Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network

Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex

Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.

Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.

Prefrontal PV interneurons facilitate attention and are linked to attentional dysfunction in a mouse model of absence epilepsy

Absence seizures are characterized by brief periods of unconsciousness accompanied by lapses in motor function that can occur hundreds of times throughout the day. Outside of these frequent moments of unconsciousness, approximately a third of people living with the disorder experience treatment-resistant attention impairments. Convergent evidence suggests prefrontal cortex (PFC) dysfunction may underlie attention impairments in affected patients. To examine this, we use a combination of slice physiology, fiber photometry, electrocorticography (ECoG), optogenetics, and behavior in the Scn8a+/-mouse model of absence epilepsy. Attention function was measured using a novel visual attention task where a light cue that varied in duration predicted the location of a food reward. In Scn8a+/-mice, we find altered parvalbumin interneuron (PVIN) output in the medial PFC (mPFC) in vitro and PVIN hypoactivity along with reductions in gamma power during cue presentation in vivo. This was associated with poorer attention performance in Scn8a+/-mice that could be rescued by gamma-frequency optogenetic stimulation of PVINs. This highlights cue-related PVIN activity as an important mechanism for attention and suggests PVINs may represent a therapeutic target for cognitive comorbidities in absence epilepsy.

Finely-tuned gamma oscillations: Spectral characteristics and links to dyskinesia

Gamma oscillations comprise a loosely defined, heterogeneous group of functionally different activities between 30 and 100 Hz in the cortical and subcortical local field potential (LFP) of the motor network. Two distinct patterns seem to emerge which are easily conflated: Finely-tuned gamma (FTG) oscillations - a narrowband activity with peaks between 60 and 90 Hz - have been observed in multiple movement disorders and are induced by dopaminergic medication or deep brain stimulation (DBS). FTG has been linked with levodopa or DBS-induced dyskinesias, which makes it a putative biomarker for adaptive DBS. On the other hand, gamma activity can also present as a broad phenomenon (30-100 Hz) in the context of motor activation and dynamic processing. Here, we contrast FTG, either levodopa-induced or DBS-induced, from movement-related broadband gamma synchronisation and further elaborate on the functional role of FTG and its potential implications for adaptive DBS. Given the unclear distinction of FTG and broad gamma in literature, we appeal for more careful separation of the two. To better characterise cortical and subcortical FTG as biomarkers for dyskinesia, their sensitivity and specificity need to be investigated in a large clinical trial.

No effects of transcranial direct current stimulation on visual evoked potential and peak gamma frequency

Evidence suggests that the visual evoked potential (VEP) and gamma oscillations elicited by visual stimuli reflect the balance of excitatory and inhibitory (E-I) cortical processes. As tDCS has been shown to modulate E-I balance, the current study investigated whether amplitudes of VEP components (N1 and P2) and peak gamma frequency are modulated by transcranial direct current stimulation (tDCS). Healthy adults underwent two electroencephalography (EEG) recordings while viewing stimuli designed to elicit a robust visual response. Between the two recordings, participants were randomly assigned to three tDCS conditions (anodal-, cathodal-, and sham-tDCS) or received no-tDCS. tDCS electrodes were placed over the occipital cortex (Oz) and the left cheek with an intensity of 2 mA for 10 min. Data of 39 participants were analysed for VEP amplitudes and peak gamma frequency using mixed-model ANOVAs. The results showed no main effects of tDCS in any metric. Possible explanations for the absence of tDCS effects are discussed.

Lipopolysaccharide-induced neuroinflammation disrupts functional connectivity and community structure in primary cortical microtissues

Three-dimensional (3D) neural microtissues are a powerful in vitro paradigm for studying brain development and disease under controlled conditions, while maintaining many key attributes of the in vivo environment. Here, we used primary cortical microtissues to study the effects of neuroinflammation on neural microcircuits. We demonstrated the use of a genetically encoded calcium indicator combined with a novel live-imaging platform to record spontaneous calcium transients in microtissues from day 14-34 in vitro. We implemented graph theory analysis of calcium activity to characterize underlying functional connectivity and community structure of microcircuits, which are capable of capturing subtle changes in network dynamics during early disease states. We found that microtissues cultured for 34 days displayed functional remodeling of microcircuits and that community structure strengthened over time. Lipopolysaccharide, a neuroinflammatory agent, significantly increased functional connectivity and disrupted community structure 5-9 days after exposure. These microcircuit-level changes have broad implications for the role of neuroinflammation in functional dysregulation of neural networks.

Family-Based Cohort Association Study of PRKCB1, CBLN1 and KCNMB4 Gene Polymorphisms and Autism in Polish Population

The aim of the study was to perform family-based association analysis of PRKCB1, CBLN1 and KCNMB4 gene polymorphisms and autism disorder. We comprised 206 Caucasian children with autistic spectrum disorder (ASD) and their biological parents. In transmission/disequilibrium test we observed that T-allele of the rs198198 polymorphism of the PRKCB1 gene was more often transmitted to affected children in the male subgroup (p = 0.010). Additionally, the T carrier state was significantly associated with hypotonia (p = 0.048). In the female subgroup, the T-allele carriers more often showed more mobile/vital behavior (p = 0.046). In conclusion, our study showed that the rs198198 of the PRKCB1 gene may be associated with ASD in men and with some features characteristic for the disorder.

No Evidence for Entrainment: Endogenous Gamma Oscillations and Rhythmic Flicker Responses Coexist in Visual Cortex

Over the past decades, numerous studies have linked cortical gamma oscillations (∼30-100 Hz) to neurocomputational mechanisms. Their functional relevance, however, is still passionately debated. Here, we asked whether endogenous gamma oscillations in the human brain can be entrained by a rhythmic photic drive >50 Hz. Such a noninvasive modulation of endogenous brain rhythms would allow conclusions about their causal involvement in neurocognition. To this end, we systematically investigated oscillatory responses to a rapid sinusoidal flicker in the absence and presence of endogenous gamma oscillations using magnetoencephalography (MEG) in combination with a high-frequency projector. The photic drive produced a robust response over visual cortex to stimulation frequencies of up to 80 Hz. Strong, endogenous gamma oscillations were induced using moving grating stimuli as repeatedly done in previous research. When superimposing the flicker and the gratings, there was no evidence for phase or frequency entrainment of the endogenous gamma oscillations by the photic drive. Unexpectedly, we did not observe an amplification of the flicker response around participants' individual gamma frequencies (IGFs); rather, the magnitude of the response decreased monotonically with increasing frequency. Source reconstruction suggests that the flicker response and the gamma oscillations were produced by separate, coexistent generators in visual cortex. The presented findings challenge the notion that cortical gamma oscillations can be entrained by rhythmic visual stimulation. Instead, the mechanism generating endogenous gamma oscillations seems to be resilient to external perturbation.SIGNIFICANCE STATEMENT We aimed to investigate to what extent ongoing, high-frequency oscillations in the gamma-band (30-100 Hz) in the human brain can be entrained by a visual flicker. Gamma oscillations have long been suggested to coordinate neuronal firing and enable interregional communication. Our results demonstrate that rhythmic visual stimulation cannot hijack the dynamics of ongoing gamma oscillations; rather, the flicker response and the endogenous gamma oscillations coexist in different visual areas. Therefore, while a visual flicker evokes a strong neuronal response even at high frequencies in the gamma-band, it does not entrain endogenous gamma oscillations in visual cortex. This has important implications for interpreting studies investigating the causal and neuroprotective effects of rhythmic sensory stimulation in the gamma-band.

The visual cortex produces gamma band echo in response to broadband visual flicker

The aim of this study is to uncover the network dynamics of the human visual cortex by driving it with a broadband random visual flicker. We here applied a broadband flicker (1-720 Hz) while measuring the MEG and then estimated the temporal response function (TRF) between the visual input and the MEG response. This TRF revealed an early response in the 40-60 Hz gamma range as well as in the 8-12 Hz alpha band. While the gamma band response is novel, the latter has been termed the alpha band perceptual echo. The gamma echo preceded the alpha perceptual echo. The dominant frequency of the gamma echo was subject-specific thereby reflecting the individual dynamical properties of the early visual cortex. To understand the neuronal mechanisms generating the gamma echo, we implemented a pyramidal-interneuron gamma (PING) model that produces gamma oscillations in the presence of constant input currents. Applying a broadband input current mimicking the visual stimulation allowed us to estimate TRF between the input current and the population response (akin to the local field potentials). The TRF revealed a gamma echo that was similar to the one we observed in the MEG data. Our results suggest that the visual gamma echo can be explained by the dynamics of the PING model even in the absence of sustained gamma oscillations.

Neural Mechanisms Underlying Human Auditory Evoked Responses Revealed By Human Neocortical Neurosolver

Auditory evoked fields (AEFs) are commonly studied, yet their underlying neural mechanisms remain poorly understood. Here, we used the biophysical modelling software Human Neocortical Neurosolver (HNN) whose foundation is a canonical neocortical circuit model to interpret the cell and network mechanisms contributing to macroscale AEFs elicited by a simple tone, measured with magnetoencephalography. We found that AEFs can be reproduced by activating the neocortical circuit through a layer specific sequence of feedforward and feedback excitatory synaptic drives, similar to prior simulation of somatosensory evoked responses, supporting the notion that basic structures and activation patterns are preserved across sensory regions. We also applied the modeling framework to develop and test predictions on neural mechanisms underlying AEF differences in the left and right hemispheres, as well as in hemispheres contralateral and ipsilateral to the presentation of the auditory stimulus. We found that increasing the strength of the excitatory synaptic cortical feedback inputs to supragranular layers simulates the commonly observed right hemisphere dominance, while decreasing the input latencies and simultaneously increasing the number of cells contributing to the signal accounted for the contralateral dominance. These results provide a direct link between human data and prior animal studies and lay the foundation for future translational research examining the mechanisms underlying alteration in this fundamental biomarker of auditory processing in healthy cognition and neuropathology.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject's hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

Oscillatory Bursting as a Mechanism for Temporal Coupling and Information Coding

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

Control of brain network dynamics across diverse scales of space and time

The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with activity on short length scales and slow timescales. Conversely, brain regions that facilitate control of difficult transitions are associated with activity on long length scales and fast timescales. Built on linear dynamical models, our results offer parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.

Synchronicity of excitatory inputs drives hippocampal networks to distinct oscillatory patterns

The rodent hippocampus expresses a variety of neuronal network oscillations depending on the behavioral state of the animal. Locomotion and active exploration are accompanied by theta-nested gamma oscillations while resting states and slow-wave sleep are dominated by intermittent sharp wave-ripple complexes. It is believed that gamma rhythms create a framework for efficient acquisition of information whereas sharp wave-ripples are thought to be involved in consolidation and retrieval of memory. While not strictly mutually exclusive, one of the two patterns usually dominates in a given behavioral state. Here we explore how different input patterns induce either of the two network states, using an optogenetic stimulation approach in hippocampal brain slices of mice. We report that the pattern of the evoked oscillation depends strongly on the initial synchrony of activation of excitatory cells within CA3. Short, synchronous activation favors the emergence of sharp wave-ripple complexes while persistent but less synchronous activity-as typical for sensory input during exploratory behavior-supports the generation of gamma oscillations. This dichotomy is reflected by different degrees of synchrony of excitatory and inhibitory synaptic currents within these two states. Importantly, the induction of these two fundamental network patterns does not depend on the presence of any neuromodulatory transmitter like acetylcholine, but is merely based on a different synchrony in the initial activation pattern.

Transient neocortical gamma oscillations induced by neuronal response modulation

In this paper a mean field model of spatio-temporal electroencephalographic activity in the neocortex is used to computationally study the emergence of neocortical gamma oscillations as a result of neuronal response modulation. It is shown using a numerical bifurcation analysis that gamma oscillations emerge robustly in the solutions of the model and transition to beta oscillations through coordinated modulation of the responsiveness of inhibitory and excitatory neuronal populations. The spatio-temporal pattern of the propagation of these oscillations across the neocortex is illustrated by solving the equations of the model using a finite element software package. Thereby, it is shown that the gamma oscillations remain localized to the regions of neuronal modulation. Moreover, it is discussed that the inherent spatial averaging effect of commonly used electrocortical measurement techniques can significantly alter the amplitude and pattern of fast oscillations in neocortical recordings, and hence can potentially affect physiological interpretations of these recordings.

Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

Neonatal EEG linked to individual differences in socioemotional outcomes and autism risk in toddlers

Research using electroencephalography (EEG) as a measure of brain function and maturation has demonstrated links between cortical activity and cognitive processes during infancy and early childhood. The current study examines whether neonatal EEG is correlated with later atypical socioemotional behaviors or neurocognitive delays. Parental report developmental assessments were administered to families with children ages 24 to 36 months who had previously participated in a neonatal EEG study (N = 129). Significant associations were found between neonatal EEG (higher frequencies in the frontal polar, temporal, and parietal brain regions) and BITSEA ASD risk scores. Infants with lower EEG power in these brain areas were more likely to have higher risk of socioemotional problems. When examining sex differences, significant links were found for males but not for females. These results demonstrate some promising associations between early neural biomarkers and later risk for atypical behaviors, which may shape early neurobehavioral development and could lead to earlier identification and intervention.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise

BACKGROUND

Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

NEW METHOD

Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

RESULTS

The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications.

COMPARISON WITH EXISTING METHOD(S)

We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods.

CONCLUSIONS

The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.

Persistent Gamma Spiking in SI Nonsensory Fast Spiking Cells Predicts Perceptual Success

Gamma oscillations (30-55 Hz) are hypothesized to temporally coordinate sensory encoding, enabling perception. However, fast spiking interneurons (FS), key gamma generators, can be highly sensory responsive, as is the gamma band local field potential (LFP). How can FS-mediated gamma act as an impartial temporal reference for sensory encoding, when the sensory drive itself presumably perturbs the pre-established rhythm? Combining tetrode recording in SI barrel cortex with controlled psychophysics, we found a unique FS subtype that was not sensory responsive and spiked regularly at gamma range intervals (gamma regular nonsensory FS [grnsFS]). Successful detection was predicted by a further increase in gamma regular spiking of grnsFS, persisting from before to after sensory onset. In contrast, broadband LFP power, including gamma, negatively predicted detection and did not cohere with gamma band spiking by grnsFS. These results suggest that a distinct FS subtype mediates perceptually relevant oscillations, independent of the LFP and sensory drive.

Cycle-by-cycle analysis of neural oscillations

Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. This has successfully uncovered numerous links between oscillations and cognition or disease. However, neural data are nonsinusoidal, and these nonsinusoidal features are increasingly linked to a variety of behavioral and cognitive states, pathophysiology, and underlying neuronal circuit properties. We present a new analysis framework, one that is complementary to existing Fourier and Hilbert transform-based approaches, that quantifies oscillatory features in the time domain on a cycle-by-cycle basis. We have released this cycle-by-cycle analysis suite as "bycycle," a fully documented, open-source Python package with detailed tutorials and troubleshooting cases. This approach performs tests to assess whether an oscillation is present at any given moment and, if so, quantifies each oscillatory cycle by its amplitude, period, and waveform symmetry, the latter of which is missed with the use of conventional approaches. In a series of simulated event-related studies, we show how conventional Fourier and Hilbert transform approaches can conflate event-related changes in oscillation burst duration as increased oscillatory amplitude and as a change in the oscillation frequency, even though those features were unchanged in simulation. Our approach avoids these errors. Furthermore, we validate this approach in simulation and against experimental recordings of patients with Parkinson's disease, who are known to have nonsinusoidal beta (12-30 Hz) oscillations.NEW & NOTEWORTHY We introduce a fully documented, open-source Python package, bycycle, for analyzing neural oscillations on a cycle-by-cycle basis. This approach is complementary to traditional Fourier and Hilbert transform-based approaches but avoids specific pitfalls. First, bycycle confirms an oscillation is present, to avoid analyzing aperiodic, nonoscillatory data as oscillations. Next, it quantifies nonsinusoidal aspects of oscillations, increasingly linked to neural circuit physiology, behavioral states, and diseases. This approach is tested against simulated and real data.

Gamma Oscillations in the Basolateral Amygdala: Biophysical Mechanisms and Computational Consequences

The basolateral nucleus of the amygdala (BL) is thought to support numerous emotional behaviors through specific microcircuits. These are often thought to be comprised of feedforward networks of principal cells (PNs) and interneurons. Neither well-understood nor often considered are recurrent and feedback connections, which likely engender oscillatory dynamics within BL. Indeed, oscillations in the gamma frequency range (40 − 100 Hz) are known to occur in the BL, and yet their origin and effect on local circuits remains unknown. To address this, we constructed a biophysically and anatomically detailed model of the rat BL and its local field potential (LFP) based on the physiological and anatomical literature, along with in vivo and in vitro data we collected on the activities of neurons within the rat BL. Remarkably, the model produced intermittent gamma oscillations (∼50 − 70 Hz) whose properties matched those recorded in vivo, including their entrainment of spiking. BL gamma-band oscillations were generated by the intrinsic circuitry, depending upon reciprocal interactions between PNs and fast-spiking interneurons (FSIs), while connections within these cell types affected the rhythm’s frequency. The model allowed us to conduct experimentally impossible tests to characterize the synaptic and spatial properties of gamma. The entrainment of individual neurons to gamma depended on the number of afferent connections they received, and gamma bursts were spatially restricted in the BL. Importantly, the gamma rhythm synchronized PNs and mediated competition between ensembles. Together, these results indicate that the recurrent connectivity of BL expands its computational and communication repertoire.

A Prospective Study of the Impact of Transcranial Alternating Current Stimulation on EEG Correlates of Somatosensory Perception

The (8-12 Hz) neocortical alpha rhythm is associated with shifts in attention across sensory systems, and is thought to represent a sensory gating mechanism for the inhibitory control of cortical processing. The present preliminary study sought to explore whether alpha frequency transcranial alternating current stimulation (tACS) could modulate endogenous alpha power in the somatosensory system, and whether the hypothesized modulation would causally impact perception of tactile stimuli at perceptual threshold. We combined electroencephalography (EEG) with simultaneous brief and intermittent tACS applied over primary somatosensory cortex at individuals' endogenous alpha frequency during a tactile detection task (n = 12 for EEG, n = 20 for behavior). EEG-measured pre-stimulus alpha power was higher on non-perceived than perceived trials, and analogous perceptual correlates emerged in early components of the tactile evoked response. Further, baseline normalized tactile detection performance was significantly lower during alpha than sham tACS, but the effect did not last into the post-tACS time period. Pre- to post-tACS changes in alpha power were linearly dependent upon baseline state, such that alpha power tended to increase when pre-tACS alpha power was low, and decrease when it was high. However, these observations were comparable in both groups, and not associated with evidence of tACS-induced alpha power modulation. Nevertheless, the tactile stimulus evoked response potential (ERP) revealed a potentially lasting impact of alpha tACS on circuit dynamics. The post-tACS ERP was marked by the emergence of a prominent peak ∼70 ms post-stimulus, which was not discernible post-sham, or in either pre-stimulation condition. Computational neural modeling designed to simulate macroscale EEG signals supported the hypothesis that the emergence of this peak could reflect synaptic plasticity mechanisms induced by tACS. The primary lesson learned in this study, which commanded a small sample size, was that while our experimental paradigm provided some evidence of an influence of tACS on behavior and circuit dynamics, it was not sufficient to induce observable causal effects of tACS on EEG-measured alpha oscillations. We discuss limitations and suggest improvements that may help further delineate a causal influence of tACS on cortical dynamics and perception in future studies.

Lamina-specific cortical dynamics in human visual and sensorimotor cortices

Distinct anatomical and spectral channels are thought to play specialized roles in the communication within cortical networks. While activity in the alpha and beta frequency range (7 - 40 Hz) is thought to predominantly originate from infragranular cortical layers conveying feedback-related information, activity in the gamma range (>40 Hz) dominates in supragranular layers communicating feedforward signals. We leveraged high precision MEG to test this proposal, directly and non-invasively, in human participants performing visually cued actions. We found that visual alpha mapped onto deep cortical laminae, whereas visual gamma predominantly occurred more superficially. This lamina-specificity was echoed in movement-related sensorimotor beta and gamma activity. These lamina-specific pre- and post- movement changes in sensorimotor beta and gamma activity suggest a more complex functional role than the proposed feedback and feedforward communication in sensory cortex. Distinct frequency channels thus operate in a lamina-specific manner across cortex, but may fulfill distinct functional roles in sensory and motor processes.

Motor Cortical Gamma Oscillations: What Have We Learnt and Where Are We Headed?

PURPOSE OF REVIEW

An increase in oscillatory activity in the γ-frequency band (approximately 50-100 Hz) has long been noted during human movement. However, its functional role has been difficult to elucidate. The advent of novel techniques, particularly transcranial alternating current stimulation (tACS), has dramatically increased our ability to study γ oscillations. Here, we review our current understanding of the role of γ oscillations in the human motor cortex, with reference to γ activity outside the motor system, and evidence from animal models.

RECENT FINDINGS

Evidence for the neurophysiological basis of human γ oscillations is beginning to emerge. Multimodal studies, essential given the necessarily indirect measurements acquired in humans, are beginning to provide convergent evidence for the role of γ oscillations in movement, and their relationship to plasticity.

SUMMARY

Human motor cortical γ oscillations appear to play a key role in movement, and relate to learning. However, there are still major questions to be answered about their physiological basis and precise role in human plasticity. It is to be hoped that future research will take advantage of recent technical advances and the physiological basis and functional significance of this intriguing and important brain rhythm will be fully elucidated.

Stimulus dependent neural oscillatory patterns show reliable statistical identification of autism spectrum disorder in a face perceptual decision task

OBJECTIVE

Electroencephalographic biomarkers have been widely investigated in autism, in the search for diagnostic, prognostic and therapeutic outcome measures. Here we took advantage of the information available in temporal oscillatory patterns evoked by simple perceptual decisions to investigate whether stimulus dependent oscillatory signatures can be used as potential biomarkers in autism spectrum disorder (ASD).

METHODS

We studied an extensive set of stimuli (9 categories of faces) and performed data driven classification (Support vector machine, SVM) of ASD vs. Controls with features based on the EEG power responses. We carried out an extensive time-frequency and synchrony analysis of distinct face categories requiring different processing mechanisms in terms of non-holistic vs. holistic processing.

RESULTS

We found that the neuronal oscillatory responses of low gamma frequency band, locked to photographic and abstract two-tone (Mooney) face stimulus presentation are decreased in ASD vs. the control group. We also found decreased time-frequency (TF) responses in the beta band in ASD after 350 ms, possibly related to motor preparation. On the other hand, synchrony in the 30-45 Hz band showed a distinct spatial pattern in ASD. These power changes enabled accurate classification of ASD with an SVM approach. SVM accuracy was approximately 85%. ROC curves showed about 94% AUC (area under the curve). Combination of Mooney and Photographic face stimuli evoked features enabled a better separation between groups, reaching an AUC of 98.6%.

CONCLUSION

We identified a relative decrease in EEG responses to face stimuli in ASD in the beta (15-30 Hz; >350 ms) and gamma (30-45 Hz; 55-80 Hz; 50-350 ms) frequency ranges. These can be used as input of a machine learning approach to separate between groups with high accuracy.

SIGNIFICANCE

Future studies can use EEG time-frequency patterns evoked by particular types of faces as a diagnostic biomarker and potentially as outcome measures in therapeutic trials.

Four-dimensional map of the human early visual system

OBJECTIVE

We generated a large-scale, four-dimensional map of neuronal modulations elicited by full-field flash stimulation.

METHODS

We analyzed electrocorticography (ECoG) recordings from 63 patients with focal epilepsy, and delineated the spatial-temporal dynamics of visually-elicited high-gamma_{70-110 Hz} amplitudes on a standard brain template. We then clarified the neuronal events underlying visual evoked potential (VEP) components, by correlating with high-gamma amplitude measures.

RESULTS

The medial-occipital cortex initially revealed rapid neural activation followed by prolonged suppression, reflected by augmentation of high-gamma activity lasting up to 100 ms followed by attenuation lasting up to 1000 ms, respectively. With a number of covariate factors incorporated into a prediction model, the eccentricity representation independently predicted the magnitude of post-activation suppression, which was more intense in regions representing more parafoveal visual fields compared to those of more peripheral fields. The initial negative component on VEP was sharply contoured and co-occurred with early high-gamma augmentation, whose offset then co-occurred with a large positive VEP peak. A delayed negative VEP peak was blunt and co-occurred with prolonged high-gamma attenuation.

CONCLUSIONS

Eccentricity-dependent gradient in neural suppression in the medial-occipital region may explain the functional difference between peripheral and parafoveal/central vision. Early negative and positive VEP components may reflect neural activation, whereas a delayed negative VEP peak reflecting neural suppression.

SIGNIFICANCE

Our observation provides the mechanistic rationale for transient scotoma or mild flash-blindness, characterized by physiological afterimage preferentially formed in central vision following intense but non-injurious light exposure.

Neuronal correlates of ketamine and walking induced gamma oscillations in the medial prefrontal cortex and mediodorsal thalamus

Alterations in the function of the medial prefrontal cortex (mPFC) and its major thalamic source of innervation, the mediodorsal (MD) thalamus, have been hypothesized to contribute to the symptoms of schizophrenia. The NMDAR antagonist ketamine, used to model schizophrenia, elicits a brain state resembling early stage schizophrenia characterized by cognitive deficits and increases in cortical low gamma (40-70 Hz) power. Here we sought to determine how ketamine differentially affects spiking and gamma local field potential (LFP) activity in the rat mPFC and MD thalamus. Additionally, we investigated the ability of drugs targeting the dopamine D4 receptor (D4R) to modify the effects of ketamine on gamma activity as a measure of potential cognitive therapeutic efficacy. Rats were trained to walk on a treadmill to reduce confounds related to hyperactivity after ketamine administration (10 mg/kg s.c.) while recordings were obtained from electrodes chronically implanted in the mPFC and MD thalamus. Ketamine increased gamma LFP power in mPFC and MD thalamus in a similar frequency range, yet did not increase thalamocortical synchronization. Ketamine also increased firing rates and spike synchronization to gamma oscillations in the mPFC but decreased both measures in MD thalamus. Conversely, walking alone increased both firing rates and spike-gamma LFP correlations in both mPFC and MD thalamus. The D4R antagonist alone (L-745,870) had no effect on gamma LFP power during treadmill walking, although it reversed increases induced by the D4R agonist (A-412997) in both mPFC and MD thalamus. Neither drug altered ketamine-induced changes in gamma power or firing rates in the mPFC. However, in MD thalamus, the D4R agonist increased ketamine-induced gamma power and prevented ketamine's inhibitory effect on firing rates. Results provide new evidence that ketamine differentially modulates spiking and gamma power in MD thalamus and mPFC, supporting a potential role for both areas in contributing to ketamine-induced schizophrenia-like symptoms.

Information and the Origin of Qualia

This article argues that qualia are a likely outcome of the processing of information in local cortical networks. It uses an information-based approach and makes a distinction between information structures (the physical embodiment of information in the brain, primarily patterns of action potentials), and information messages (the meaning of those structures to the brain, and the basis of qualia). It develops formal relationships between these two kinds of information, showing how information structures can represent messages, and how information messages can be identified from structures. The article applies this perspective to basic processing in cortical networks or ensembles, showing how networks can transform between the two kinds of information. The article argues that an input pattern of firing is identified by a network as an information message, and that the output pattern of firing generated is a representation of that message. If a network is encouraged to develop an attractor state through attention or other re-entrant processes, then the message identified each time physical information is cycled through the network becomes “representation of the previous message”. Using an example of olfactory perception, it is shown how this piggy-backing of messages on top of previous messages could lead to olfactory qualia. The message identified on each pass of information could evolve from inner identity, to inner form, to inner likeness or image. The outcome is an olfactory quale. It is shown that the same outcome could result from information cycled through a hierarchy of networks in a resonant state. The argument for qualia generation is applied to other sensory modalities, showing how, through a process of brain-wide constraint satisfaction, a particular state of consciousness could develop at any given moment. Evidence for some of the key predictions of the theory is presented, using ECoG data and studies of gamma oscillations and attractors, together with an outline of what further evidence is needed to provide support for the theory.

Linking canonical microcircuits and neuronal activity: Dynamic causal modelling of laminar recordings

Neural models describe brain activity at different scales, ranging from single cells to whole brain networks. Here, we attempt to reconcile models operating at the microscopic (compartmental) and mesoscopic (neural mass) scales to analyse data from microelectrode recordings of intralaminar neural activity. Although these two classes of models operate at different scales, it is relatively straightforward to create neural mass models of ensemble activity that are equipped with priors obtained after fitting data generated by detailed microscopic models. This provides generative (forward) models of measured neuronal responses that retain construct validity in relation to compartmental models. We illustrate our approach using cross spectral responses obtained from V1 during a visual perception paradigm that involved optogenetic manipulation of the basal forebrain. We find that the resulting neural mass model can distinguish between activity in distinct cortical layers - both with and without optogenetic activation - and that cholinergic input appears to enhance (disinhibit) superficial layer activity relative to deep layers. This is particularly interesting from the perspective of predictive coding, where neuromodulators are thought to boost prediction errors that ascend the cortical hierarchy.

Modeling fast and slow gamma oscillations with interneurons of different subtype

Experimental and theoretical studies demonstrate that neuronal gamma oscillations crucially depend on interneurons, but current models do not consider the diversity of known interneuron subtypes. Moreover, in CA1 of the hippocampus, experimental evidence indicates the presence of multiple gamma oscillators, two of which may be coordinated by differing interneuron populations. In this article, we show that models of networks with competing interneuron populations with different postsynaptic effects are sufficient to generate, within CA1, distinct oscillatory regimes. We find that strong mutual inhibition between the interneuron populations permits distinct fast and slow gamma states, whereas weak mutual inhibition generates mixed gamma states. We develop idealized firing rate models to illuminate dynamic properties of these competitive gamma networks, and reinforce these concepts with basic spiking models. The models make several explicit predictions about gamma oscillators in CA1. Specifically, interneurons of different subtype phase-lock to different gamma states, and one population of interneurons is silenced and the other active during fast and slow gamma events. Finally, mutual inhibition between interneuron populations is necessary to generate distinct gamma states. Previous experimental studies indicate that fast and slow gamma oscillations reflect different information processing modes, although it is unclear whether these rhythms are intrinsic or imposed. The models outlined demonstrate that basic architectures can locally generate these oscillations, as well as capture other features of fast and slow gamma, including theta-phase preference and spontaneous transitions between gamma states. These models may extend to describe general dynamics in networks with diverse interneuron populations.NEW & NOTEWORTHY The oscillatory coordination of neural signals is crucial to healthy brain function. We have developed an idealized neuronal model that generates distinct fast and slow gamma oscillations, a known feature of the rodent hippocampus. Our work provides a mechanism of this phenomenon, as well as a theoretical framework for future experiments concerning hippocampal gamma. It moreover offers a tractable model of competitive gamma oscillations that is generalizable across the nervous system.

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot-savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm(3). With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Human neocortical 15-29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting <150 ms with a stereotypical waveform. Computational modeling uniquely designed to infer the electrical currents underlying these signals showed that beta events could emerge from the integration of nearly synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (∼50 ms). This beta mechanism rigorously accounted for the beta event profiles; several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function.

When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning

Rhythms are a prominent signature of brain activity. Their expression is correlated with numerous examples of healthy information processing and their fluctuations are a marker of disease states. Yet, their causal or epiphenomenal role in brain function is still highly debated. We review recent studies showing brain rhythms are not always 'rhythmic', by which we mean representative of repeated cycles of activity. Rather, high power and continuous rhythms in averaged signals can represent brief transient events on single trials whose density accumulates in the average. We also review evidence showing time-domain signals with vastly different waveforms can exhibit identical spectral-domain frequency and power. Further, non-oscillatory waveform feature can create spurious high spectral power. Knowledge of these possibilities is essential when interpreting rhythms and is easily missed without considering pre-processed data. Lastly, we discuss how these findings suggest new directions to pursue in our quest to discover the mechanism and meaning of brain rhythms.

Computational modeling to improve treatments for essential tremor

Essential tremor (ET) is a neurological disorder of unknown etiology that is typically characterized by an involuntary periodic movement of the upper limbs. No longer considered monosymptomatic, ET patients often have additional motor and even cognitive impairments. Although there are several pharmacological treatments, no drugs have been developed specifically for ET [1], and 30-70% of patients are medication-refractory [2]. A subset of medication-refractory patients may benefit from electrical deep brain stimulation (DBS) of the ventral intermediate nucleus of the thalamus (VIM), which receives cerebellar inputs. Abnormal cerebellar input to VIM is presumed to be a major contributor to tremor symptoms, which is alleviated by DBS. Computational modeling of the effects of DBS in VIM has been a powerful tool to design DBS protocols to reduce tremor activity. However, far less is known about how these therapies affect non-tremor symptoms, and more experimental and computational modeling work is required to address these growing considerations. Models capable of addressing multiple facets of ET will lead to novel, more efficient treatment.

Effects of L-dopa priming on cortical high beta and high gamma oscillatory activity in a rodent model of Parkinson's disease

Prolonged L-dopa treatment in Parkinson's disease (PD) often leads to the expression of abnormal involuntary movements known as L-dopa-induced dyskinesia. Recently, dramatic 80 Hz oscillatory local field potential (LFP) activity within the primary motor cortex has been linked to dyskinetic symptoms in a rodent model of PD and attributed to stimulation of cortical dopamine D1 receptors. To characterize the relationship between high gamma (70-110 Hz) cortical activity and the development of L-dopa-induced dyskinesia, cortical LFP and spike signals were recorded in hemiparkinsonian rats treated with L-dopa for 7 days, and dyskinesia was quantified using the abnormal involuntary movements (AIMs) scale. The relationship between high gamma and dyskinesia was further probed by assessment of the effects of pharmacological agents known to induce or modulate dyskinesia expression. Findings demonstrate that AIMs and high gamma LFP power increase between days 1 and 7 of L-dopa priming. Notably, high beta (25-35 Hz) power associated with parkinsonian bradykinesia decreased as AIMs and high gamma LFP power increased during priming. After priming, rats were treated with the D1 agonist SKF81297 and the D2 agonist quinpirole. Both dopamine agonists independently induced AIMs and high gamma cortical activity that were similar to that induced by L-dopa, showing that this LFP activity is neither D1 nor D2 receptor specific. The serotonin 1A receptor agonist 8-OH-DPAT reduced L-dopa- and DA agonist-induced AIMs and high gamma power to varying degrees, while the serotonin 1A antagonist WAY100635 reversed these effects. Unexpectedly, as cortical high gamma power increased, phase locking of cortical pyramidal spiking to high gamma oscillations decreased, raising questions regarding the neural substrate(s) responsible for high gamma generation and the functional correlation between high gamma and dyskinesia.

Linear tuning of gamma amplitude and frequency to luminance contrast: evidence from a continuous mapping paradigm

Individual differences in the visual gamma (30–100Hz) response and their potential as trait markers of underlying physiology (particularly related to GABAergic inhibition) have become a matter of increasing interest in recent years. There is growing evidence, however, that properties of the gamma response (e.g., its amplitude and frequency) are highly stimulus dependent, and that individual differences in the gamma response may reflect individual differences in the stimulus tuning functions of gamma oscillations. Here, we measured the tuning functions of gamma amplitude and frequency to luminance contrast in eighteen participants using MEG. We used a grating stimulus in which stimulus contrast was modulated continuously over time. We found that both gamma amplitude and frequency were linearly modulated by stimulus contrast, but that the gain of this modulation (as reflected in the linear gradient) varied across individuals. We additionally observed a stimulus-induced response in the beta frequency range (10–25Hz), but neither the amplitude nor the frequency of this response was consistently modulated by the stimulus over time. Importantly, we did not find a correlation between the gain of the gamma-band amplitude and frequency tuning functions across individuals, suggesting that these may be independent traits driven by distinct neurophysiological processes.

Direction of magnetoencephalography sources associated with feedback and feedforward contributions in a visual object recognition task

Identifying inter-area communication in terms of the hierarchical organization of functional brain areas is of considerable interest in human neuroimaging. Previous studies have suggested that the direction of magneto- and electroencephalography (MEG, EEG) source currents depend on the layer-specific input patterns into a cortical area. We examined the direction in MEG source currents in a visual object recognition experiment in which there were specific expectations of activation in the fusiform region being driven by either feedforward or feedback inputs. The source for the early non-specific visual evoked response, presumably corresponding to feedforward driven activity, pointed outward, i.e., away from the white matter. In contrast, the source for the later, object-recognition related signals, expected to be driven by feedback inputs, pointed inward, toward the white matter. Associating specific features of the MEG/EEG source waveforms to feedforward and feedback inputs could provide unique information about the activation patterns within hierarchically organized cortical areas.