Multi-scale analysis of neural activity in humans: Implications for micro-scale electrocorticography.

OBJECTIVE: Electrocorticography grids have been used to study and diagnose neural pathophysiology for over 50 years, and recently have been used for various neural prosthetic applications. Here we provide evidence that micro-scale electrodes are better suited for studying cortical pathology and function, and for implementing neural prostheses. METHODS: This work compares dynamics in space, time, and frequency of cortical field potentials recorded by three types of electrodes: electrocorticographic (ECoG) electrodes, non-penetrating micro-ECoG (µECoG) electrodes that use microelectrodes and have tighter interelectrode spacing; and penetrating microelectrodes (MEA) that penetrate the cortex to record single- or multiunit activity (SUA or MUA) and local field potentials (LFP). RESULTS: While the finest spatial scales are found in LFPs recorded intracortically, we found that LFP recorded from µECoG electrodes demonstrate scales of linear similarity (i.e., correlation, coherence, and phase) closer to the intracortical electrodes than the clinical ECoG electrodes. CONCLUSIONS: We conclude that LFPs can be recorded intracortically and epicortically at finer scales than clinical ECoG electrodes are capable of capturing. SIGNIFICANCE: Recorded with appropriately scaled electrodes and grids, field potentials expose a more detailed representation of cortical network activity, enabling advanced analyses of cortical pathology and demanding applications such as brain-computer interfaces.

Quasi-periodic fluctuations in default mode network electrophysiology.

The study of human brain electrophysiology has extended beyond traditional frequency ranges identified by the classical EEG rhythms, encompassing both higher and lower frequencies. Changes in high-gamma-band (>70 Hz) power have been identified as markers of local cortical activity. Fluctuations at infra-slow (<0.1 Hz) frequencies have been associated with functionally significant cortical networks elucidated using fMRI studies. In this study, we examined infra-slow changes in band-limited power across a range of frequencies (1-120 Hz) in the default mode network (DMN). Measuring the coherence in band-limited power fluctuations between spatially separated electrodes makes it possible to detect small, spatially extended, and temporally coherent fluctuating components in the presence of much larger incoherent fluctuations. We show that the default network is characterized by significant high-gamma-band (65-110 Hz) coherence at infra-slow (<0.1 Hz) frequencies. This coherence occurs over a narrow frequency range, centered at 0.015 Hz, commensurate with the frequency of BOLD signal fluctuations seen by fMRI, suggesting that quasi-periodic, infra-slow changes in local cortical activity form the neurophysiological basis for this network.

Power-law scaling in the brain surface electric potential.

Recent studies have identified broadband phenomena in the electric potentials produced by the brain. We report the finding of power-law scaling in these signals using subdural electrocorticographic recordings from the surface of human cortex. The power spectral density (PSD) of the electric potential has the power-law form P(f ) approximately Af(-chi) from 80 to 500 Hz. This scaling index, chi = 4.0+/-0.1, is conserved across subjects, area in the cortex, and local neural activity levels. The shape of the PSD does not change with increases in local cortical activity, but the amplitude, A, increases. We observe a "knee" in the spectra at f(0) approximately 75 Hz, implying the existence of a characteristic time scale tau = (2pif(0))(-1) approximately 2 - 4ms. Below f(0), we explore two-power-law forms of the PSD, and demonstrate that there are activity-related fluctuations in the amplitude of a power-law process lying beneath the alpha/beta rhythms. Finally, we illustrate through simulation how, small-scale, simplified neuronal models could lead to these power-law observations. This suggests a new paradigm of non-oscillatory "asynchronous," scale-free, changes in cortical potentials, corresponding to changes in mean population-averaged firing rate, to complement the prevalent "synchronous" rhythm-based paradigm.

Sensitivity of firing rate to input fluctuations depends on time scale separation between fast and slow variables in single neurons.

Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the f-I curve. We introduce a novel classification of neurons into Types A, B-, and B+ according to how f-I curves are modulated by input fluctuations. In Type A neurons, the f-I curves display little sensitivity to input fluctuations when the mean current is large. In contrast, Type B neurons display sensitivity to fluctuations throughout the entire range of input means. Type B- neurons do not fire repetitively for any constant input, whereas Type B+ neurons do. We show that Type B+ behavior results from a separation of time scales between a slow and fast variable. A voltage-dependent time constant for the recovery variable can facilitate sensitivity to input fluctuations. Type B+ firing rates can be approximated using a simple "energy barrier" model.

Beyond the gamma band: the role of high-frequency features in movement classification.

Electrocorticographic spectral changes during movement show a behavioral inflection in the classic gamma band (30-70 Hz). We quantify this inflection and demonstrate that it limits classification accuracy. We call for the designation of a functionally defined band above it, which we denote the chi-band.

Quasistatic x-ray speckle metrology of microscopic magnetic return-point memory.

We have used coherent, resonant, x-ray magnetic speckle patterns to measure the statistical evolution of the microscopic magnetic domains in perpendicular magnetic films as a function of the applied magnetic field. Our work constitutes the first direct, ensemble-averaged study of microscopic magnetic return-point memory, and demonstrates the profound impact of interfacial roughness on this phenomenon. At low fields, the microscopic magnetic domains forget their past history with an exponential field dependence.