EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

Abstract: The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions

Formant Space Reconstruction From Brain Activity in Frontal and Temporal Regions Coding for Heard Vowels

Classical studies have isolated a distributed network of temporal and frontal areas engaged in the neural representation of speech perception and production. With modern literature arguing against unique roles for these cortical regions, different theories have favored either neural code-sharing or cortical space-sharing, thus trying to explain the intertwined spatial and functional organization of motor and acoustic components across the fronto-temporal cortical network. In this context, the focus of attention has recently shifted toward specific model fitting, aimed at motor and/or acoustic space reconstruction in brain activity within the language network. Here, we tested a model based on acoustic properties (formants), and one based on motor properties (articulation parameters), where model-free decoding of evoked fMRI activity during perception, imagery, and production of vowels had been successful. Results revealed that phonological information organizes around formant structure during the perception of vowels; interestingly, such a model was reconstructed in a broad temporal region, outside of the primary auditory cortex, but also in the pars triangularis of the left inferior frontal gyrus. Conversely, articulatory features were not associated with brain activity in these regions. Overall, our results call for a degree of interdependence based on acoustic information, between the frontal and temporal ends of the language network

Charge collection and capacitance–voltage analysis in irradiated n-type magnetic Czochralski silicon detectors

Abstract The depletion depth of irradiated n-type silicon microstrip detectors can be inferred from both the reciprocal capacitance and from the amount of collected charge. Capacitance voltage ( C – V ) measurements at different frequencies and temperatures are being compared with the bias voltage dependence of the charge collection on an irradiated n-type magnetic Czochralski silicon detector. Good agreement between the reciprocal capacitance and the median collected charge is found when the frequency of the C – V measurement is selected such that it scales with the temperature dependence of the leakage current. Measuring C – V characteristics at prescribed combinations of temperature and frequency allows then a realistic estimate of the depletion characteristics of irradiated silicon strip detectors based on C – V data alone

Signal processing and mathematical modelling approaches in Bioengineering: applications in sleep and epidemiological research

In the last decades, impressive technological advances have brought research in biosciences to a new interdisciplinary and translational dimension, in which computation has emerged as fundamental partner in scientific investigation. In this context, mathematical tools typically inherent to engineering can indeed magnify the investigation potentials of the sector specialists, allowing to efficiently exploit the stifling deluge of data and information, and finding simultaneous applications in a heterogeneous range of different fields. A clear example is provided in sleep research by the advent of high-density electroencephalographic (EEG) systems. Simultaneously recording signals from hundreds of electrodes (up to 257) distributed over the scalp highly increase the spatial resolution of the investigation but make standard analysis methods, based on visual inspection and manual scoring that have been traditionally used both in clinics and research, totally inadequate. Moreover, such techniques, in combination with novel analytical approaches, revealed previously unknown features of the sleep EEG activity, such as the local, experience-dependent regulation of particular oscillations, including so-called slow waves and spindles. These and other properties of the EEG signal that can be studied only through the development of adequate automated tools. Importantly, REM sleep, a state associated with rapid eye movements (REMs) and a “tonically activated” EEG similar to that of wakefulness, poses further methodological challenges that have limited its analysis until now. In fact, REM sleep is not a homogeneous phase but it is episodically interrupted by phasic components, including REMs, muscle twitches and micro-arousals that can generate artefactual potentials corrupting the corresponding EEG signals. This great richness of signals and features, whose functional role and reciprocal relation is still under debate, brings about the strong need of optimized and validated automated procedures, allowing to efficiently manage and decode this large amount of data. In response to the listed requirements, a multi-functional software was developed in the Matlab environment that allows automatic analysis of REM sleep high-density EEG data. The tool includes different functionalities that have been developed and validated against the visual scoring of a board certified electrophysiologist and sleep specialist, with optimal results. The first stage of the procedure allows to detect the occurrence of each elementary rapid eye movement and provides a complete characterization of ocular activity in terms of time density, aggregation tendency and directional properties (movements are in fact classified according their main direction). REMs represent a peculiar aspect of REM sleep whose physiological origin is still unclear and whose occurrence pattern has been correlated to learning processes and various psychiatric disorders. This functionality represents hence an important investigation tool both in basic REM sleep research and in clinical practice. REMs represent also one of the main sources of contamination that affect the study of cerebral activity during REM sleep. For this reason, the global procedure considers also an ocular artifact removal stage that integrates the information provided by the REMs detection algorithm to activate a correction scheme based on adaptive filtering. The capacity of the correction algorithm in reconstructing the true EEG signals was objectively evaluated by artificially simulating the propagation of ocular potentials, showing how the proposed artifact removal procedure reaches greatly improved performance with respect to standard methods based on adaptive filters only. Finally, the toolbox includes an EEG activation detection algorithm that precisely identifies abrupt and relative shifts in the EEG instantaneous frequency, potentially reflecting cortical desynchronization events. Growing experimental evidence has in fact suggested that the electrophysiological features of both sleep and wake can appear in an extremely local manner independently from the global vigilance state of the brain. In order to potentially detect also localized activations, the algorithm is intended to work independently channel by channel, automatically adapting to the different features of signals, without strong a priori assumptions about the frequencies involved in the activation, but always detecting frequency increases relative to the current background. Since standard clinical criteria assume that EEG activation must be accompanied by a simultaneous increase in EMG activity for the identification of a micro-arousal, also an EMG activation detection procedure was herein developed, in order to compare the properties of micro-arousals and the detected localized EEG activations. After the development, the different stages of the REM sleep toolbox listed above were applied to various sets of overnight high-density recordings, in order to characterize the REM electrophysiological features, to gain further insight into their functional meaning. Mathematical details of the algorithms are described in the first section. Toolbox functionalities are based on several signal processing techniques that go thought Fourier and wavelet transform, adaptive filtering to non linear instantaneous energy estimators, and allowed to automatically extract significant information from a great amount of data. However, mathematical tools, and in particular dynamical models, can also be used to integrate the available data and current knowledge in order to predict the system future states. In epidemiological research, this model-based approach is an often used and valuable tool.. Herein, we provide an example of a further evolution of this approach, in which we apply optimal control to a realistically parameterized age-structured model for the Varicella-Zoster-Virus transmission to investigate whether feasible varicella immunizations paths that are optimal in controlling both varicella and zoster exist. In fact, herpes zoster is a disease arising from reactivation of the Varicella-Zoster-Virus (VZV), causing varicella in children. As reactivation occurs when cell-mediated immunity (CMI) declines, and there is evidence that re-exposure to VZV boosts CMI, mass varicella immunization might increase the zoster burden, at least for some decades. Fear of this natural zoster boom is the main reason for the paralysis of varicella immunization in Europe. We analyze the optimality system numerically focusing on the role of the cost functional, of the relative zoster-varicella cost, and of the planning horizon length. We show that optimal programs will mostly be unfeasible for public health due to their complex temporal profiles. This complexity is the consequence of the intrinsically antagonistic nature of varicella immunization programs when aimed to control both varicella and zoster. However we show that gradually increasing – thereby feasible - vaccination schedules, can perform largely better than routine programs with constant vaccine uptake. Finally we show the optimal profiles of feasible programs targeting mitigation of the post-immunization natural zoster boom with priority

Analisi e rimozione degli artefatti oculari da segnali elettroencefalografici durante il sonno REM

Analisi e rimozione degli artefatti oculari da segnali elettroencefalografici durante il sonno REM

A Classification method for eye movements direction during REM sleep trained on wake electro-oculographic recordings

Rapid eye movements (REMs) are a peculiar and intriguing aspect of REM sleep, even if their physiological function still remains unclear. During this work, a new automatic tool was developed, aimed at a complete description of REMs activity during the night, both in terms of their timing of occurrence that in term of their directional properties. A classification stage of each singular movement detected during the night according to its main direction, was in fact added to our procedure of REMs detection and ocular artifact removal. A supervised classifier was constructed, using as training and validation set EOG data recorded during voluntary saccades of five healthy volunteers. Different classification methods were tested and compared. The further information about REMs directional characteristic provided by the procedure would represent a valuable tool for a deeper investigation into REMs physiological origin and functional meaning

Combining pharmacological therapy and vaccination in Chronic Myeloid Leukemia via model predictive control

This paper describes a simulation study which aims at optimizing the therapy for the control of Chronic Myeloid Leukemia according to the following objectives: the reduction of the administered drug and vaccine amounts, the establishment of a auto-immune response and the long-term control of disease without reducing the effective of therapy with respect to the full treatment. A therapy optimization method is developed defining and solving a Model Predictive Control algorithm, preceded by an accurate Initial Guess search based on Monte-Carlo like approach. Simulation results show that the suggested procedure achieves the proposed goals