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Background: It has been suggested that the use of window functions, other than the rectangular, in the sliding window method, may be beneficial for reducing the effects of motion-related outliers in the time-series, when assessing dynamic functional connectivity (dFC) in resting-state fMRI (rs-fMRI). Methodology: Ten window functions for a wide range of window lengths (20−150 s) combined with Pearson and Kendall correlation metrics, were investigated. One hundred high quality rs-fMRI datasets from healthy controls, were used to systematically assess the effect of varying the window function and length on dFC assessment. To this end, two approaches were implemented: a) simulated outliers were added to the experimental data and b) the experimental data were divided into low and high motion subgroups. Results: The presence of experimental motion-noise tended to inflate the number of dynamic connections for longer (≥100 s) wide-shaped windows, while shorter (20−30 s) narrow-shaped windows exhibited increased sensitivity in the presence of simulated outliers. Moreover, window sizes from 60 s to 90 s were mildly affected by motion-related effects. In most cases, the number of dynamic connections increased, and gradually lower frequencies were captured, with an increasing window size. Conclusions: Subject motion considerably affects the obtained dFC patterns; thus, it is preferable to perform motion artefact removal in the pre-processing stage rather than using alternative window functions to mitigate their effects. Provided that motion-noise is not excessive, the choice of a rectangular window is adequate. Finally, low frequency oscillations in functional connectivity seem to play an important role in the context of dFC assessment. © 2019 Elsevier B.V.

We present a random forest (RF) classification and regression technique to predict, intraoperatively, the unified Parkinson's disease rating scale (UPDRS) improvement after deep brain stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson's disease patients undergoing DBS surgery. We modified the employed RFs to account for unbalanced datasets and multiple observations per patient, and showed, for the first time, that only five neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. This finding suggests that subthalamic nucleus (STN) electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STN-DBS. © 1964-2012 IEEE.

This paper deals with the estimation of glucose levels in ICU patients by the application of statistical filter theory to the data provided by a commercial continuous glucose monitoring system using a microdialysis sensor. Kalman and particle filtering are applied to simple models of the glucose dynamics. The particle filter enables the joint filtering and calibration of the sensor. The results show that the proposed filters lead to significant reduction in the estimation error with computational cost well within the capabilities of modern digital equipment. Additionally, the filters can be used for the automatic recognition of sensor faults. These results show that suitable filters can help in the construction of an artificial pancreas. © 2015 Elsevier Ltd. All rights reserved.

Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts but only moderate accuracies for individual spikes. Our aim was to utilize a data-driven approach, without relying on feature selection, to predict individual spike times. Methods: The relationship between LFPs and multi-unit spike trains in monkey early visual cortex during passive viewing of grating stimuli was analyzed using a variant of the general Volterra approach (Laguerre-Volterra network). Network parameters were trained based on a hybrid Genetic Algorithm ? Interior Point optimization method, and model selection was achieved via cross-validation. The Matthews Correlation Coefficient (-1 Results: Single trial MCCs ranged from 0.45 to 0.66 (median=0.60). Superior performance of 2nd order relative to 1st order models indicated a nonlinear relationship between LFPs and spikes in visual cortex. Consistent with other studies, the PDMs of the identified system exhibited low-pass (theta frequency) and high-pass (gamma frequency) characteristics. Conclusions: We successfully predicted multi-unit spike times from local LFPs with reasonable accuracy and without selection of a-priori features. Our approach enhances our understanding of spike precision and spike timing, and of the network principles underlying the neural code