Stimulus-dependent spiking relationships with the EEG

Abstract

Snyder AC, Smith MA. Stimulus-dependent spiking relationships with the EEG. J Neurophysiol 114: 1468 -1482. First published June 24, 2015 doi:10.1152/jn.00427.2015The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. EEG; local field potential; spiking activity ELECTROENCEPHALOGRAPHY (EEG) has been a boon to human neuroscience. Because EEG is noninvasive, it is one of the few methods that can be used to measure human brain activity in both health and disease. Moreover, EEG has advantages over other neuroimaging methods. The cost is relatively low, compared with approaches such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG); there is no ionizing radiation, as with positron emission tomography (PET); the materials are portable (even usable with actively moving subjects; De Sanctis et al. 2012;Makeig et al. 2009), unlike nearly all alternatives; and the temporal resolution is exquisitely fine, unlike functional near-infrared spectroscopy (fNIRS) and fMRI. EEG covers a wide range of neurophysiological applications, from diagnosis of brain stem lesions to basic research on complex cognition.The principal limitation of EEG is spatial resolution. Typically, EEG is useful for implicating activity in parts of the brain on the order of a cubic centimeter (Sejnowski et al. 2014). The computational machinations of the neurons within ...

Interaction between steady-state visually evoked potentials at nearby flicker frequencies

“…Despite the widespread use of SSVEP, its precise mechanisms remain unclear, although the dominant view holds that the SSVEP results from the synchronized activity of spatially homogeneous cortical neurons in an open-field arrangement 31 . This is a simplified assumption, however, since the surface EEG can relate to the underlying neural activity in more complex ways [32][33][34][35] . Attempts to probe the nature of TF normalization at multiple scales of neural recording, therefore, could potentially shed light on the mechanisms of SSVEP generation as well.…”

Establishing a Statistical Link between Network Oscillations and Neural Synchrony

“…This simple scenario was used to demonstrate the effectiveness of the procedure, in principle, and to investigate its statistical power. The assumption that two neurons have different phase modulation curves has been reported both experimentally [ 20 , 28 ] and theoretically [ 29 ]. Jia et al [ 20 ] have shown that neurons in area V1 have various preferred phases and the distribution of the preferred phase can change in response to different stimuli.…”

“…In this experiment, spike trains from a pair of neural units in V4 were simultaneously recorded ( Fig 8A and 8D ) with a multi-electrode array during a fixation task in which spontaneous activity was measured. These data have been analyzed in another paper [ 28 ], which examined the relationship between individual neuron’s activity and large-scale network state. Here we wanted to test whether network-wide oscillations contribute to the excess pairwise synchrony.…”

“…For each neuron, we defined its surrounding LFP as the average of LFPs recorded at its adjacent electrodes (see Materials and Methods ). The spike-triggered average of the LFP for two neurons showed that the two neurons are phase locked to their surrounding field potential ( S2C and S2D Fig and [ 28 ]). We also found that the LFP showed a prominent slow oscillation ( Fig 8B and 8E ).…”

“…Experimental procedures were approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh. A separate analysis of these data has been previously reported ([ 28 , 54 ]).…”