A view-based decision mechanism for rewards in the primate amygdala

Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey’s current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice. Using neural-network modeling, we identified a sequence of computations by which amygdala neurons implemented view-based decision making and eventually recovered the chosen object’s identity when the monkeys acted on their choice. These findings reveal a neural mechanism in the amygdala that derives object choices from abstract, view-based computations, suggesting an efficient solution for decision problems with many objects

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create “archetype” Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10−5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity—especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state

Evaluación y requerimientos nutricionales en pacientes hospitalizados por COVID-19

La COVID-19 desencadena un cuadro clínico que puede impactar negativamente en el estado nutricional y en el pronóstico de los pacientes. La respuesta inflamatoria exacerbada en casos críticos, puede traer consigo una serie de alteraciones metabólicas y catabólicas que afectan directamente el estado nutricional, generando pérdida de masa muscular esquelética y desnutrición, lo que se asocia a peores desenlaces y mayores complicaciones. La valoración del estado nutricional, mediante el tamizaje y la evaluación nutricional en pacientes con COVID-19, nos permite preservar el estado nutricional y/o prevenir o tratar la desnutrición asociada y así, reducir las complicaciones asociadas a la misma. Actualmente no existe tratamiento nutricional específico frente al COVID-19, sin embargo, las diversas sociedades científicas del mundo han establecido una serie de recomendaciones para confrontar la situación nutricional. Así, en el presente trabajo se exponen los requerimientos nutricionales en pacientes con COVID-19 no ventilados en hospitalización general y en la Unidad de Cuidados Intensivos (UCI) en base al aporte de energía, carbohidratos, proteínas, lípidos y micronutrientes (vitaminas y minerales) necesarios para mejorar su pronóstico durante la estancia hospitalaria. Palabras claves: Evaluación Nutricional, Necesidades Nutricionales, Infección por Coronavirus 2019-nCoV. (fuente: DeCS)   DOI: http://dx.doi.org/10.17268/rmt.2021.v16i03.1

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Modèles probabilistes pour l'étude de la variabilité dans l'activité de neurones individuels et d'ensembles de neurones

Une des caractéristiques les plus singulières de l’activité corticale est son degré élevé de variabilité. Ma thèse dedoctorat s’est focalisée sur l’étude de (i) l’irrégularité des intervalles entre potentiels d’action (PAs)successivement émis par un neurone, et (ii) la variabilité dans l’évolution temporelle de l’activité d’un ensemblede neurones. Premièrement, j’ai étudié l’irrégularité des neurones enregistrés dans le cortex moteur de singesmacaques performant une tâche d’estimation du temps et de préparation à l’action. J’ai montré que l’irrégularitén’est pas un paramètre libre de l’activité neuronale, contrairement au taux de PAs, mais est déterminée par lescontraintes structurelles des réseaux neuronaux. Deuxièmement, j’ai utilisé le modèle de Markov caché (MMC)pour analyser l’activité d’ensembles de neurones enregistrés dans plusieurs aires corticales, sensorielles etmotrices, de singes exécutant une tâche de discrimination tactile. J’ai montré que les processus sensoriels etdécisionnels sont distribués dans plusieurs aires corticales. Les résultats suggèrent que l’action et la décision surlaquelle elle est basée sont reliées par une cascade d’évènements non stationnaires et stochastiques. Finalement,j’ai utilisé le MMC pour caractériser l’activité spontanée d’un ensemble de neurones du cortex préfrontal d’unrat. Les résultats montrèrent que l’alternance entre les états UP et DOWN est un processus stochastique etdynamique. La variabilité apparaît donc aussi bien pendant l’activité spontanée que pendant le comportementactif et semble être contrainte par des facteurs structurels qui, à leur tour, contraignent le mode d’opération desréseaux neuronaux.A hallmark of cortical activity is its high degree of variability. The present work focused on (i) the variability ofintervals between spikes that single neurons emit, called spike time irregularity (STI), and (ii) the variability inthe temporal evolution of the collective neuronal activity. First, I studied the STI of macaque motor corticalneurons during time estimation and movement preparation. I found that although the firing rate of the neuronstransmitted information about these processes, the STI of a neuron is not flexible and is determined by thebalance of excitatory and inhibitory inputs. These results were obtained by means of an irregularity measure thatI compared to other existing measures. Second, I analyzed the neuronal ensemble activity of severalsomatosensory and motor cortical areas of macaques during tactile discrimination. I showed that ensembleactivity can be effectively described by the Hidden Markov Model (HMM). Both sensory and decision-makingprocesses were distributed across many areas. Moreover, I showed that decision-related changes in neuronalactivity rely on a noise-driven mechanism and that the maintenance of the decision relies on transient dynamics,subtending the conversion of a decision into an action. Third, I characterized the statistics of spontaneous UP andDOWN states in the prefrontal cortex of a rat, using the HMM. I showed that state alternation is stochastic andthe activity during UP states is dynamic. Hence, variability is prominent both during active behavior andspontaneous activity and is determined by structural factors, thus rending it inherent to cortical organization andshaping the function of neural networks

Modèles probabilistes pour l'étude de la variabilité dans l'activité de neurones individuels et d'ensembles de neurones

Une des caractéristiques les plus singulières de l activité corticale est son degré élevé de variabilité. Ma thèse dedoctorat s est focalisée sur l étude de (i) l irrégularité des intervalles entre potentiels d action (PAs)successivement émis par un neurone, et (ii) la variabilité dans l évolution temporelle de l activité d un ensemblede neurones. Premièrement, j ai étudié l irrégularité des neurones enregistrés dans le cortex moteur de singesmacaques performant une tâche d estimation du temps et de préparation à l action. J ai montré que l irrégularitén est pas un paramètre libre de l activité neuronale, contrairement au taux de PAs, mais est déterminée par lescontraintes structurelles des réseaux neuronaux. Deuxièmement, j ai utilisé le modèle de Markov caché (MMC)pour analyser l activité d ensembles de neurones enregistrés dans plusieurs aires corticales, sensorielles etmotrices, de singes exécutant une tâche de discrimination tactile. J ai montré que les processus sensoriels etdécisionnels sont distribués dans plusieurs aires corticales. Les résultats suggèrent que l action et la décision surlaquelle elle est basée sont reliées par une cascade d évènements non stationnaires et stochastiques. Finalement,j ai utilisé le MMC pour caractériser l activité spontanée d un ensemble de neurones du cortex préfrontal d unrat. Les résultats montrèrent que l alternance entre les états UP et DOWN est un processus stochastique etdynamique. La variabilité apparaît donc aussi bien pendant l activité spontanée que pendant le comportementactif et semble être contrainte par des facteurs structurels qui, à leur tour, contraignent le mode d opération desréseaux neuronaux.A hallmark of cortical activity is its high degree of variability. The present work focused on (i) the variability ofintervals between spikes that single neurons emit, called spike time irregularity (STI), and (ii) the variability inthe temporal evolution of the collective neuronal activity. First, I studied the STI of macaque motor corticalneurons during time estimation and movement preparation. I found that although the firing rate of the neuronstransmitted information about these processes, the STI of a neuron is not flexible and is determined by thebalance of excitatory and inhibitory inputs. These results were obtained by means of an irregularity measure thatI compared to other existing measures. Second, I analyzed the neuronal ensemble activity of severalsomatosensory and motor cortical areas of macaques during tactile discrimination. I showed that ensembleactivity can be effectively described by the Hidden Markov Model (HMM). Both sensory and decision-makingprocesses were distributed across many areas. Moreover, I showed that decision-related changes in neuronalactivity rely on a noise-driven mechanism and that the maintenance of the decision relies on transient dynamics,subtending the conversion of a decision into an action. Third, I characterized the statistics of spontaneous UP andDOWN states in the prefrontal cortex of a rat, using the HMM. I showed that state alternation is stochastic andthe activity during UP states is dynamic. Hence, variability is prominent both during active behavior andspontaneous activity and is determined by structural factors, thus rending it inherent to cortical organization andshaping the function of neural networks.AIX-MARSEILLE2-Bib.electronique (130559901) / SudocSudocFranceF

Modèles probabilistes pour l'étude de la variabilité dans l'activité de neurones individuels et d'ensembles de neurones

Une des caractéristiques les plus singulières de l activité corticale est son degré élevé de variabilité. Ma thèse dedoctorat s est focalisée sur l étude de (i) l irrégularité des intervalles entre potentiels d action (PAs)successivement émis par un neurone, et (ii) la variabilité dans l évolution temporelle de l activité d un ensemblede neurones. Premièrement, j ai étudié l irrégularité des neurones enregistrés dans le cortex moteur de singesmacaques performant une tâche d estimation du temps et de préparation à l action. J ai montré que l irrégularitén est pas un paramètre libre de l activité neuronale, contrairement au taux de PAs, mais est déterminée par lescontraintes structurelles des réseaux neuronaux. Deuxièmement, j ai utilisé le modèle de Markov caché (MMC)pour analyser l activité d ensembles de neurones enregistrés dans plusieurs aires corticales, sensorielles etmotrices, de singes exécutant une tâche de discrimination tactile. J ai montré que les processus sensoriels etdécisionnels sont distribués dans plusieurs aires corticales. Les résultats suggèrent que l action et la décision surlaquelle elle est basée sont reliées par une cascade d évènements non stationnaires et stochastiques. Finalement,j ai utilisé le MMC pour caractériser l activité spontanée d un ensemble de neurones du cortex préfrontal d unrat. Les résultats montrèrent que l alternance entre les états UP et DOWN est un processus stochastique etdynamique. La variabilité apparaît donc aussi bien pendant l activité spontanée que pendant le comportementactif et semble être contrainte par des facteurs structurels qui, à leur tour, contraignent le mode d opération desréseaux neuronaux.A hallmark of cortical activity is its high degree of variability. The present work focused on (i) the variability ofintervals between spikes that single neurons emit, called spike time irregularity (STI), and (ii) the variability inthe temporal evolution of the collective neuronal activity. First, I studied the STI of macaque motor corticalneurons during time estimation and movement preparation. I found that although the firing rate of the neuronstransmitted information about these processes, the STI of a neuron is not flexible and is determined by thebalance of excitatory and inhibitory inputs. These results were obtained by means of an irregularity measure thatI compared to other existing measures. Second, I analyzed the neuronal ensemble activity of severalsomatosensory and motor cortical areas of macaques during tactile discrimination. I showed that ensembleactivity can be effectively described by the Hidden Markov Model (HMM). Both sensory and decision-makingprocesses were distributed across many areas. Moreover, I showed that decision-related changes in neuronalactivity rely on a noise-driven mechanism and that the maintenance of the decision relies on transient dynamics,subtending the conversion of a decision into an action. Third, I characterized the statistics of spontaneous UP andDOWN states in the prefrontal cortex of a rat, using the HMM. I showed that state alternation is stochastic andthe activity during UP states is dynamic. Hence, variability is prominent both during active behavior andspontaneous activity and is determined by structural factors, thus rending it inherent to cortical organization andshaping the function of neural networks.AIX-MARSEILLE2-Bib.electronique (130559901) / SudocSudocFranceF

Behavioral/Systems/Cognitive On the Anticipatory Precue Activity in Motor Cortex

International audienceMotor cortical neurons are activated during movement preparation and execution, and in response to task-relevant visual cues. A few studies also report activation before the expected presentation of cues. Here, we study specifically this anticipatory activity preceding visual cues in motor cortical areas. We recorded the activity of 1215 neurons in the motor cortex of two macaque monkeys while they performed a center-out reaching task, including two consecutive delays of equal duration, known in advance. During the first delay (D1), they had to await the spatial cue and only reach to the cued target after the second delay (D2). Forty-two percent of the neurons displayed anticipatory activity during D1. Among these anticipatory neurons, 59% increased (D1up) their activity and the remaining decreased (D1down) their activity. By classifying the neurons according to these firing rate profiles during D1, we found that the activity during D2 differed in a systematic way. The D1up neurons were more likely to discharge phasically soon after the spatial cue and were less active during movement execution, whereas the D1down neurons showed the opposite pattern. But, regardless of their temporal activity profiles, the two categories seemed equally involved in early and late motor preparation, as reflected in their directional selectivity. This precue activity in motor cortex may reflect two complementary, coexisting processes: the facilitation of incoming spatial information in parallel with the downregulation of corticospinal excitability to prevent a premature response