Differential phase-amplitude coupling in nucleus accumbens and orbitofrontal cortex reflects decision-making during a delay discounting task.

BACKGROUND: The impulsive choice is characterized by the preference for a small immediate reward over a bigger delayed one. The mechanisms underlying impulsive choices are linked to the activity in the Nucleus Accumbens (NAc), the orbitofrontal cortex (OFC), and the dorsolateral striatum (DLS). While the study of functional connectivity between brain areas has been key to understanding a variety of cognitive processes, it remains unclear whether functional connectivity differentiates impulsive-control decisions. METHODS: To study the functional connectivity both between and within NAc, OFC, and DLS during a delay discounting task, we concurrently recorded local field potential in NAc, OFC, and DLS in rats. We then quantified the degree of phase-amplitude coupling (PAC), coherence, and Granger Causality between oscillatory activities in animals exhibiting either a high (HI) or low (LI) tendency for impulsive choices. RESULTS: Our results showed a differential pattern of PAC during decision-making in OFC and NAc, but not in DLS. While theta-gamma PAC in OFC was associated with self-control decisions, a higher delta-gamma PAC in both OFC and NAc biased decisions toward impulsive choices in both HI and LI groups. Furthermore, during the reward event, Granger Causality analysis indicated a stronger NAc➔OFC gamma contribution in the HI group, while the LI group showed a higher OFC➔NAc gamma contribution. CONCLUSIONS: The overactivity in NAc during reward in the HI group suggests that exacerbated contribution of NAcCore can lead to an overvaluation of reward that biases the behavior toward the impulsive choice.

BOARD-FTD-PACC: a graphical user interface for the synaptic and cross-frequency analysis derived from neural signals.

In order to understand the link between brain functional states and behavioral/cognitive processes, the information carried in neural oscillations can be retrieved using different analytic techniques. Processing these different bio-signals is a complex, time-consuming, and often non-automatized process that requires customization, due to the type of signal acquired, acquisition method implemented, and the objectives of each individual research group. To this end, a new graphical user interface (GUI), named BOARD-FTD-PACC, was developed and designed to facilitate the visualization, quantification, and analysis of neurophysiological recordings. BOARD-FTD-PACC provides different and customizable tools that facilitate the task of analyzing post-synaptic activity and complex neural oscillatory data, mainly cross-frequency analysis. It is a flexible and user-friendly software that can be used by a wide range of users to extract valuable information from neurophysiological signals such as phase-amplitude coupling and relative power spectral density, among others. BOARD-FTD-PACC allows researchers to select, in the same open-source GUI, different approaches and techniques that will help promote a better understanding of synaptic and oscillatory activity in specific brain structures with or without stimulation.

Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis.

Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Communication Through Coherence by Means of Cross-frequency Coupling.

The theory of communication through coherence (CTC) posits the synchronization of brain oscillations as a key mechanism for information sharing and perceptual binding. In a parallel literature, hippocampal theta activity (4-10 Hz) has been shown to modulate the appearance of neocortical fast gamma oscillations (100-150 Hz), a phenomenon known as cross-frequency coupling (CFC). Even though CFC has also been previously associated with information routing, it remains to be determined whether it directly relates to CTC. In particular, for the theta-fast gamma example at hand, a critical question is to know if the phase of the theta cycle influences gamma synchronization across the neocortex. To answer this question, we combined CFC (modulation index) and CTC (phase-locking value) metrics in order to detect the modulation of the cross-regional high-frequency synchronization by the phase of slower oscillations. Upon applying this method, we found that the inter-hemispheric synchronization of neocortical fast gamma during REM sleep depends on the instantaneous phase of the theta rhythm. These results show that CFC is likely to aid long-range information transfer by facilitating the synchronization of faster rhythms, thus consistent with classical CTC views.

Bifurcation structure determines different phase-amplitude coupling patterns in the activity of biologically plausible neural networks.

Phase-amplitude cross frequency coupling (PAC) is a rather ubiquitous phenomenon that has been observed in a variety of physical domains; however, the mechanisms underlying the emergence of PAC and its functional significance in the context of neural processes are open issues under debate. In this work we analytically demonstrate that PAC phenomenon naturally emerges in mean-field models of biologically plausible networks, as a signature of specific bifurcation structures. The proposed analysis, based on bifurcation theory, allows the identification of the mechanisms underlying oscillatory dynamics that are essentially different in the context of PAC. Specifically, we found that two PAC classes can coexist in the complex dynamics of the analyzed networks: 1) harmonic PAC which is an epiphenomenon of the nonsinusoidal waveform shape characterized by the linear superposition of harmonically related spectral components, and 2) nonharmonic PAC associated with "true" coupled oscillatory dynamics with independent frequencies elicited by a secondary Hopf bifurcation and mechanisms involving periodic excitation/inhibition (PEI) of a network population. Importantly, these two PAC types have been experimentally observed in a variety of neural architectures confounding traditional parametric and nonparametric PAC metrics, like those based on linear filtering or the waveform shape analysis, due to the fact that these methods operate on a single one-dimensional projection of an intrinsically multidimensional system dynamics. We exploit the proposed tools to study the functional significance of the PAC phenomenon in the context of Parkinson's disease (PD). Our results show that pathological slow oscillations (e.g. ß band) and nonharmonic PAC patterns emerge from dissimilar underlying mechanisms (bifurcations) and are associated to the competition of different BG-thalamocortical loops. Thus, this study provides theoretical arguments that demonstrate that nonharmonic PAC is not an epiphenomenon related to the pathological ß band oscillations, thus supporting the experimental evidence about the relevance of PAC as a potential biomarker of PD.