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• Propofol susceptibility correlates well with pre-anesthesia dynamic frontal-parietal communication. • Robustness of the results is verified on three data sets. • Propofol administration should take pre-operative brain functional state into consideration. We investigated whether pre-anesthesia dynamic frontal-parietal functional connectivity was correlated with the observed interindividual differences in propofol susceptibility. Three resting-state EEG datasets were used in the study (N = 29, N = 21 and N = 20). We estimated the pre-anesthesia strength and fluctuations of frontal-parietal functional connectivity by using sliding-window analysis. Propofol served as the sole anesthetic drug, and it was administered by using a target-controlled infusion system. Individual susceptibility to propofol was assessed by the induction time, from infusion onset until a bispectral index value of 60 was reached, for subjects in dataset-1 and dataset-2, and susceptibility was assessed by behavioral data for subjects in the external dataset. We observed in the three datasets that subjects with high susceptibility to propofol had lower pre-anesthesia strength and lower fluctuation of frontal-parietal functional connectivity than the low-susceptibility group at alpha band. Moreover, the induction time was significantly correlated with the estimated pre-anesthesia frontal-parietal functional connectivity measures. We also validated the robustness of these findings by using different window lengths in sliding-window analysis. Subjects with weaker pre-anesthesia dynamic frontal-parietal communication are more likely to be anesthetized. These observations suggest that the titration procedure for propofol should consider the pre-anesthesia brain functional state. [ABSTRACT FROM AUTHOR]

• Developed an accurate prediction system for the progression of mild cognitive impairment to AD. • Used longitudinal structural MRI data without the segmentation of regions of interest. • Applied dictionary learning to exploit the nuances between mild cognitive impairment patients. Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. The developed scheme is expected to advance the clinical research and treatment of MCI patients. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic brain functional network during anesthesia is supported by 5 metastable network patterns. • The occurrence of each metastable network pattern is associated with the level of anesthesia. • An anatomical connectivity supported functional network pattern persists across conscious levels. Abstract Objective We investigated the changes of dynamic brain functional network from awaken state to the anesthesia level suitable for surgery. Methods 60-channel EEG data of 22 subjects are acquired at wakefulness, light anesthesia and deep anesthesia. The activity of 68 cortical regions are obtained by using EEG source imaging. Sliding window analysis is employed to obtain a dynamic sequence of brain functional network. K-means clustering algorithm is then employed to identify the common brain functional network patterns. Results Five common brain functional network patterns were identified across all conscious levels. The occurrence of each meta-stable network pattern was associated with the level of anesthesia. A transition functional network pattern was found to transfer to the anesthesia dominating or wakefulness dominating network pattern depending on the conscious level. Furthermore, a functional network pattern persisted during both wakefulness and anesthesia is found to be supported by the anatomical connectivity. Conclusions Dynamic changes of brain functional network exist in both awaken and anesthesia state. Significance These findings suggest that dynamic brain functional network analysis plays a critical role in decoding the mechanism of general anesthesia. The obtained five metastable network patterns may be employed for monitoring the depth of anesthesia. [ABSTRACT FROM AUTHOR]