One slope, many causes: a multiscale framework for interpreting 1/f-like neural spectra

Aperiodic, 1/f-like structure is prominent in power spectra from local field potential (LFP), electrocorticography (ECoG), magnetoencephalography (MEG), and scalp electroencephalography (EEG) recordings. Related scale-like structure is studied in slower blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) time series, although BOLD is hemodynamic rather than electromagnetic. Once treated mainly as background, aperiodic activity is now used to characterize neural state, cognition, development, disease, anesthesia, arousal, and pharmacological perturbation. Although an exponent, offset, or knee describes a recorded spectrum, it does not identify a microscopic mechanism. This review organizes interpretation around five questions: ingredients, dynamics, visibility, description, and test. Cellular and synaptic kinetics provide temporal ingredients; circuit interactions combine them; source geometry and recording modality determine visibility; spectral analysis compresses the measured mixture into fitted parameters; and perturbations and generative models test candidate explanations. The framework explains why excitation/inhibition balance is model-dependent, why a knee supports a timescale only under a specified relaxation model, and why different processes can yield the same finite-band exponent. Intracranial examples further show that parameter values vary across sources and should not be treated as modality-independent reference values. Mechanistic inference is strongest when selective perturbations and source-aware forward or generative models produce synthetic recordings analyzed with the same pipeline as the data. Thus, aperiodic spectra can reveal meaningful differences in neural state, but biological interpretation requires an explicit account of how activity is generated, projected, measured, and summarized.