There is an association between angioarchitectural features such as the number of draining veins and the pathological structure of the AVM wall. These abnormalities may contribute to AVM rupture.

Background and objectiveMirror-like intracranial aneurysms (MirAn) occur symmetrically at the corresponding intracranial arteries and are a subgroup of multiple intracranial aneurysms. The aim of this study was to analyze the risk factors, treatment, and prognosis of MirAn.MethodsWe retrospectively analyzed 43 cases of MirAn diagnosed between January 2000 and December 2009. The control groups comprised patients with non-mirror-like multiple aneurysms (nMirAn) and single aneurysms (SingAn). Sex, age, localization of MirAn, hypertension, diabetes, smoking, and rupture were identified as potential risk factors for MirAn.ResultsThe male to female ratio of the MirAn patients was 1.0:5.1, which was significantly different from that of the nMirAn patients (1.0:1.9, P=0.037) and SingAn patients (1.0:1.3, P<0.001). There was no difference in age (P=0.8741), smoking (P=0.301), diabetes (P=0.267), or hypertension (P=0.874) between the MirAn and nMirAn patients. The aneurysms in 28 MirAn patients (65.1%) involved the internal carotid-posterior communicating arteries; in these patients, the rupture risk was significantly higher for larger aneurysms compared with smaller aneurysms (P<0.05).ConclusionMore women suffered from MirAn than nMirAn or SingAn. The most common MirAn sites were the internal carotid-posterior communicating arteries. Our results suggest that MirAn was not associated with age, smoking, hypertension, or diabetes.

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High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. HFA often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research on quantitative measurement and separation of such patterns is still lacking. In this paper, spatial pattern clustering of HFA (SPC-HFA) is developed. The process is composed of three steps: (1) feature extraction: skewness which quantifies the intensity of HFA is extracted; (2) clustering: k-means clustering is applied to separate column vectors within the feature matrix into intrinsic spatial patterns; (3) localization: the determination of epileptic tissue is performed based on the cluster centroid with HFA expanding to the largest spatial extent. Experiments were conducted on a public iEEG dataset with 20 patients. Compared with existing localization methods, SPC-HFA demonstrates improvement (Cohen's d > 0.2) and ranks top in 10 out of 20 patients in terms of the area under the curve. In addition, after extending SPC-HFA to high-frequency oscillation detection algorithms, corresponding localization results also improve with effect size Cohen's d ≥ 0.48. Therefore, SPC-HFA can be utilized to guide clinical and surgical treatment of refractory epilepsy.