Nonlinear dynamic analysis provides new methods for the processing of the electroencephalogram (EEG). We demonstrate here that the EEG dynamics of major depressive subjects is more predictable, that ...is less complex, than that of control subjects. Moreover, the consequence of treatment upon the EEG dynamics seems to be dependent on the appearance of the illness. Although the specificity of this dynamic signature for different stages of depression is to be confirmed, the assumption of a strong link between a healthy system and a high level of complexity in dynamics is further supported.
The detection and characterization of bursting activity remains a topic where no consensual definition has been reached so far. We compare here three different approaches of spike trains variability: ...statistical characterization (average frequency, coefficient of variation), burst detection (Poisson and rank surprise) and multi-scale analysis (detrended fluctuations analysis). Using both real and simulated data, we show that Poisson surprise provides information closely related to the coefficient of variation and that rank surprise detects significant bursts which are associated with long-range correlations. Since these long-range correlations are only adequately characterized with multi-scale analysis, this study emphasizes the complementarity of these approaches for the complete characterization of spike trains.
TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and ...neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting.
While entropy per unit time is a meaningful index to quantify the dynamic features of experimental time series, its estimation is often hampered in practice by the finite length of the data. We here ...investigate the performance of entropy estimation procedures, relying either on block entropies or Lempel-Ziv complexity, when only very short symbolic sequences are available. Heuristic analytical arguments point at the influence of temporal correlations on the bias and statistical fluctuations, and put forward a reduced effective sequence length suitable for error estimation. Numerical studies are conducted using, as benchmarks, the wealth of different dynamic regimes generated by the family of logistic maps and stochastic evolutions generated by a Markov chain of tunable correlation time. Practical guidelines and validity criteria are proposed. For instance, block entropy leads to a dramatic overestimation for sequences of low entropy, whereas it outperforms Lempel-Ziv complexity at high entropy. As a general result, the quality of entropy estimation is sensitive to the sequence temporal correlation hence self-consistently depends on the entropy value itself, thus promoting a two-step procedure. Lempel-Ziv complexity is to be preferred in the first step and remains the best estimator for highly correlated sequences.
The influence of the network topology on the dynamics of systems of coupled excitable units is studied numerically and demonstrates a lower dynamical variability for power-law networks than for ...Poisson ones. This effect which reflects a robust collective excitable behavior is however lower than that observed for diffusion processes or network robustness. Instead, the presence (and number) of triangles and larger loops in the networks appears as a parameter with strong influence on the considered dynamics.
Ionic currents across neuron and glial cells membranes lie at the origin of the entire brain electrophysiology. They are the common root of functional brain dynamics and mesoscopic or macroscopic ...phenomena such as extracellular fields. In particular, they provide the relevant basis to relate cellular electrophysiology and macroscopic dipole models. In order to derive robust features and to envision the multi-scale approaches required to connect the different levels of observation, an essential prerequisite is to have minimal model of elementary ionic motions. In this paper, we propose a general cellular automata framework allowing to investigate the distribution of ionic currents in heterogeneous media interspersed with membranes, from which follows the local electromagnetic field.
Schizophrenic speech has been studied both at the clinical and linguistic level. Nevertheless, the statistical methods used in these studies do not specifically take into account the dynamical ...aspects of language. In the present study, we quantify the dynamical properties of linguistic production in schizophrenic and control subjects. Subjects' recall of a short story was encoded according to the succession of macro- and micro-propositions, and symbolic dynamical methods were used to analyze these data. Our results show the presence of a significant temporal organization in subjects' speech. Taking this structure into account, we show that schizophrenics connect micro-propositions significantly more often than controls. This impairment in accessing language at the highest level supports the hypothesis of a deficit in maintaining a discourse plan in schizophrenia.
Autobiographical memory in depression is characterized by an increase in general memory evocation. The aim of this study is to compare autobiographical memory in patients with a first depressive ...episode and in recurrent patients before and after recovery, using Williams' and Scott's autobiographical memory test. Our results show an increase of the number of general memories only with positive cue words in both groups of patients during the depressive episode. After clinical improvement, this specificity remains in recurrent patients who, in addition, recall more general memories for negative words. By contrast, patients with a first depressive episode are no longer different from controls. These results show both an overgeneralization and a deficit in positive memory access during the depressive episode, whatever the number of previous episodes. Moreover, recurrence chronically modifies access to emotional memories.
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