Waves traveling over a map of visual space can ignite short-term predictions of sensory input.

Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate the excitability of local networks and perceptual sensitivity. The general computational role of these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that traveling waves endow the visual system with the capacity to predict complex and naturalistic inputs. We present a network model whose connections can be rapidly and efficiently trained to predict individual natural movies. After training, a few input frames from a movie trigger complex wave patterns that drive accurate predictions many frames into the future solely from the network's connections. When the recurrent connections that drive waves are randomly shuffled, both traveling waves and the ability to predict are eliminated. These results suggest traveling waves may play an essential computational role in the visual system by embedding continuous spatiotemporal structures over spatial maps.

Wide spectrum of neuronal and network phenotypes in human stem cell-derived excitatory neurons with Rett syndrome-associated MECP2 mutations.

Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily caused by heterozygous loss-of-function mutations in the X-linked gene MECP2 that is a global transcriptional regulator. Mutations in the methyl-CpG binding domain (MBD) of MECP2 disrupt its interaction with methylated DNA. Here, we investigate the effect of a novel MECP2 L124W missense mutation in the MBD of an atypical RTT patient with preserved speech in comparison to severe MECP2 null mutations. L124W protein had a limited ability to disrupt heterochromatic chromocenters due to decreased binding dynamics. We isolated two pairs of isogenic WT and L124W induced pluripotent stem cells. L124W induced excitatory neurons expressed stable protein, exhibited increased input resistance and decreased voltage-gated Na+ and K+ currents, and their neuronal dysmorphology was limited to decreased dendritic complexity. Three isogenic pairs of MECP2 null neurons had the expected more extreme morphological and electrophysiological phenotypes. We examined development and maturation of L124W and MECP2 null excitatory neural network activity using micro-electrode arrays. Relative to isogenic controls, L124W neurons had an increase in synchronous network burst frequency, in contrast to MECP2 null neurons that suffered a significant decrease in synchronous network burst frequency and a transient extension of network burst duration. A biologically motivated computational neural network model shows the observed changes in network dynamics are explained by changes in intrinsic Na+ and K+ currents in individual neurons. Our multilevel results demonstrate that RTT excitatory neurons show a wide spectrum of morphological, electrophysiological and circuitry phenotypes that are dependent on the severity of the MECP2 mutation.

State-dependent activity dynamics of hypothalamic stress effector neurons.

The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRHPVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRHPVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRHPVN neurons.

Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states.

Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated "asynchronous-irregular" dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo.

Desigualdade de gênero nos estados brasileiros e análise dos stakeholders do conselho nacional dos direitos da mulher / Desigualdad de género en los estados brasileños y análisis de stakeholders del consejo nacional para los derechos de la mujer / Gender gap in brazilian states and stakeholder analysis of the national council for women's rights

Resumo O objetivo deste artigo é analisar a desigualdade entre homens e mulheres nas unidades federativas brasileiras e a atuação dos stakeholders do Conselho Nacional dos Direitos da Mulher (CNDM). Para tal, realizaram-se duas etapas de pesquisa. A primeira é baseada no cálculo de um índice sobre a disparidade entre gêneros nos estados brasileiros a partir da metodologia aplicada no Global Gender Gap Report (GGGR), elaborado pelo Fórum Econômico Mundial. Na segunda, foram mapeados os stakeholders do CNDM e analisada sua atuação diante das lacunas enfatizadas na primeira fase do estudo, à luz de referencial teórico amparado em modelos de análise de stakeholders. No Brasil, as dimensões que apresentaram o pior desempenho foram a política e a econômica, sendo as unidades federativas que obtiveram os melhores índices gerais Amapá, Distrito Federal e Maranhão, e os piores, Mato Grosso, Minas Gerais e Paraná. Por outro lado, os stakeholders do CNDM que atuam nas políticas públicas ligadas às dimensões econômica e política parecem não ter grande relevância e capacidade de influência. As evidências indicam que existe concentração de esforços em políticas nas áreas em que o país apresenta bons indicadores no GGGR, como saúde e educação.

Resumen El objetivo de este artículo es analizar la brecha de género en las unidades federativas brasileñas y las acciones de los stakeholders del Consejo Nacional para los Derechos de la Mujer (CNDM). Con este fin, se realizaron dos etapas de investigación. La primera se basa en el cálculo de un índice sobre la brecha de género en los estados brasileños a partir de la metodología aplicada en el Global Gender Gap Report (GGGR), preparado por el Foro Económico Mundial. En la segunda etapa, se mapeareon los stakeholders del CNDM y se analizó su desempeño en vista de las brechas destacadas en la primera fase del estudio, a la luz del marco teórico respaldado por los modelos de análisis de stakeholders. En Brasil, las dimensiones de peor desempeño fueron la política y la económica, las unidades federativas que obtuvieron los mejores índices generales fueron Amapá, Distrito Federal y Maranhão, y las peores fueron Mato Grosso, Minas Gerais y Paraná. Por otro lado, los stakeholders del CNDM que actúan en políticas públicas relacionadas con las dimensiones económica y política no parecen tener gran relevancia e influencia. La evidencia indica que hay una concentración de esfuerzos en políticas en áreas donde el país tiene buenos indicadores de GGGR (salud y educación).

Abstract This paper aims to analyze the gender gap in Brazilian states and the actions of stakeholders of the National Council for Women's Rights (NCWR). Two research was conducted in two steps. In the first step, a gender gap index in Brazilian states was calculated based on the Global Gender Gap Report (GGGR) methodology, prepared by the World Economic Forum. In the second, the NCWR stakeholders were mapped, and their performance was analyzed, considering the gender gap indexes obtained in the first step and the theoretical framework supported by stakeholder analysis models. The political and economic dimensions presented the worst performance. The Brazilian states with the best general indexes were Amapá, Distrito Federal, and Maranhão, and the worst were Mato Grosso, Minas Gerais, and Paraná. On the other hand, NCWR stakeholders acting in public policies related to the economic and political dimensions do not seem to have great relevance and influence capacity. Evidence indicates a concentration of policy efforts in areas where the country has good GGGR indicators (health and education).

Schrödinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact.

In EEG data acquired in the presence of fMRI, gradient-related spike artifacts contaminate the signal following the common preprocessing step of average artifact subtraction. Spike artifacts compromise EEG data quality since they overlap with the EEG signal in frequency, thereby confounding frequency-based inferences on activity. As well, spike artifacts can inflate or deflate correlations among time series, thereby confounding inferences on functional connectivity. We present Schrödinger filtering, which uses the Schrödinger equation to decompose the spike-containing input. The basis functions of the decomposition are localized and pulse-shaped, and selectively capture the various input peaks, with the spike components clustered at the beginning of the spectrum. Schrödinger filtering automatically subtracts the spike components from the data. On real and simulated data, we show that Schrödinger filtering (1) simultaneously accomplishes high spike removal and high signal preservation without affecting evoked activity, and (2) reduces spurious pairwise correlations in spontaneous activity. In these regards, Schrödinger filtering was significantly better than three other despiking techniques: median filtering, amplitude thresholding, and wavelet denoising. These results encourage the use of Schrödinger filtering in future EEG-fMRI pipelines, as well as in other spike-related applications (e.g., fMRI motion artifact removal or action potential extraction).