High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.
Objective: Altered functional activities and hypometabolism have been found in medial temporal lobe epilepsy patients with hippocampal sclerosis (mTLE-HS). Hybrid PET/MR scanners provide opportunities to explore the relationship between resting-state energy consumption and functional activities, but whether repeated seizures disturb the bioenergetic coupling and its relationship with seizure outcomes remain unknown. Methods: 18 F-FDG PET and restingstate functional MRI (rs-fMRI) scans were performed with hybrid PET/MR in 26 patients with mTLE-HS and in healthy controls. Energy consumption was quantified by 18 F-FDG standardized uptake value ratio(SUVR) relative to cerebellum. Spontaneous neural activities were estimated using regional homogeneity (ReHo), fractional amplitude of low frequency fluctuations (fALFF) from rs-fMRI. Between-group differences in SUVR and rs-fMRI derived metrics were evaluated by two-sample t test. Voxel-wise spatial correlations were explored between SUVR and ReHo, fALFF across gray matter and compared between groups. Furthermore, the relationships between altered fALFF/SUVR and ReHo/ SUVR coupling and surgical outcomes were evaluated. Results: Both the patients and healthy controls showed significant positive correlations between SUVR and rs-fMRI metrics. Spatial correlations between SUVR and fMRIderived metrics across gray matter were significantly higher in patients with mTLE-HS compared with healthy controls (fALFF/SUVR, P < 0.001; ReHo/ SUVR, P = 0.022). Higher fALFF/SUVR couplings were found in patients who had Engel class IA after surgery than all other (P = 0.025), while altered ReHo/ SUVR couplings (P = 0.097) were not. Conclusion: These findings demonstrated altered bioenergetic coupling across gray matter and its relationship with seizure outcomes, which may provide novel insights into pathogenesis of mTLE-HS and potential biomarkers for epilepsy surgery planning.
High-frequency oscillations (HFOs) have been proposed as a promising biomarker of the epileptogenic zone (EZ). But accurate delineation of EZ based on HFOs is still challenging. Our study compared HFOs from EZ and non-EZ on the basis of their associations with interictal slow waves, aiming at exploring a new way to localize EZ. Nineteen medically intractable epilepsy patients with good surgical outcome were included. Five minute interictal intracranial electroencephalography (EEG) epochs of slow-wave sleep were randomly selected; then ripples (80-200 Hz), fast ripples (FRs; 200-500 Hz), and slow waves (0.1-4 Hz) were automatically analyzed. The EZ and non-EZ were identified by resection range during the surgeries. We found that both ripples and FRs superimposed more frequently on slow waves in EZ than in non-EZ (P < 0.01). Although ripples preferred to occur on the down state of slow waves in both two groups, ripples in EZ tended to be closer to the down-state peak of slow wave than in non-EZ (-174 vs.-231 ms, P = 0.008). As for FR, no statistical difference was found between the two groups (P = 0.430). Additionally, slow wave-containing ripples in EZ had a steeper slope (1.7 vs. 1.5 µV/ms, P < 0.001) and wider distribution ratio (32.3 vs. 30.1%, P < 0.001) than those in the non-EZ. But for slow wave-containing FR, only a steeper slope (1.7 vs. 1.4 µV/ms, P < 0.001) was observed. Our study innovatively compared the different features of association between HFOs and slow wave in EZ and non-EZ from refractory focal epilepsy with good surgical outcome, proposing a new method to localize EZ and facilitating the surgical plan.
Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.
The cover image is based on the Research Article Exosomal TUBB3 mRNA expression of metastatic castration‐resistant prostate cancer patients: Association with patient outcome under abiraterone by Hao Zeng et al., https://doi.org/10.1002/cam4.4168.
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