Extrapyramidal system | EPFL Graph Search

In anatomy, the extrapyramidal system is a part of the motor system network causing involuntary actions. The system is called extrapyramidal to distinguish it from the tracts of the motor cortex that reach their targets by traveling through the pyramids of the medulla. The pyramidal tracts (corticospinal tract and corticobulbar tracts) may directly innervate motor neurons of the spinal cord or brainstem (anterior (ventral) horn cells or certain cranial nerve nuclei), whereas the extrapyramidal system centers on the modulation and regulation (indirect control) of anterior (ventral) horn cells. Extrapyramidal tracts are chiefly found in the reticular formation of the pons and medulla, and target lower motor neurons in the spinal cord that are involved in reflexes, locomotion, complex movements, and postural control. These tracts are in turn modulated by various parts of the central nervous system, including the nigrostriatal pathway, the basal ganglia, the cerebellum, the vestibular nu

Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons

Alain Destexhe, Henry Markram

We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

2008

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Enrico Amico

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the "clinical fingerprint" to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King's disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the "clinical fingerprint" was predictive of the ALSFRS-r (p = 0.0397; beta = 32.8), the King's (p = 0.0001; beta = -7.40), and the MiToS (p = 0.0025; beta = -4.9) scores. Accordingly, it negatively correlated with the King's (Spearman's rho = -0.6041, p = 0.0003) and MiToS scales (Spearman's rho = -0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management.

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Breakdown of Effective Connectivity During Slow Wave Sleep: Investigating the Mechanism Underlying a Cortical Gate Using Large-Scale Modeling

Sean Lewis Hill

Esser SK, Hill S, Tononi G. Breakdown of effective connectivity during slow wave sleep: investigating the mechanism underlying a cortical gate using large-scale modeling. J Neurophysiol 102: 2096-2111, 2009. First published August 5, 2009; doi: 10.1152/jn.00059.2009. Effective connectivity between cortical areas decreases during slow wave sleep. This decline can be observed in the reduced interareal propagation of activity evoked either directly in cortex by transcranial magnetic stimulation (TMS) or by sensory stimulation. We present here a large-scale model of the thalamocortical system that is capable of reproducing these experimental observations. This model was constructed according to a large number of physiological and anatomical constraints and includes over 30,000 spiking neurons interconnected by more than 5 million synaptic connections and organized into three cortical areas. By simulating the different effects of arousal promoting neuromodulators, the model can produce a waking or a slow wave sleep-like mode. In this work, we also seek to explain why intercortical signal transmission decreases in slow wave sleep. The traditional explanation for reduced brain responses during this state, a thalamic gate, cannot account for the reduced propagation between cortical areas. Therefore we propose that a cortical gate is responsible for this diminished intercortical propagation. We used our model to test three candidate mechanisms that might produce a cortical gate during slow wave sleep: a propensity to enter a local down state following perturbation, which blocks the propagation of activity to other areas, increases in potassium channel conductance that reduce neuronal responsiveness, and a shift in the balance of synaptic excitation and inhibition toward inhibition, which decreases network responses to perturbation. Of these mechanisms, we find that only a shift in the balance of synaptic excitation and inhibition can account for the observed in vivo response to direct cortical perturbation as well as many features of spontaneous sleep.

2009

Breakdown of Effective Connectivity During Slow Wave Sleep: Investigating the Mechanism Underlying a Cortical Gate Using Large-Scale Modeling

Sean Lewis Hill

2009