The Detection of Phase Amplitude Coupling during Sensory Processing.
- Authors
- Seymour, Robert A; Rippon, Gina; Kessler, Klaus
- Year
- 2017
- Journal
- Frontiers in neuroscience
- PMID
- 28919850
- DOI
- 10.3389/fnins.2017.00487
- PMCID
- PMC5585190
There is increasing interest in understanding how the phase and amplitude of distinct neural oscillations might interact to support dynamic communication within the brain. In particular, previous work has demonstrated a coupling between the phase of low frequency oscillations and the amplitude (or power) of high frequency oscillations during certain tasks, termed phase amplitude coupling (PAC). For instance, during visual processing in humans, PAC has been reliably observed between ongoing alpha (8-13 Hz) and gamma-band (>40 Hz) activity. However, the application of PAC metrics to electrophysiological data can be challenging due to numerous methodological issues and lack of coherent approaches within the field. Therefore, in this article we outline the various analysis steps involved in detecting PAC, using an openly available MEG dataset from 16 participants performing an interactive visual task. Firstly, we localized gamma and alpha-band power using the Fieldtrip toolbox, and extracted time courses from area V1, defined using a multimodal parcelation scheme. These V1 responses were analyzed for changes in alpha-gamma PAC, using four common algorithms. Results showed an increase in alpha (7-13 Hz)-gamma (40-100 Hz) PAC in response to the visual grating stimulus, though specific patterns of coupling were somewhat dependent upon the algorithm employed. Additionally, analyses showed that these results were not driven by the presence of non-sinusoidal oscillations, and that trial length was sufficient to obtain reliable PAC estimates. Finally, throughout the article, methodological issues and practical guidelines for ongoing PAC research will be discussed.
The structure of the engaging sensory paradigm. Each trial started with a 1,500, 2,500, or 3,500 ms baseline period in which a square black box (the βportholeβ) was centrally presented. This was followed by presentation of the visual grating stimulus (two cycles/degree) around the central porthole for 1,500 ms. A picture of an alien (target) or astronaut (non-target) was then shown within the porthole for 500 ms. Participants were instructed to respond after the appearance of an alien picture (maximum response time: 1,500 ms). Correct or incorrect responses were conveyed to the participant through audio-visual feedback in which the porthole turned green (correct) or red (incorrect) and a correct/incorrect tone was played. The times corresponding to the analyzed baseline and visual grating periods are labeled in orange/blue, respectively.
Illustration of the phase amplitude coupling (PAC) analysis procedure. The V1 time-series were filtered to obtain estimates of phase and amplitude, using a narrow (Β±1 Hz) bandwidth for the phase and a variable bandwidth (Β±0.4 times the center frequency) for the amplitude. Phase and amplitude information were obtained via the Hilbert transform. The coupling between phase and amplitude was then quantified using Mean Vector Length, Kullback-Leiber, or Phase Locking Value algorithms to produce a Modulation Index value.
Whole-brain oscillatory power changes following the presentation of the visual grating are marked by (A) increases in the gamma-band (40β60 Hz) and (B) decreases in the alpha-band (8β13 Hz), localized primarily in the ventral occipital cortex. Power maps were thresholded at a value which allowed prominent patterns of power changes to be determined, indicated by the white dotted line. Time-courses were extracted from bilateral visual area V1, defined using the atlas region shown in (C) from the HCP-MMP 1.0 parcelation (Glasser et al., 2016). (D) These V1 responses showed reductions in alpha/beta power and increases in gamma-band (40β70 Hz) power.
Phase-amplitude comodulograms produced by statistically comparing modulation index (MI)-values from 300 to 1,500 ms post-grating onset to a 1,200 ms baseline period, using four separate approaches. Comodulograms for (A) raw MI values and (B) MI values normalized by surrogate data are shown separately. The black dotted line represents significantly different phase-amplitude coupling frequencies (p < 0.05; for details of non-parametric cluster-based statistics see Section Methods).
Results of the simulated PAC analysis. (A) Phase-amplitude comodulograms produced using the MVL-MI-Canolty, MVL-MI-Γzkurt, PLV-MI-Cohen, and KL-MI-Tort algorithms were able to successfully detect the 1.2 s of simulated coupling between 10 Hz phase and 50β70 Hz amplitude. (B) The coupling between 10 Hz phase and 60 Hz amplitude was calculated as a function of simulated data trial length. For trial data under 1 s, all four algorithms produced artificially inflated PAC.
| Name | Type |
|---|---|
| AAL atlas local | anatomy |
| alpha-band local | drug |
| Alpha band (8-13 Hz) local | phenotype |
| alpha/beta power local | phenotype |
| alpha-gamma PAC | phenotype |
| alpha-gamma phase amplitude coupling local | phenotype |
| alpha oscillations | phenotype |
| alpha power | phenotype |
| auditory cortex | anatomy |
| Auditory feedback local | phenotype |
| autism spectrum disorder | phenotype |
| Conte69 brain local | anatomy |
| cortical inhibition | phenotype |
| Cross frequency coupling local | phenotype |
| Cross-frequency coupling | phenotype |
| gamma-band local | drug |
| Gamma band (40-60 Hz) local | phenotype |
| gamma oscillations | phenotype |
| gamma power | phenotype |
| hippocampus | anatomy |
| Human Connectome Project | cohort |
| infragranular cortical layers local | anatomy |
| insufficient data local | phenotype |
| neurocognitive networks local | anatomy |
| neurological illness local | phenotype |
| non-sinusoidal oscillations local | phenotype |
| Non-sinusoidal oscillations local | phenotype |
| normal vision | phenotype |
| occipital cortex | anatomy |
| oscillatory decay-time local | phenotype |
| oscillatory rise-time local | phenotype |
| PAC | phenotype |
| parietal cortex | anatomy |
| Parkinson's disease | phenotype |
| participants | cohort |
| phase-amplitude coupling | phenotype |
| prefrontal cortex | anatomy |
| primary visual cortex | anatomy |
| psychiatric disorders | phenotype |
| schizophrenia | phenotype |
| sensorimotor cortex | anatomy |
| Sensorimotor mu rhythms local | phenotype |
| supragranular cortical layers local | anatomy |
| temporal region | anatomy |
| theta-gamma coupling | phenotype |
| theta oscillations | phenotype |
| trial length local | phenotype |
| ventral occipital cortex local | anatomy |
| visual area V1 local | anatomy |
| Visual area V1 local | anatomy |
| visual MEG dataset local | cohort |
| visual sensory processing local | phenotype |
| Visual stimulus local | phenotype |
| visual system | anatomy |
| visual working memory | phenotype |
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| Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy. | Fujita Y et al. | β | 2022 | β |
| Alpha-beta decoupling relevant to inhibition deficits leads to suicide attempt in major depressive disorder. | Dai Z et al. | β | 2022 | β |
| Attenuated alpha-gamma coupling in emotional dual pathways with right-Amygdala predicting ineffective antidepressant response. | Dai Z et al. | β | 2022 | β |
| Cortical oscillatory dysrhythmias in visual snow syndrome: a magnetoencephalography study. | Hepschke JL et al. | β | 2022 | β |
| Cross-frequency coupling in psychiatric disorders: A systematic review. | Yakubov B et al. | β | 2022 | β |
| Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis. | Batista Tsukahara VH et al. | β | 2022 | β |
| Detection of Cross-Frequency Coupling Between Brain Areas: An Extension of Phase Linearity Measurement. | Sorrentino P et al. | β | 2022 | β |
| EEG phase-amplitude coupling to stratify encephalopathy severity in the developing brain. | Wang X et al. | β | 2022 | β |
| Hemispheric Utilization of Alpha Oscillatory Dynamics as a Unique Biomarker of Neural Compensation in Females with Fragile X Syndrome. | Norris JE et al. | β | 2022 | β |
| Phase-amplitude coupling measures for determination of the epileptic network: A methodological comparison. | Ali R et al. | β | 2022 | β |
| Phase- targeted stimulation modulates phase-amplitude coupling in the motor cortex of the human brain. | Salimpour Y et al. | β | 2022 | β |
| Sensory processing dysregulations as reliable translational biomarkers in SYNGAP1 haploinsufficiency. | CarreΓ±o-MuΓ±oz MI et al. | β | 2022 | β |
| Coordination of top-down influence on V1 responses by interneurons and brain rhythms. | Tani R et al. | β | 2021 | β |
| Cortical entrainment to hierarchical contextual rhythms recomposes dynamic attending in visual perception. | Yuan P et al. | β | 2021 | β |
| Decreased Phase-Amplitude Coupling Between the mPFC and BLA During Exploratory Behaviour in Chronic Unpredictable Mild Stress-Induced Depression Model of Rats. | Wang Z et al. | β | 2021 | β |
| Defining the filter parameters for phase-amplitude coupling from a bispectral point of view. | Zandvoort CS et al. | β | 2021 | β |
| Dysmorphic neurons as cellular source for phase-amplitude coupling in Focal Cortical Dysplasia Type II. | Rampp S et al. | β | 2021 | β |
| Event-related and oscillatory signatures of response inhibition: A magnetoencephalography study with subclinical high and low impulsivity adults | Jauregi A et al. | β | 2021 | β |
| Motor and sensory cortical processing of neural oscillatory activities revealed by human swallowing using intracranial electrodes. | Hashimoto H et al. | β | 2021 | β |
| Multitaper estimates of phase-amplitude coupling. | Lepage KQ et al. | β | 2021 | β |
| Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis. | Juan CH et al. | β | 2021 | β |
| Intact Auditory Cortical Cross-Frequency Coupling in Early and Chronic Schizophrenia. | Murphy N et al. | β | 2020 | β |
| Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb. | Losacco J et al. | β | 2020 | β |
| Midline frontal and occipito-temporal activity during error monitoring in dyadic motor interactions. | Moreau Q et al. | β | 2020 | β |
| Oscillations in the central brain of <i>Drosophila</i> are phase locked to attended visual features. | Grabowska MJ et al. | β | 2020 | β |
| Pain phenotypes classified by machine learning using electroencephalography features. | Levitt J et al. | β | 2020 | β |
| Profound regional spectral, connectivity, and network changes reflect visual deficits in posterior cortical atrophy: an EEG study. | Briels CT et al. | β | 2020 | β |
| Rhythmic light flicker rescues hippocampal low gamma and protects ischemic neurons by enhancing presynaptic plasticity. | Zheng L et al. | β | 2020 | β |
| The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling. | Zhang W et al. | β | 2020 | β |
| A causal role for the precuneus in network-wide theta and gamma oscillatory activity during complex memory retrieval. | Hebscher M et al. | β | 2019 | β |
| Cross-Frequency Coupling Based Neuromodulation for Treating Neurological Disorders. | Salimpour Y et al. | β | 2019 | β |
| Dysregulated oscillatory connectivity in the visual system in autism spectrum disorder. | Seymour RA et al. | β | 2019 | β |
| Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex. | An J et al. | β | 2019 | β |
| Synchronised spiking activity underlies phase amplitude coupling in the subthalamic nucleus of Parkinson's disease patients. | Meidahl AC et al. | β | 2019 | β |
| Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations. | Munia TTK et al. | β | 2019 | β |
| Cortico-Striatal Cross-Frequency Coupling and Gamma Genesis Disruptions in Huntington's Disease Mouse and Computational Models. | Naze S et al. | β | 2018 | β |
| Oscillatory networks of high-level mental alignment: A perspective-taking MEG study. | Seymour RA et al. | β | 2018 | β |