Variability and stability of large-scale cortical oscillation patterns

Cox, Roy; Schapiro, Anna C.; Stickgold, Robert

doi:10.1162/netn_a_00046

Cited by 19 publications

(20 citation statements)

References 89 publications

(196 reference statements)

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“…The slope of the power spectrum in schizophrenia patients was significantly steeper (1.94 +/-0.48 MAD) compared to controls (1.68 +/-0.55) (Wilcoxon rank sum test, W = 14104, p = 1.2e-05) (Figure 2 f-h ). Spectral power varies between cortical regions and can be used to identify individual subjects, and tasks (Cox, R. et al, 2016). We therefore employed a spatial multivariate classification approach to predict clinical group (i.e., control or schizophrenia) from either oscillatory band power, spectral slope, or both (see Methods ).…”

Section: /F Spectral Slope Decreases In Schizophrenia and Best Predimentioning

confidence: 99%

Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

Rosen

Belger

et al. 2017

Preprint

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Schizophrenia has been associated with separate irregularities in several neural oscillatory frequency bands, including theta, alpha, and gamma. Our multivariate classification suggests that instead of irregularities in many frequency bands, schizophrenia-related EEG 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 oscillatory power. 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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Section: /F Spectral Slope Decreases In Schizophrenia and Best Predimentioning

confidence: 99%

Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

Rosen

Belger

et al. 2017

Preprint

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“…To date, most fingerprinting analyses have focused on features derived from brain networks in which nodes represent positioned electrodes [34] or brain regions [14,35]. Recently, we proposed an alternative model of brain connectivity that focuses on interactivity among a network's connections (or "edges") [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Subject identification using edge-centric functional connectivity

Faskowitz

Esfahlani

et al. 2020

Preprint

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Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed "fingerprinting" analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

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“…Previously, researchers have used such features in attempts to identify general blueprints of brain activity, the shared patterns or contrasting abnormalities of which within a group of subjects would allow the assignment of individuals to a certain population (e.g., to healthy subjects or to subjects with diagnosable neurological or psychiatric disorders). Yet, despite gross inter-individual similarities, there is reason to believe that a discernible portion of the individual spontaneous cortical activity is subject-specific (Valizadeh et al 2019); that is, it varies substantially from one subject to another (Barch et al 2013;Finn et al 2015;Valizadeh et al 2019) and can be recognized and attributed to that same individual several months later (Chu et al 2012;Cox et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers

et al. 2020

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In neuroscience, neural oscillations and other features of brain activity recorded by electroencephalography (EEG) are typically statistically assessed on the basis of the study's population mean to identify possible blueprints for healthy subjects, or subjects with diagnosable neurological or psychiatric disorders. Despite some inter-individual similarities, there is reason to believe that a discernible portion of the individual brain activity is subject-specific. In order to encompass the potential individual source of variance in EEG data and psychometric parameters, we introduce an innovative application of linear mixed-effects models (LMM) as an alternative procedure for the analysis of resting-state EEG data. Using LMM, individual differences can be modelled through the assumptions of idiosyncrasy of all responses and dependency among data points (e.g., from the same subject within and across units of time) via random effects parameters. This report provides an example of how LMM can be used for the statistical analysis of resting-state EEG data in a heterogeneous group of subjects; namely, people who suffer from tinnitus (ringing in the ear/s). Results from 49 participants (38 male, mean age of 46.69 ± 12.65 years) revealed that EEG signals were not only associated with specific recording sites, but exhibited regional specific oscillations in conjunction to symptom severity. Tinnitus distress targeted the frequency bands beta3 (23.5-35 Hz) and gamma in right frontal regions, whereas delta (0.5-4 Hz) exhibited significant changes in temporal-parietal sources. Further, 57.8% of the total variance in EEG power was subject-specific and acknowledged by the LMM framework and its prediction. Thus, a deeper understanding of both the underlying statistical and physiological patterns of EEG data was gained.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Variability and stability of large-scale cortical oscillation patterns

Cited by 19 publications

References 89 publications

Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

Subject identification using edge-centric functional connectivity

Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers

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