Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up.

The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness

Dimensionality Reduction in spatio-temporal MaxEnt models and analysis of Retinal Ganglion Cell Spiking Activity in experiments

International audienceRetinal spike response to stimuli is constrained, on one hand by short range correlations (receptive field overlap) and on the other hand by lateral connectivity (cells connectivity). This last effect is difficult to handle fromstatistics because it requires to consider spatio-temporal correlations with a time delay long enough to take into account the time of propagation along synapses. Although MaxEnt model are useful to fit optimal model(maximizing entropy) under the constraints of reproducing observed correlations, they do address spatio-temporal correlations in their classical form (Ising or higher order interactions but without time delay). Binning insuch models somewhat integrates propagation effects, but in an implicit form, and increasing binning severely bias data [1]. To resolve this issue we have considered spatio-temporal MaxEnt model formerly developed e.g.by Vasquez et al. [2]. The price to pay, however is a huge set of parameters that must be fitted to experimental data to explain the observed spiking patterns statistics. There is no a priori knowledge of which parameters arerelevant and which ones are contributing to overfitting. We propose here a method of dimension reduction, i.e. a projection on a relevant subset of parameters, relying on the so-called Susceptibility matrix closely related tothe Fisher information. In contrast to standard methods in information geometry though, this matrix handle space and time correlations.We have applied this method for retina data obtained in a diurnal rodent (Octodon degus, having 30% of cones photoreceptors) and a 252-MEA system. Three types of stimuli were used: spatio-temporal uniform light, whitenoise and a natural movie. We show the role played by time-delayed pairwise interactions in the neural response to stimuli both for close and distant cells. Our conclusion is that, to explain the population spiking statisticswe need both short-distance interactions as well as long-distance interactions, meaning that the relevant functional correlations are mediated not only by common input (i.e. receptive field overlap, electrical coupling;spillover) but also by long range connections

Dimensionality Reduction on Maximum Entropy Models on Spiking Networks

Maximum entropy models (MEM) have been widely used in the last 10 years formodelling, explaining and predicting the statistics of networks of spiking neurons.However, as the network size increases, the number of model parameters increasesrapidily, hindering its interpretation and fast computation. However, these parametersare not necessarily independent from each other; when some of them are related byhidden dependencies, their number can be reduced, allowing to map the MEM into alower dimensional space. Here, we present a novel framework for MEM dimensionalityreduction that uses the geometrical properties of MEM to find the subset of dimensionsthat best captures the network high-order statistics, without fitting the model to data.This allows us define a parameter somehow representing the degree of compressibility ofthe code. The method was tested on synthetic data where the underlying statistics isknown and on retinal ganglion cells (RGC) data recorded using multi-electrode arrays(MEA) under different stimuli. We found that MEM dimensionality reduction dependson the interdependences between the network activity, the density of the raster and thenumber of observed events. For RGC data we found that the activity is highlyinterdependent, with a dimensionality reduction of almost 50%, compared to a randomraster, showing that the network activity is highly compressible, possibly due to thenetwork redundancies. This dimensionality reduction depends on the stimuli statistics,supporting the idea that sensory networks adapts to stimuli statistics, modifying thelevel of redundancy, i.e. the coding strategy

Characterization of Retinal Functionality at Different Eccentricities in a Diurnal Rodent

Although the properties of the neurons of the visual system that process central and peripheral regions of the visual field have been widely researched in the visual cortex and the LGN, they have scarcely been documented for the retina. The retina is the first step in integrating optical signals, and despite considerable efforts to functionally characterize the different types of retinal ganglion cells (RGCs), a clear account of the particular functionality of cells with central vs. peripheral fields is still wanting. Here, we use electrophysiological recordings, gathered from retinas of the diurnal rodent Octodon degus, to show that RGCs with peripheral receptive fields (RF) are larger, faster, and have shorter transient responses. This translates into higher sensitivity at high temporal frequencies and a full frequency bandwidth when compared to RGCs with more central RF. We also observed that imbalances between ON and OFF cell populations are preserved with eccentricity. Finally, the high diversity of functional types of RGCs highlights the complexity of the computational strategies implemented in the early stages of visual processing, which could inspire the development of bio-inspired artificial systems

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Introduction -- Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods -- We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results -- Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion --Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling

INTRODUCTION Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings

Sembradora Horsch

Nesta edição da Revista Cultivar Máquinas apresentamos um teste de campo com a semeadora da marca Horsch, modelo Maestro Evolution 36.50, realizado em Barreiras na Bahia. Este modelo é uma evolução da maestro SW, antes comercializada no Brasil. A qualidade da tecnologia alemã realmente, além do conceito mundial, apresentou-se plenamente nesta máquina. Esta série de máquinas da Horsch é oferecida com versões de 24, 30, 36, 39 e 40 linhas e espaçamentos de 45 a 60 cm. Contudo, o espaçamento de 60 cm só é oferecido no modelo de 30 linhas e a versão de 40 linhas somente com espaçamento de 45 cm. A versão de 39 linhas é chamada de Split row e só tem oferta com 45 cm e serve para o algodão de 90 cm de espaçamento. Nos demais casos o cliente pode escolher o espaçamento. O modelo que tivemos disponível para teste era de 36 linhas, com espaçamento de 50 cm e é um dos modelos mais comercializados para as regiões de grandes áreas de agricultura, como o Mato Grosso e o Nordeste. Embora esta máquina tenha projeto e tecnologia alemã, ela é fabricada em Curitiba, PR, com grande nacionalização de componentes, possuindo credenciamento no Programa Finame do BNDES desde 2017. Além de várias inovações tecnológicas a máquina caracteriza-se por ser articulada e auto transportável. Articulada porque possui uma barra de plantio com seções que se adaptam às ondulações do terreno e auto transportável porque a barra pode ser recolhida lateralmente ao depósito reduzindo a largura de transporte da máquina para 3,20 m.Peer ReviewedPostprint (author's final draft

A mechanistic model of the neural entropy increase elicited by psychedelic drugs

Funder: Ad Astra Chandaria FoundationAbstract: Psychedelic drugs, including lysergic acid diethylamide and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, mediating both acute alterations in consciousness and long-term effects. However, no clear mechanistic explanation for this entropy increase has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in some regions and decreased in others, suggesting a topographical reconfiguration mediated by 5HT2A-R activation. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brain’s anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the 16 statistics of networks of spiking neurons. A major drawback of this approach is that the number of parameters used in the statistical model increases very fast with the network size, hindering its interpretation and fast computation. Here, we present a novel framework of dimensionality reduction for generalized MEM handling spatio-temporal correlations. This formalism is based on information geometry where a MEM is a point on a large-dimensional manifold. We exploit the geometrical properties of this manifold in order to find a projection on a lower dimensional space that best captures the high-order statistics. This allows us to define a quantitative criterion that we call the "degree of compressibility" of the neuronal code. A powerful aspect of this method is that it does not require fitting the model. Indeed, the matrix defining the metric of the manifold is computed directly via the data without parameters fitting. The method is first validated using synthetic data generated by a known statistics. We then analyze a MEM having more parameters than the underlying data statistics and show that our method detects the extra dimensions. We then test it on experimental retinal data. We record retinal ganglion cells (RGC) spiking data using multi-electrode arrays (MEA) under different visual stimuli: spontaneous activity, white noise stimulus, and natural scene. Using our method, we report a dimensionality reduction up to 50% for retinal data. As we show, this is quite a huge reduction compared to a randomly generated spike train, suggesting that the neuronal code, in these experiments, is highly compressible. This additionally shows that the dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics by modifying the level of redundancy

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the 16 statistics of networks of spiking neurons. A major drawback of this approach is that the number of parameters used in the statistical model increases very fast with the network size, hindering its interpretation and fast computation. Here, we present a novel framework of dimensionality reduction for generalized MEM handling spatio-temporal correlations. This formalism is based on information geometry where a MEM is a point on a large-dimensional manifold. We exploit the geometrical properties of this manifold in order to find a projection on a lower dimensional space that best captures the high-order statistics. This allows us to define a quantitative criterion that we call the "degree of compressibility" of the neuronal code. A powerful aspect of this method is that it does not require fitting the model. Indeed, the matrix defining the metric of the manifold is computed directly via the data without parameters fitting. The method is first validated using synthetic data generated by a known statistics. We then analyze a MEM having more parameters than the underlying data statistics and show that our method detects the extra dimensions. We then test it on experimental retinal data. We record retinal ganglion cells (RGC) spiking data using multi-electrode arrays (MEA) under different visual stimuli: spontaneous activity, white noise stimulus, and natural scene. Using our method, we report a dimensionality reduction up to 50% for retinal data. As we show, this is quite a huge reduction compared to a randomly generated spike train, suggesting that the neuronal code, in these experiments, is highly compressible. This additionally shows that the dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics by modifying the level of redundancy