The functional role of supplementary eye field underlying value-based decision making

In nearly every moment of our lives, we make decisions. However, the neuronal mechanism underlying decision-making is still not clear. Currently, there is a debate on whether value-based decisions are based on the selection of goals or the selection of actions. We investigated this question by recording from the supplementary eye field (SEF) of monkeys during an oculomotor gambling task. We found that SEF neurons initially encode the option and action values associated with both task options. Later on, the competitive interactions between the different options result in their selection. Specifically, competition occurred in both action space and value space as represented in SEF. However, SEF encodes the chosen option 60~100 ms before the chosen action. When neuronal activity in SEF was reversibly inactivated, the monkeys’ selection of the less valuable option was significantly increased. These results suggest that SEF is actively engaged in value based decision-making by forming a map of the competing saccade targets. Activity within this map reflects the chosen option first, and then later the corresponding necessary action . This SEF population activity is causally related to the selection of a saccade based on subjective value, and reflects both the selection of goals and of actions. Moreover, in contrary to the two major decision hypothesis under the debate, our results suggest value based decision rely on the selection of both goals and actions. This study therefore supports a new cascade choice theory of value-based decision making in which the competition is present for both goals and actions. The early competition between goals (value) can further bias the competitive process between actions

Standard Biological Part Automatic Modeling Database Language (MoDeL)

This BioBricks Foundation Request for Comments (BBF RFC) describes the Standard Biological Part Automatic Modeling Database Language (MoDeL). MoDeL provides a language and syntax standard for automatic modeling databases used by synthetic biology software. Meanwhile, MoDeL allows detailed description of biological complex, and presents the concept of Chain-Node Model

Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the issue of inconsistency between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies at current time step. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor sensor data forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches

Parietal Cortex Regulates Visual Salience and Salience-Driven Behavior

Chen et al. show that inactivation of parietal cortex selectively reduces salience signals within prefrontal cortex and diminishes the influence of salience on visually guided behavior. The results demonstrate a causal role of parietal cortex in regulating salience signals within the brain and in controlling salience-driven behavior

The Contribution of Parietal Cortex to Visual Salience

Unique stimuli stand out. In spite of an abundance of competing sensory stimuli, the detection of the most salient ones occurs without effort, and that detection contributes to the guidance of adaptive behavior. Neurons sensitive to the salience of visual stimuli are widespread throughout the primate visual system and are thought to shape the selection of visual targets. However, the source of the salience computation has remained elusive. Among the possible candidates are areas within posterior parietal cortex, which appear to be crucial in the control of visual attention and are thought to play a unique role in representing stimulus salience. Here we show that reversible inactivation of parietal cortex not only selectively reduces the representation of visual salience within the brain, but it also diminishes the influence of salience on visually guided behavior. These results demonstrate a distinct contribution of parietal areas to vision and visual attention

The Contribution of Parietal Cortex to Visual Salience

Unique stimuli stand out. In spite of an abundance of competing sensory stimuli, the detection of the most salient ones occurs without effort, and that detection contributes to the guidance of adaptive behavior. Neurons sensitive to the salience of visual stimuli are widespread throughout the primate visual system and are thought to shape the selection of visual targets. However, the source of the salience computation has remained elusive. Among the possible candidates are areas within posterior parietal cortex, which appear to be crucial in the control of visual attention and are thought to play a unique role in representing stimulus salience. Here we show that reversible inactivation of parietal cortex not only selectively reduces the representation of visual salience within the brain, but it also diminishes the influence of salience on visually guided behavior. These results demonstrate a distinct contribution of parietal areas to vision and visual attention

Transcriptome-wide association study reveals novel susceptibility genes for coronary atherosclerosis

BackgroundGenetic risk factors substantially contributed to the development of coronary atherosclerosis. Genome-wide association study (GWAS) has identified many risk loci for coronary atherosclerosis, but the translation of these loci into therapeutic targets is limited for their location in non-coding regions. Here, we aimed to screen the potential coronary atherosclerosis pathogenic genes expressed though TWAS (transcriptome wide association study) and explore the underlying mechanism association.MethodsFour TWAS approaches (PrediXcan, JTI, UTMOST, and FUSION) were used to screen genes associated with coronary atherosclerosis. Enrichment analysis of TWAS-identified genes was applied through the Metascape website. The summary-data-based Mendelian randomization (SMR) analysis was conducted to provide the evidence of causal relationship between the candidate genes and coronary atherosclerosis. At last, the cell type-specific expression of the intersection genes was examined by using human coronary artery single-cell RNA-seq, interrogating the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity.ResultsWe identified 19 genes by at least three approaches and 1 gene (NBEAL1) by four approaches. Enrichment analysis enriching the genes identified at least by two TWAS approaches, suggesting that these genes were markedly enriched in asthma and leukocyte mediated immunity reaction. Further, the summary-data-based Mendelian randomization (SMR) analysis provided the evidence of causal relationship between NBEAL1 gene and coronary atherosclerosis, confirming the protecting effects of NBEAL1 gene and coronary atherosclerosis. At last, the single cell cluster analysis demonstrated that NBEAL1 gene has differential expressions in macrophages, plasma cells and endothelial cells.ConclusionOur study identified the novel genes associated with coronary atherosclerosis and suggested the potential biological function for these genes, providing insightful guidance for further biological investigation and therapeutic approaches development in atherosclerosis-related diseases

Inactivation of Medial Frontal Cortex Changes Risk Preference.

Humans and other animals need to make decisions under varying degrees of uncertainty. These decisions are strongly influenced by an individuals risk preference; however, the neuronal circuitry by which risk preference shapes choice is still unclear [1]. Supplementary eye field (SEF), an oculomotor area within primate medial frontal cortex, is thought to be an essential part of the neuronal circuit underlying oculomotor decision making, including decisions under risk [2-5]. Consistent with this view, risk-related action value and monitoring signals have been observed in SEF [6-8]. However, such activity has also been observed in other frontal areas, including orbitofrontal [9-11], cingulate [12-14], and dorsal-lateral frontal cortex [15]. It is thus unknown whether the activity in SEF causally contributes to risky decisions, or whether it is merely a reflection of neural processes in other cortical regions. Here, we tested a causal role of SEF in risky oculomotor choices. We found that SEF inactivation strongly reduced the frequency of risky choices. This reduction was largely due to a reduced attraction to reward uncertainty and high reward gain, but not due to changes in the subjective estimation of reward probability or average expected reward. Moreover, SEF inactivation also led to increased sensitivity to differences between expected and actual reward during free choice. Nevertheless, it did not affect adjustments of decisions based on reward history

Sequential selection of economic good and action in medial frontal cortex of macaques during value-based decisions.

Value-based decisions could rely either on the selection of desired economic goods or on the selection of the actions that will obtain the goods. We investigated this question by recording from the supplementary eye field (SEF) of monkeys during a gambling task that allowed us to distinguish chosen good from chosen action signals. Analysis of the individual neuron activity, as well as of the population state-space dynamic, showed that SEF encodes first the chosen gamble option (the desired economic good) and only ~100 ms later the saccade that will obtain it (the chosen action). The action selection is likely driven by inhibitory interactions between different SEF neurons. Our results suggest that during value-based decisions, the selection of economic goods precedes and guides the selection of actions. The two selection steps serve different functions and can therefore not compensate for each other, even when information guiding both processes is given simultaneously