Bradley Voytek

This study investigates aperiodic dynamics during sleep K-complexes, finding a genuine cortical state shift associated with these events after accounting for measurement artifacts.

The paper shows that action potential waveforms are state-dependent and not random, challenging the binary spike assumption in neural coding.

The paper simulates field potentials to disentangle neural mechanisms of human temporal attention, identifying signatures of attentional bias and anticipatory activity that predict perceptual accuracy.

This study uses tRAS to manipulate aperiodic neural activity and shows it improves visual working memory precision in younger adults.

The paper examines the coupling between respiration and neural oscillations by analyzing breath-by-breath waveform shapes in human brain recordings.

The paper examines longitudinal changes in aperiodic EEG activity and links them to age-related declines in sustained attention but not executive function.

ScaleSurfer rapidly estimates whole-brain morphometric features using a multi-scale vision transformer, achieving speeds up to five orders of magnitude faster than traditional methods.

NeuroVLM generates and evaluates vision-language models for human neuroimaging using 30,826 neuroimage-text pairs to perform tasks like text-to-image generation and image-to-text interpretation.

This study challenges conventional time-frequency analyses by showing that post-stimulus theta power increases may reflect changes in aperiodic activity rather than genuine oscillations.

This paper investigates how theta and alpha oscillations contribute to visual working memory, finding that aperiodic neural activity explains apparent theta paradoxes and reveals novel aperiodic dynamics.

This paper introduces two methods to estimate timescales in resting-state fMRI, showing the time-domain method is more accurate and provides valid statistical inference with Python implementations.

The paper quantifies the contribution of aperiodic activity to oscillatory functional networks in EEG data and finds that aperiodic activity dominates most networks.

The paper reanalyzes EEG data to distinguish aperiodic activity from alpha oscillations in visual working memory, finding distinct roles for each in encoding and maintaining spatial location.

The paper analyzes EEG data to show that aperiodic neural timescales in prefrontal cortex increase with higher task abstraction during a cognitive control task.

This study evaluates and compares different measures of aperiodic neural activity, showing shared variance across methods and differences in their sensitivity to oscillatory activity.

This study examines aperiodic EEG activity in Parkinson's disease during cognitive tasks, finding increased offsets and task-related differences but no behavioral or clinical correlations.

The paper analyzes resting-state alpha and mu rhythms in children to assess developmental changes and their potential as biomarkers for ADHD and autism, finding no significant differences between groups.

This study maps neurophysiological dynamics to cortical micro-architecture, finding spatial correlations between brain activity patterns and microstructural features.

This study shows that aperiodic neural activity, not oscillatory power, better predicts schizophrenia diagnosis and severity using EEG data during a target detection task.

This paper investigates the trade-off between computational error and population-level synchrony in neural oscillations and identifies a threshold for pathological synchrony.

The paper reanalyzes EEG data to show that both oscillations and aperiodic activity are co-modulated during memory encoding.

The paper shows that EEG spectral exponents reflect changes in E:I balance and selective attention, and track stimulus spectral exponents in sensory cortices, predicting behavioral performance.

The paper uses local field potentials in zebra finches to decode vocal sequence elements and shows similarities to human motor areas.

This paper introduces data-driven spatial filters to enhance neuronal oscillation measurement in intracranial electrophysiological recordings, showing improved signal-to-noise ratio and better analysis of oscillation properties.

The paper examines 1/f aperiodic activity in cortical layers across species, finding steeper slopes and greater magnitudes in superficial layers.

The paper measures the costs and benefits of converting aperiodic neural codes to rhythmic ones in two models, finding that oscillations can enhance communication by correcting undersampling but depend on specific physical details.

This study examines longitudinal changes in periodic and aperiodic neuronal activity in infants using EEG data, showing early flattening of the EEG power spectrum and the need to separate these signals for accurate oscillation measurement.

An open-source Python algorithm decomposes neural power spectra into a dynamic aperiodic 1/f background and Gaussian oscillatory peaks without requiring predefined canonical frequency bands.

The paper infers neuronal timescales from human intracranial recordings and shows they align with cortical microarchitecture and cognitive functions.

This paper shows that band ratio measures in electrophysiological data reflect both periodic and aperiodic activity, challenging their typical interpretation as oscillatory power ratios.