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Voltage of the apical dendrite (upper) and intrinsic or synaptic currents (lower) in the basal and apical dendrites of an IB cell during beta1. In the lower figure we show the h-current (blue) and the fast sodium current (gray) of the apical dendrite, and the NMDA synaptic current (red) to the basal dendrite. Negative values indicate inward currents. Inhibitory input from an LTS interneuron arrives at the arrow (upper panel) and activates the h-current of the apical dendrite (lower panel) but has little effect on the NMDA current of the basal dendrite.
Source publication
Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems. Contemporary observations suggest that coexistent oscillations interact and, in time, may switch in dominance. We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex....
Citations
... Not much is known about β-bands in rodents. Scientists have managed to generate this rhythm in vitro during an intracellular recording of rat somatosensory cortex slices using microelectrodes filled with potassium acetate [29,30]. The findings suggest that the βrhythm functionally separates activity in the superficial layers of the somatosensory cortex, represented mainly by the θ-rhythm, from output pathways in the deep layers, consisting of the β2-band. ...
Electrophysiological studies have long established themselves as reliable methods for assessing the functional state of the brain and spinal cord, the degree of neurodegeneration, and evaluating the effectiveness of therapy. In addition, they can be used to diagnose, predict functional outcomes, and test the effectiveness of therapeutic and rehabilitation programs not only in clinical settings, but also at the preclinical level. Considering the urgent need to develop potential stimulators of neuroregeneration, it seems relevant to obtain objective data when modeling neurological diseases in animals. Thus, in the context of the application of electrophysiological methods, not only the comparison of the basic characteristics of bioelectrical activity of the brain and spinal cord in humans and animals, but also their changes against the background of neurodegenerative and post-traumatic processes are of particular importance. In light of the above, this review will contribute to a better understanding of the results of electrophysiological assessment in neurodegenerative and post-traumatic processes as well as the possibility of translating these methods from model animals to humans.
... Several theories for PAC have been proposed [2,30,31]. Most (but not all [85], [51]) involve two interacting subsystems oscillating at different frequencies with different intrinsic timescales [32-36, 86], or a single fast system externally driven by a slow one [87][88][89][90]. Detailed CA1 models generating coupled oscillations [71] also include many components with different timescales. ...
Phase amplitude coupling (PAC) between slow and fast oscillations is found throughout the brain and plays important functional roles. Its neural origin remains unclear. Experimental findings are often puzzling and sometimes contradictory. Most computational models rely on pairs of pacemaker neurons or neural populations tuned at different frequencies to produce PAC. Here, using a data-driven model of a hippocampal microcircuit, we demonstrate that PAC can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma. The model suggests the conditions under which a CA1 microcircuit can operate to elicit theta-gamma PAC, and highlights the modulatory role of OLM and PVBC cells, recurrent connectivity, and short term synaptic plasticity. Surprisingly, the results suggest the experimentally testable prediction that the generation of the slow population oscillation requires the fast one and cannot occur without it.
... Canolty's study showed that neurons are sensitive to multiple frequencies [110,111]. The cellular and network origins of distinct brain frequencies are the focus of ongoing research [112]; however, the period of concatenation hypothesis [113] provides a supportive mechanism accounting for the generation of the frequency bands observed in the neocortex. Each generated distinct brain frequency can be independently controlled by different neuronal ensembles. ...
Source activity was extracted from resting-state magnetoencephalography data of 103 subjects, aged 18–60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed us to estimate the probability distribution of the DoCM, independently for phase and amplitude. The results support earlier observations of a posterior to anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior to anterior information flow in {α1, α2, γ}, a sensory-motor β oriented pattern and anterior to posterior pattern in {δ, θ}. The DoCM between intra and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence reaching a peak in adulthood and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.
... proposed [38], [65], [66]. Alternatively, golden rhythms may emerge when two input rhythms undergo a nonlinear transformation [62], [68]. ...
While brain rhythms appear fundamental to brain function, why brain rhythms consistently organize into the small set of discrete frequency bands observed remains unknown. Here we propose that rhythms separated by factors of the golden ratio ($\phi=(1+ \sqrt{2})/5$) optimally support segregation and integration of information transmission in the brain. Organized by the golden ratio, pairs of rhythms support multiplexing by reducing interference between separate communication channels, and triplets of rhythms support integration of signals to establish a hierarchy of cross-frequency interactions. We illustrate this framework in simulation and apply this framework to propose three testable hypotheses.
... An alternative model for the generation of short periods of beta activity was proposed more recently, relying on the coincident input to apical and proximal dendrites of pyramidal cells (Sherman et al. 2016). Furthermore, it was shown that intrinsic bursting cells can produce spontaneous beta oscillations when their axons contain M-currents (Roopun et al. 2006;Kramer et al. 2008). The M-current builds up through burst spiking and, through hyperpolarization, prevents fur-ther spiking, with the decay time constant determining the period of these beta oscillations. ...
... The M-current builds up through burst spiking and, through hyperpolarization, prevents fur-ther spiking, with the decay time constant determining the period of these beta oscillations. When these intrinsic bursting cells were combined with RS, FS and LTS cells, the model circuit alternated between slow gamma, fast beta and a 'period-concatenated' slow beta, which period was the sum of the other two rhythms (Kramer et al. 2008). It is unclear whether this mechanism extends to other areas than motor cortex. ...
Neural circuits contain a wide variety of interneuron types, which differ in their biophysical properties and connectivity patterns. The two most common interneuron types, parvalbumin-expressing and somatostatin-expressing cells, have been shown to be differentially involved in many cognitive functions. These cell types also show different relationships with the power and phase of oscillations in local field potentials. The mechanisms that underlie the emergence of different oscillatory rhythms in neural circuits with more than one interneuron subtype, and the roles specific interneurons play in those mechanisms, are not fully understood. Here, we present a comprehensive analysis of all possible circuit motifs and input regimes that can be achieved in circuits comprised of excitatory cells, PV-like fast-spiking interneurons and SOM-like low-threshold spiking interneurons. We identify 18 unique motifs and simulate their dynamics over a range of input strengths. Using several characteristics, such as oscillation frequency, firing rates, phase of firing and burst fraction, we cluster the resulting circuit dynamics across motifs in order to identify patterns of activity and compare these patterns to behaviors that were generated in circuits with one interneuron type. In addition to the well-known PING and ING gamma oscillations and an asynchronous state, our analysis identified three oscillatory behaviors that were generated by the three-cell-type motifs only: theta-nested gamma oscillations, stable beta oscillations and theta-locked bursting behavior, which have also been observed in experiments. Our characterization provides a map to interpret experimental activity patterns and suggests pharmacological manipulations or optogenetics approaches to validate these conclusions.
... Unlike indirect functional magnetic resonance imaging (fMRI), neural oscillations measured using non-invasive methods, such as magnetoencephalography (MEG), arise directly from the synchronous oscillatory-activation of post-synaptic currents in ensembles of principal cells in cortex ( Brunel and Wang, 2003 ;Buzsáki et al., 2004 ;Wang, 2010 ), predominantly those in superficial layers ( Buzsáki et al., 2012 ;Xing et al., 2012 ). Converging evidence from experimental ( Kramer et al., 2008 ;Whittington et al., 1995 ) and computational ( Brunel and Wang, 2003 ;Pinotsis et al., 2013 ;Shaw et al., 2017 ) studies suggests that the attributes of these oscillations -peak frequency and amplitude -are determined by more fundamental, unobserved, neurophysiological processes. For example, the peak frequency of oscillations across many frequency bands has been linked with inhibitory neurotransmission ( Buzsáki et al., 2004 ;Hall et al., 2011 ;Roopun et al., 2006 ;Whittington et al., 2000 ) mediated by γ -aminobutyric acid (GABA). ...
As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) plays a crucial role in shaping the frequency and amplitude of oscillations, which suggests a role for GABA in shaping the topography of functional connectivity and activity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) using the GABA transporter 1 (GAT1) blocker, tiagabine (15 mg). In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo. We quantified whole brain activity and functional connectivity in discrete frequency bands. Drug-by-session (2 × 4) analysis of variance in connectivity revealed interaction and main effects. Post-hoc permutation testing of each post-drug recording vs. respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, across 1- 3- and 5- hour recordings following tiagabine only. The same analysis applied to activity revealed significant increases across frontal regions, coupled with reductions in posterior regions, across delta, theta, alpha and beta frequencies. Crucially, the spatial distribution of tiagabine-induced changes overlap with group-averaged maps of the distribution of GABAA receptors, from flumazenil (FMZ-VT) PET, demonstrating a link between GABA availability, GABAA receptor distribution, and low-frequency network oscillations. Our results indicate that the relationship between PET receptor distributions and MEG effects warrants further exploration, since elucidating the nature of this relationship may uncover electrophysiologically-derived maps of oscillatory activity as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging.
... In vitro investigations of somatosensory cortex identified intrinsic bursting triggered through the M-current as a possible driver of faster-frequency β2 rhythms, in which gap-junction coupling is necessary for coherent rhythm generation (Roopun et al., 2006). Such bursting-based oscillatory dynamics have been incorporated into more complex multi-layer models (Kramer et al., 2008;Kopell et al., 2011;Lee et al., 2013). An intralaminar mechanism has been proposed to account for how superficial-layer γ rhythms, arising from fast-spiking interneurons and regular spiking excitatory cells, are interleaved (termed period concatenation) with deep-layer intrinsic bursters that are reciprocally coupled to superficial cells. ...
Extrastriate visual neurons show no firing rate change during a working memory (WM) task in the absence of sensory input, but both αβ oscillations and spike phase locking are enhanced, as is the gain of sensory responses. This lack of change in firing rate is at odds with many models of WM, or attentional modulation of sensory networks. In this article we devised a computational model in which this constellation of results can be accounted for via selective activation of inhibitory subnetworks by a top-down working memory signal. We confirmed the model prediction of selective inhibitory activation by segmenting cells in the experimental neural data into putative excitatory and inhibitory cells. We further found that this inhibitory activation plays a dual role in influencing excitatory cells: it both modulates the inhibitory tone of the network, which underlies the enhanced sensory gain, and also produces strong spike-phase entrainment to emergent network oscillations. Using a phase oscillator model we were able to show that inhibitory tone is principally modulated through inhibitory network gain saturation, while the phase-dependent efficacy of inhibitory currents drives the phase locking modulation. The dual contributions of the inhibitory subnetwork to oscillatory and non-oscillatory modulations of neural activity provides two distinct ways for WM to recruit sensory areas, and has relevance to theories of cortical communication.
... We refer to these two phenomena as movement related beta decrease (MRBD) and post-movement beta rebound (PMBR), respectively ( Figure 1A). The two principal sources of beta are sensorimotor cortex (Jensen et al., 2005;Roopun et al., 2006;Kramer et al., 2008;Yamawaki et al., 2008;Kopell et al., 2011) and basal ganglia ( Figure 1C; Holgado et al., 2010;McCarthy et al., 2011;Tachibana et al., 2011;Mirzaei et al., 2017). There is debate as to whether they originate independently in each area or if they are an emergent property of the cortico-basal ganglia networks (Pavlides et al., 2015;Sherman et al., 2016;Reis et al., 2019). ...
Beta oscillations have been predominantly observed in sensorimotor cortices and basal ganglia structures and they are thought to be involved in somatosensory processing and motor control. Although beta activity is a distinct feature of healthy and pathological sensorimotor processing, the role of this rhythm is still under debate. Here we review recent findings about the role of beta oscillations during experimental manipulations (i.e., drugs and brain stimulation) and their alteration in aging and pathology. We show how beta changes when learning new motor skills and its potential to integrate sensory input with prior contextual knowledge. We conclude by discussing a novel methodological approach analyzing beta oscillations as a series of transient bursting events.
... On connection lines: red trianglesexcitation, blue circles-inhibition, dented lines-gap junctions. Based on Kramer et al., 2008;Wang, 2010;Whittington et al., 2000). ...
... However, if the driving input is reduced by application of 2.5 μM NBQX (2,3-dioxo-6-nitro-7-sulfamoyl-benzo[f]quinoxaline, an AMPA receptor antagonist), beta1 (~15 Hz) oscillation appears, which is sensitive to lesion at L4. This band transition can be explained by a model of inter-layer interaction and period concatenation suggested by Kopell and colleagues Kramer et al., 2008;Roopun et al., 2008). According to this model ( Figure 1.9D), depolarization is conveyed via slow N-Methyl-D-aspartate (NMDA) glutamatergic receptors. ...
The brain is constantly active. The environment requires us to adapt our behavior in response to external cues. Therefore, the interaction between spontaneous and externally evoked neuronal activity is key for understanding cognition. Imaging studies describe functionally connected networks termed resting state networks (RSNs), which are activated spontaneously and during tasks. RSNs were suggested to oppose each other and thus play a role in the interaction between internal and external cues, but this suggestion is under debate. Recent evidence indicates that spontaneously occurring bursts of high-power local field potential (LFP) oscillations in the 15-30 Hz beta-band represent activation of RSNs. LFP-bursts can be detected in fine timescales, thus allow investigating their impact on behavior on the single-trial level. The behavioral impact of intrinsic LFP-bursts was demonstrated on a variety of behaviors, from perception, through working memory and attention, to movement in health and disease. A repeating effect of beta-bursts in the literature is inhibitory, manifested as a masking-effect on perception or maintaining the status-quo in movement. Spontaneously occurring beta oscillations, representing network input, is anti-correlated with the stimulus-evoked spiking output. This may indicate an ongoing local competition between inputs and outputs, or spontaneous and evoked activities. Yet, a demonstration of the effect of beta-bursts in real-time is missing, and little is known about the underlying mechanism.
In this work, we propose that oscillatory bursts may constitute the mesoscopic link between macroscopic functional networks and microscopic assemblies of spiking neurons. Packets of spikes from a sender within one RSN are translated via cellular and network mechanisms into LFP-bursts by the receiver in another RSN. The anti-correlation between spiking activity and beta-power within a region on one hand, and between RSNs on the other, may represent the competition between functional networks. Based on this synthesis, we hypothesized that high levels of spontaneous activity, manifested as beta-bursts, could influence behavior in real-time.
To test this hypothesis, we developed a method to measure short and narrowband LFP-bursts in real-time. In a first study, we demonstrated the capability of the method to detect behaviorally relevant bursts; we trained freely moving rats to increase the occurrence of bursts and were able to decode the occurrence of bursts from movement. Then, we combined the real-time method with a novel forepaw detection task including a tactile vibrational stimulus and multi-electrode recordings, to investigate the real-time masking effect of beta-bursts on sensory inputs and the relationship between neuronal populations and bursts. We found that by bidirectional adjustment of the stimulus intensity according to real-time detected bursting levels, the masking effect of beta-bursts can be counterbalanced. Further, we found that bursts in a wide range of frequencies were associated with transient synchronization of cell assemblies. Only in the beta band, bursts were anti-correlated with stimulus-evoked activity and were followed by a reduction of firing-rate. Our studies suggest that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.
... Neural oscillations, including rhythms in the beta1 band (12)(13)(14)(15)(16)(17)(18)(19)(20), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network's behavior. ...
... There is a large variety of brain rhythms and mechanisms that produce them [1]. Here we focus on a rhythm of frequency about 15 Hz (beta1 band) that appears in the parietal cortex [11,12], because of its unusual and functionally important dynamics, as well as the fact that there exists a biophysical computational model for it. An important property of this rhythm is that the beta1 period arises from a concatenation of two shorter cycles of physiologically relevant lengths: a beta2 (∼ 40 ms) and a gamma (∼ 25 ms) timescale. ...
... We use an adapted version of a model for the parietal beta1 rhythm [12]. It consists of a network of Hodgkin-Huxley type neurons, of four different types. ...
Neural oscillations, including rhythms in the beta1 band (12–20 Hz), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network’s behavior. We use a simplified, yet biophysical, model of a beta1 rhythm that occurs in the parietal cortex, in order to study its response to oscillatory inputs. We demonstrate that a cell has the ability to respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to that of the other. We show that this is a very general phenomenon, independent of the model used. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it.
