Instantaneous voltage as an alternative to power- and phase-based interpretation of oscillatory brain activity (original) (raw)
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Frontiers in Human Neuroscience, 2015
A central goal of neuroscience is to determine how the brain's relatively static anatomy can support dynamic cortical function, i.e., cortical function that varies according to task demands. In pursuit of this goal, scientists have produced a large number of experimental results and established influential conceptual frameworks, in particular communication-through-coherence (CTC) and gating-by-inhibition (GBI), but these data and frameworks have not provided a parsimonious view of the principles that underlie cortical function. Here I synthesize these existing experimental results and the CTC and GBI frameworks, and propose the function-through-biased-oscillations (FBO) hypothesis as a model to understand dynamic cortical function. The FBO hypothesis suggests that oscillatory voltage amplitude is the principal measurement that directly reflects cortical excitability, that asymmetries in voltage amplitude explain a range of brain signal phenomena, and that predictive variations in such asymmetric oscillations provide a simple and general model for information routing that can help to explain dynamic cortical function.
Dominant frequencies of resting human brain activity as measured by the electrocorticogram
NeuroImage, 2013
The brain's spontaneous, intrinsic activity is increasingly being shown to reveal brain function, delineate large scale brain networks, and diagnose brain disorders. One of the most studied and clinically utilized types of intrinsic brain activity are oscillations in the electrocorticogram (ECoG), a relatively localized measure of cortical synaptic activity. Here we objectively characterize the types of ECoG oscillations commonly observed over particular cortical areas when an individual is awake and immobile with eyes closed, using a surface-based cortical atlas and cluster analysis. Both methods show that [1] there is generally substantial variability in the dominant frequencies of cortical regions and substantial overlap in dominant frequencies across the areas sampled (primarily lateral central, temporal, and frontal areas), [2] theta (4-8 Hz) is the most dominant type of oscillation in the areas sampled with a mode around 7 Hz, [3] alpha (8-13 Hz) is largely limited to parietal and occipital regions, and [4] beta (13-30 Hz) is prominent peri-Rolandically, over the middle frontal gyrus, and the pars opercularis. In addition, the cluster analysis revealed seven types of ECoG spectral power densities (SPDs). Six of these have peaks at 3, 5, 7 (narrow), 7 (broad), 10, and 17 Hz, while the remaining cluster is broadly distributed with less pronounced peaks at 8, 19, and 42 Hz. These categories largely corroborate conventional sub-gamma frequency band distinctions (delta, theta, alpha, and beta) and suggest multiple sub-types of theta. Finally, we note that gamma/high gamma activity (30+ Hz) was at times prominently observed, but was too infrequent and variable across individuals to be reliably characterized. These results should help identify abnormal patterns of ECoG oscillations, inform the interpretation of EEG/MEG intrinsic activity, and provide insight into the functions of these different oscillations and the networks that produce them. Specifically, our results support theories of the importance of theta oscillations in general cortical function, suggest that alpha activity is primarily related to sensory processing/attention, and demonstrate that beta networks extend far beyond primary sensorimotor regions.
High-frequency gamma oscillations and human brain mapping with electrocorticography
Progress in Brain Research, 2006
Invasive EEG recordings with depth and/or subdural electrodes are occasionally necessary for the surgical management of patients with epilepsy refractory to medications. In addition to their vital clinical utility, electrocorticographic (ECoG) recordings provide an unprecedented opportunity to study the electrophysiological correlates of functional brain activation in greater detail than non-invasive recordings. The proximity of ECoG electrodes to the cortical sources of EEG activity enhances their spatial resolution, as well as their sensitivity and signal-to-noise ratio, particularly for high-frequency EEG activity. ECoG recordings have, therefore, been used to study the event-related dynamics of brain oscillations in a variety of frequency ranges, and in a variety of functional-neuroanatomic systems, including somatosensory and somatomotor systems, visual and auditory perceptual systems, and cortical networks responsible for language. These ECoG studies have confirmed and extended the original non-invasive observations of ERD/ERS phenomena in lower frequencies, and have discovered novel event-related responses in gamma frequencies higher than those previously observed in non-invasive recordings. In particular, broadband event-related gamma responses greater than 60 Hz, extending up to $200 Hz, have been observed in a variety of functional brain systems. The observation of these ''high gamma'' responses requires a recording system with an adequate sampling rate and dynamic range (we use 1000 Hz at 16-bit A/D resolution) and is facilitated by event-related time-frequency analyses of the recorded signals. The functional response properties of high-gamma activity are distinct from those of ERD/ERS phenomena in lower frequencies. In particular, the timing and spatial localization of high-gamma ERS often appear to be more specific to the putative timing and localization of functional brain activation than alpha or beta ERD/ERS. These findings are consistent with the proposed role of synchronized gamma oscillations in models of neural computation, which have in turn been inspired by observations of gamma activity in animal preparations, albeit at somewhat lower frequencies. Although ECoG recordings cannot directly measure the synchronization of action potentials among assemblies of neurons, they may demonstrate event-related interactions between gamma oscillations in macroscopic local field potentials (LFP) generated by different large-scale populations of neurons engaged by the same functional task. Indeed, preliminary studies suggest that such interactions do occur in gamma frequencies, including high-gamma frequencies, at latencies consistent with the timing of task performance. The neuronal mechanisms underlying high-gamma activity and its unique response properties in humans are still largely unknown, but their investigation through invasive methods is expected to facilitate and expand their potential clinical and research applications, including functional brain mapping, brain-computer interfaces, and neurophysiological studies of human cognition.
Large-scale cortical correlation structure of spontaneous oscillatory activity
Nature Neuroscience, 2012
Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8-32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4-6 Hz), in lateral parietal areas in the alpha to beta frequency range (8-23 Hz) and in sensorimotor areas for higher frequencies (32-45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.
Power and phase properties of oscillatory neural responses in the presence of background activity
Journal of Computational Neuroscience, 2012
Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by 'inter-trial phase coherence', is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.
A common challenge in neuroscience is how to decompose noisy, multi-source signals measured in experiments into biophysically interpretable components. Analysis of cortical surface electrical potentials (CSEPs) measured using electrocorticography arrays (ECoG) typifies this problem. We hypothesized that high frequency (70-1,000 Hz) CSEPs are composed of broadband (i.e., power-law) and bandlimited components with potentially differing biophysical origins. In particular, the high-gamma band (70-150 Hz) has been shown to be highly predictive for encoding and decoding behaviors and stimuli. Despite its demonstrated importance, whether high-gamma is composed of a bandlimited signal is poorly understood. To address this gap, we recorded CSEPs from rat auditory cortex and demonstrate that the evoked CSEPs are composed of multiple distinct frequency components, including high-gamma. We then show, using a novel robust regression method, that at fast timescales and on single trials during spe...
Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG
Biosensors
The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly differen...
Probing for cortical excitability
2011
This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus via intracranial electroencephalography (iEEG). Stimuli consisted of 100 single bi-phasic pulses, delivered every 10 minutes. Neural excitability is estimated by extracting a feature from the iEEG responses to the stimuli, which we dub the mean phase variance (PV). We show that the mean PV increases with the rate of inter-ictal discharges in one patient. In another patient, we show that the mean PV changes with sleep and an epileptic seizure. The results demonstrate a proof-of-principal for the method to be applied in a seizure anticipation framework.