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

Arousal effects on oscillatory dynamics in the non-human primate brain

Arousal states are thought to influence many aspects of cognition and behavior by broadly modulating neural activity. Many studies have observed arousal-related modulations of alpha (~8 to 15 Hz) and gamma (~30 to 50 Hz) power and coherence in local field potentials across relatively small groups of brain regions. However, the global pattern of arousal-related oscillatory modulation in local field potentials is yet to be fully elucidated. We simultaneously recorded local field potentials in numerous cortical and subcortical regions in the primate brain and assessed oscillatory activity and inter-regional coherence associated with arousal state. In high arousal states, we found a uniquely strong and coherent gamma oscillation between the amygdala and basal forebrain. In low arousal rest-like states, a relative increase in coherence at alpha frequencies was present across sampled brain regions, with the notable exception of the medial temporal lobe. We consider how these patterns of activity may index arousal-related brain states that support the processing of incoming sensory stimuli during high arousal states and memory-related functions during rest.

Basolateral amygdala oscillations enable fear learning in a biophysical model

The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3-6 Hz), high theta (~6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

Basolateral amygdala oscillations enable fear learning in a biophysical model

The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3-6 Hz), high theta (~6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

The amygdala mediates the facilitating influence of emotions on memory through multiple interacting mechanisms

Emotionally arousing experiences are better remembered than neutral ones, highlighting that memory consolidation differentially promotes retention of experiences depending on their survival value. This paper reviews evidence indicating that the basolateral amygdala (BLA) mediates the facilitating influence of emotions on memory through multiple mechanisms. Emotionally arousing events, in part by triggering the release of stress hormones, cause a long-lasting enhancement in the firing rate and synchrony of BLA neurons. BLA oscillations, particularly gamma, play an important role in synchronizing the activity of BLA neurons. In addition, BLA synapses are endowed with a unique property, an elevated post-synaptic expression of NMDA receptors. As a result, the synchronized gamma-related recruitment of BLA neurons facilitates synaptic plasticity at other inputs converging on the same target neurons. Given that emotional experiences are spontaneously remembered during wake and sleep, and that REM sleep is favorable to the consolidation of emotional memories, we propose a synthesis for the various lines of evidence mentioned above: gamma-related synchronized firing of BLA cells potentiates synapses between cortical neurons that were recruited during an emotional experience, either by tagging these cells for subsequent reactivation or by enhancing the effects of reactivation itself.

Inferring Pyramidal Neuron Morphology using EAP Data

We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

Reverse engineering information processing in lateral amygdala during auditory tones

Learning in the mammalian lateral amygdala (LA) during auditory fear conditioning (tone - foot shock pairing), one form of associative learning, requires N-methyl-D-aspartate (NMDA) receptor-dependent plasticity. Despite this fact being known for more than two decades, the biophysical details related to signal flow and the involvement of the coincidence detector, NMDAR, in this learning, remain unclear. Here we use a 4000-neuron computational model of the LA (containing two types of pyramidal cells, types A and C, and two types of interneurons, fast spiking FSI and low-threshold spiking LTS) to reverse engineer changes in information flow in the amygdala that underpin such learning; with a specific focus on the role of the coincidence detector NMDAR. The model also included a Ca^2s based learning rule for synaptic plasticity. The physiologically constrained model provides insights into the underlying mechanisms that implement habituation to the tone, including the role of NMDARs in generating network activity which engenders synaptic plasticity in specific afferent synapses. Specifically, model runs revealed that NMDARs in tone-FSI synapses were more important during the spontaneous state, although LTS cells also played a role. Training trails with tone only also suggested long term depression in tone-PN as well as tone-FSI synapses, providing possible hypothesis related to underlying mechanisms that might implement the phenomenon of habituation.

Domain-specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities

Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience. The challenge for a domain scientist lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics when: investigating new methods, developing new tools, or integrating datasets. In this paper, we propose a novel "domain-specific topic model" (DSTM) to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplary scientific domains. Our DSTM is a generative model that extends the Latent Dirichlet Allocation (LDA) model and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include more than 25,000 of papers over the last ten years, featuring hundreds of tools and datasets that are commonly used in relevant studies. Evaluation experiments based on generalization and information retrieval metrics show that our model has better performance than the state-of-the-art baseline models for discovering highly-specific latent topics within a domain. Lastly, we demonstrate applications that benefit from our DSTM to discover intra-domain, cross-domain and trend knowledge patterns.

Neural Oscillations in Aversively Motivated Behavior

Fear and anxiety-based disorders are highly debilitating and among the most prevalent psychiatric disorders. These disorders are associated with abnormal network oscillations in the brain, yet a comprehensive understanding of the role of network oscillations in the regulation of aversively motivated behavior is lacking. In this review, we examine the oscillatory correlates of fear and anxiety with a particular focus on rhythms in the theta and gamma-range. First, we describe neural oscillations and their link to neural function by detailing the role of well-studied theta and gamma rhythms to spatial and memory functions of the hippocampus. We then describe how theta and gamma oscillations act to synchronize brain structures to guide adaptive fear and anxiety-like behavior. In short, that hippocampal network oscillations act to integrate spatial information with motivationally salient information from the amygdala during states of anxiety before routing this information via theta oscillations to appropriate target regions, such as the prefrontal cortex. Moreover, theta and gamma oscillations develop in the amygdala and neocortical areas during the encoding of fear memories, and interregional synchronization reflects the retrieval of both recent and remotely encoded fear memories. Finally, we argue that the thalamic nucleus reuniens represents a key node synchronizing prefrontal-hippocampal theta dynamics for the retrieval of episodic extinction memories in the hippocampus.

Detecting Spontaneous Neural Oscillation Events in Primate Auditory Cortex

Electrophysiological oscillations in the brain have been shown to occur as multicycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We developed an open-source wavelet-based tool to detect and characterize such oscillation events (OEvents) and exemplify the use of this tool in both simulations and two invasively-recorded electrophysiology datasets: one from human, and one from nonhuman primate (NHP) auditory system. After removing incidentally occurring event-related potentials (ERPs), we used OEvents to quantify oscillation features. We identified ∼2 million oscillation events, classified within traditional frequency bands: δ, θ, α, β, low γ, γ, and high γ. Oscillation events of 1-44 cycles could be identified in at least one frequency band 90% of the time in human and NHP recordings. Individual oscillation events were characterized by nonconstant frequency and amplitude. This result necessarily contrasts with prior studies which assumed frequency constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intraevent rhythmicity, there was also evidence of interevent rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano factor (FF) measures differed significantly from a Poisson distribution assumption. Overall, our study provides an easy-to-use tool to study oscillation events at the single-trial level or in ongoing recordings, and demonstrates that rhythmic, multicycle oscillation events dominate auditory cortical dynamics.

Gamma Oscillations in the Basolateral Amygdala: Localization, Microcircuitry, and Behavioral Correlates

The lateral (LA) and basolateral (BL) nuclei of the amygdala regulate emotional behaviors. Despite their dissimilar extrinsic connectivity, they are often combined, perhaps because their cellular composition is similar to that of the cerebral cortex, including excitatory principal cells reciprocally connected with fast-spiking interneurons (FSIs). In the cortex, this microcircuitry produces gamma oscillations that support information processing and behavior. We tested whether this was similarly the case in the rat (males) LA and BL using extracellular recordings, biophysical modeling, and behavioral conditioning. During periods of environmental assessment, both nuclei exhibited gamma oscillations that stopped upon initiation of active behaviors. Yet, BL exhibited more robust spontaneous gamma oscillations than LA. The greater propensity of BL to generate gamma resulted from several microcircuit differences, especially the proportion of FSIs and their interconnections with principal cells. Furthermore, gamma in BL but not LA regulated the efficacy of excitatory synaptic transmission between connected neurons. Together, these results suggest fundamental differences in how LA and BL operate. Most likely, gamma in LA is externally driven, whereas in BL it can also arise spontaneously to support ruminative processing and the evaluation of complex situations.SIGNIFICANCE STATEMENT The basolateral amygdala (BLA) participates in the production and regulation of emotional behaviors. It is thought to perform this using feedforward circuits that enhance stimuli that gain emotional significance and directs them to valence-appropriate downstream effectors. This perspective overlooks the fact that its microcircuitry is recurrent and potentially capable of generating oscillations in the gamma band (50-80 Hz), which synchronize spiking activity and modulate communication between neurons. This study found that BLA gamma supports both of these processes, is associated with periods of action selection and environmental assessment regardless of valence, and differs between BLA subnuclei in a manner consistent with their heretofore unknown microcircuit differences. Thus, it provides new mechanisms for BLA to support emotional behaviors.

Model neocortical microcircuit supports beta and gamma rhythms

Gamma and beta rhythms in neocortical circuits are thought to be caused by distinct subcircuits involving different type of interneurons. However, it is not clear how these distinct but inter-linked intrinsic circuits interact with afferent drive to engender the two rhythms. We report a biophysical computational model to investigate the hypothesis that tonic and phasic drive might engender beta and gamma oscillations, respectively, in a neocortical circuit.

Inferring Morphology of a Neuron from In Vivo LFP Data

We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.

Noise in Neurons and Synapses Enables Reliable Associative Memory Storage in Local Cortical Circuits

Neural networks in the brain can function reliably despite various sources of errors and noise present at every step of signal transmission. These sources include errors in the presynaptic inputs to the neurons, noise in synaptic transmission, and fluctuations in the neurons’ postsynaptic potentials (PSPs). Collectively they lead to errors in the neurons’ outputs which are, in turn, injected into the network. Does unreliable network activity hinder fundamental functions of the brain, such as learning and memory retrieval? To explore this question, this article examines the effects of errors and noise on the properties of model networks of inhibitory and excitatory neurons involved in associative sequence learning. The associative learning problem is solved analytically and numerically, and it is also shown how memory sequences can be loaded into the network with a biologically more plausible perceptron-type learning rule. Interestingly, the results reveal that errors and noise during learning increase the probability of memory recall. There is a trade-off between the capacity and reliability of stored memories, and, noise during learning is required for optimal retrieval of stored information. What is more, networks loaded with associative memories to capacity display many structural and dynamical features observed in local cortical circuits in mammals. Based on the similarities between the associative and cortical networks, this article predicts that connections originating from more unreliable neurons or neuron classes in the cortex are more likely to be depressed or eliminated during learning, while connections onto noisier neurons or neuron classes have lower probabilities and higher weights.

Immunohistochemical Identification of Interneuronal Subpopulations in the Basolateral Amygdala of the Rhesus Monkey (Macaca mulatta)

Inhibitory circuits in the basolateral nuclear complex of the amygdala (BNC) critical for controlling the acquisition, expression, and extinction of emotional responses are mediated by GABAergic interneurons (INs). Studies in rodents have demonstrated that separate IN subpopulations, identified by their expression of calcium-binding proteins and neuropeptides, play discrete roles in the intrinsic circuitry of the BNC. Far less is known about IN subpopulations in primates. In order to fill in this gap in our understanding of primate INs, the present investigation used dual-labeling immunohistochemistry for IN markers to identify subpopulations expressing cholecystokinin (CCK), calbindin (CB), calretinin (CR), and somatostatin (SOM) in somata and axon terminals in the monkey BNC. In general, colocalization patterns seen in somata and axon terminals were similar. It was found that there was virtually no colocalization of CB and CR, the two calcium-binding proteins investigated. Three subtypes of CCK-immunoreactive (CCK+) INs were identified on the basis of their expression of CR or CB: (1) CCK+/CR+; (2) CCK+/CB+); and (3) CCK+/CR-/CB-. Almost no colocalization of CCK with SOM was observed, but there was extensive colocalization of SOM and CB. CCK+, CR+, and CCK+/CR+ double-labeled axon terminals were seen surrounding pyramidal cell somata in basket-like plexuses, as well as in the neuropil. CB+, SOM+, and CB+/SOM+ terminals did not form baskets, suggesting that these IN subpopulations are mainly dendrite-targeting neurons. In general, the IN subpopulations in the monkey are not dissimilar to those seen in rodents but, unlike rodents, CB+ INs in the monkey are not basket cells.

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.

A time-dependent role for the transcription factor CREB in neuronal allocation to an engram underlying a fear memory revealed using a novel in vivo optogenetic tool to modulate CREB function

The internal representation of an experience is thought to be encoded by long-lasting physical changes to the brain ("engrams") . Previously, we and others showed within the lateral amygdala (LA), a region critical for auditory conditioned fear, eligible neurons compete against one other for allocation to an engram. Neurons with relatively higher function of the transcription factor CREB were more likely to be allocated to the engram. In these studies, though, CREB function was artificially increased for several days before training. Precisely when increased CREB function is important for allocation remains an unanswered question. Here, we took advantage of a novel optogenetic tool (opto-DN-CREB) to gain spatial and temporal control of CREB function in freely behaving mice. We found increasing CREB function in a small, random population of LA principal neurons in the minutes, but not 24 h, before training was sufficient to enhance memory, likely because these neurons were preferentially allocated to the underlying engram. However, similarly increasing CREB activity in a small population of random LA neurons immediately after training disrupted subsequent memory retrieval, likely by disrupting the precise spatial and temporal patterns of offline post-training neuronal activity and/or function required for consolidation. These findings reveal the importance of the timing of CREB activity in regulating allocation and subsequent memory retrieval, and further, highlight the potential of optogenetic approaches to control protein function with temporal specificity in behaving animals.

Classification of Brainwaves Using Convolutional Neural Network

Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.

Embracing Complexity in Defensive Networks

The neural basis of defensive behaviors continues to attract much interest, not only because they are important for survival but also because their dysregulation may be at the origin of anxiety disorders. Recently, a dominant approach in the field has been the optogenetic manipulation of specific circuits or cell types within these circuits to dissect their role in different defensive behaviors. While the usefulness of optogenetics is unquestionable, we argue that this method, as currently applied, fosters an atomistic conceptualization of defensive behaviors, which hinders progress in understanding the integrated responses of nervous systems to threats. Instead, we advocate for a holistic approach to the problem, including observational study of natural behaviors and their neuronal correlates at multiple sites, coupled to the use of optogenetics, not to globally turn on or off neurons of interest, but to manipulate specific activity patterns hypothesized to regulate defensive behaviors.

Optogenetic Stimulation of the Basolateral Amygdala Increased Theta-Modulated Gamma Oscillations in the Hippocampus

The amygdala can modulate declarative memory. For example, previous research in rats and humans showed that brief electrical stimulation to the basolateral complex of the amygdala (BLA) prioritized specific objects to be consolidated into long term memory in the absence of emotional stimuli and without awareness of stimulation. The capacity of the BLA to influence memory depends on its substantial projections to many other brain regions, including the hippocampus. Nevertheless, how activation of the BLA influences ongoing neuronal activity in other regions is poorly understood. The current study used optogenetic stimulation of putative glutamatergic neurons in the BLA of freely exploring rats to determine whether brief activation of the BLA could increase in the hippocampus gamma oscillations for which the amplitude was modulated by the phase of theta oscillations, an oscillatory state previously reported to correlate with good memory. BLA neurons were stimulated in 1-s bouts with pulse frequencies that included the theta range (8 Hz), the gamma range (50 Hz), or a combination of both ranges (eight 50-Hz bursts). Local field potentials were recorded in the BLA and in the pyramidal layer of CA1 in the intermediate hippocampus. A key question was whether BLA stimulation at either theta or gamma frequencies could combine with ongoing hippocampal oscillations to result in theta-modulated gamma or whether BLA stimulation that included both theta and gamma frequencies would be necessary to increase theta–gamma comodulation in the hippocampus. All stimulation conditions elicited robust responses in BLA and CA1, but theta-modulated gamma oscillations increased in CA1 only when BLA stimulation included both theta and gamma frequencies. Longer bouts (5-s) of BLA stimulation resulted in hippocampal activity that evolved away from the initial oscillatory states and toward those characterized more by prominent low-frequency oscillations. The current results indicated that one mechanism by which the amygdala might influence declarative memory is by eliciting neuronal oscillatory states in the hippocampus that benefit memory.