Anesthetic Effect on the Subthalamic Nucleus in Microelectrode Recording and Local Field Potential of Parkinson's Disease.

OBJECTIVES: Anesthetic agents used during deep brain stimulation (DBS) surgery might interfere with microelectrode recording (MER) and local field potential (LFP) and thus affect the accuracy of surgical target localization. This review aimed to identify the effects of different anesthetic agents on neuronal activity of the subthalamic nucleus (STN) during the MER procedure. MATERIALS AND METHODS: We used Medical Subject Heading terms to search the PubMed, EMBASE, EBSCO, and ScienceDirect data bases. MER characteristics were sorted into quantitative and qualitative data types. Quantitative data included the burst index, pause index, firing rate (FR), and interspike interval. Qualitative data included background activity, burst discharge (BD), and anesthetic agent effect. We also categorized the reviewed manuscripts into those describing local anesthesia with sedation (LAWS) and those describing general anesthesia (GA) and compiled the effects of anesthetic agents on MER and LFP characteristics. RESULTS: In total, 26 studies on MER were identified, of which 12 used LAWS and 14 used GA. Three studies on LFP also were identified. We found that the FR was preserved under LAWS but tended to be lower under GA, and BD was reduced in both groups. Individually, propofol enhanced BD but was better used for sedation, or the dosage should be minimized in GA. Similarly, low-dose dexmedetomidine sedation did not disturb MER. Opioids could be used as adjunctive anesthetic agents. Volatile anesthesia had the least adverse effect on MER under GA, with minimal alveolar concentration at 0.5. Dexmedetomidine anesthesia did not affect LFP, whereas propofol interfered with the power of LFP. CONCLUSIONS: The effects of the tested anesthetics on the STN in MER and LFP of Parkinson's disease varied; however, identifying the STN and achieving a good clinical outcome are possible under controlled anesthetic conditions. For patient comfort, anesthesia should be considered in STN-DBS.

Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation.

Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model information sent from local clients to update the parameters for a global model. Sample mean is the simplest and most commonly used aggregation method. However, it is not robust for data with outliers or under the Byzantine problem, where Byzantine clients send malicious messages to interfere with the learning process. Some robust aggregation methods were introduced in literature including marginal median, geometric median and trimmed-mean. In this article, we propose an alternative robust aggregation method, named γ-mean, which is the minimum divergence estimation based on a robust density power divergence. This γ-mean aggregation mitigates the influence of Byzantine clients by assigning fewer weights. This weighting scheme is data-driven and controlled by the γ value. Robustness from the viewpoint of the influence function is discussed and some numerical results are presented.

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images.

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers. Then, cell correspondences in the correspondent colonies are computed using the recorded cell centers. The examples show the proposed cell tracking method can achieve high tracking accuracy for time-lapse 3D images of undifferentiated but self-renewing mouse embryonic stem (mES) cells where the number and mobility of ES cells in a cell colony may change suddenly by a colony merging or splitting, and cell proliferation or death. The geometric data in the hierarchical data structures also help the visualization and quantitation of the cell shapes and mobility.