Spontaneous Neuronal Oscillations in the Human Insula are Hierarchically Organized Traveling Waves

The insula plays a fundamental role in a wide range of adaptive human behaviors, but its electrophysiological dynamics are poorly understood. Here we used human intracranial electroencephalographic recordings to investigate the electrophysiological properties and hierarchical organization of spontaneous neuronal oscillations within the insula. We analyzed the neuronal oscillations of the insula directly and found that rhythms in the theta and beta frequency oscillations are widespread and spontaneously present. These oscillations are largely organized along the anterior-posterior axis of the insula. Both the left and right insula showed anterior-to-posterior decreasing gradients for the power of oscillations in the beta frequency band. The left insula also showed a posterior-to-anterior decreasing frequency gradient and an anterior-to-posterior decreasing power gradient in the theta frequency band. In addition to measuring the power of these oscillations, we also examined the phase of these signals across simultaneous recording channels and found that the insula oscillations in the theta and beta bands are traveling waves. The strength of the traveling waves in each frequency was positively correlated with the amplitude of each oscillation. However, the theta and beta traveling waves were uncoupled to each other in terms of phase and amplitude, which suggested that insula traveling waves in the theta and beta bands operate independently. Our findings provide new insights into the spatiotemporal dynamics and hierarchical organization of neuronal oscillations within the insula, which, given its rich connectivity with widespread cortical regions, indicates that oscillations and traveling waves have an important role in intra- and inter-insula communication.

Baseline Shift in Neuronal Oscillations and Its Implications for the Interpretation of Evoked Activity Obtained with EEG/MEG

Oscillations and evoked responses are two types of neuronal activity recorded non-invasively with EEG/MEG. Although typically studied separately, they might in fact represent the same process. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which would indicate that stimulus-relating amplitude modulation of neuronal oscillations should lead to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model generating alpha rhythm, we indeed demonstrated that the oscillatory mean is nonzero for a large range of model-parameter values. In EEG data we detected non-zero mean alpha oscillations in about 96% of the participants. Furthermore, using neuronal-ensemble modelling, we provided an explanation for the often observed discrepancies between amplitude modulation and baseline shifts. Overall, our results provide strong support for the unification of neuronal oscillations and evoked responses.

Individual Stability of Pain- and Touch-Related Neuronal Gamma Oscillations

Background: The processing of brief pain and touch stimuli has been associated with an increase of neuronal oscillations in the gamma range (40-90 Hz). However, some studies report divergent gamma effects across single participants. Methods: In two repeated sessions we recorded gamma responses to pain and touch stimuli using EEG. Individual gamma responses were extracted from EEG channels and from ICA components that contain a strong gamma amplitude. Results: We observed gamma responses in the majority of the participants. If present, gamma synchronisation was always bound to a component that contained a laser-evoked response. We found a broad variety of individual cortical processing: some participants showed a clear gamma effect, others did not exhibit any gamma. For both modalities, the effect was reproducible between sessions. In addition, participants with a strong gamma response showed a similar time-frequency pattern across sessions. Conclusions: Our results indicate that current measures of reproducibility of research results do not reflect the complex reality of the diverse individual processing pattern of applied pain and touch. The present findings raise the question of whether we would find similar quantitatively different processing patterns in other domains in neuroscience: group results would be replicable but the overall effect is driven by a subgroup of the participants.

Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

Neuronal oscillations, their inter-areal synchronization and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. However, the genetic underpinnings of oscillatory dynamics have remained poorly understood. We recorded resting-state magnetoencephalography (MEG) from 82 participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase ( COMT ) Val 158 Met and brain-derived neurotrophic factor ( BDNF ) Val 66 Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Brain criticality framework and computational modelling of near-critical synchronization dynamics suggested that COMT and BDNF polymorphisms influenced local oscillations via differences in net excitation-inhibition balance. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in local and large-scale synchronization dynamics of neuronal oscillations.

Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data

Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni's working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. 'Normalised shapes' can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.