Gamma oscillations and episodic memory

Enhanced gamma oscillatory activity (30-80 Hz) accompanies the successful formation and retrieval of episodic memories. While this co-occurrence is well documented, the mechanistic contributions of gamma oscillatory activity to episodic memory remain unclear. Here, we review how gamma oscillatory activity may facilitate spike timing-dependent plasticity, neural communication, and sequence encoding/retrieval, thereby ensuring the successful formation and/or retrieval of an episodic memory. Based on the evidence reviewed, we propose that multiple, distinct forms of gamma oscillation can be found within the canonical gamma band, each of which has a complementary role in the neural processes listed above. Further exploration of these theories using causal manipulations may be key to elucidating the relevance of gamma oscillatory activity to episodic memory.

Towards Generalizing the Information Theory for Neural Communication

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Anatomy and Neural Pathways Modulating Distinct Locomotor Behaviors in Drosophila Larva

The control of movements is a fundamental feature shared by all animals. At the most basic level, simple movements are generated by coordinated neural activity and muscle contraction patterns that are controlled by the central nervous system. How behavioral responses to various sensory inputs are processed and integrated by the downstream neural network to produce flexible and adaptive behaviors remains an intense area of investigation in many laboratories. Due to recent advances in experimental techniques, many fundamental neural pathways underlying animal movements have now been elucidated. For example, while the role of motor neurons in locomotion has been studied in great detail, the roles of interneurons in animal movements in both basic and noxious environments have only recently been realized. However, the genetic and transmitter identities of many of these interneurons remains unclear. In this review, we provide an overview of the underlying circuitry and neural pathways required by Drosophila larvae to produce successful movements. By improving our understanding of locomotor circuitry in model systems such as Drosophila, we will have a better understanding of how neural circuits in organisms with different bodies and brains lead to distinct locomotion types at the organism level. The understanding of genetic and physiological components of these movements types also provides directions to understand movements in higher organisms.

Neurobiology of cannabinoid receptor signaling

The endocannabinoid system (ECS) is a highly versatile signaling system within the nervous system. Despite its widespread localization, its functions within the context of distinct neural processes are very well discernable and specific. This is remarkable, and the question remains as to how such specificity is achieved. One key player in the ECS is the cannabinoid type 1 receptor (CB₁), a G protein-coupled receptor characterized by the complexity of its cell-specific expression, cellular and subcellular localization, and its adaptable regulation of intracellular signaling cascades. CB₁ receptors are involved in different synaptic and cellular plasticity processes and in the brain's bioenergetics in a context-specific manner. CB₂ receptors are also important in several processes in neurons, glial cells, and immune cells of the brain. As polymorphisms in ECS components, as well as external impacts such as stress and metabolic challenges, can both lead to dysregulated ECS activity and subsequently to possible neuropsychiatric disorders, pharmacological intervention targeting the ECS is a promising therapeutic approach. Understanding the neurobiology of cannabinoid receptor signaling in depth will aid optimal design of therapeutic interventions, minimizing unwanted side effects.
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Theta Modulated Neural Phase Coherence Facilitates Speech Fluency in Adults Who Stutter

Adults who stutter (AWS) display altered patterns of neural phase coherence within the speech motor system preceding disfluencies. These altered patterns may distinguish fluent speech episodes from disfluent ones. Phase coherence is relevant to the study of stuttering because it reflects neural communication within brain networks. In this follow-up study, the oscillatory cortical dynamics preceding fluent speech in AWS and adults who do not stutter (AWNS) were examined during a single-word delayed reading task using electroencephalographic (EEG) techniques. Compared to AWNS, fluent speech preparation in AWS was characterized by a decrease in theta-gamma phase coherence and a corresponding increase in theta-beta coherence level. Higher spectral powers in the beta and gamma bands were also observed preceding fluent utterances by AWS. Overall, there was altered neural communication during speech planning in AWS that provides novel evidence for atypical allocation of feedforward control by AWS even before fluent utterances.

Reviewing the Diverse Effects of Protein Phosphorylation in Neural Signaling Transduction

Signaling transduction pathways are now known to be foundational mechanisms for a wide variety of biological function. Nobel laureate Paul Greengard dedicated his early career to the exploration of how these molecular cascades are triggered by neurotransmitters, hence applying a general phenomenon to the nervous system. A review by Hemmings, Nairn, McGuinness, Huganir, and Greengard published in the FASEB journal in 1989 identifies three different effects of protein phosphorylation, namely vesicle release, modulation of receptor sensitivity, and initiation of positive or negative feedback systems. The work's focus on three specific examples, rather than the exhaustive approach taken by many other reviews, provides students with an accessible framework within which to learn fundamental concepts in molecular neuroscience. The review could be incorporated as assigned reading for introductory neuroscience or even upper level molecular neurobiology, as it holds very versatile teaching potential.

Navigation of brain networks

We show that the combination of topology and geometry in mammalian cortical networks allows for near-optimal decentralized communication under navigation routing. Following a simple propagation rule based on local knowledge of the distance between cortical regions, we demonstrate that brain networks can be successfully navigated with efficiency that is comparable to shortest paths routing. This finding helps to conciliate the major progress achieved over more than a decade of connectomics research, under the assumption of communication via shortest paths, with recent questions raised by the biologically unrealistic requirements involved in the computation of optimal routes. Our results reiterate the importance of the brain’s spatial embedding, suggesting a three-way relationship between connectome geometry, topology, and communication.

Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse, and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45–60% reductions in navigation performance. We found that the human connectome cannot be progressively randomized or clusterized to result in topologies with substantially improved navigation performance (>5%), suggesting a topological balance between regularity and randomness that is conducive to efficient navigation. Navigation was also found to (i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks, and (ii) explain significant variation in functional connectivity. Unlike commonly studied communication strategies in connectomics, navigation does not mandate assumptions about global knowledge of network topology. We conclude that the topology and geometry of brain networks are conducive to efficient decentralized communication.

The Lag Structure of Intrinsic Activity is Focally Altered in High Functioning Adults with Autism

The behaviors that define autism spectrum disorders (ASDs) have been hypothesized to result from disordered communication within brain networks. Several groups have investigated this question using resting-state functional magnetic resonance imaging (RS-fMRI). However, the published findings to date have been inconsistent across laboratories. Prior RS-fMRI studies of ASD have employed conventional analysis techniques based on the assumption that intrinsic brain activity is exactly synchronous over widely separated parts of the brain. By relaxing the assumption of synchronicity and focusing, instead, on lags between time series, we have recently demonstrated highly reproducible patterns of temporally lagged activity in normal human adults. We refer to this analysis technique as resting-state lag analysis (RS-LA). Here, we report RS-LA as well as conventional analyses of RS-fMRI in adults with ASD and demographically matched controls. RS-LA analyses demonstrated significant group differences in rs-fMRI lag structure in frontopolar cortex, occipital cortex, and putamen. Moreover, the degree of abnormality in individuals was highly correlated with behavioral measures relevant to the diagnosis of ASD. In this sample, no significant group differences were observed using conventional RS-fMRI analysis techniques. Our results suggest that altered propagation of intrinsic activity may contribute to abnormal brain function in ASD.

Neural communication through theta-gamma cross-frequency coupling in a bistable motion perception task

Functional significance of the neural oscillations has been debated since long. In particular, oscillations have been suggested to play a major role in formation of communication channels between brain regions. It has been previously suggested that gamma coherence increases during communication between hemispheres when subjects perceive a horizontal motion in Stroboscopic Alternative Motion (SAM) stimulus. In addition, disruption of this coherence may change the horizontal perception of SAM. In this study, we investigated the changes of Cross-Frequency Coupling (CFC) in EEG signals from parietal and occipital cortices during horizontal and vertical perception of SAM. Our results suggested that while the strength of CFC in parietal electrodes showed no significant change, CFC in P3-P4 electrode-pair demonstrated a significant correlation during horizontal perception of SAM. Therefore, the CFC between theta- and gamma-band oscillations seems to be correlated with changes in functional interactions between brain regions. Accordingly, we propose that in addition to gamma coherence, CFC is perhaps another neurophysiological mechanism involved in neural communication.

Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions

Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions.

Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.

The dependence of neuronal encoding efficiency on Hebbian plasticity and homeostatic regulation of neurotransmitter release

Synapses act as information filters by different molecular mechanisms including retrograde messenger that affect neuronal spiking activity. One of the well-known effects of retrograde messenger in presynaptic neurons is a change of the probability of neurotransmitter release. Hebbian learning describe a strengthening of a synapse between a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive. In this work, a theory of homeostatic regulation of neurotransmitter release by retrograde messenger and Hebbian plasticity in neuronal encoding is presented. Encoding efficiency was measured for different synaptic conditions. In order to gain high encoding efficiency, the spiking pattern of a neuron should be dependent on the intensity of the input and show low levels of noise. In this work, we represent spiking trains as zeros and ones (corresponding to non-spike or spike in a time bin, respectively) as words with length equal to three. Then the frequency of each word (here eight words) is measured using spiking trains. These frequencies are used to measure neuronal efficiency in different conditions and for different parameter values. Results show that neurons that have synapses acting as band-pass filters show the highest efficiency to encode their input when both Hebbian mechanism and homeostatic regulation of neurotransmitter release exist in synapses. Specifically, the integration of homeostatic regulation of feedback inhibition with Hebbian mechanism and homeostatic regulation of neurotransmitter release in the synapses leads to even higher efficiency when high stimulus intensity is presented to the neurons. However, neurons with synapses acting as high-pass filters show no remarkable increase in encoding efficiency for all simulated synaptic plasticity mechanisms. This study demonstrates the importance of cooperation of Hebbian mechanism with regulation of neurotransmitter release induced by rapid diffused retrograde messenger in neurons with synapses as low and band-pass filters to obtain high encoding efficiency in different environmental and physiological conditions.

Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease

Efficient neural communication between premotor and motor cortical areas is critical for manual motor control. Here, we used high-density electroencephalography to study cortical connectivity in patients with Parkinson's disease (PD) and age-matched healthy controls while they performed repetitive movements of the right index finger at maximal repetition rate. Multiple source beamformer analysis and dynamic causal modeling were used to assess oscillatory coupling between the lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1) in the contralateral hemisphere. Elderly healthy controls showed task-related modulation in connections from lPM to SMA and M1, mainly within the γ-band (>30 Hz). Nonmedicated PD patients also showed task-related γ-γ coupling from lPM to M1, but γ coupling from lPM to SMA was absent. Levodopa reinstated physiological γ-γ coupling from lPM to SMA and significantly strengthened coupling in the feedback connection from M1 to lPM expressed as β-β as well as θ-β coupling. Enhancement in cross-frequency θ-β coupling from M1 to lPM was correlated with levodopa-induced improvement in motor function. The results show that PD is associated with an altered neural communication between premotor and motor cortical areas, which can be modulated by dopamine replacement.