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Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available. Impact statement Past research has showcased multiple changes to the brain's structural and functional properties caused by aging. Here we expand prior work through recent advancements in multivariate information theory, which provide richer and more theoretically principled analyses than existing alternatives. We show that the brains of older participants contain more redundant information at multiple spatial scales-that is, activation in different brain regions is less diverse, compared with younger participants-and identify a "redundancy core" constituted by prefrontal and motor cortices, which might explained impaired performance in the old population in functions such as working memory and executive control. ispartof: BRAIN CONNECTIVITY vol:11 issue:9 pages:734-744 ispartof: location:United States status: published

Psychedelic drugs, including lysergic acid diethylamide (LSD) 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 all regions, but the larger effect were localised in visuo-occipital regions. 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.

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to fMRI brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.

El presente estudio plantea que el aprendizaje estructurado a traves del contexto funcional, es un recurso para la formacion de los estudiantes de kinesiologia que se expresa conceptualmente en una dinamica axial, cuyo epicentro se da con el usuario respecto de la expresion de movimiento con el sentido que el declare y, se operacionaliza por medio de actuaciones profesionales que estan modeladas a la base de su racionalidad por el modelo funcion disfuncion del movimiento humano (MFDMH). Al cierre del primer semestre se solicito a traves de una entrevista a los profesores de la Linea de Razonamiento Profesional que describieran el uso didactico de esquemas conceptuales del MFDMH, con el proposito de mostrar los componentes basicos y los atributos esenciales que poseen estas racionalidades en la toma de decisiones para la formacion de kinesiologos a lo largo del ciclo formativo. Doi: 10.21703/rexe.20201939escobar11

Different whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive – to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances – including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm – the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics.HighlightsWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics.Using analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck.The FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data.Our advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model.

Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholineα4β2correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain’s connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits.Author summaryModern neuroimaging techniques allow us to study how the human brain’s anatomical architecture (a.k.a. structural connectome) changes under different conditions or interventions. Recently, using functional neuroimaging data, we have shown that complex patterns of interactions between brain areas change along the lifespan, exhibiting increased redundant interactions in the older population. However, the mechanisms that underlie these functional differences are still unclear. Here, we extended this work and hypothesized that the variations of functional patterns can be explained by the dynamics of the brain’s anatomical networks, which are known to degenerate as we age. To test this hypothesis, we implemented a whole-brain model of neuronal activity, where different brain regions are anatomically wired using real connectomes from 161 participants with ages ranging from 10 to 80 years old. Analyzing different functional aspects of brain activity when varying the empirical connectomes, we show that the increased redundancy found in the older group can indeed be explained by precise rules affecting anatomical connectivity, thus emphasizing the critical role that the brain connectome plays for shaping complex functional interactions and the efficiency in the global communication of the human brain.

En la actualidad la obesidad es considerada como una pandemia, ademas de los cambios metabolicos ya sabidos, se producen alteraciones estructurales como las generadas en el sistema ventilatorio. Sin embargo, se ha observado que dependiendo de la forma de categorizacion de esta condicion, puede o no existir alteracion sobre este sistema generando asi una complicacion al momento de la eleccion de esta herramienta. Por lo tanto el objetivo de esta investigacion fue comparar parametros de la funcion pulmonar en individuos obesos (O) y normo-peso (Np) segun indice de masa corporal (IMC) y porcentaje de grasa corporal (PGC). Se midio la funcion ventilatoria en 57 participantes, posteriormente estas variables se contrastaron entre los sujetos normo-peso (Np) y obesos (O) segun IMC y PGC. Se observo un aumento significativo en el valor de la capacidad inspiratoria (CI) de O versus Np y del volumen de reserva espiratoria (VRE) y capacidad residual funcional (CRF) de Np en relacion a O, tanto para la division por IMC como por PGC, en el grupo dividido por IMC las variables de resistencia especifica de las vias aereas (sRaw) y presion inspiratoria maxima (PImax) fueron significativamente mayor en los participantes O, asimismo, la conductancia especifica de las vias aereas (sGaw) fue significativamente mayor en sujetos Np. En conclusion, el IMC mostro mayor versatilidad como predictor de la funcion ventilatoria, sin embargo, se necesitan mayores estudios de los efectos mecanicos de este indicador y del PGC sobre las vias aereas.

The structural connectivity of human brain allows the coexistence of segregated and integrated states of activity. Neuromodulatory systems facilitate the transition between these functional states and recent computational studies have shown how an interplay between the noradrenergic and cholinergic systems define these transitions. However, there is still much to be known about the interaction between the structural connectivity and the effect of neuromodulation, and to what extent the connectome facilitates dynamic transitions. In this work, we use a whole brain model, based on the Jasen and Rit equations plus a human structural connectivity matrix, to find out which structural features of the human connectome network define the optimal neuromodulatory effects. We simulated the effect of the noradrenergic system as changes in filter gain, and studied its effects related to the global-, local-, and meso-scale features of the connectome. At the global-scale, we found that the ability of the network of transiting through a variety of dynamical states is disrupted by randomization of the connection weights. By simulating neuromodulation of partial subsets of nodes, we found that transitions between integrated and segregated states are more easily achieved when targeting nodes with greater connection strengths—local feature—or belonging to the rich club—meso-scale feature. Overall, our findings clarify how the network spatial features, at different levels, interact with neuromodulation to facilitate the switching between segregated and integrated brain states and to sustain a richer brain dynamics.

In recent years, advances in science, technology, and the way in which we view our world have led to an increasingly broad use of the term “intelligence”. As we learn more about biological systems, we find more and more examples of complex and precise adaptive behavior in animals and plants. Similarly, as we build more complex computational systems, we recognize the emergence of highly sophisticated structures capable of solving increasingly complex problems. These behaviors show characteristics in common with the sort of complex behaviors and learning capabilities we find in humans, and therefore it is common to see them referred to as “intelligent”. These analogies are problematic as the term intelligence is inextricably associated with human-like capabilities. While these issues have been discussed by leading researchers of AI and renowned psychologists and biologists highlighting the commonalities and differences between AI and biological intelligence, there have been few rigorous attempts to create an interdisciplinary approach to the modern problem of intelligence. This article proposes a comparative framework to discuss what we call “purposeful behavior”, a characteristic shared by systems capable of gathering and processing information from their surroundings and modifying their actions in order to fulfill a series of implicit or explicit goals. Our aim is twofold: on the one hand, the term purposeful behavior allows us to describe the behavior of these systems without using the term “intelligence”, avoiding the comparison with human capabilities. On the other hand, we hope that our framework encourages interdisciplinary discussion to help advance our understanding of the relationships among different systems and their capabilities., Theoretical framework; Artificial intelligence; Philosophy; Non-human intelligence

We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.

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.

We propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity is already organized in clearly distinguishable functional ensembles. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. Additionally, we found that our method outperforms current alternative methodologies. Finally, we provide a Graphic User Interface, which aims to facilitate our method’s use by the scientific community.Author summaryNeuronal ensembles are strongly interconnected groups of neurons that tend to fire together (Hebb 1949). However, even when this concept was proposed more than 70 years ago, only recent advances in multi-electrode arrays and calcium imaging, statistical methods, and computing power have made it possible to record and analyze multiple neurons’ activities spiking simultaneously, providing a unique opportunity to study how groups of neurons form ensembles spontaneously and under different stimuli scenarios. Using our method, we found that retinal ganglion cells show a consistent stimuli-evoked ensemble activity, and, when validated with synthetic data, the method shows good performance by detecting the number of ensembles, the activation times, and the core-cells for a wide range of firing rates and number of ensembles accurately.

The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of scienThe Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.ce, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

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. Fil: Cofré, Rodrigo. Universidad de Valparaíso; Chile Fil: Herzog, Rubén. Universidad de Valparaíso; Chile Fil: Mediano, Pedro A.M.. University of Cambridge; Estados Unidos Fil: Piccinini, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Rosas, Fernando E.. Imperial College London; Reino Unido Fil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina

Psychedelic drugs, including lysergic acid diethylamide (LSD) 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, encoding both acute alterations in consciousness and mediating long-term effects. However, no clear mechanistic explanation for this ‘entropic’ phenomenon 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.

RESUMEN: La expresión de los genes puede impactar sobre el rendimiento muscular. En este aspecto el polimorfismo del gen de la Enzima Convertidora de Angiotensina (ECA), dependiendo de su inserción (Ins) o deleción (Del) puede potenciar diferentes cualidades musculares. Del mismo modo, si consideramos la ventilación como un proceso vital, sería relevante investigar si existe una influencia de este polimorfismo sobre los músculos que llevan a cabo tan importante función. El objetivo de este trabajo consistió en determinar el rendimiento muscular inspiratorio y espiratorio según alelos y polimorfismos del gen de la ECA. Se sometió a 83 sujetos (18 a 35 años), 46 hombres y 37 mujeres, a evaluaciones de capacidad vital forzada (CVF), presión inspiratoria máxima y presión espiratoria máxima (PIM-PEM). Posterior a esto, la genotipificación fue realizada por polimerase chain reaction (PCR) y electroforesis en gel de agarosa. Las variables fueron sometidas al análisis estadístico correspondiente según su distribución paramétrica, el nivel de significancia establecido fue un p

El objetivo del siguiente estudio fue determinar las dimensiones del tórax y su relación con la función ventilatoria en un grupo de nadadores correspondientes a la selección de natación de la Universidad Católica del Maule. Se evaluaron 42 sujetos de sexo masculino entre 18 y 26 años, 18 seleccionados de natación y 24 estudiantes sedentarios, todos de la Universidad Católica del Maule. Se les realizó antropometría corporal básica (peso y talla corporal) y específica de tórax, diámetro antero-posterior (DAT) y transverso (DTT) de tórax y perímetro mesoesternal (PME). Posterior a esto, se evaluó la función ventilatoria a través de pletismografía corporal. Para el análisis estadístico se determinó la normalidad de los datos posteriormente se utilizó t de student o U de Mann-Whitney y Pearson o Spearman según correspondiera, para establecer significancia estadística o correlación, respectivamente. Se consideró un nivel de significancia estadística de p

Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.

Obesity is associated with a rapid decrease in ventilatory function. The most common way of assessing nutritional status and measuring abdominal fat and hips are the body mass index (BMI) and the waist-hip ratio (WHR). There is scarce evidence suggesting their relation to functional residual capacity (FRC). Our objective was to determine the relation among BMI, WHR, and FRC in obese children in the city of Talca, Chile.Male and female children were recruited (6-12 years). Weight, height, BMI, WHR, and pulmonary function were assessed; the latter with body plethysmography. Depending on data distribution, Student's t test or the Mann-Whitney U test were used for independent samples, while Pearson's or Spearman's r test was used to establish the correlation between WHR and FRC.Children were divided into normal weight (n = 18) and obese (n = 18). A significant reduction in FRC (p = 0.025) was reported in obese children, while a reverse association was observed between WHR and FRC, which was moderate in normal weight children (s = -0.489; p = 0.03) and high in obese children (r = -0.681; p = 0.001).Obese children showed a lower FRC compared to normal weight children, which, in turn, was associated with WHR. These results are indicative of the systemic effects caused by obesity on children's ventilatory function and the need to use body fat distribution indicators at an early age.Introducción. La obesidad está asociada a un descenso acelerado de la función ventilatoria. Las formas más frecuentes de evaluar el estado nutricional y medir la grasa abdominal y las caderas son el índice de masa corporal (IMC) y el índice cintura-cadera (ICC). Existe escasa evidencia que sugiera su relación con la capacidad residual funcional (CRF). Nuestro objetivo fue determinar la relación entre el IMC, el ICC y la CRF en niños obesos de la ciudad de Talca, Chile. Población y métodos. Se reclutaron niños de ambos sexos (6-12 años). Se evaluaron peso, talla, IMC, ICC y función pulmonar a través de pletismografía corporal. Dependiendo de la distribución de los datos, se utilizó la prueba t de Student o U de Mann-Whitney para muestras independientes y, la prueba r de Pearson o Spearman para establecer la correlación entre ICC y CRF. Resultados. Los niños se dividieron en normopeso (n= 18) y obesos (n= 18). Se reportó una disminución significativa de la CRF (p= 0,025) en niños obesos y una relación inversa entre ICC y CRF, la cual fue moderada en niños normopeso (s= -0,489; p= 0,03) y alta en obesos (r= -0,681; p= 0,001). Conclusiones. Los niños obesos mostraron una menor CRF respecto de los normopeso, que, a su vez, se relacionó con el ICC. Estos resultados indican efectos sistémicos que produce la obesidad en la función ventilatoria en niños y la necesidad de incorporar indicadores de distribución de grasa corporal a temprana edad.

Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

Network representations often cannot fully account for the structural richness of complex systems spanning multiple levels of organisation. Recently proposed high-order information-theoretic signals are well-suited to capture synergistic phenomena that transcend pairwise interactions; however, the exponential-growth of their cardinality severely hinders their applicability. In this work, we combine methods from harmonic analysis and combinatorial topology to construct efficient representations of high-order information-theoretic signals. The core of our method is the diagonalisation of a discrete version of the Laplace-de Rham operator, that geometrically encodes structural properties of the system. We capitalise on these ideas by developing a complete workflow for the construction of hyperharmonic representations of high-order signals, which is applicable to a wide range of scenarios., Version after referee reports

Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain., Author summary Segregation of brain activity refers to the fact that some brain regions are specialized to handle particular features of external and internal stimuli. However, to produce a coherent behavioral outcome, the brain must coordinate the activity of these specialized brain areas, and this is called integration of brain activity. Based on a fixed connectome (the brain anatomical structure), the neuromodulatory systems are one of the plausible candidates to manage the transitions of brain states in short timescales. Understanding the role of neuromodulators in brain dynamics and the segregation/integration balance is relevant, in particular, as it is known that in several neuropsychiatric disorders the segregation/integration balance its impaired. Here, we used a computational model of the whole brain to study the dual effect of the cholinergic and noradrenergic neuromodulatory systems in the switching from segregated to integrated brain states. The novelty of our work is the inclusion of a homeostatic local inhibitory loop. This specific inhibition, modulated by the cholinergic system, maintains the excitation/inhibition balance while promoting integration. Our work links the local effects of cholinergic neuromodulation, with the more global influences of the structural connectivity and neuromodulatory systems. This constitutes a step forward in the understanding of the neural mechanisms behind the segregation/integration balance of brain activity.

Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the 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.Author SummaryMaximum entropy models (MEM) have been widely used to characterize the statistics of networks of spiking neurons. However, as the network size increases, the number of model parameters increases rapidly, hindering its interpretation and fast computation. Here, we propose a method to evaluate the dimensionality reduction of MEM, based on the geometrical properties of the manifold best capturing the network high-order statistics. Our method is validated with synthetic data using independent or correlated neural responses. Importantly, we show that dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics modifying the level of redundancy.

Child and adolescent obesity is an epidemiological problem in developing countries. Its prevalence among preschoolers and schoolchildren is over 30%. It has been associated with a wide range of health complications, including rapid loss of lung function leading to changes in physiology and ventilatory mechanics. The objective of this study was to analyze the association between obesity and the increase in specific airway resistance (sRaw) in a sample of obese children and adolescents from the district of Talca.In a sample of 36 subjects with an average age of 9.38 ± 1.99 years, divided into 2 groups (normal weight and obese), the tricipital, subscapular, and abdominal skinfolds and lung volumes were measured. For the statistical analysis, data normality was determined and then the Student's t test or the Mann-Whitney U test and Pearson's or Spearman's correlations were used, as applicable. A value of p0.05 was considered statistically significant.When comparing normal weight and obese subjects, a significant increase in sRaw and a significant reduction in specific airway conductance (sGaw) were observed in obese subjects. In addition, an adequate and significant correlation was observed between sRaw and fat percentage.Obese subjects showed an increased sRaw and a reduced sGaw.La obesidad infantojuvenil es un problema epidemiológico en los países en vías de desarrollo. Su prevalencia entre los niños en edad preescolar y escolar supera el 30%. Esta se asocia a una amplia variedad de complicaciones de salud, entre ellas, una acelerada pérdida de la función pulmonar, que provoca cambios en la fisiología y mecánica ventilatoria. El objetivo de este estudio fue estudiar la asociación entre obesidad y aumento de la resistencia específica de las vías aéreas (specific resistance airway; sRaw, por sus siglas en inglés) en una muestra infantojuvenil obesa de la comuna de Talca.En una muestra de 36 sujetos con un promedio de edad de 9,38 ± 1,99 años, dividida en dos grupos, peso normal y obesos, se midieron los pliegues tricipital, subescapular, abdominal y volúmenes pulmonares. Para el análisis estadístico, se determinó la normalidad de los datos y, posteriormente, se utilizaron los test t de Student o U de Mann-Whitney y Pearson o Spearman según correspondiera. Se consideró un nivel de significancia estadística de p0,05.Al comparar sujetos de peso normal con obesos, se observó un aumento significativo de la sRaw y una disminución significativa de la conductancia específica de la vía aérea en sujetos obesos. Además, se observó una correlación buena y significativa entre sRaw y porcentaje de grasa.Los sujetos obesos mostraron un aumento de la sRaw y disminución de la conductancia específica de la vía aérea.

The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

In recent years there has been a debate over the alleged superiority of achievement tests over aptitude tests on the grounds that the first would be fairer for college admissions and less influenced by family background. The switch from aptitude tests to achievement tests in Chile presented a unique opportunity to examine this claim. Regression analysis was used to assess the impact of the change in test performance using data from seven cohorts of test-takers. The evidence does not support the superiority of achievement tests, particularly when these assess extensive contents.

Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models., arXiv admin note: text overlap with arXiv:1309.5873

This paper is based on a lecture given in the LACONEU summer school, Valparaiso, January 2012. We introduce Gibbs distribution in a general setting, including non stationary dynamics, and present then three examples of such Gibbs distributions, in the context of neural networks spike train statistics: (i) Maximum entropy model with spatio-temporal constraints; (ii) Generalized Linear Models; (iii) Conductance based Inte- grate and Fire model with chemical synapses and gap junctions., Comment: 23 pages, submitted

We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based Integrate-and-Fire neural network, driven by a Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or Generalized Linear Models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions., 42 pages, 1 figure, submitted

International audience; In this talk we shall argue that Gibbs distributions, considered in more general setting than the initial concept coming from statistical physics and thermodynamics, are canonical models for spike train statistics analysis. This statement is based on the three following facts, developed in the talk. 1. The so-called Maximum Entropy Principle allows one to propose models for spike statistics based on empirical observations, providing constraints for the model. Although this approach has been initially devoted to show the role of weak instantaneous pairwise correlations in the retina [7], it has been recently applied to investigating the role of more complex events such as instantaneous triplets [4] or spatio-temporal events [8]. Probability distributions arising from the Maximum Entropy Principle are Gibbs distributions. The main strength of this method is to be able to include any type of spatio-temporal correlations as constraints for statistical estimations. Its mean weaknesses are (i) It assumes stationarity; (ii) It does not tell how to select constraints among the huge set of possibilities. 2. Other approaches such as the Linear-Non Linear (LN) or Generalized Linear Models (GLM) propose an ad hoc form for the conditional probability that a neuron fires given the past network activity and given the stimulus. Those models have been proven quite effcient for retina spike trains analysis [6]. They are not limited by the constraint of stationarity, but they are based on a questionable assumption of conditional independence between neurons. As we show, the probability distributions coming out from those models are also Gibbs distributions, although in a more general sense than 1. 3. Recent investigations on neural network models (conductance based IF with chemical and electric synapses) show that statistics of spike trains generated by these models are Gibbs distributions reducing to 1 when dynamics is stationary, and reducing to 2 in specific cases [1, 2, 3]. In the general case, the spike trains produced by these models have Gibbs distributions which neither match 1 nor 2. Additionally, spike correlations are not only produced by a shared stimulus: they have a strong dynamical component due to neurons' interactions. We shall also briefly discuss a software implementation, designed at INRIA, for MEA spike train analysis using Gibbs distributions [5]. [1] Cessac B. A discrete time neural network model with spiking neurons ii. dynamics with noise. J. Math. Biol., 62:863-900, 2011 [2] Cessac B. Statistics of spike trains in conductance-based neural networks: Rigorous results. J Mathem Neurosci, 1, 2011. [3] Cofré R, Cessac B. Dynamics and spike trains statistics in conductance-based integrate-and-_re neural networks with chemical and electric synapses. Chaos, Solitons and Fractals, 2012. submitted. [4] Ganmor E, Segev R, Schneidman E. The architecture of functional interaction networks in the retina. J Neurosci, 31:3044-3054, 2011. [5] Nasser H, Marre O, Cessac B. Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and monte-carlo method. J Statist Mech, submitted, 2012. [6] Pillow JW, Ahmadian Y, Paninski L. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Comput., 23:1-45, 2011. [7] Schneidman E, Berry MJ, Segev R, Bialek W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440:1007-1012, 2006. [8] Vasquez JC, Marre O, Palacios AG, Berry MJ, Cessac B. Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells. J. Physiol. Paris, 2012. in press.

Modified electrodes coated by adsorbed cobalt phthalocyanines are known to show substantial electrocatalytic activity for the electro-oxidation of several thiols in alkaline aqueous solution. In this context, we explore in this study the electrocatalytic activity of adsorbed cobalt phthalocyanine (CoPc) on ordinary pyrolytic graphite electrode for the oxidation of reduced L-glutathione GSH and the reduction of its disulfide GSSG at physiological pH. To do so, cyclic and rotating disk voltammetries were performed and the amperometric results show that a stable electrochemical sensing material, with good reproducibility and sensitivity (in accordance with the concentrations of GSH expected in biological media), can be easily achieved. This opens the way for the design of an electrochemical sensor able to detect these two analytes in biologically relevant experimental conditions (in terms of pH).

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.