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In this article, a correlation metric $\kappa_C$ is proposed for the inference of the dynamical state of neuronal networks. $\kappa_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed., Comment: 8 pages, 8 figures

While the support for the relevance of critical dynamics to brain function is increasing, there is much less agreement on the exact nature of the advocated critical point. Thus, a considerable number of theoretical efforts are currently concentrated on which mechanisms and what type/s of transition can be exhibited by neuronal networks models. In that direction, the present work describes the effect of incorporating a fraction of inhibitory neurons on the collective dynamics. As we show, this results in the appearence of a tricritical point for highly connected networks and non-zero fraction of inhibitory neurons. We discuss the relation of the present results with relevant experimental evidence., Comment: 7 pages, 7 figures

Neurons in cortex synchronize their spiking in response to local and distant inputs. These synchronized assemblies are fundamental to cortex function, yet basic dynamical aspects about their size and duration are largely unknown. Using 2-photon imaging of neurons in superficial cortex of awake mice, we show that synchronized assemblies organize as scale-invariant avalanches that quadratically grow with duration. This quadratic expansion was found only for correlated neurons and required temporal coarse graining to compensate for spatial subsampling when network dynamics are critical, as demonstrated in simulations. The corresponding time course of an inverted parabola with exponent of χ = 2 described avalanches of up to 5 s duration and maximized temporal complexity in the ongoing activity of prefrontal and somatosensory cortex and in visual responses of primary visual cortex. Our results identify a scale-invariant order in the synchronization of highly diverse cortical cell assemblies in the form of parabolic avalanches.Significance StatementThe synchronization of cell assemblies is fundamental to many brain theories. Here we show such synchronization to grow according to an inverted parabola that maximizes temporal complexity. This quadratic scaling found for cell assemblies of highly diverse size and duration is in line with prediction for neuronal avalanches and the cortex being in a critical state.

Many complex systems exhibit large fuctuations both across space and over time. These fuctuations have often been linked to the presence of some kind of critical phenomena, where it is well known that the emerging correlation functions in space and time are closely related to each other. Here we test whether the time correlation properties allow systems exhibiting a phase transition to self-tune to their critical point. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek focking model and a small-world neuronal network model. We demonstrate that feedback from the autocorrelation function of the order parameter fuctuations shifts the system towards its critical point. Our results rely on universal properties of critical systems and are expected to be relevant to a variety of other settings., Instituto de Física de Líquidos y Sistemas Biológicos

Prolonged periods of sleep restriction seem to be common in the contemporary world. Sleep loss causes perturbations of circadian rhythmicity and degradation of waking alertness as reflected in attention, cognitive efficiency and memory. Understanding whether and how the human brain recovers from chronic sleep loss is important not only from a scientific but also from a public health perspective. In this work we report on behavioral, motor, and neurophysiological correlates of sleep loss in healthy adults in an unprecedented study conducted in natural conditions and comprising 21 consecutive days divided into periods of 4 days of regular life (a baseline), 10 days of chronic partial sleep restriction (30% reduction relative to individual sleep need) and 7 days of recovery. Throughout the whole experiment we continuously measured the spontaneous locomotor activity by means of actigraphy with 1-minute resolution. On a daily basis the subjects were undergoing EEG measurements (64-electrodes with 500 Hz sampling frequency): resting state with eyes open and closed (8 minutes long each) followed by Stroop task lasting 22 minutes. Altogether we analyzed actigraphy (distributions of rest and activity durations), behavioral measures (reaction times and accuracy from Stroop task) and EEG (amplitudes, latencies and scalp maps of event-related potentials from Stroop task and power spectra from resting states). We observed unanimous deterioration in all the measures during sleep restriction. Further results indicate that a week of recovery subsequent to prolonged periods of sleep restriction is insufficient to recover fully. Only one measure (mean reaction time in Stroop task) reverted to baseline values, while the others did not. Fil: Ochab, Jeremi K.. Jagiellonian University. Marian Smoluchowski Institute of Physics; Polonia. Jagiellonian University. Mark Kac Complex Systems Research Centre; Polonia Fil: Szwed, Jerzy. Jagiellonian University. Marian Smoluchowski Institute of Physics; Polonia. Jagiellonian University. Mark Kac Complex Systems Research Centre; Polonia Fil: Oles, Katarzyna. Jagiellonian University. Marian Smoluchowski Institute of Physics; Polonia Fil: Beres, Anna. Jagiellonian University. Department of Cognitive Neuroscience and Neuroergonomics; Polonia Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina Fil: Domagalik, Aleksandra. Jagiellonian University. Department of Cognitive Neuroscience and Neuroergonomics; Polonia Fil: Frafrowicz, Magdalena. Jagiellonian University. Department of Cognitive Neuroscience and Neuroergonomics; Polonia Fil: Oginska, Halszka. Jagiellonian University. Department of Cognitive Neuroscience and Neuroergonomics; Polonia Fil: Gudowska-Nowak, Ewa. Jagiellonian University. Marian Smoluchowski Institute of Physics; Polonia. Jagiellonian University. Małopolska Center of Biotechnology ; Polonia Fil: Marek, Tadeusz. Jagiellonian University. Department of Cognitive Neuroscience and Neuroergonomic; Polonia. Jagiellonian University. Małopolska Center of Biotechnology; Argentina Fil: Nowak, Maciej A.. Jagiellonian University. Marian Smoluchowski Institute of Physics; Polonia. Jagiellonian University. Mark Kac Complex Systems Research Centre; Polonia

The scaling of correlations as a function of size provides important hints to understand critical phenomena on a variety of systems. Its study in biological structures offers two challenges: usually they are not of infinite size, and, in the majority of cases, dimensions can not be varied at will. Here we discuss how finite-size scaling can be approximated in an experimental system of fixed and relatively small extent, by computing correlations inside of a reduced field of view of various widths (we will refer to this procedure as "box-scaling"). A relation among the size of the field of view, and measured correlation length, is derived at, and away from, the critical regime. Numerical simulations of a neuronal network, as well as the ferromagnetic 2D Ising model, are used to verify such approximations. Numerical results support the validity of the heuristic approach, which should be useful to characterize relevant aspects of critical phenomena in biological systems., Facultad de Ciencias Exactas, Instituto de Física de Líquidos y Sistemas Biológicos

This report is concerned with the relevance of the microscopic rules that implement individual neuronal activation, in determining the collective dynamics, under variations of the network topology. To fix ideas we study the dynamics of two cellular automaton models, commonly used, rather in-distinctively, as the building blocks of large-scale neuronal networks. One model, due to Greenberg and Hastings (GH), can be described by evolution equations mimicking an integrate-and-fire process, while the other model, due to Kinouchi and Copelli (KC), represents an abstract branching process, where a single active neuron activates a given number of postsynaptic neurons according to a prescribed "activity" branching ratio. Despite the apparent similarity between the local neuronal dynamics of the two models, it is shown that they exhibit very different collective dynamics as a function of the network topology. The GH model shows qualitatively different dynamical regimes as the network topology is varied, including transients to a ground (inactive) state, continuous and discontinuous dynamical phase transitions. In contrast, the KC model only exhibits a continuous phase transition, independently of the network topology. These results highlight the importance of paying attention to the microscopic rules chosen to model the interneuronal interactions in large-scale numerical simulations, in particular when the network topology is far from a mean-field description. One such case is the extensive work being done in the context of the Human Connectome, where a wide variety of types of models are being used to understand the brain collective dynamics.

Simple sensory stimuli or motor outputs engage large populations of neurons in the mammalian cortex. When stimuli or outputs repeat, the robust population response contrasts with fluctuating responses of individual neurons, known as trial-by-trial variability. To understand this apparent discrepancy, a detailed identification of the underlying spatiotemporal correlations is required. Here, we analyze spatial correlations in the instantaneous fluctuations between neurons relative to the neuronal population. Using 2-photon imaging of visual and auditory responses in primary cortices of awake mice, we show that these correlations grow linearly with the size of the observed cortical area. We extend these observations to the cortical mesoscale by analyzing local field potentials in behaving nonhuman primates. In network simulations, we demonstrate this linear growth in spatial correlation to emerge at criticality. Our findings suggest that trial-by-trial variability is a signature of critical dynamics in cortex maintaining robust, long-range spatial correlations among neurons.

Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information. Fil: Cifre, I.. Universitat Ramon Llull; España Fil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Horovitz, S. G.. National Institutes of Health; Estados Unidos Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility. Fil: Bachli, M. Belen. Universidad Nacional de San Martín; Argentina Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Ochab, Jeremi K.. No especifíca; Fil: Piguet, Olivier. University Of Technology Sydney.; Australia Fil: Kumfor, Fiona. University Of Technology Sydney.; Australia Fil: Reyes, Pablo. Hospital Universitario San Ignacio; Colombia Fil: Torralva, Teresa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Roca, María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Cardona, Juan Felipe. Universidad del Valle; Colombia Fil: Gonzalez Campo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Herrera, Eduar. Universidad Icesi; Colombia Fil: Slachevsky, Andrea. Universidad del Desarrollo; Chile Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia Fil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Adolfo Ibañez; Chile Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina

Biomarkers represent a critical research area in neurodegeneration disease as they can contribute to studying potential disease-modifying agents, fostering timely therapeutic interventions, and alleviating associated financial costs. Functional connectivity (FC) analysis represents a promising approach to identify early biomarkers in specific diseases. Yet, virtually no study has tested whether potential FC biomarkers prove to be reliable and reproducible across different centers. As such, their implementation remains uncertain due to multiple sources of variability across studies: the numerous international centers capable conducting FC research vary in their scanning equipment and their samples’ socio-cultural background, and, more troublingly still, no gold-standard method exists to analyze FC. In this unprecedented study, we aim to address both issues by performing the first multicenter FC research in the behavioral-variant frontotemporal dementia (bvFTD), and by assessing multiple FC approaches to propose a gold-standard method for analysis. We enrolled 52 bvFTD patients and 60 controls from three international clinics (with different fMRI recording parameters), and three additional neurological patient groups. To evaluate FC, we focused on seed analysis, inter-regional connectivity, and several graph-theory approaches. Only graph-theory analysis, based on weighted-matrices, yielded consistent differences between bvFTD and controls across centers. Also, graph metrics robustly discriminated bvFTD from the other neurological conditions. The consistency of our findings across heterogeneous contexts highlights graph-theory as a potential gold-standard approach for brain network analysis in bvFTD. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Sleep deprivation is an important societal problem that affects millions of people around the world on a daily basis. Our study aimed to examine the impact of a partial sleep restriction and following recovery processes on cognitive information processing, as evaluated by the Stroop test. We tested 15 participants over a period of 21 consecutive days, divided into 3 sleep conditions: 4 days of typical daily routine (baseline, ‘base’), 10 days of partial sleep deprivation (‘SD’), and 7 days of recovery (‘rec’). Each day, participants took part in an EEG experiment in which they performed a Stroop test, lasting for about 30 minutes, that required them to make an appropriate response to congruent and incongruent stimuli. Additionally, every day they answered a number of questions regarding their subjective levels of sleepiness and mood. During the whole period of 21 days, participants’ spontaneous locomotor activity was measured with the use of actigraphy. We have found behavioural and neural changes associated with different sleep conditions, such that the 10-day period of partial sleep restriction was linked with poorer behavioural performance on the Stroop test and an attenuated P300 neural response, compared to the baseline, followed by the observation of slow and gradual return in the period of recovery. This study, the first longitudinal study of its kind, shows that partial sleep deprivation has detrimental, long-term consequences on both behavioural and neural levels. This adds to the growing body of literature on this important issue in modern societies.SummarySleep deprivation, a world-wide problem in the 21st century, is associated with a number of complications, such as motor vehicle accidents (Lyznicki et al., 1998; Goel et al., 2009), medical errors (Barger et al., 2006), poorer health (in Colten & Altevogt, 2006), as well as cognitive deficits including problems with working memory and attention (van Dongen et al., 2003; Lim & Dinges, 2008). While total sleep deprivation (that is, a complete lack of sleep in a 24-hour period) is usually reserved only to certain professions (such as medical doctors), partial sleep restriction (that is, reducing one’s sleep time in a 24-hour period to fewer hours than typically required) is world-wide and affects a large proportion of the population across the globe. Taking this global impact into account, and thus increasing our understanding of the neurophysiological and cognitive processes that are linked with partial sleep deprivation, could largely inform the public discussion over what kind of impact, if any, restricting our sleep has on our daily functioning. This 21-day long EEG study investigated the effects of a prolonged (10-day) sleep restriction, and the recovery processes (over a 7-day period) that followed. Each day participants performed a Stroop test, known to measure attentional levels, and completed a number of sleep-related questionnaires. We have found that while behavioural responses are easier to recover, the neurophysiological responses are heavily affected after a period of sleep deprivation, with one week of recovery being insufficient to return to a pre-testing performance of an individual.

Evidence of critical dynamics has been recently found in both experiments and models of large scale brain dynamics. The understanding of the nature and features of such critical regime is hampered by the relatively small size of the available connectome, which prevent among other things to determine its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world network that share some topological features with the human connectome. We found that varying the topological parameters can give rise to a scale-invariant behavior belonging either to mean field percolation universality class or having non universal critical exponents. In addition, we found certain regions of the topological parameters space where the system presents a discontinuous (i.e., non critical) dynamical phase transition into a percolated state. Overall these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics., Comment: 6 pages, 6 figures

The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed "Multiplication of Temporal Derivatives" (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods., 12 pages, 7 figures

Why life is complex and - most importantly - what is the origin of the over abundance of complexity in nature? This is a fundamental scientific question which, paraphrasing the late Per Bak, “is screaming to be answered but seldom is even being asked”. In this article, we review recent attempts across several scales to understand the origins of complex biological problems from the perspective of critical phenomena. To illustrate the approach, three cases are discussed, namely the large scale brain dynamics, the characterization of spontaneous fluctuations of proteins, and the physiological complexity of the cell mitochondria network. Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions. Fil: Zamponi, Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Zamponi, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Billoni, Orlando Vito. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Helguera, Pablo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín; Argentina

Objectives: Behavioral variant frontotemporal dementia (bvFTD) is characterized by early atrophy in the frontotemporoinsular regions. These regions overlap with networks that are engaged in social cognition-executive functions, two hallmarks deficits of bvFTD. We examine (i) whether Network Centrality (a graph theory metric that measures how important a node is in a brain network) in the frontotemporoinsular network is disrupted in bvFTD, and (ii) the level of involvement of this network in social-executive performance. Methods: Patients with probable bvFTD, healthy controls, and frontoinsular stroke patients underwent functional MRI resting-state recordings and completed social-executive behavioral measures. Results: Relative to the controls and the stroke group, the bvFTD patients presented decreased Network Centrality. In addition, this measure was associated with social cognition and executive functions. To test the specificity of these results for the Network Centrality of the frontotemporoinsular network, we assessed the main areas from six resting-state networks. No group differences or behavioral associations were found in these networks. Finally, Network Centrality and behavior distinguished bvFTD patients from the other groups with a high classification rate. Conclusions: bvFTD selectively affects Network Centrality in the frontotemporoinsular network, which is associated with high-level social and executive profile. Fil: Sedeño, Lucas. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Diego Portales; Chile Fil: Couto, Juan Blas Marcos. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: García Cordero, Indira Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina Fil: Melloni, Margherita. Universidad Diego Portales; Chile. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Báez Buitrago, Sandra Jimena. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina Fil: Sepúlveda, Juan Pablo Morales. Universidad Diego Portales; Chile Fil: Fraiman Borrazás, Daniel Edmundo. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Pontificia Universidad Católica de Chile; Chile Fil: Huepe, David. Universidad Diego Portales; Chile Fil: Hurtado, Esteban. Pontificia Universidad Católica de Chile; Chile. Universidad Diego Portales; Chile Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia Fil: Kuljis, Rodrigo. Universidad Diego Portales; Chile Fil: Torralva, Teresa. Universidad Diego Portales; Chile. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Macquarie University; Australia Fil: Chialvo, Dante Renato. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sigman, Mariano. University of New South Wales; Australia Fil: Piguet, Olivier. University of New South Wales; Australia. Macquarie University; Australia Fil: Manes, Facundo Francisco. Macquarie University; Australia. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina Fil: Ibáñez Barassi, Agustín Mariano. Macquarie University; Australia. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Autónoma del Caribe; Colombia

Mounting evidence implicates chronic oxidative stress as a critical driver of the aging process. Down syndrome (DS) is characterized by a complex phenotype, including early senescence. DS cells display increased levels of reactive oxygen species (ROS) and mitochondrial structural and metabolic dysfunction, which are counterbalanced by sustained Nrf2-mediated transcription of cellular antioxidant response elements (ARE). Here, we show that caspase 3/PKCδdependent activation of the Nrf2 pathway in DS and Dp16 (a mouse model of DS) cells is necessary to protect against chronic oxidative damage and to preserve cellular functionality. Mitochondria-targeted catalase (mCAT) significantly reduced oxidative stress, restored mitochondrial structure and function, normalized replicative and wound healing capacity, and rendered the Nrf2-mediated antioxidant response dispensable. These results highlight the critical role of Nrf2/ARE in the maintenance of DS cell homeostasis and validate mitochondrial-specific interventions as a key aspect of antioxidant and antiaging therapies. Fil: Zamponi, Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Zamponi, Nahuel. Cornell University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Coskun, Pinar. University of California at Irvine; Estados Unidos Fil: Quassollo Infanzon, Gonzalo Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Lorenzo, Alfredo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Pigino, Gustavo Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín; Argentina Fil: Gardiner, Katheleen. University of Colorado Denver. School of Medicine; Estados Unidos Fil: Busciglio, Jorge. University of California at Irvine; Estados Unidos Fil: Helguera, Pablo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina

Among ERα-positive breast cancers, approximately half fail to respond to endocrine therapy, and the causes of this resistance are unknown. Sampayo et al. show that fibronectin (FN) influences the trafficking of ERα-positive vesicles. FN promotes ERα localization in Rab11+ vesicles, rescues ERα from lysosomal degradation, and reinforces ERα trafficking to the nucleus and transcriptional activity in tumor cells., Estrogen receptor α (ERα) is expressed in tissues as diverse as brains and mammary glands. In breast cancer, ERα is a key regulator of tumor progression. Therefore, understanding what activates ERα is critical for cancer treatment in particular and cell biology in general. Using biochemical approaches and superresolution microscopy, we show that estrogen drives membrane ERα into endosomes in breast cancer cells and that its fate is determined by the presence of fibronectin (FN) in the extracellular matrix; it is trafficked to lysosomes in the absence of FN and avoids the lysosomal compartment in its presence. In this context, FN prolongs ERα half-life and strengthens its transcriptional activity. We show that ERα is associated with β1-integrin at the membrane, and this integrin follows the same endocytosis and subcellular trafficking pathway triggered by estrogen. Moreover, ERα+ vesicles are present within human breast tissues, and colocalization with β1-integrin is detected primarily in tumors. Our work unravels a key, clinically relevant mechanism of microenvironmental regulation of ERα signaling.

The study of neuronal interactions is currently at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, the Brainome, etc.) which attempt to obtain a detailed map of the entire brain. Under certain constraints, mathematical theory can advance predictions of the expected neural dynamics based solely on the statistical properties of the synaptic interaction matrix. This work explores the application of free random variables to the study of large synaptic interaction matrices. Besides recovering in a straightforward way known results on eigenspectra in types of models of neural networks proposed by Rajan and Abbott, we extend them to heavy-tailed distributions of interactions. More importantly, we derive analytically the behavior of eigenvector overlaps, which determine the stability of the spectra. We observe that upon imposing the neuronal excitation/inhibition balance, despite the eigenvalues remaining unchanged, their stability dramatically decreases due to the strong non-orthogonality of associated eigenvectors. It leads us to the conclusion that the understanding of the temporal evolution of asymmetric neural networks requires considering the entangled dynamics of both eigenvectors and eigenvalues, which might bear consequences for learning and memory processes in these models. Considering the success of free random variables theory in a wide variety of disciplines, we hope that the results presented here foster the additional application of these ideas in the area of brain sciences., 25 pages + 5 pages of refs, 9 figures

The motion of social insects is often used as a paradigmatic example of complex adaptive dynamics arising from decentralized individual behaviour. In this paper, we revisit the topic of the ruling laws behind the burst of activity in ants. The analysis, done over previously reported data, reconsiders the causation arrows, proposed at individual level, not finding any link between the duration of the ants' activity and their moving speed. Secondly, synthetic trajectories created from steps of different ants demonstrate that a Markov process can explain the previously reported speed shape profile. Finally, we show that as more ants enter the nest, the faster they move, which implies a collective property. Overall, these results provide a mechanistic explanation for the reported behavioural laws, and suggest us a formal way to further study the collective properties in these scenarios., R.G. acknowledges funding from the SESAR Joint Undertaking under grant agreement no. 699260 included in the European Union's Horizon 2020 Research and Innovation Programme. D.R.C. thanks support from CONICET and Universidad Nacional de San Martín, Argentina.

The study of fluctuation-induced transport is concerned with the directed motion of particles on a substrate when subjected to a fluctuating external field. Work over the last two decades provides now precise clues on how the average transport depends on three fundamental aspects: the shape of the substrate, the correlations of the fluctuations and the mass, geometry, interaction and density of the particles. These three aspects, reviewed here, acquire additional relevance because the same notions apply to a bewildering variety of problems at very different scales, from the small nano or micro-scale, where thermal fluctuations effects dominate, up to very large scales including ubiquitous cooperative phenomena in granular materials., Comment: 12 pages, 9 figures

The study of rapid changes in brain dynamics and functional connectivity (FC) is of increasing interest in neuroimaging. Brain states departing from normal waking consciousness are expected to be accompanied by alterations in the aforementioned dynamics. In particular, the psychedelic experience produced by psilocybin (a substance found in "magic mushrooms") is characterized by unconstrained cognition and profound alterations in the perception of time, space and selfhood. Considering the spontaneous and subjective manifestation of these effects, we hypothesize that neural correlates of the psychedelic experience can be found in the dynamics and variability of spontaneous brain activity fluctuations and connectivity, measurable with functional Magnetic Resonance Imaging (fMRI). Fifteen healthy subjects were scanned before, during and after intravenous infusion of psilocybin and an inert placebo. Blood-Oxygen Level Dependent (BOLD) temporal variability was assessed computing the variance and total spectral power, resulting in increased signal variability bilaterally in the hippocampi and anterior cingulate cortex. Changes in BOLD signal spectral behavior (including spectral scaling exponents) affected exclusively higher brain systems such as the default mode, executive control, and dorsal attention networks. A novel framework enabled us to track different connectivity states explored by the brain during rest. This approach revealed a wider repertoire of connectivity states post-psilocybin than during control conditions. Together, the present results provide a comprehensive account of the effects of psilocybin on dynamical behavior in the human brain at a macroscopic level and may have implications for our understanding of the unconstrained, hyper-associative quality of consciousness in the psychedelic state.

The recent upraise of interest in the dynamics of the brain resting activity opens a number of new and different questions. A fundamental one is related to the character of correlations of healthy large scale brain activity. These studies focus on the linear correlation of the spontaneous activity between brain sites. Here we present a different approach, instead to estimate the average linear correlation of activity between pairs of brain sites, we ask: what are average sequels in space and time of a big event (i.e., a thunder). By strobing these events we find that on average the activity variations with opposite sign are correlated in time, over a temporal scale of few seconds, exposing a critical balance between excitation and depression opposing forces. Fil: Lombardi, F.. ETH Zurich; Suiza Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Herrmann, H. J.. ETH Zurich; Suiza. Universidade Federal do Ceará; Brasil Fil: de Arcangelis, L.. Seconda Universita Degli Studi Di Napoli; Italia

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance. Fil: Tagliazucchi, Enzo. Christian Albrechts Universitat Zu Kiel.; Alemania. University Frankfurt am Main; Alemania Fil: Siniatchkin, Michael. Christian Albrechts Universitat Zu Kiel.; Alemania Fil: Laufs, Helmut. University Frankfurt am Main; Alemania. University Hospital Schleswig Holstein; Alemania Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina

Based on protein structural ensembles determined by nuclear magnetic resonance, we study the position fluctuations of residues by calculating distance-dependent correlations and conducting finite-size scaling analysis. The fluctuations exhibit high susceptibility and long-range correlations up to the protein sizes. The scaling relations between the correlations or susceptibility and protein sizes resemble those in other physical and biological systems near their critical points. These results indicate that, at the native states, motions of each residue are felt by every other one in the protein. We also find that proteins with larger susceptibility are more frequently observed in nature. Overall, our results suggest that the protein's native state is critical. Fil: Tang, Qian Yuan. Nanjing University; China Fil: Zhang, Yang Yang. Nanjing University; China Fil: Wang, Jun. Nanjing University; China Fil: Wang, Wei. Nanjing University; China Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. In this study, we arrive at a basic model explaining these observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness and during the subsequent recovery. We observed that during unconsciousness activity in frontothalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from anatomical constraints. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This framework unifies different observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent from its causes. Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Christian Albrechts University zu Kiel; Alemania. Goethe Universitat Frankfurt; Alemania Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Siniatchkin, Michael. Christian Albrechts University zu Kiel; Alemania Fil: Amico, Enrico. Université de Liège; Bélgica Fil: Brichant, Jean Francois. Université de Liège; Bélgica Fil: Bonhomme, Vincent. Université de Liège; Bélgica Fil: Noirhomme, Quentin. Université de Liège; Bélgica Fil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania Fil: Laureys, Steven. Université de Liège; Bélgica

Motor activity of humans displays complex temporal fluctuations which can be characterized by scale-invariant statistics, thus documenting that structure and fluctuations of such kinetics remain similar over a broad range of time scales. Former studies on humans regularly deprived of sleep or suffering from sleep disorders predicted change in the invariant scale parameters with respect to those representative for healthy subjects. In this study we investigate the signal patterns from actigraphy recordings by means of characteristic measures of fractional point processes. We analyse spontaneous locomotor activity of healthy individuals recorded during a week of regular sleep and a week of chronic partial sleep deprivation. Behavioural symptoms of lack of sleep can be evaluated by analysing statistics of duration times during active and resting states, and alteration of behavioural organization can be assessed by analysis of power laws detected in the event count distribution, distribution of waiting times between consecutive movements and detrended fluctuation analysis of recorded time series. We claim that among different measures characterizing complexity of the actigraphy recordings and their variations implied by chronic sleep distress, the exponents characterizing slopes of survival functions in resting states are the most effective biomarkers distinguishing between healthy and sleep-deprived groups., Communicated at UPON2015, 14-17 July 2015, Barcelona. 21 pages, 11 figures; updated: figures 4-7, text revised, expanded Sec. 1,3,5

Nonlinear systems driven by noise and periodic forces with more than one frequency exhibit the phenomenon of Ghost Stochastic Resonance (GSR) found in a wide and disparate variety of fields ranging from biology to geophysics. The common novel feature is the emergence of a "ghost" frequency in the system's output which it is absent in the input. As reviewed here, the uncovering of this phenomenon helped to understand a range of problems, from the perception of pitch in complex sounds or visual stimuli, to the explanation of climate cycles. Recent theoretical efforts show that a simple mechanism with two ingredients are at work in all these observations. The first one is the linear interference between the periodic inputs and the second a nonlinear detection of the largest constructive interferences, involving a noisy threshold. These notes are dedicated to review the main aspects of this phenomenon, as well as its different manifestations described on a bewildering variety of systems ranging from neurons, semiconductor lasers, electronic circuits to models of glacial climate cycles., Comment: To appear in Contemporary Physics

Objective The present study examines the possibility that a chronic pain condition, such as fibromyalgia, was associated with deficits in decision making and associative learning. Methods Fifteen patients with fibromyalgia (aged 42–59 years) and 15 healthy controls (aged 39–61 years) participated in the experiment. Subjects completed anxiety (STAI) and depression (BDI) questionnaires, as well as standardized neuropsychological tests (Stroop and WAIS subscales). In addition, an emotional decision-making task (Iowa Gambling Task) and a conditional associative learning task (CALT) were administered to all participants. Results Results indicated that fibromyalgia had a poorer performance than healthy controls in both tasks, showing more perseveration errors in the learning task, and more disadvantageous decisions, as well as a more random behavior in the gambling task. Moreover, we observed that poor performance on the associative learning task was mediated by depression, whereas performance on the gambling task was not influenced by depression. No group differences were found on the standardized neuropsychological tests. Conclusion These findings indicate that pain and depressive symptoms in fibromyalgia might lead to significant deficits in emotionally charged cognitive tasks. Furthermore, it suggests that chronic pain might impose a high cost on executive control, undermining mainly affective processes involved in learning, memory, attention, and decision-making.

Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages (“rBeta”) can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.

Adaptive behaviour, cognition and emotion are the result of a bewildering variety of brain spatio-temporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e. they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatio-temporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state functional magnetic resonance imaging data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/ f frequency behaviour of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being in both health and disease.

In these notes we review emergent phenomena in complex systems, emphasizing ways to identify potential underlying universal mechanisms that generate complexity. The discussion focuses on the emergence of collective behavior in dynamical systems when they are poised near a critical point of a phase transition, either by tuning or by self-organization. We then present the rationale for our proposal that the brain is naturally poised near criticality and review recent results as well as the implications of this view of the functioning brain.

We describe a method for the measurement, analysis and display of cerebral cortical data obtained from coronal brain sections of the adult rat. In this method, regions-of-interest (ROI) are selected in the cortical mantle in a semiautomated fashion using a radial grid overlay, spaced in 15 degrees intervals from the midline. ROI measurements of intensity are mapped on a flattened two-dimensional surface. Topographic maps of statistical significance at each ROI allow for the rapid viewing of group differences. Cortical z-scores are displayed with the boundaries of brain regions defined according to a standard atlas of the rat brain. This method and accompanying software implementation (Matlab, Labview) allow for compact data display in a variety of autoradiographic and histologic studies of the structure and function of the rat brain.

Chronic pain patients suffer from more than just pain; depression and anxiety, sleep disturbances, and decision-making abnormalities (Apkarian et al., 2004a) also significantly diminish their quality of life. Recent studies have demonstrated that chronic pain harms cortical areas unrelated to pain (Apkarian et al., 2004b; Acerra and Moseley, 2005), but whether these structural impairments and behavioral deficits are connected by a single mechanism is as of yet unknown. Here we propose that long-term pain alters the functional connectivity of cortical regions known to be active at rest, i.e., the components of the “default mode network” (DMN). This DMN (Raichle et al., 2001; Greicius et al., 2003; Vincent et al., 2007) is marked by balanced positive and negative correlations between activity in component brain regions. In several disorders, however this balance is disrupted (Fox and Raichle, 2007). Using well validated functional magnetic resonance imaging (fMRI) paradigms to study the DMN (Fox et al., 2005), we investigated whether the impairments of chronic pain patients could be rooted in disturbed DMN dynamics. Studying with fMRI a group of chronic back pain (CBP) patients and healthy controls while executing a simple visual attention task, we discovered that CBP patients, despite performing the task equally well as controls, displayed reduced deactivation in several key DMN regions. These findings demonstrate that chronic pain has a widespread impact on overall brain function, and suggest that disruptions of the DMN may underlie the cognitive and behavioral impairments accompanying chronic pain.

To navigate in space most vertebrates need precise positional cues provided by a variety of sensors, including structures in the inner ear, which are exquisitely sensitive to gravity and linear acceleration. Although these structures have been described in many vertebrates, no information is available for anuran larvae. The purpose of our study was to describe, for the first time, the size, complexity and microchemistry of the saccular otoliths of the larva of 13 anuran species from central Argentina, using electron microscopy and X-ray spectroscopy (N = 65). We concluded that a) these structures differ in area, perimeter, otolith relative size and fractal dimension, but are similar in terms of their microchemistry when compared by spatial guilds, b) that nektonic species have larger otoliths than nektonic-benthic and benthic species and c) that benthic species have larger otolith relative size than nektonic-benthic and nektonic species. Fil: Basso, Agustin. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina Fil: Peltzer, Paola. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Saneamiento Ambiental. Cátedra de Ecotoxicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Lajmanovich, Rafael Carlos. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Saneamiento Ambiental. Cátedra de Ecotoxicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Attademo, Andres Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Saneamiento Ambiental. Cátedra de Ecotoxicología; Argentina Fil: Junges, Celina Maria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Saneamiento Ambiental. Cátedra de Ecotoxicología; Argentina Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina

The brain exhibits a wide variety of spatiotemporal patterns of neuronal activity recorded using functional magnetic resonance imaging as the so-called blood-oxygenated-level–dependent (BOLD) signal. An active area of work includes efforts to best describe the plethora of these patterns evolving continuously in the brain. Here we explore the third-moment statistics of the brain BOLD signals in the resting state as a proxy to capture extreme BOLD events. We find that the brain signal exhibits typically nonzero skewness, with positive values for cortical regions and negative values for subcortical regions. Furthermore, the combined analysis of structural and functional connectivity demonstrates that relatively more connected regions exhibit activity with high negative skewness. Overall, these results highlight the relevance of recent results emphasizing that the spatiotemporal location of the relatively large-amplitude events in the BOLD time series contains relevant information to reproduce a number of features of the brain dynamics during resting state in health and disease. Fil: Amor, T. A.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina Fil: Russo, R.. Università Degli Studi Di Napoli Federico Ii; Italia Fil: Diez, I.. Cruces University Hospital; España Fil: Bharath, P.. VA West Los Angeles Medical Center; Estados Unidos Fil: Zirovich, P.. VA West Los Angeles Medical Center; Estados Unidos Fil: Stramaglia, S.. Universita Degli Studi Di Bari; Italia. Basque Center for Applied Mathematics; España Fil: Cortes, J. M.. Cruces University Hospital; España. The Basque Foundation for Science; España. University of the Basque Country; España Fil: de Arcangelis, L.. Seconda Universita Degli Studi Di Napoli; Italia Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico Rosario; Argentina

Living with unrelenting pain (chronic pain) is maladaptive and is thought to be associated with physiological and psychological modifications, yet there is a lack of knowledge regarding brain elements involved in such conditions. Here, we identify brain regions involved in spontaneous pain of chronic back pain (CBP) in two separate groups of patients (n= 13 andn= 11), and contrast brain activity between spontaneous pain and thermal pain (CBP and healthy subjects,n= 11 each). Continuous ratings of fluctuations of spontaneous pain during functional magnetic resonance imaging were separated into two components: high sustained pain and increasing pain. Sustained high pain of CBP resulted in increased activity in the medial prefrontal cortex (mPFC; including rostral anterior cingulate). This mPFC activity was strongly related to intensity of CBP, and the region is known to be involved in negative emotions, response conflict, and detection of unfavorable outcomes, especially in relation to the self. In contrast, the increasing phase of CBP transiently activated brain regions commonly observed for acute pain, best exemplified by the insula, which tightly reflected duration of CBP. When spontaneous pain of CBP was contrasted to thermal stimulation, we observe a double-dissociation between mPFC and insula with the former correlating only to intensity of spontaneous pain and the latter correlating only to pain intensity for thermal stimulation. These findings suggest that subjective spontaneous pain of CBP involves specific spatiotemporal neuronal mechanisms, distinct from those observed for acute experimental pain, implicating a salient role for emotional brain concerning the self.

We examined mRNA expression of the pro-inflammatory cytokine IL-1beta in the brainstem, thalamus, and prefrontal cortex in two rat models of neuropathic pain. Rats received a neuropathic injury: spared nerve injury (SNI) or chronic constriction injury (CCI), sham injury, or were minimally handled (control). Neuropathic pain-like behavior was monitored by tracking tactile thresholds. SNI-injured animals showed a robust decrease in tactile thresholds of the injured foot, while CCI-injured animals did not show tactile threshold changes. Ten or 24 days after nerve injury, IL-1beta gene expression in the brain was determined by RT-PCR. IL-1beta expression changes were observed mainly at 10 days after injury in the SNI animals, contralateral to the injury side, with increased expression in the brainstem and prefrontal cortex. The results indicate that neuro-immune activation in neuropathic pain conditions includes supraspinal brain regions, suggesting cytokine modulation of supraspinal circuitry of pain in neuropathic conditions.