Background and PurposeObstructive sleep apnea (OSA) is associated with cerebral white-matter changes (WMC), but the underlying mechanisms are not completely understood. Our aim was to identify the cardiovascular autonomic characteristics during sleep that are associated with cerebral WMC in OSA patients.MethodsWe recruited subjects from our sleep-center database who underwent both polysomnography and brain MRI within a 1-year period. Sixty patients who had OSA with WMC (OSA+WMC), 44 patients who had OSA without WMC (OSA−WMC), and 31 control subjects who had neither OSA nor WMC were analyzed. Linear and nonlinear indices of heart-rate variability (HRV) were analyzed in each group according to different sleep stages and also over the entire sleeping period.ResultsAmong the nonlinear HRV indices, the Poincaré ratio (SD12) during the entire sleep period was significantly increased in the OSA+WMC group, even after age adjustment. Meanwhile, detrended fluctuation analysis 1 during non-rapid-eye-movement sleep tended to be lowest in the OSA+WMC group. These indices were altered regardless of the presence of hypertension or diabetes. In the subgroup analysis of middle-aged OSA patients, approximate entropy during rapid-eye-movement sleep was significantly lower in OSA+WMC patients than in OSA−WMC patients. Overall, the nonlinear HRV indices suggest that sympathetic activity was higher in the OSA+WMC group than in the OSA−WMC and control groups.ConclusionsOur findings suggest that dysregulation of HRV, especially overactivation of sympathetic tone, could be a pathophysiologic mechanism underlying the development of WMC in OSA patients.
SummaryObjective: Patients with temporal lobe epilepsy (TLE) show brain connectivity changes in association with cognitive impairment. Seizure frequency and lateralization are 2 important clinical factors that characterize epileptic seizures. In this study, we sought to examine an interactive effect of the 2 seizure factors on intratemporal effective connectivity based on resting-state functional magnetic resonance imaging (rsfMRI) in patients with TLE. Methods: For rsfMRI data acquired from 48 TLE patients and 45 healthy controls, we applied stochastic dynamical causal modeling to infer effective connectivity between 3 medial temporal lobe (MTL) regions, including the hippocampus (Hipp), parahippocampal gyrus (PHG), and amygdala (Amyg), ipsilateral to the seizure focus. We searched for the effect of the 2 seizure factors, seizure frequency (good vs poor seizure control) and lateralization (left vs right TLE), on connection strengths and their relationship with the level of verbal memory and language impairment. Results: Impairment of verbal memory and language function was mainly affected by seizure lateralization, consistent with preferential involvement of the left MTL in verbal mnemonic processing. For the fully connected model, which was selected as the effective connectivity structure that best explained the observed rsfMRI time series, alterations in connection strengths were primarily influenced by seizure frequency; there was an increase in the strength of the Hipp to PHG connection in TLE patients with poor seizure control, whereas the strength of the Amyg to PHG connection increased in those with good seizure control. Furthermore, the association between connection strength alterations and cognitive impairment was interactively affected by both seizure frequency and lateralization. Significance: These findings suggest an interactive effect as well as an individual effect of seizure frequency and lateralization on neuroimaging features and cognitive function. This potential interaction needs to be evaluated in the consideration of multiple seizure factors. K E Y W O R D Seffective connectivity, functional MRI, language, memory, seizure frequency, seizure lateralization, temporal lobe epilepsy
The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40% FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.
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