Parameterizing neural power spectra into periodic and aperiodic components

By Thomas Donoghue, Matar Haller, Erik J. Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Antonio H. Lara, J. Wallis, Robert T. Knight and 2 more. Nature Neuroscience, 2020

1,825 citations recorded by the source corpus.

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis. A method for parameterizing electrophysiological neural power spectra into periodic and aperiodic components is introduced, addressing limitations of common approaches. The method is validated in simulation and demonstrated on real data applications.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9505733780b78ae59efa5530213a97208be526fa

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In Cognitive Neuroscience

Citations in the corpus

Aperiodic neural timescales in prefrontal cortex dilate with increased task abstraction (2025, unknown): “We model PSDs using the Python-based, open-source FOOOF toolbox (36).”
Distinct laminar origins of sensory-evoked high-gamma and low-frequency ECoG signals revealed by optogenetics (2026, unknown): “CSEPs contain both periodic and aperiodic signal components with energy across multiple frequencies (12).”
Cortical-limbic circuit dynamics of approach-avoidance conflict in humans (2026, unknown): “We used the FOOOF (Fitting Oscillations and One-Over-F) algorithm — which decomposes the power spectrum into its aperiodic (1/f) and oscillatory components — to these contacts and found evidence of theta oscillations in the MFG in each patientinatleastonecontact 37 .”
Scale-Free Neurodynamics as Functional Fingerprint of Brain Regions (2026, unknown): “While previous studies have interpreted the 1/ f slope as a proxy for cortical excitability or E/I balance [14,16,17,34], such conclusions typically rely on task-based paradigms, developmental comparisons, or direct behavioral associations.”
Distinct laminar origins of high-gamma and low-frequency ECoG signals revealed by optogenetics (2025, unknown): “CSEPs contain both periodic and aperiodic signal components with energy across multiple frequencies 12 .”
The development of aperiodic neural activity in the human brain (2024, unknown): “Studies using scalp electroencephalography (EEG) during passive (that is, task-free) states have consistently demonstrated a flattening of the slope and a downward shift in the offset with advancing age throughout adulthood 8,16,27,28 .”
Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity (2024, unknown): “Each of these time series have well-studied features that can be extracted, such as measurements of periodic or rhythmic activity (Buzs ´ aki and Draguhn, 2004; Engel et al., 2001) and of phase and amplitude of aperiodic activity (Donoghue et al., 2020).”
Human alpha frequency and expression across the lifespan: a 2,172-participant electrophysiology-MRI atlas (2026, unknown): “This agrees with the view of alpha as a multidimensional and state-sensitive phenomenon [6,7,13,29,30].”
Characterization of aperiodic and theta activity in preterm infants using EEG: Insights into cerebral maturation and inter-individual variability (2026, unknown): “Yet distinguishing aperiodic and oscillatory activity is particularly important in developmental research, as underlying developmental mechanisms may differ (Donoghue et al., 2020; Schaworonkow and Voytek, 2021).”
Non-Neural Sources Systematically Impact Aperiodic EEG Activity (2026, unknown): “The resulting PSD was then parameterized using the specparam algorithm 1 , which decomposes the power spectrum into an aperiodic component and oscillatory peaks.”
Relating ADHD to the aperiodic component of the EEG signal in both males and females across development (2026, unknown): “The Specparam (previously Fitting Oscillations and One-Over-Frequency; FOOOF) toolbox was used to decompose the PSDs into periodic (oscillatory) and aperiodic components (Donoghue et al., 2020b).”
Revolutionizing Pediatric Neurophysiology With Magnetoencephalography (2026, unknown): “…activity (contributing power across all frequencies if decomposed into sinusoids) co-existing with periodic oscillations (e.g., actual neural oscillations visible as peaks in the power spectrum, such as the RS dominant oscillation) (He 2014; Freeman and Zhai 2009; Donoghue, Haller, et al. 2020).”
The Interaction between Sleep and Development on Wake EEG Oscillations (2026, unknown): “To dissociate periodic and aperiodic spectral power, we used the MATLAB extension of specparam [formerly known as FOOOF (Donoghue et al., 2020)].”
Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “A growing literature relies on the ‘spec-param ’ toolbox (Donoghue, 2020; Ostlund, 2022) to separately param-eterize periodic and aperiodic activity (Donoghue et al., 2020; Ostlund, 2022; He et al., 2019).”
A human electrophysiological signature of Fragile X pathophysiology is shared in V1 of Fmr1-/y mice (2026, unknown): “An effective way to model the 1/f signal of the absolute power spectrum across a variety of datasets is to use a Lorentzian function 49 .”
Confirmation of a Useful Dark‐Room Resting‐State Procedure: Periodic and Aperiodic MEG Activity in Children (2026, unknown): “Thus, what appear to be peaks in the power spectrum, such as RS alpha activity, may overestimate that periodic activity (He 2014; Freeman and Zhai 2009; Donoghue, Haller, et al. 2020).”