Changes in the spatiotemporal pattern of spontaneous activity across a cortical column after noise trauma.

In the auditory modality, noise trauma has often been used to investigate cortical plasticity as it causes cochlear hearing loss. One limitation of these past studies, however, is that the effects of noise trauma have been mostly documented at the granular layer, which is the main cortical recipient of thalamic inputs. Importantly, the cortex is composed of six different layers each having its own pattern of connectivity and specific role in sensory processing. The present study aims at investigating the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity (SA) in primary auditory cortex (A1) of the anesthetized guinea pig. We show that spontaneous activity is dramatically altered across cortical layers after acute and chronic noise-induced hearing loss. First, spontaneous activity was globally enhanced across cortical layers, both in terms of firing rate and amplitude of spike-triggered average of local field potentials. Second, current source density on (spontaneous) spike-triggered average of local field potentials indicates that current sinks develop in the supra- and infragranular layers. These latter results suggest that supragranular layers become a major input recipient and the propagation of spontaneous activity over a cortical column is greatly enhanced after acute and chronic noise-induced hearing loss. We discuss the possible mechanisms and functional implications of these changes.NEW & NOTEWORTHY The present study investigates the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity in the primary auditory cortex. Our study is first to report that noise trauma alters the sequence of cortical column activation during ongoing activity. In particular, we show that the supragranular layer becomes a major input recipient and the synaptic activity in the infragranular layers is enhanced.

Neural changes in the auditory cortex of awake guinea pigs after two tinnitus inducers: salicylate and acoustic trauma.

Tinnitus, also called phantom auditory perception, is a major health problem in western countries. As such, a significant amount of effort has been devoted to understanding its mechanisms, including studies in animals wherein a supposed "tinnitus state" can be induced. Here, we studied on the same awake animals the effects of a high-dose of salicylate and of an acoustic trauma both at levels known to induce tinnitus. Recordings of cortical activity (local field potentials) from chronically implanted electrodes in the same animals under each condition allowed direct comparison of the effects of salicylate and trauma (noise trauma was carried out several days after full recovery from salicylate administration). Salicylate induced a systematic and reversible increase in amplitude of cortical responses evoked by tone bursts over a wide range of frequencies and intensities. The effects of noise trauma, though much more variable than those of salicylate, resulted in both increases and decreases in the amplitude of cortical responses. These alterations of cortical response amplitudes likely reflect associated hypoacusis and hyperacusis. The effects of salicylate administration and noise trauma on spontaneous activity were also studied. Fourier analysis did not reveal any increase in power within any given frequency band; rather, both treatments induced a decrease of power spectrum over a relatively broad frequency band (approximately 10-30 Hz). Entropy rate of spontaneous activity, a measure of complexity (temporal correlations), was found to decrease after salicylate but not after acoustic trauma. The present data on evoked potentials confirm salicylate effects at the cortical level and partially extend such effects to acoustic trauma. While the present study showed that both salicylate and noise trauma induced some changes of spontaneous activity in auditory cortex, none of these changes are interpretable in terms of potential neural correlate of tinnitus.

[Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry]. / Paradigme dynamique en psychopathologie: la "Théorie du chaos", de la physique à la psychiatrie.

For the last thirty years, progress in the field of physics, known as "Chaos theory"--or more precisely: non-linear dynamical systems theory--has increased our understanding of complex systems dynamics. This framework's formalism is general enough to be applied in other domains, such as biology or psychology, where complex systems are the rule rather than the exception. Our goal is to show here that this framework can become a valuable tool in scientific fields such as neuroscience and psychiatry where objects possess natural time dependency (i.e. dynamical properties) and non-linear characteristics. The application of non-linear dynamics concepts on these topics is more precise than a loose metaphor and can throw a new light on mental functioning and dysfunctioning. A class of neural networks (recurrent neural networks) constitutes an example of the implementation of the dynamical system concept and provides models of cognitive processes (15). The state of activity of the network is represented in its state space and the time evolution of this state is a trajectory in this space. After a period of time those networks settle on an equilibrium (a kind of attractor). The strength of connections between neurons define the number and relations between those attractors. The attractors of the network are usually interpreted as "mental representations". When an initial condition is imposed to the network, the evolution towards an attractor is considered as a model of information processing (27). This information processing is not defined in a symbolic manner but is a result of the interaction between distributed elements. Several properties of dynamical models can be used to define a way where the symbolic properties emerge from physical and dynamical properties (28) and thus they can be candidates for the definition of the emergence of mental properties on the basis of neuronal dynamics (42). Nevertheless, mental properties can also be considered as the result of an underlying dynamics without explicit mention of the neuronal one (47). In that case, dynamical tools can be used to elucidate the Freudian psychodynamics (34, 35). Recurrent neuronal networks have been used to propose interpretation of several mental dysfunctions (12). For example in the case of schizophrenia, it has been proposed that troubles in the cortical pruning during development (13) may cause a decrease in neural network storage ability and lead to the creation of spurious attractors. Those attractors do not correspond to stored memories and attract a large amount of initial conditions: they were thus associated to reality distorsion observed in schizophrenia (14). Nevertheless, the behavior of these models are too simple to be directly compared with real physiological data. In fact, equilibrium attractors are hardly met in biological dynamics. More complex behaviors (such as oscillations or chaos) should thus to be taken into account. The study of chaotic behavior have lead to the development of numerical methods devoted to the analysis of complex time series (17). These methods may be used to characterise the dynamical processes at the time-scales of both the cerebral dynamics and the clinical symptoms variations. The application of these methods to physiological signals have shown that complex behaviors are related to healthy states whereas simple dynamics are related to pathology (8). These studies have thus confirmed the notion of "dynamical disease" (20, 21) which denotes pathological conditions characterised by changes in physiological rhythms. Depression has been studied within this framework (25, 32) in order to define possible changes in brain electrical rhythms related to this trouble and its evolution. It has been shown that controls' brain dynamics is more complex than depressive one and that the recovery of a complex brain activity depends on the number of previous episodes. In the case of the symptoms time evolution, several studies have demonstrated that non-linear dynamical process may be involved in the recurrence of symptoms in troubles such as manic-depressive illness (9) or schizophrenia (51). These observations can contribute to more parcimonious interpretation of the time course of these illnesses than usual theories. In the search of a relationship between brain dynamics and mental troubles, it has been shown in three depressed patients an important correlation between the characteristics of brain dynamics and the intensity of depressive mood (49). This preliminary observation is in accordance with the emergence hypothesis according which changes in neuronal dynamics should be related to changes in mental processes. We reviewed here some theoretical and experimental results related to the use of "physical" dynamical theory in the field of psychopathology. It has been argued that these applications go beyond metaphor and that they are empirically founded. Nevertheless, these studies only constitute first steps on the way of a cautious development and definition of a "dynamical paradigm" in psychopathology. The introduction of concepts from dynamics such as complexity and dynamical changes (i.e. bifurcations) permits a new perspective on function and dysfunction of the mind/brain and the time evolution of symptoms. Moreover, it offers a ground for the hypothesis of the emergence of mental properties on the basis of neuronal dynamics (42). Since this theory can help to throw light on classical problems in psychopathology, we consider that a precise examination of both its theoretical and empirical consequences is requested to define its validity on this topic.

Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson's disease.

OBJECTIVES: Modifications of brain activity in the early stages of Parkinson's disease (PD) are difficult to detect using electroencephalography (EEG) signals and are often biased by L-DOPA treatment. We compare here the performances of both linear and non-linear methods in differentiating EEG of L-DOPA naive PD patients from that of control subjects. METHODS: Resting multichannel EEG (20 electrodes, 30 s epochs) of 9 patients with PD in Hoehn and Yahr stages 1-2 (4 women, 5 men, mean age 54.3 years, range 48-63 years) were compared with those of 9 control subjects (7 women, two men, mean age 51.3 years, range 43-61 years). The following measurements were computed: theta-, alpha- and beta-band relative powers constituted the linear indices; localized entropy, slope asymmetry and number of non-linear EEG segments constituted the non-linear indices. RESULTS: In the case of linear quantification, only a decrease in the beta-band was observed for patients. Significant non-linear structures were observed in our EEG data. Non-linear quantifiers demonstrate an increase in entropy and in the number of non-linear EEG segments for the patients. CONCLUSIONS: Changes in EEG dynamics observed here in L-DOPA naive PD patients may represent early signs of cortical dysfunction produced by subcortical dopamine depletion.

Why bother to spatially embed EEG? Comments on Pritchard et al., Psychophysiology, 33, 362-368, 1996.

In a recent paper, Pritchard, Krieble, and Duke (Psychophysiology, 33, 362-368, 1996) studied the validity of spatial embedding of electroencephalographic (EEG) data and rejected this method in favor of time-delay embedding. The present paper describes the nonlinear characterization of brain dynamics using either spatial or time-delay embedding. We discuss the arguments published in Pritchard et al. (1996) and demonstrate that the spatial embedding cannot be rejected on this basis. We also point out the limitations of both spatial and time-delay embeddings related to the spatial extension and the high-dimensional dynamics of brain activity.

Dynamical systems and depression: a framework for theoretical perspectives.

The theory of dynamical systems allows one to describe the change in a system's macroscopic behavior as a bifurcation in the underlying dynamics. We show here, from the example of depressive syndrome, the existence of a correspondence between clinical and electro-physiological dimensions and the association between clinical remission and brain dynamics reorganization (i.e. bifurcation). On the basis of this experimental study, we discuss the interest of such results concerning the question of normality versus pathology in psychiatry and the relationship between mind and brain.

Entropy maps characterize drug effects on brain dynamics in Alzheimer's disease.

Non-linear quantifiers of brain electrical dynamics (entropy maps computed from the degradation of temporal forecasting of EEG signals) were studied in relation to drug treatment of Alzheimer's disease. A placebo condition was compared to three drug doses (50, 100 and 200 mg). A significant general effect of the drug was found when compared to placebo and specific contrasts between placebo and each of the three drug doses only reveal a significant entropy increase for the highest dose. These effects were localized bilaterally in fronto-temporal areas and support changes in the dynamics of the cerebral structures involved in memory processes.

[Static balance is controlled by a non-linear dynamic system]. / L'équilibre statique est contrôlé par un système dynamique non-linéaire.

Different techniques of stabilometric signal analysis have been used in order to study the adaptation of the fine postural control system to the wearing of corrective glasses with or without prisms. The comparison between the results obtained with conventional techniques and those obtained with non-linear dynamic methods demonstrates the better efficiency of the latter. These results confirm that the postural system behaves as a non-linear dynamical system and may explain the outstanding sensitivity of the fine postural system to small perturbations.

Spatial extension of brain activity fools the single-channel reconstruction of EEG dynamics.

We report here on a first attempt to settle the methodological controversy between advocates of two alternative reconstruction approaches for temporal dynamics in brain signals: the single-channel method (using data from one recording site and reconstructing by time-lags), and the multiple-channel method (using data from a spatially distributed set of recordings sites and reconstructing by means of spatial position). For the purpose of a proper comparison of these two techniques, we computed a series of EEG-like measures on the basis of well-known dynamical systems placed inside a spherical model of the head. For each of the simulations, the correlation dimension estimates obtained by both methods were calculated and compared, when possible, with the known (or estimated) dimension of the underlying dynamical system. We show that the single-channel method fails to reliably quantify spatially extended dynamics, while the multichannel method performs better. It follows that the latter is preferable, given the known spatially distributed nature of brain processes. Hum. Brain Mapping 5:26-47, 1997. (c) 1997 Wiley-Liss, Inc.

Depression as a dynamical disease.

Mathematical models are helpful in the understanding of diseases through the use of dynamical indicators. A previous study has shown that brain activity can be characterized by a decrease of dynamical complexity in depressive subjects. The present paper confirms and extends these conclusions through the use of recent methodological advances: first episode and recurrent patients strongly differ in their dynamical response to therapeutic interventions. These results emphasize the need for clinical follow-ups to avoid recurrence and the necessity of specific therapeutic intervention in the case of recurrent patients.

Non-linear forecasting measurements of multichannel EEG dynamics.

This work presents a new method for studying the underlying dynamics of multichannel EEG on the basis of the mathematical theory of dynamical systems. It computes the local loss of predictability and Kolmogorov entropy of the dynamics reconstructed from brain electrical activity. This reconstruction uses multichannel recordings in order to quantify an equivalent of spatio-temporal mapping. Five experimental conditions have been studied: closed eyes at rest, closed eyes and counting even numbers, staring at a spotlight, passive and active auditive odd-ball tasks. The entropy is positive for all the experimental conditions which proves that the underlying EEG dynamics are chaotic. Moreover, on the basis of the dynamical signature it is possible to differentiate 3 types of EEG activity: the rest closed eyes activity, the task closed eyes activity (counting and odd-ball tasks) and the open eyes activity (staring at a spotlight). It is inferred that this index could characterize task-related changes in brain activity.

Decrease of complexity in EEG as a symptom of depression.

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.