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Objective To assess the neuropsychological function and quality of life of the patients after awake craniotomy (AC). Methods A case ⁃ control study was conducted among 81 patients who underwent awake craniotomy, and a 1⁃to⁃1 control group (matched by age, gender, degree of education, tumor location and characteristic) undergoing general anesthesia (GA) in glioma resections was assembled. The incidence of postoperative neurological deficits, psychological disorders and recurrence were investigated during telephone follow⁃ups, and Medical Outcomes Study Short Form 36 (SF⁃36) was adopted to evaluate the life quality of patients. Results Almost 73 pairs of patients fulfilled the survey of AC and GA group respectively. There were 21 patients and 28 patients with postoperative neurological deficits, and 12 patients and 8 patients with psychological disorders in AC and GA group respectively. Thirty patients of AC group had the recollection of being awake during the surgery. There were 9 patients in CA group having long ⁃ term ( > 6 months) neurological deficits, which was less than the number of GA group (18 patients, P = 0.038). According to the assessment in short⁃term, medium⁃term and long⁃term postoperative neurological deficits, there was no significant difference in the quality⁃of⁃life scores between the two groups (P > 0.05, for all). Conclusion Awake craniotomy can be the main method for removing the lesions located in or close to functional areas with lower incidence of long⁃term postoperative neurological deficits, and it has no significant impact on the psychological status and the quality of life postoperatively． DOI：10.3969/j.issn.1672⁃6731.2012.06.013

Subarachnoid hemorrhage caused by the rupture of cerebral aneurysms is extremely fatal and disabling. It’s imperative for radiologists to achieve efficient screening with the help of deep learning-based models. To improve the detection sensitivity of time of flight-magnetic resonance angiography (TOF-MRA) images, this study proposed a neural network named DCAU-Net which is based on fuzzy labels, 3D U-Net variant, and dual-branch channel attention (DCA), and able to adaptively adjust the response of channel features to improve feature extraction capability. First, TOF-MRA images from 260 subjects were preprocessed, and the data were split into the training set (N=174), validation set (N=43) and testing set (N=43). Then the preprocessed data were used for training and validating DCAU-Net. The results show that DCAU-Net scores 90.69% of sensitivity, 0.83 per case of false positive count and 0.52 of positive predicted value in the testing set, providing a promising tool for detecting cerebral aneurysms.