scholarly journals Modeling the Generation of Phase-Amplitude Coupling in Cortical Circuits: From Detailed Networks to Neural Mass Models

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Roberto C. Sotero
Keyword(s):  
Computational Models ◽  
High Frequency Oscillation ◽  
Mathematical Framework ◽  
Cortical Circuits ◽  
Frequency Oscillation ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Phase Amplitude ◽  
The Brain ◽  
Neuroscience Community

Phase-amplitude coupling (PAC), the phenomenon where the amplitude of a high frequency oscillation is modulated by the phase of a lower frequency oscillation, is attracting an increasing interest in the neuroscience community due to its potential relevance for understanding healthy and pathological information processing in the brain. PAC is a diverse phenomenon, having been experimentally detected in at least ten combinations of rhythms: delta-theta, delta-alpha, delta-beta, delta-gamma, theta-alpha, theta-beta, theta-gamma, alpha-beta, alpha-gamma, and beta-gamma. However, a complete understanding of the biophysical mechanisms generating this diversity is lacking. Here we review computational models of PAC generation that range from detailed models of neuronal networks, where each cell is described by Hodgkin-Huxley-type equations, to neural mass models (NMMs) where only the average activities of neuronal populations are considered. We argue that NMMs are an appropriate mathematical framework (due to the small number of parameters and variables involved and the richness of the dynamics they can generate) to study the PAC phenomenon.

scholarly journals Topology, cross-frequency, and same-frequency band interactions shape the generation of phase-amplitude coupling in a neural mass model of a cortical column

2015 ◽  
Author(s):  
Roberto C. Sotero
Keyword(s):  
Frequency Band ◽  
Clustering Coefficient ◽  
Neural Mass Model ◽  
Cortical Column ◽  
Phase Coupling ◽  
Mass Model ◽  
Important Indicator ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Phase Amplitude

AbstractPhase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we propose a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). The conditional transfer entropies (cTE) from the phases to the amplitudes of the generated oscillations are estimated by means of the conditional mutual information. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between them and, on average, did not depend upon the presence or absence of a direct (anatomical) connection. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more efficient when transmitting information. Since more than 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation on of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.

scholarly journals Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome

2021 ◽  
Author(s):  
Naoto Kuroda ◽  
Masaki Sonoda ◽  
Makoto Miyakoshi ◽  
Hiroki Nariai ◽  
Jeong-Won Jeong ◽  
...  
Keyword(s):  
Standard Model ◽  
High Frequency ◽  
Classification Accuracy ◽  
Modulation Index ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Model Accuracy ◽  
Intracranial Eeg ◽  
Postoperative Seizure ◽  
Phase Amplitude

Abstract Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving postoperative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥ 80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3-4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG, and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across nonepileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the nonepileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League Against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of postoperative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.

Correlation between inspiratory unit activity in the brain stem and phrenic high-frequency oscillation in rabbits

1990 ◽  
Vol 31 (3) ◽  
pp. 259
Author(s):  
Kazuo Takano ◽  
Fusao Kato ◽  
Naofumi Kimura ◽  
Yuan Wen-jun ◽  
Takehiko Hukuhara
Keyword(s):  
Brain Stem ◽  
Unit Activity ◽  
High Frequency ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
The Brain

Dynamics of EEGs as Signals of Neuronal Populations

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Dynamic Systems ◽  
Neuronal Networks ◽  
Computational Models ◽  
Physiological Data ◽  
Biophysical Properties ◽  
Eeg Signals ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Topological Models ◽  
Different Levels

This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.

scholarly journals Multitaper Estimates of Phase-Amplitude Coupling

2021 ◽  
Author(s):  
Kyle Q. Lepage ◽  
Cavan N. Fleming ◽  
Mark Witcher ◽  
Sujith Vijayan
Keyword(s):  
High Frequency ◽  
Broad Band ◽  
Low Frequency ◽  
Partial Coherence ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Statistical Measures ◽  
Frequency Components ◽  
Phase Amplitude

AbstractPhase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing phase-amplitude coupling with existing statistical measures have been well-documented. The limitations of these measures include: (i) response to non-oscillatory, high-frequency, broad-band activity, (ii) response to high-frequency components of the low-frequency oscillation, (iii) adhoc selection of analysis frequency-intervals, and (iv) reliance upon data shuffling to assess statistical significance. In this work, a multitaper phase-amplitude coupling estimator is proposed that addresses issues (i)-(iv) above. Specifically, issue (i) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (ii) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (iii) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (iv).

Scalp-HFO indexes are biomarkers for the lateralization and localization of the epileptogenic zone in preoperative assessment

2021 ◽  
Vol 126 (4) ◽  
pp. 1148-1158
Author(s):  
Yujiao Yang ◽  
Wei Wang ◽  
Jing Wang ◽  
Mengyang Wang ◽  
Xiaonan Li ◽  
...  
Keyword(s):  
Brain Region ◽  
High Frequency ◽  
Quantitative Assessment ◽  
Preoperative Assessment ◽  
Assessment Method ◽  
Regions Of Interest ◽  
High Frequency Oscillation ◽  
Epileptogenic Zone ◽  
Frequency Oscillation ◽  
The Brain

We proposed the scalp-high-frequency oscillation (HFO) index (HI) as a quantitative assessment method for scalp HFOs to locate the epileptogenic zone (EZ). Our results showed that the HI in regions of interest (ROIs) was significantly higher than in contra-ROIs. Sensitivity and specificity of HI based on ripple rates (n-HI) for EZ localization were 90% and 79.58%, respectively. If the n-HI of the brain region was >1.35, it was more likely to be an epileptogenic region. Clinical application of HIs as an indicator may facilitate localization of the EZ.

scholarly journals Introducing divisive inhibition in the Wilson-Cowan model

2019 ◽  
Author(s):  
Christoforos A. Papasavvas ◽  
Yujiang Wang
Keyword(s):  
Computational Models ◽  
Neural Field ◽  
Cortical Circuits ◽  
Cortical Networks ◽  
Crucial Step ◽  
Inhibitory Mechanisms ◽  
Neural Mass ◽  
Different Types ◽  
Neural Field Models

AbstractBoth subtractive and divisive inhibition has been recorded in cortical circuits and recent findings suggest that different interneuronal populations are responsible for the different types of inhibition. This calls for the formulation and description of these inhibitory mechanisms in computational models of cortical networks. Neural mass and neural field models typically only feature subtractive inhibition. Here, we introduce how divisive inhibition can be incorporated in such models, using the Wilson-Cowan modelling formalism as an example. In addition, we show how the subtractive and divisive modulations can be combined. Including divisive inhibition in neural mass models is a crucial step in understanding its role in shaping oscillatory phenomena in cortical networks.

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