No abstract

Deep brain stimulation (DBS) surgery of the subthalamic nucleus (STN) under general anesthesia (GA) had been used in Parkinson's disease (PD) patients who are unable tolerate awake surgery. The effect of anesthetics on intraoperative microelectrode recording (MER) remains unclear. Understanding the effect of anesthetics on MER is important in performing STN DBS surgery with general anesthesia. In this study, we retrospectively performed qualitive and quantitative analysis of STN MER in PD patients received STN DBS with controlled desflurane anesthesia or LA and compared their clinical outcome. From January 2005 to March 2006, 19 consecutive PD patients received bilateral STN DBS surgery in Hualien Tzu-Chi hospital under either desflurane GA (n = 10) or LA (n = 9). We used spike analysis (frequency and modified burst index [MBI]) and the Hilbert transform to obtain signal power measurements for background and spikes, and compared the characterizations of intraoperative microelectrode signals between the two groups. Additionally, STN firing pattern characteristics were determined using a combined approach based on the autocorrelogram and power spectral analysis, which was employed to investigate differences in the oscillatory activities between the groups. Clinical outcomes were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) before and after surgery. The results revealed burst firing was observed in both groups. The firing frequencies were greater in the LA group and MBI was comparable in both groups. Both the background and spikes were of significantly greater power in the LA group. The power spectra of the autocorrelograms were significantly higher in the GA group between 4 and 8 Hz. Clinical outcomes based on the UPDRS were comparable in both groups before and after DBS surgery. Under controlled light desflurane GA, burst features of the neuronal firing patterns are preserved in the STN, but power is reduced. Enhanced low-frequency (4–8 Hz) oscillations in the MERs for the GA group could be a characteristic signature of desflurane's effect on neurons in the STN.

No abstract

Tuberous sclerosis complex (TSC) is a rare hereditary disease, which results from the mutation of either TSC1 or TSC2, and its clinical features include benign tumors and dysfunctions in numerous organs, including the brain. Many individuals with TSC manifest neuropsychiatric symptoms, such as learning impairments, cognitive deficits and anxiety. Current pharmacological treatment for TSC is the use of mTOR inhibitors. However, they are not effective in treating neuropsychiatric symptoms. We previously used curcumin, a diet‐derived mTOR inhibitor, which possesses both anti‐inflammatory and antiproliferative properties, to improve learning and memory deficits in Tsc2+/− mice. Diffusion tensor imaging (DTI) provides microstructural information in brain tissue and has been used to study the neuropathological changes in TSC. In this study, we confirmed that the impaired recognition memory and increased anxiety‐like behavior in Tsc2+/− mice can be reversed by curcumin treatment. Second, we found altered fractional anisotropy and mean diffusivity in the anterior cingulate cortex and the hippocampus of the Tsc2+/− mice, which may indicate altered circuitry. Finally, the mTOR complex 1 hyperactivity was found in the cortex and hippocampus, coinciding with abnormal cortical myelination and increased glial fibrillary acidic protein expression in the hippocampal CA1 of Tsc2+/− mice, both of which can be rescued with curcumin treatment. Overall, DTI is sensitive to the subtle alterations that cannot be detected by conventional imaging, suggesting that noninvasive DTI may be suitable for longitudinally monitoring the in vivo neuropathology associated with the neuropsychiatric symptoms in TSC, thereby facilitating future clinical trials of pharmacological treatments.

Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications.