Identifying robust and sensitive frequency bands for interrogating neural oscillations

AbstractNeural oscillations are routinely analyzed using methods that measure activity in canonical frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals is not fixed and varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm (“ORCA”), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands which incorporates two new methods for frequency band identification. ORCA uses the instantaneous power, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed “resting” conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that will allow researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.

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073 Whole-brain network analysis of neural oscillations during light sleep

SLEEP ◽

10.1093/sleep/zsab072.072 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A30-A31

Author(s):

Joline Fan ◽

Kiwamu Kudo ◽

Kamalini Ranasinghe ◽

Hirofumi Morise ◽

Anne Findlay ◽

...

Keyword(s):

Functional Connectivity ◽

Information Flow ◽

Phase Transfer ◽

Sleep Onset ◽

Adaptive Beamforming ◽

Neural Oscillations ◽

High Temporal Resolution ◽

Whole Brain ◽

Frequency Bands ◽

Brain Functional Connectivity

Abstract Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. Methods In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. Results We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Conclusion Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Support (if any) Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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Imagined Temporal Groupings Tune Oscillatory Neural Activity for Processing Rhythmic Sounds

Timing & Time Perception ◽

10.1163/22134468-03002042 ◽

2015 ◽

Vol 3 (1-2) ◽

pp. 172-188

Author(s):

Brandon T. Paul ◽

Per B. Sederberg ◽

Lawrence L. Feth

Keyword(s):

Multivariate Analysis ◽

Neural Activity ◽

Neural Oscillations ◽

Neural Activation ◽

Focus Attention ◽

Complex Sound ◽

Frequency Bands ◽

Hierarchical Timing ◽

Electroencephalogram Eeg ◽

The Brain

Temporal patterns within complex sound signals, such as music, are not merely processed after they are heard. We also focus attention to upcoming points in time to aid perception, contingent upon regularities we perceive in the sounds’ inherent rhythms. Such organized predictions are endogenously maintained as meter — the patterning of sounds into hierarchical timing levels that manifest as strong and weak events. Models of neural oscillations provide potential means for how meter could arise in the brain, but little evidence of dynamic neural activity has been offered. To this end, we conducted a study instructing participants to imagine two-based or three-based metric patterns over identical, equally-spaced sounds while we recorded the electroencephalogram (EEG). In the three-based metric pattern, multivariate analysis of the EEG showed contrasting patterns of neural oscillations between strong and weak events in the delta (2–4 Hz) and alpha (9–14 Hz), frequency bands, while theta (4–9 Hz) and beta (16–24 Hz) bands contrasted two hierarchically weaker events. In two-based metric patterns, neural activity did not drastically differ between strong and weak events. We suggest the findings reflect patterns of neural activation and suppression responsible for shaping perception through time.

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Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

10.1101/2020.07.28.225698 ◽

2020 ◽

Author(s):

Parham Mostame ◽

Sepideh Sadaghiani

Keyword(s):

Information Exchange ◽

Spatial Organization ◽

Brain Activity ◽

The State ◽

Neural Oscillations ◽

Cognitive State ◽

State Dependency ◽

Frequency Bands ◽

Cognitive States ◽

The Impact

AbstractFunctional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

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1/f neural noise is a better predictor of schizophrenia than neural oscillations

10.1101/113449 ◽

2017 ◽

Cited By ~ 8

Author(s):

Erik J. Peterson ◽

Burke Q. Rosen ◽

Alana M. Campbell ◽

Aysenil Belger ◽

Bradley Voytek

Keyword(s):

Background Noise ◽

Inhibitory Neuron ◽

Predictive Biomarkers ◽

Neural Oscillations ◽

Behavioral Performance ◽

Oscillatory Frequency ◽

Frequency Bands ◽

Neural Noise ◽

Oscillatory Power

AbstractSchizophrenia has been associated with separate irregularities in several neural oscillatory frequency bands, including theta, alpha, and gamma. Our multivariate classification of human EEG suggests that instead of irregularities in many frequency bands, schizophrenia-related electrophysiological differences may better be explained by an overall shift in neural noise, reflected by a change in the 1/f slope of the power spectrum.Significance statementUnderstanding the neurobiological origins of schizophrenia, and identifying reliable biomarkers, are both of critical importance in improving treatment of that disease. While we lack predictive biomarkers, numerous studies have observed disruptions to neural oscillations in schizophrenia patients. This literature has, in part, lead to schizophrenia being characterized as disease of disrupted neural coordination. We report however that changes to background noise (i.e., 1/f noise) are a substantially better predictor of schizophrenia than both oscillatory power and participants own behavioral performance. The observed alterations in neural noise are consistent with inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and neurobiological deficits.

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Desynchronization between speech rhythms and neural oscillations: a possible cause of phonological problems in dyslexia

ANALES RANM ◽

10.32440/ar.2018.135.02.supl01.art05 ◽

2018 ◽

Vol 135 (135(02)) ◽

pp. 47-51

Author(s):

Mikel Lizarazu ◽

Marie Lallier ◽

Nicola Molinaro

Keyword(s):

Auditory Perception ◽

Neural Mechanisms ◽

Theoretical Research ◽

Neural Oscillations ◽

Neuronal Oscillations ◽

Research Framework ◽

Frequency Bands ◽

Neural Bases ◽

Possible Cause

The main objective of our studies is to understand the neural bases underlying phonological difficulties in dyslexia. First, we will review the theoretical research framework generated around the phonological theory of dyslexia. Second, we will review what are the neural mechanisms involved in the segmentation of speech in control readers. In this section we will demonstrate that the synchronization between speech rhythms and neural oscillations at different frequency bands plays a key role in the segmentation of speech. Next, we will present different studies that suggest that dyslexic readers present a desynchronization between speech rhythms and neuronal oscillations in auditory regions. This lack of synchronization could cause the auditory perception problems and the phonological difficulties that we observe in readers with dyslexia. Finally, we will present recent studies from our laboratory that support the theory of neuronal desynchronization in dyslexia and show that these problems are also present in children with dyslexia.

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The Oscillatory ReConstruction Algorithm adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings

Journal of Neurophysiology ◽

10.1152/jn.00292.2020 ◽

2020 ◽

Vol 124 (6) ◽

pp. 1914-1922 ◽

Cited By ~ 1

Author(s):

Andrew J. Watrous ◽

Robert J. Buchanan

Keyword(s):

Spectral Decomposition ◽

Reconstruction Algorithm ◽

Brain Regions ◽

Neural Oscillations ◽

Fixed Frequency ◽

Neural Signals ◽

Frequency Bands ◽

Neural Recordings ◽

Analytic Tool ◽

Researcher Bias

Neural oscillations show substantial variability within and across individuals and brain regions, yet most existing studies analyze oscillations using canonical, fixed-frequency bands. Thus, there is an ongoing need for tools that capture oscillatory variability in neural signals. Toward this end, Oscillatory ReConstruction Algorithm is a novel and adaptive analytic tool that allows researchers to measure neural oscillations with more precision and less researcher bias.

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The predictive roles of neural oscillations in speech motor adaptability

Journal of Neurophysiology ◽

10.1152/jn.00043.2016 ◽

2016 ◽

Vol 115 (5) ◽

pp. 2519-2528 ◽

Cited By ~ 4

Author(s):

Ranit Sengupta ◽

Sazzad M. Nasir

Keyword(s):

Brain Regions ◽

Brain Network ◽

Neural Oscillations ◽

Motor Speech ◽

Frequency Bands ◽

Time Frequency ◽

Continuous Eeg ◽

Outcome Differences ◽

Eeg Recordings ◽

Speech Motor

The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations.

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DREAM

Neuroinformatics ◽

10.1007/s12021-020-09500-9 ◽

2021 ◽

Author(s):

Zhu-Qing Gong ◽

Peng Gao ◽

Chao Jiang ◽

Xiu-Xia Xing ◽

Hao-Ming Dong ◽

...

Keyword(s):

Brain Activity ◽

Worked Examples ◽

Individual Variability ◽

Head Movements ◽

Neural Oscillations ◽

Head Motion ◽

Multiple Frequency ◽

Frequency Bands ◽

Human Connectome Project ◽

The Brain

AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation’s amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

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