-Neural Sources Systematically Impact Aperiodic EEG Activity (2026, unknown): “Datasets rated as poor quality were excluded from further processing (see Supplement 7 for a detailed description of the pipeline).”
Relating ADHD to the aperiodic component of the EEG signal in both males and females across development (2026, unknown): “…neuronal activity) and aperiodic activity (irregular arrhythmic and quasi-periodic bursts of broadband neural activity, traditionally seen as signal noise, now thought to represent the intrinsic background EEG state; Buzs ´ aki and V ¨ or ¨ oslakos, 2023; Donoghue et al., 2020b; He, 2014).”
Revolutionizing Pediatric Neurophysiology With Magnetoencephalography (2026, unknown): “Readers are also referred to papers discussing best practices for data collection, analysis, and reporting for EEG (Picton et al. 2000; Keil et al. 2022, 2014) and MEG (Keil et al. 2022, 2014; Gross et al. 2013; Clarke et al. 2022).”
Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “RS neural activity exhibits aperiodic background activity (contributing power across all frequencies when subjected to standard power-spectrum analysis) that co-exists with periodic oscillations (He, 2014; Freeman and Zhai, 2009; Donoghue, 2020).”
Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age (2026, unknown): “…’ (i.e., it is not restricted to a speci ﬁ c A full list of af ﬁ liations appears at the end of the paper. e-mail: ilksuu@utu. ﬁ temporalscale) 32 ,age-relatedchangescouldalsobedetectedusingaperiodic parameters of the resting-state functional MRI (rs-fMRI) blood-oxygen-level-dependent (BOLD)…”
Exploring Background Aperiodic Electroencephalography (EEG) Activity in the Bucharest Early Intervention Project (2024, unknown): “He, 2014; B. J.”
The development of aperiodic and periodic resting-state power between early childhood and adulthood: New insights from optically pumped magnetometers (2024, background): “He, 2014).”
Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research (2024, background): “These two EEG components have been shown to exhibit distinct properties and dynamics across tasks (He, 2014).”