scholarly journals Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes

2021 ◽  
Author(s):  
Brian Nils Lundstrom ◽  
Benjamin Brinkmann ◽  
Gregory Worrell
Keyword(s):  
Positive Predictive Value ◽  
Patient Outcomes ◽  
Predictive Value ◽  
Low Frequency ◽  
Seizure Onset ◽  
Eeg Activity ◽  
Seizure Onset Zone ◽  
Interictal Eeg ◽  
Eeg Power ◽  
Frequency Power

Abstract Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5,111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2–4 Hz) and beta-gamma (20–50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modeling suggests changes in neural adaptation may be related to the observed low frequency power changes.

scholarly journals Low frequency interictal EEG biomarker for localizing seizures

2021 ◽  
Author(s):  
Brian Lundstrom ◽  
Benjamin Brinkmann ◽  
Gregory Worrell
Keyword(s):  
Positive Predictive Value ◽  
Surgical Resection ◽  
Patient Outcomes ◽  
Predictive Value ◽  
Low Frequency ◽  
Seizure Onset ◽  
Prognostic Information ◽  
Eeg Activity ◽  
Epileptiform Discharges ◽  
Interictal Eeg

Objective: We want to identify seizure onset zone (SOZ) from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Methods: Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5,111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Results: Interictal infraslow frequency EEG activity was decreased for SOZ electrodes while faster frequencies such as delta (2-4 Hz) and beta-gamma (20-50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal SOZ electrodes compared to non-SOZ electrodes. Interictal EEG biomarkers correctly classified mesial temporal SOZ electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interpretation: Interictal infraslow EEG activity is decreased near the SOZ while higher frequency power is increased, suggesting distinct underlying physiologic mechanisms. Decreased interictal infraslow activity may reflect the loss of neural inhibition. Narrowband interictal EEG power bands provide information about the SOZ and can help predict mesial temporal involvement in seizure onset. Together with interictal epileptiform discharges and high frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection.

scholarly journals Seizure Onset Zone Identification Using Phase-Amplitude Coupling and Multiple Machine Learning Approaches for Interictal Electrocorticogram

2021 ◽  
Author(s):  
Yao Miao ◽  
Yasushi Iimura ◽  
Hidenori Sugano ◽  
Kosuke Fukumori ◽  
Toshihisa Tanaka
Keyword(s):  
Machine Learning ◽  
Focal Cortical Dysplasia ◽  
Low Frequency ◽  
Classification Performance ◽  
Learning Approaches ◽  
Seizure Onset ◽  
Statistical Measures ◽  
Seizure Onset Zone ◽  
Significant Difference ◽  
Phase Amplitude

Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC served as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low frequency rhythms (0.5–24 Hz) and high frequency oscillations (HFOs) (80–560 Hz). We used statistical measures to test the significant difference in PAC between SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to automatically learn the characteristics of PAC features obtained and classify them to identify SOZ. Besides, to conquer the imbalance of datasets, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed the time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only illustrate that the significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ but also indicate the effectiveness of PAC features and the proposed models with better classification performance.

scholarly journals Subliminal perception can be predicted from prestimulus activity

2020 ◽  
Author(s):  
Henry Railo ◽  
Roberto Piccin ◽  
Karolina M. Lukasik
Keyword(s):  
Low Frequency ◽  
Discrimination Accuracy ◽  
Electrophysiological Response ◽  
Eeg Activity ◽  
Subliminal Perception ◽  
Low Contrast ◽  
Subliminal Stimulus ◽  
Eeg Power ◽  
Evoked Activity ◽  
Complex Manner

AbstractHumans sometimes make accurate guesses about stimuli they report not consciously seeing—this phenomenon is known as “subliminal perception.” We asked participants (N = 31) to discriminate the location of a briefly presented low-contrast visual stimulus, and then rate how well they saw the stimulus. The behavioral accuracy of discriminating the location of a subjectively subliminal stimulus could be predicted in a trial-by-trial manner from lateralized low frequency (1–15 Hz) electroencephalographic (EEG) activity before the stimulus. This effect was observed up to 1 s before the stimulus was presented. Lateralized occipital prestimulus EEG power modulated the stimulus-evoked activity in a complex manner, but the amplitude of the stimulus-evoked electrophysiological response was not strongly modulated by subliminal objective discrimination accuracy. Signal detection analyses indicated that the participants’ capacity to discriminate subliminal stimuli lay on the same continuum as conscious vision. The results suggest that subliminal perception is not an automatic stimulus-evoked process but relies on perceptual decisions about weak perceptual signals that may be available for introspection.

Correlates to the seizure onset zone in interictal EEG/fMRI recordings

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
M Hauf ◽  
K Schindler ◽  
O Scheidegger ◽  
K Jann ◽  
T Koenig ◽  
...  
Keyword(s):  
Seizure Onset ◽  
Seizure Onset Zone ◽  
Interictal Eeg

Changes of EEG synchronization during low-frequency electric stimulation of the seizure onset zone

2007 ◽  
Vol 77 (2-3) ◽  
pp. 108-119 ◽  
Author(s):  
Kaspar Schindler ◽  
Christian E. Elger ◽  
Klaus Lehnertz
Keyword(s):  
Electric Stimulation ◽  
Low Frequency ◽  
Seizure Onset ◽  
Seizure Onset Zone ◽  
Stimulation Of

scholarly journals Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

2021 ◽  
Vol 15 ◽  
Author(s):  
Maryam Faramarzi ◽  
Florian H. Kasten ◽  
Gamze Altaş ◽  
André Aleman ◽  
Branislava Ćurčić-Blake ◽  
...  
Keyword(s):  
Low Frequency ◽  
Neural Correlates ◽  
Auditory Signal ◽  
Perceptual Awareness ◽  
Sensory Stimuli ◽  
Eeg Activity ◽  
Power Enhancement ◽  
Related Potentials ◽  
Low Frequency Power ◽  
Frequency Power

Hallucinations and illusions are two instances of perceptual experiences illustrating how perception might diverge from external sensory stimulations and be generated or altered based on internal brain states. The occurrence of these phenomena is not constrained to patient populations. Similar experiences can be elicited in healthy subjects by means of suitable experimental procedures. Studying the neural mechanisms underlying these experiences not only has the potential to expand our understanding of the brain’s perceptual machinery but also of how it might get impaired. In the current study, we employed an auditory signal detection task to induce auditory illusions by presenting speech snippets at near detection threshold intensity embedded in noise. We investigated the neural correlates of auditory false perceptions by examining the EEG activity preceding the responses in speech absent (false alarm, FA) trials and comparing them to speech present (hit) trials. The results of the comparison of event-related potentials (ERPs) in the activation period vs. baseline revealed the presence of an early negativity (EN) and a late positivity (LP) similar in both hits and FAs, which were absent in misses, correct rejections (CR) and control button presses (BPs). We postulate that the EN and the LP might represent the auditory awareness negativity (AAN) and centro-parietal positivity (CPP) or P300, respectively. The event-related spectral perturbations (ERSPs) exhibited a common power enhancement in low frequencies (<4 Hz) in hits and FAs. The low-frequency power enhancement has been frequently shown to be accompanied with P300 as well as separately being a marker of perceptual awareness, referred to as slow cortical potentials (SCP). Furthermore, the comparison of hits vs. FAs showed a significantly higher LP amplitude and low frequency power in hits compared to FAs. Generally, the observed patterns in the present results resembled some of the major neural correlates associated with perceptual awareness in previous studies. Our findings provide evidence that the neural correlates associated with conscious perception, can be elicited in similar ways in both presence and absence of externally presented sensory stimuli. The present findings did not reveal any pre-stimulus alpha and beta modulations distinguishing conscious vs. unconscious perceptions.

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