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.

AimTo quantify age-dependent iron deposition changes in healthy subjects using Susceptibility Weighted Imaging (SWI).Materials and MethodsIn total, 143 healthy volunteers were enrolled. All underwent conventional MR and SWI sequences. Subjects were divided into eight groups according to age. Using phase images to quantify iron deposition in the head of the caudate nucleus and the lenticular nucleus, the angle radian value was calculated and compared between groups. ANOVA/Pearson correlation coefficient linear regression analysis and polynomial fitting were performed to analyze the relationship between iron deposition in the head of the caudate nucleus and lenticular nucleus with age.ResultsIron deposition in the lenticular nucleus increased in individuals aged up to 40 years, but did not change in those aged over 40 years once a peak had been reached. In the head of the caudate nucleus, iron deposition peaked at 60 years (p<0.05). The correlation coefficients for iron deposition in the L-head of the caudate nucleus, R-head of the caudate nucleus, L-lenticular nucleus and R-lenticular nucleus with age were 0.67691, 0.48585, 0.5228 and 0.5228 (p<0.001, respectively). Linear regression analyses showed a significant correlation between iron deposition levels in with age groups.ConclusionsIron deposition in the lenticular nucleus was found to increase with age, reaching a plateau at 40 years. Iron deposition in the head of the caudate nucleus also increased with age, reaching a plateau at 60 years.

We aim to determine if visit‐to‐visit blood pressure variability (BPV) adds prognostic value for all‐cause mortality independently of the Framingham risk score (FRS) in the systolic blood pressure intervention trial (SPRINT). We defined BPV as variability independent of the mean (VIM) and the difference of maximum minus minimum (MMD) of the systolic blood pressure (SBP). Multivariable Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Based on FRS stratification, there were 1035, 2911, and 4050 participants in the low‐, intermediate‐, and high‐risk groups, respectively. During the trial, 230 deaths occurred since the 12th month with an average follow‐up of 2.5 years. In continuous analysis, 1‐SD increase of SBP VIM and MMD were significantly associated with all‐cause mortality (HR 1.18, 95% CI 1.05–1.32, p = .005; and HR 1.21, 95% CI 1.09–1.35, p < .001, respectively). In category analysis, the highest quintile of BPV compared with the lowest quintile had significantly higher risk of all‐cause mortality. Cross‐tabulation analysis showed that the 3rd tertile of SBP VIM in the high‐risk group had the highest HR of all‐cause mortality in total population (HR 4.99; 95% CI 1.57–15.90; p = .007), as well as in intensive‐therapy group (HR 7.48; 95% CI 1.01–55.45; p = .05) analyzed separately. Cross‐tabulation analysis of SBP MMD had the same pattern as VIM showed above. In conclusion, visit‐to‐visit BPV was an independent predictor of all‐cause mortality, when accounting for conventional risk factors or FRS. BPV combined with FRS conferred an increased risk for all‐cause mortality in the SPRINT trial.