Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

Jacobs, Daniel; Hilton, Trevor; Campo, Martín del; Carlen, Peter L.; Bardakjian, Berj L.

doi:10.1109/tbme.2018.2797919

Cited by 50 publications

(32 citation statements)

References 41 publications

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“…Based on γ-distribution fits of interseizure and seizure duration histograms, seizure onset has been projected as randomly occurring (Suffczynski et al, 2006), whereas seizure termination has been demonstrated to be predictable (Bauer et al, 2017). However, there may be features that govern seizure sub-states that have predictable underlying patterns for both the onset and termination of the ictal events (Jacobs et al, 2018). In particular, using CFC to classify the ictal state may be more accurate and provide greater insight into state dynamics than visual inspection or amplitude-based measurements.…”

Section: Introductionmentioning

confidence: 99%

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

Breton

Bardakjian

Carlen

2019

eNeuro

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In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised two-state hidden Markov model (HMM). γ-Distributions of their durations indicated that both are predictable. Furthermore, omitting 4 Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-aminopyridine (4-AP)-induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12 Hz, inhibitory currents dominantly phase locked at 4 Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.

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Section: Introductionmentioning

confidence: 99%

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

Breton

Bardakjian

Carlen

2019

eNeuro

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“…Recent years have also witnessed great advances in neuroimaging modalities such as magneto-and electroencephalography (MEG/EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI), which provide valuable tools for the identification of networks (Teipel et al, 2009Ciuciu et al, 2014Bönstrup et al, 2015;Wu et al, 2017;Dimitriadis et al, 2018). Among the multimodal neuroimaging techniques, EEG may have some major assets from a clinical perspective since it is non-invasive, inexpensive, and easy to use (Fan and Chou, 2018;Jacobs et al, 2018). Compared to most fMRI techniques, the high temporal resolution of EEG also enables the detection of fast neural oscillations that are related to the perception and information exchange between cortical areas (Babiloni et al, 2014;Sigala et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease

et al. 2020

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Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

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“…2,4 The underlying mechanisms of these two patterns were also shown to be different in experimental animal models. [8][9][10][11][12] PAC has been shown to be higher during the ictal period, and in the seizure-onset zone (SOZ) compared to nonepileptic regions. 5 Different high-frequency oscillation (HFO) patterns are also observed during LVF and PS seizures in animals.…”

Section: Introductionmentioning

confidence: 99%

“…7 Phase-amplitude coupling (the coupling between the amplitude of high-frequency activity and the phase of the lowfrequency band; PAC) has been the subject of many studies, not only in normal brain activity, but also in patients with epilepsy. [8][9][10][11][12] PAC has been shown to be higher during the ictal period, and in the seizure-onset zone (SOZ) compared to nonepileptic regions. 13,14 The interaction of HFOs and slow waves can also help distinguish physiologic and pathologic HFOs.…”

Section: Introductionmentioning

confidence: 99%

Interictal coupling of HFOs and slow oscillations predicts the seizure‐onset pattern in mesiotemporal lobe epilepsy

2019

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Objective Low‐voltage fast activity (LVF) and low‐frequency high‐amplitude periodic spiking (PS) are the two most common seizure‐onset patterns in mesiotemporal lobe epilepsy, with different underlying mechanisms, pathology, and postsurgical outcome. The present work aims to investigate whether specific coupling patterns of high‐frequency oscillations (HFOs >80 Hz) and low‐frequency waves in the interictal period may distinguish these two patterns, and also seizure‐onset zone (SOZ) from non‐SOZ as a secondary aim. Methods We used intracranial electroencephalography (iEEG) data (during non–rapid eye movement [NREM] sleep) of 18 patients with either LVF or PS seizure‐onset patterns. We investigated the interaction between HFOs (ripples: 80‐250 Hz and fast ripples: >250 Hz) and slow oscillations (slow‐delta, delta, and theta waves). We compared classic features (amplitude, duration, frequency, and power) and phase of coupling between HFOs and slower oscillations inside and outside the SOZ. We then used these features to classify HFOs and subsequently patients into LVF and PS groups. Results Ripples in the LVF group had significantly longer duration, lower frequency, and higher amplitude than in the PS group. The phase of slow oscillations at which HFOs occur is different between the LVF and PS HFOs (LVF, mostly at the peak or the transition of peak to trough; PS, mostly during the transition of trough to peak). HFOs associated with theta waves best discriminate seizure‐onset patterns. The coupling phase improves the classification of HFOs and patients to either LVF or PS groups, and also the classification of HFOs in SOZ and non‐SOZ. Significance The phase of coupling of HFOs and low‐frequency waves may help to not only identify the SOZ, but also to classify patients with different types of seizure‐onset patterns. It likely reflects that different disease processes are involved in these patterns during the interictal period.

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Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

Cited by 50 publications

References 41 publications

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease

Interictal coupling of HFOs and slow oscillations predicts the seizure‐onset pattern in mesiotemporal lobe epilepsy

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