Laurent Pezard

Distinct Patterns of Striatal Medium Spiny Neuron Activity during the Natural Sleep–Wake Cycle

Striatal medium-sized spiny neurons (MSNs) integrate and convey information from the cerebral cortex to the output nuclei of the basal ganglia. Intracellular recordings from anesthetized animals show that MSNs undergo spontaneous transitions between hyperpolarized and depolarized states. State transitions, regarded as necessary for eliciting action potential firing in MSNs, are thought to control basal ganglia function by shaping striatal output. Here, we use an anesthetic-free rat preparation to show that the intracellular activity of MSNs is not stereotyped and depends critically on vigilance state. During slow-wave sleep, much as during anesthesia, MSNs displayed rhythmic step-like membrane potential shifts, correlated with cortical field potentials. However, wakefulness was associated with a completely different pattern of temporally disorganized depolarizing synaptic events of variable amplitude. Transitions from slow-wave sleep to wakefulness converted striatal discharge from a cyclic brisk firing to an irregular pattern of action potentials. These findings illuminate different capabilities of information processing in basal ganglia networks, suggesting in particular that a novel style of striatal computation is associated with the waking state.

Neuroleptic-Induced Catalepsy: Electrophysiological Mechanisms of Functional Recovery Induced by High-Frequency Stimulation of the Subthalamic Nucleus

High-frequency stimulation (HFS) of the subthalamic nucleus (STN) remarkably alleviates motor disorders in parkinsonian patients. The mechanisms by which STN HFS exerts its beneficial effects were investigated in anesthetized rats, using a model of acute interruption of dopaminergic transmission. Combined systemic injections of SCH-23390 [R(+)-7-chloro-8-hydroxy-3-methyl-1-phenyl-2,3,4,5,-tetrahydro-1H-3-benzazepine] and raclopride, antagonists of the D1 and D2 classes of dopaminergic receptors, respectively, were performed, and the parameters of STN HFS that reversed the neuroleptic-induced catalepsy were determined in freely moving animals. The effects of neuroleptics and the impact of STN HFS applied at parameters alleviating neuroleptic-induced catalepsy were analyzed in the substantia nigra pars reticulata (SNR), a major basal ganglia output structure, by recording the neuronal firing pattern and the responses evoked by cortical stimulation. Neuroleptic injection altered the tonic and regular mode of discharge of SNR neurons, most of them becoming irregular with bursts of spikes and pauses. The inhibitory component of the cortically evoked response, which is attributable to the activation of the direct striatonigral circuit, was decreased, whereas the late excitatory response resulting from the indirect striato-pallido-subthalamo-nigral circuit was reinforced. During STN HFS, the spontaneous firing of SNR cells was either increased or decreased with a global enhancement of the firing rate in the overall population of SNR cells recorded. However, in all of the cases, SNR firing pattern was regularized, and the bias between the trans-striatal and trans-subthalamic circuits was reversed. By these effects, STN HFS restores the functional properties of the circuits by which basal ganglia contribute to motor activity.

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.

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.

Autobiographical Memory in Major Depression: A Comparison between First-Episode and Recurrent Patients

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.

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.

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.

Entropy quantification of human brain spatio-temporal dynamics

Abstract We present a procedure to quantify spatio-temporal dynamics applied here to brain surface recordings during three distinct cognitive tasks. The method uses 19 sites of EEG recording as spatial embedding for the reconstruction of trajectories, global and local linear indices, and non-linear forecasting methods to quantify the global and local information loss of the dynamics (K-entropy). We show that K-entropy can differentiate between raw and multivariate phase random surrogate data in a significant percentage of EEG segments, and that relevant non-linear indices are best studied in time segments not longer than 4s. We also find a certain complementarity between local non-linear and linear indices for the discrimination between the three cognitive tasks. Moreover, localized projections onto electrode site of K-entropy provide a new kind of brain mapping with functional significance.

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 Alzheimers 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.

Assessment of bursting activity and interspike intervals variability: A case study for methodological comparison

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.