Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

Co-first authors 6 +Correspondence: 7 Fabrice Wendling 8 fabrice.wendling@inserm.fr 9 Abstract 12Computational model of consciousness 2 This is a provisional file, not the final typeset article 2009). However, most of these models are limited in terms of spatial scale and the represented micro-85 circuitry. This limitation hinders bridging the micro-circuit scale with the brain-scale, which is of 86 interest in consciousness. The present study proposes to fill this gap, and provides new links between 87 different levels of description (from local neuronal population to whole-brain scale). 88Using a bottom-up approach, we developed a new computational model of brain-scale 89 electrophysiological activity. The model starts from neuronal micro-circuits involving different 90 cellular subtypes that have been reliably identified through neurobiological studies. The basic unit of 91 the model is the neural mass, representing a local population of a few thousands of neurons, which 92 has proven its ability to capture the dynamics of actual neuronal assemblies (Wendling, Bartolomei et 93 al. 2002). At the local level, the model includes subsets of pyramidal neurons (glutamatergic), and 94 three different types of interneurons (GABAergic) with appropriate physiologically-based kinetics 95(fast vs. slow). At the global level, the large-scale model is then constructed on the basis of a standard 96 66-region brain atlas (Desikan, Segonne et al. 2006), with one neural mass representing the local 97 field activity of one atlas region. Neural masses are spatially distributed over the cortex, using the 98 template brain morphology (Colin). As they account for distinct cortical regions, neural masses are 99 synaptically connected through long-range glutamatergic projections among pyramidal neurons and 100GABAergic interneurons, Connectivity is derived from DTI (Diffusion Tensor Imaging) data. 101Results show that the model captures the large-scale structure of brain connectivity between regions, 102while accounting for the properties of local micro-circuits. It can accurately reproduce EEG activity 103for different conscious states (e.g., sleep vs. wake), and the breakdown of functional connectivity 104 during sleep as assessed through the replication of TMS-EEG experiments. 105