Alina Peter

Parametric variation of gamma frequency and power with luminance contrast: A comparative study of human MEG and monkey LFP and spike responses

Gamma oscillations contribute significantly to the manner in which neural activity is bound into functional assemblies. The mechanisms that underlie the human gamma response, however, are poorly understood. Previous computational models of gamma rely heavily on the results of invasive recordings in animals, and it is difficult to assess whether these models hold in humans. Computational models of gamma predict specific changes in gamma spectral response with increased excitatory drive. Hence, differences and commonalities between spikes, LFPs and MEG in the spectral responses to changes in excitatory drive can lead to a refinement of existing gamma models. We compared gamma spectral responses to varying contrasts in a monkey dataset acquired previously (Roberts et al., 2013) with spectral responses to similar contrast variations in a new human MEG dataset. We found parametric frequency shifts with increasing contrast in human MEG at the single-subject and the single-trial level, analogous to those observed in the monkey. Additionally, we observed parametric modulations of spectral asymmetry, consistent across spikes, LFP and MEG. However, while gamma power scaled linearly with contrast in MEG, it saturated at high contrasts in both the LFP and spiking data. Thus, while gamma frequency changes to varying contrasts were comparable across spikes, LFP and MEG, gamma power changes were not. This indicates that gamma frequency may be a more stable parameter across scales of measurements and species than gamma power. The comparative approach undertaken here represents a fruitful path towards a better understanding of gamma oscillations in the human brain.

Input-Dependent Frequency Modulation of Cortical Gamma Oscillations Shapes Spatial Synchronization and Enables Phase Coding

Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

A quantitative theory of gamma synchronization in macaque V1

Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other’s phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1 and is likely generalizable to other brain regions and rhythms.

Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.

Synchronization principles of gamma rhythms in monkey visual cortex

Neural synchronization in the gamma-band (25-80Hz) can enhance and route information flow during sensory and cognitive processing. However, it is not understood how synchronization between neural groups is robustly achieved and regulated despite of large variability in the precise oscillation frequency. A common belief is that continuous frequency matching over time is required for synchronization and that thus rhythms with different frequencies cannot establish preferred phase-relations. Here, by studying gamma rhythms in monkey visual area V1, we found that the temporal variation of the frequency difference was to the contrary essential for synchronization. Gamma rhythms synchronized by continuously varying their frequency difference in a phase-dependent manner. The synchronization level and the preferred phase-relation were determined by the amplitude and the mean of the frequency difference variations. Strikingly, stronger variation of the frequency difference led to stronger synchronization. These observations were reproduced by a biophysical model of gamma rhythms and were explained within the theory of weakly coupled oscillators. Using a single and general equation, we derived analytical predictions that precisely matched our V1 gamma data across different stimulus conditions. Our work reveals the principles of how gamma rhythms synchronize, where phase-dependent frequency variations play a central role. These frequency variations are characteristic for the intermittent synchronization regime, a non-stationary regime naturally occurring between the state of complete synchrony and asynchrony. This regime allows for synchronization between rhythms of variable frequencies, which is essential for achieving robust synchronization in the complex and noisy networks of the brain.Neuronal gamma-band synchronization shapes information flow during sensory and cognitive processing. A common view is that a stable and shared frequency over time is required for robust and functional synchronization. To the contrary, we found that non-stationary instantaneous frequency modulations were essential for synchronization. First, we recorded gamma rhythms in monkey visual area V1, and found that they synchronized by continuously modulating their frequency difference in a phase-dependent manner. The frequency modulation properties regulated both the phase-locking and the preferred phase-relation between gamma rhythms. Second, our experimental observations were in agreement with a biophysical model of gamma rhythms and were accurately predicted by the theory of weakly coupled oscillators revealing the underlying theoretical principles that govern gamma synchronization. Thus, synchronization through instantaneous frequency modulations represents a fundamental principle of gamma-band neural coordination that is likely generalizable to other brain rhythms.

Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

The integration of direct bottom-up inputs with contextual information is a canonical motif in neocortical circuits. In area V1, neurons may reduce their firing rates when the (classical) receptive field input can be predicted by the spatial context. We previously hypothesized that gamma-synchronization (30-80Hz) provides a complementary signal to rates, encoding whether stimuli are predicted from spatial context by preferentially synchronizing neuronal populations receiving predictable inputs. Here we investigated how rates and synchrony are modulated by predictive context. Large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma-synchronization, but only when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma-synchronization while increasing firing rates. Differences between colors, including strong gamma-responses to red, arose because of stimulus adaptation to a full-screen background, with a prominent difference in adaptation between M- and L-cone signaling pathways. Thus, synchrony signals whether RF inputs are predicted from spatial context and may encode relationships across space, while firing rates increase when stimuli are unpredicted from the context.