Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.

BACKGROUND: Cognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods. NEW METHOD: To address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance. RESULTS: The characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used. COMPARISON WITH EXISTING METHODS: Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts. CONCLUSIONS: This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Amygdala Stimulation Leads to Functional Network Connectivity State Transitions in the Hippocampus.

Several studies have shown that direct brain stimulation can enhance memory in humans and animal models. Investigating the neurophysiological changes induced by brain stimulation is an important step towards understanding the neural processes underlying memory function. Furthermore, it paves the way for developing more efficient neuromodulation approaches for memory enhancement. In this study, we utilized a combination of unsupervised and supervised machine learning approaches to investigate how amygdala stimulation modulated hippocampal network activities during the encoding phase. Using a sliding window in time, we estimated the hippocampal dynamic functional network connectivity (dFNC) after stimulation and during sham trials, based on the covariance of local field potential recordings in 4 subregions of the hippocampus. We extracted different network states by combining the dFNC samples from 5 subjects and applying k-means clustering. Next, we used the between-state transition numbers as the latent features to classify between amygdala stimulation and sham trials across all subjects. By training a logistic regression model, we could differentiate stimulated from sham trials with 67% accuracy across all subjects. Using elastic net regularization as a feature selection method, we identified specific patterns of hippocampal network state transition in response to amygdala stimulation. These results offer a new approach to better understanding of the causal relationship between hippocampal network dynamics and memory-enhancing amygdala stimulation.

Closed-loop control of gamma oscillations in the amygdala demonstrates their role in spatial memory consolidation.

Gamma is a ubiquitous brain rhythm hypothesized to support cognitive, perceptual, and mnemonic functions by coordinating neuronal interactions. While much correlational evidence supports this hypothesis, direct experimental tests have been lacking. Since gamma occurs as brief bursts of varying frequencies and durations, most existing approaches to manipulate gamma are either too slow, delivered irrespective of the rhythm's presence, not spectrally specific, or unsuitable for bidirectional modulation. Here, we overcome these limitations with an approach that accurately detects and modulates endogenous gamma oscillations, using closed-loop signal processing and optogenetic stimulation. We first show that the rat basolateral amygdala (BLA) exhibits prominent gamma oscillations during the consolidation of contextual memories. We then boost or diminish gamma during consolidation, in turn enhancing or impairing subsequent memory strength. Overall, our study establishes the role of gamma oscillations in memory consolidation and introduces a versatile method for studying fast network rhythms in vivo.

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.

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.

Intra- and interregional cortical interactions related to sharp-wave ripples and dentate spikes.

The hippocampus generates population events termed sharp-wave ripples (SWRs) and dentate spikes (DSs). While little is known about DSs, SWR-related hippocampal discharges during sleep are thought to replay prior waking activity, reactivating the cortical networks that encoded the initial experience. During slow-wave sleep, such reactivations likely occur during up-states, when most cortical neurons are depolarized. However, most studies have examined the relationship between SWRs and up-states measured in single neocortical regions. As a result, it is currently unclear whether SWRs are associated with particular patterns of widely distributed cortical activity. Additionally, no such investigation has been carried out for DSs. The present study addressed these questions by recording SWRs and DSs from the dorsal hippocampus simultaneously with prefrontal, sensory (visual and auditory), perirhinal, and entorhinal cortices in naturally sleeping rats. We found that SWRs and DSs were associated with up-states in all cortical regions. Up-states coinciding with DSs and SWRs exhibited increased unit activity, power in the gamma band, and intraregional gamma coherence. Unexpectedly, interregional gamma coherence rose much more strongly in relation to DSs than to SWRs. Whereas the increase in gamma coherence was time locked to DSs, that seen in relation to SWRs was not. These observations suggest that SWRs are related to the strength of up-state activation within individual regions throughout the neocortex but not so much to gamma coherence between different regions. Perhaps more importantly, DSs coincided with stronger periods of interregional gamma coherence, suggesting that they play a more important role than previously assumed. NEW & NOTEWORTHY: Off-line cortico-hippocampal interactions are thought to support memory consolidation. We surveyed the relationship between hippocampal sharp-wave ripples (SWRs) and dentate spikes (DSs) with up-states across multiple cortical regions. SWRs and DSs were associated with increased cortical gamma oscillations. Interregional gamma coherence rose much more strongly in relation to DSs than to SWRs. Moreover, it was time locked to DSs but not SWRs. These results have important implications for current theories of systems memory consolidation during sleep.