Phase-amplitude coupling supports phase coding in human ECoG

Prior studies have shown that high-frequency activity (HFA) is modulated by the phase of low-frequency activity. This phenomenon of phase-amplitude coupling (PAC) is often interpreted as reflecting phase coding of neural representations, although evidence for this link is still lacking in humans. Here, we show that PAC indeed supports phase-dependent stimulus representations for categories. Six patients with medication-resistant epilepsy viewed images of faces, tools, houses, and scenes during simultaneous acquisition of intracranial recordings. Analyzing 167 electrodes, we observed PAC at 43% of electrodes. Further inspection of PAC revealed that category specific HFA modulations occurred at different phases and frequencies of the underlying low-frequency rhythm, permitting decoding of categorical information using the phase at which HFA events occurred. These results provide evidence for categorical phase-coded neural representations and are the first to show that PAC coincides with phase-dependent coding in the human brain.

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Visualization of Phase-Amplitude Coupling Using Rhythmic High-Frequency Activity

10.1101/2020.08.12.247775 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hiroaki Hashimoto ◽

Hui Ming Khoo ◽

Takufumi Yanagisawa ◽

Naoki Tani ◽

Satoru Oshino ◽

...

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

Low Frequency ◽

Θ Rhythm ◽

Human Epilepsy ◽

High Frequency Activity ◽

Phase Amplitude ◽

Intracranial Electrode ◽

Intracranial Electrodes ◽

Frequency Phase

AbstractObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are gaining attention as key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.MethodsWe used intracranial electrodes to record seizures of symptomatic focal epilepsy (15 seizures in seven patients). Ripples (80–250 Hz), as representative of HFAs, were evaluated along with PAC. The synchronization index (SI), representing PAC, was used to analyze the coupling between the amplitude of ripples and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were represented by circles that were scaled linearly to the power changes of ripple.ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The video showed that fluctuation of the diameter of these circles indicated the rhythmic changes during significant high values of θ-ripple PAC.ConclusionsWe inferred that ripple activities occurring during seizure evolution were modulated by θ rhythm. In addition, we concluded that rhythmic circles’ fluctuation presented in the video represents the PAC phenomenon. Our video is thus a useful tool for understanding how ripple activity is modulated by the low-frequency phase in relation with PAC.

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High-frequency neural activity predicts word parsing in ambiguous speech streams

Journal of Neurophysiology ◽

10.1152/jn.00074.2016 ◽

2016 ◽

Vol 116 (6) ◽

pp. 2497-2512 ◽

Cited By ~ 29

Author(s):

Anne Kösem ◽

Anahita Basirat ◽

Leila Azizi ◽

Virginie van Wassenhove

Keyword(s):

High Frequency ◽

Low Frequency ◽

Acoustic Properties ◽

Neural Oscillations ◽

Speech Comprehension ◽

Top Down ◽

Linguistic Representations ◽

High Frequency Activity ◽

Speech Percept ◽

The Brain

During speech listening, the brain parses a continuous acoustic stream of information into computational units (e.g., syllables or words) necessary for speech comprehension. Recent neuroscientific hypotheses have proposed that neural oscillations contribute to speech parsing, but whether they do so on the basis of acoustic cues (bottom-up acoustic parsing) or as a function of available linguistic representations (top-down linguistic parsing) is unknown. In this magnetoencephalography study, we contrasted acoustic and linguistic parsing using bistable speech sequences. While listening to the speech sequences, participants were asked to maintain one of the two possible speech percepts through volitional control. We predicted that the tracking of speech dynamics by neural oscillations would not only follow the acoustic properties but also shift in time according to the participant's conscious speech percept. Our results show that the latency of high-frequency activity (specifically, beta and gamma bands) varied as a function of the perceptual report. In contrast, the phase of low-frequency oscillations was not strongly affected by top-down control. Whereas changes in low-frequency neural oscillations were compatible with the encoding of prelexical segmentation cues, high-frequency activity specifically informed on an individual's conscious speech percept.

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Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

10.1101/077263 ◽

2016 ◽

Author(s):

K. Kessler ◽

R. A. Seymour ◽

G. Rippon

Keyword(s):

High Frequency ◽

Low Frequency ◽

Autism Spectrum ◽

List Type ◽

Brain Oscillations ◽

New Techniques ◽

Low Frequencies ◽

High Frequency Activity ◽

Recent Classification ◽

The Brain

AbstractAlthough atypical social behaviour remains a key characterisation of ASD, the presence of sensory and perceptual abnormalities has been given a more central role in recent classification changes. An understanding of the origins of such aberrations could thus prove a fruitful focus for ASD research. Early neurocognitive models of ASD suggested that the study of high frequency activity in the brain as a measure of cortical connectivity might provide the key to understanding the neural correlates of sensory and perceptual deviations in ASD. As our review shows, the findings from subsequent research have been inconsistent, with a lack of agreement about the nature of any high frequency disturbances in ASD brains. Based on the application of new techniques using more sophisticated measures of brain synchronisation, direction of information flow, and invoking the coupling between high and low frequency bands, we propose a framework which could reconcile apparently conflicting findings in this area and would be consistent both with emerging neurocognitive models of autism and with the heterogeneity of the condition.HighlightsSensory and perceptual aberrations are becoming a core feature of the ASD symptom prolife.Brain oscillations and functional connectivity are consistently affected in ASD.Relationships (coupling) between high and low frequencies are also deficient.Novel framework proposes the ASD brain is marked by local dysregulation and reduced top-down connectivityThe ASD brain’s ability to predict stimuli and events in the environment may be affectedThis may underlie perceptual sensitives and cascade into social processing deficits in ASD

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Phase-tuned neuronal firing encodes human contextual representations for navigational goals

10.1101/202374 ◽

2017 ◽

Cited By ~ 1

Author(s):

Andrew J Watrous ◽

Jonathan Miller ◽

Salman E Qasim ◽

Itzhak Fried ◽

Joshua Jacobs

Keyword(s):

Firing Rate ◽

Medial Temporal Lobe ◽

Single Neuron ◽

Detection Algorithm ◽

Neuronal Firing ◽

Neural Firing ◽

Categorical Information ◽

High Frequency Activity ◽

Oscillation Detection ◽

Intracranial Recordings

AbstractWe previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

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High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone

Epilepsy Currents ◽

10.1177/1535759719842236 ◽

2019 ◽

Vol 19 (3) ◽

pp. 184-186 ◽

Cited By ~ 3

Author(s):

Omar J. Ahmed ◽

Shyam Kumar Sudhakar

Keyword(s):

High Frequency ◽

Low Frequency ◽

Seizure Onset ◽

Seizure Onset Zone ◽

High Frequency Activity

[Box: see text]

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Opuntia Ficus Indica Increases Heart-Rate Variability in High-Level Athletes

International Journal of Sport Nutrition and Exercise Metabolism ◽

10.1123/ijsnem.18.2.169 ◽

2008 ◽

Vol 18 (2) ◽

pp. 169-178 ◽

Cited By ~ 5

Author(s):

Laurent Schmitt ◽

Jean-Pierre Fouillot ◽

Gérard Nicolet ◽

Alain Midol

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

High Frequency ◽

Low Frequency ◽

Physiological Effects ◽

Opuntia Ficus Indica ◽

Diet Supplement ◽

High Frequency Activity ◽

High Level

Opuntia ficus indica (OFI) has many physiological effects, but a relationship between OFI and heart-rate variability (HRV) has never been established. The aim of this study was to describe the effects of a diet supplement of OFI on HRV in athletes. The first day, heart rate (HR) was measured at rest in supine (SU) and standing (ST) positions to analyze HRV in 10 athletes, followed by a randomized assignment to an OFI (5) or placebo (5) group. The next day, the athletes repeated the HRV test. One month later the crossover protocol was applied. In OFI, the high-frequency-activity HFSU (1,773 ± 2,927 vs. 5,856 ± 8,326 ms2, p < .05), HFST (295 ± 313 vs. 560 ± 515 ms2, p < .05), and low-frequency LFSU (1,621 ± 1,795 vs. 6,029 ± 9,007 ms2, p < .01) increased. HRSU (66 ± 13 vs. 57 ± 11 beats/min, p < .01) and HRST (87 ± 11 vs. 76 ± 9 beats/min, p < .01) decreased. A diet supplement of OFI increases HF and LF activities and decreases HR.

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Intracranial recordings from human auditory cortex reveal a neural population selective for song

10.1101/696161 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sam V Norman-Haignere ◽

Jenelle Feather ◽

Dana Boebinger ◽

Peter Brunner ◽

Anthony Ritaccio ◽

...

Keyword(s):

Human Brain ◽

Superior Temporal Gyrus ◽

Data Driven ◽

Neural Population ◽

Acoustic Features ◽

Neural Representations ◽

Cortical Responses ◽

Different Types ◽

Neural Selectivity ◽

Intracranial Recordings

AbstractHow are neural representations of music organized in the human brain? While neuroimaging has suggested some segregation between responses to music and other sounds, it remains unclear whether finer-grained organization exists within the domain of music. To address this question, we measured cortical responses to natural sounds using intracranial recordings from human patients and inferred canonical response components using a data-driven decomposition algorithm. The inferred components replicated many prior findings including distinct neural selectivity for speech and music. Our key novel finding is that one component responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features and was co-located with speech- and music-selective responses in the middle and anterior superior temporal gyrus. These results suggest that neural representations of music are fractionated into subpopulations selective for different types of music, at least one of which is specialized for the analysis of song.

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A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects

eLife ◽

10.7554/elife.44287 ◽

2019 ◽

Vol 8 ◽

Author(s):

Jessica K Nadalin ◽

Louis-Emmanuel Martinet ◽

Ethan B Blackwood ◽

Meng-Chen Lo ◽

Alik S Widge ◽

...

Keyword(s):

High Frequency ◽

Statistical Power ◽

Brain Activity ◽

Low Frequency ◽

Modeling Framework ◽

Frequency Coupling ◽

Phase Amplitude ◽

Cross Frequency Coupling ◽

Frequency Phase

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

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Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

10.1101/392886 ◽

2018 ◽

Cited By ~ 1

Author(s):

Juan L.P. Soto ◽

Felipe V.D. Prado ◽

Etienne Combrisson ◽

Karim Jerbi

Keyword(s):

High Frequency ◽

Brain Activity ◽

Low Frequency ◽

Preliminary Evidence ◽

Visuomotor Coordination ◽

Frequency Signal ◽

High Frequency Signal ◽

Electrophysiological Measurements ◽

High Gamma ◽

Phase Amplitude

AbstractMany functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that both approaches have comparable accuracy, except in the presence of interfering signals with frequencies near the high-frequency band. As for the visuomotor data, the most noticeable impact of the choice of approach was observed when evaluating task-based changes in PAC between the delta (2-5 Hz) and the high-gamma (60-90 Hz) frequency bands, as we were able to identify widespread brain areas with statistically significant effects only with the Hilbert approach. These results provide preliminary evidence of the advantages of the Hilbert approach over the wavelet approach, at least in the context of PAC estimates.

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