Time-varying effective EEG source connectivity: the optimization of model parameters*

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways

Population overlap and habitat segregation in wintering Black-tailed Godwits Limosa limosa

Distinct breeding populations of migratory species may overlap both spatially and temporally, but differ in patterns of habitat use. This has important implications for population monitoring and conservation. To quantify the extent to which two distinct breeding populations of a migratory shorebird, the Black-tailed Godwit Limosa limosa, overlap spatially, temporally and in their use of different habitats during winter. We use mid-winter counts between 1990 and 2001 to identify the most important sites in Iberia for Black-tailed Godwits. Monthly surveys of estuarine mudflats and rice-fields at one major site, the Tejo estuary in Portugal in 2005-2007, together with detailed tracking of colour-ringed individuals, are used to explore patterns of habitat use and segregation of the Icelandic subspecies L. l. islandica and the nominate continental subspecies L. l. limosa. In the period 1990-2001, over 66 000 Black-tailed Godwits were counted on average in Iberia during mid-winter (January), of which 80% occurred at just four sites: Tejo and Sado lower basins in Portugal, and Coto Dontildeana and Ebro Delta in Spain. Icelandic Black-tailed Godwits are present throughout the winter and forage primarily in estuarine habitats. Continental Black-tailed Godwits are present from December to March and primarily use rice-fields. Iberia supports about 30% of the Icelandic population in winter and most of the continental population during spring passage. While the Icelandic population is currently increasing, the continental population is declining rapidly. Although the estuarine habitats used by Icelandic godwits are largely protected as Natura 2000 sites, the habitat segregation means that conservation actions for the decreasing numbers of continental godwits should focus on protection of rice-fields and re-establishment of freshwater wetlands

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge. Author summary During normal behavior, brains transition between functional network states several times per second. This allows humans to quickly read a sentence, and a frog to catch a fly. Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. To overcome these difficulties, we designed a new Kalman filter (STOK) by innovating on previous solutions from control theory and state-space modelling. We show that STOK accurately models fast network changes in simulations and real neural data, making it an essential new tool for modelling fast brain networks in the time and frequency domain

A tunable local field potentials computer simulator to assess minimal requirements for phase–amplitude cross-frequency-coupling estimation

The quantitative study of cross-frequency coupling (CFC) is a relevant issue in neuroscience. In local field potentials (LFPs), measured either in the cortex or in the hippocampus, how γ-oscillation amplitude is modulated by changes in θ-rhythms-phase is thought to be important in memory formation. Several methods were proposed to quantify CFC, but reported evidence suggests that experimental parameters affect the results. Therefore, a simulation tool to support the determination of minimal requirements for CFC estimation in order to obtain reliable results is particularly useful. An approach to generate computer-simulated signals having CFC intensity, sweep duration, signal-to-noise ratio (SNR), and multiphasic-coupling tunable by the user has been developed. Its utility has been proved by a study evaluating minimal sweep duration and SNR required for reliable θ–γ CFC estimation from signals simulating LFP measured in the mouse hippocampus. A MATLAB® software was made available to facilitate methodology reproducibility. The analysis of the synthetic LFPs created by the simulator shows how the minimal sweep duration for achieving accurate θ–γ CFC estimates increases as SNR decreases and the number of CFC levels to discriminate increases. In particular, a sufficient reliability in discriminating five different predetermined CFC levels is reached with 35-s sweep with SNR = 20, while SNR = 5 requires at least 140-s sweep

Hypoglycemia-induced EEG complexity changes in Type 1 diabetes assessed by fractal analysis algorithm

In recent years, hypoglycemia-induced changes in the EEG signal of patients with Type 1 diabetes (T1D) have been quantified and studied mainly by linear approaches. So far, sample entropy (SampEn) is the only nonlinear measure used in the literature. SampEn has the disadvantage of being computationally demanding and, hence, difficult to be used in real-time settings. The present study investigates whether other nonlinear indicators, less computationally demanding than SampEn, can be equally sensitive to changes in the EEG signal induced by hypoglycemia. For such a scope, we considered a database obtained from 19 T1D patients who underwent a hyperinsulinemic-hypoglycemic clamp while continuous EEG was recorded. We analyzed the P3-C3 EEG derivation data using three measures of signal complexity based on an approach originally proposed by Higuchi in the 80s: the original measure of fractal dimension and two new indexes based on the Higuchi's curve. All the three indicators revealed a statistically significant decrease in EEG complexity in the hypo- versus euglycemic state, which is in line with the results previously obtained with SampEn. However, the lower computational cost of the proposed indicators ( 3cO(N) versus 3cO(N2)) makes them potentially more suited for real-time applications such as the use of EEG to trigger hypoglycemia alerts