scholarly journals A neuronal prospect theory model in the brain reward circuitry

2021 ◽  
Author(s):  
Yuri Imaizumi ◽  
Agnieszka Tymula ◽  
Yasuhiro Tsubo ◽  
Masayuki Matsumoto ◽  
Hiroshi Yamada
Keyword(s):  
Prospect Theory ◽  
Neural Coding ◽  
Theory Model ◽  
Neuron Activity ◽  
Reward Circuitry ◽  
Subcortical Regions ◽  
The Brain ◽  
Human Neuroimaging ◽  
Theory Framework ◽  
Theoretical Accuracy

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, reflects prospect theory remains unknown. Here, we show with theoretical accuracy equivalent to that of human neuroimaging studies that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards, parameterized as a multiplicative combination of a utility and probability weighting functions in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reliably reconstructed risk preferences and subjective probability perceptions revealed by the animals' choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

scholarly journals Synergistic Effect of Several Neurotransmitters in PFC-NAc-VTA Neural Circuit for the Anti-Depression Effect of Shuganheweitang in a Chronic Unpredictable Mild Stress Model

2021 ◽  
Vol 16 (3) ◽  
pp. 1934578X2110024
Author(s):  
Xin Chen ◽  
Yuanchun Ma ◽  
Xiongjun Mou ◽  
Hao Liu ◽  
Hao Ming ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Neural Circuit ◽  
Concentration Level ◽  
Brain Regions ◽  
Mild Stress ◽  
Depression Effect ◽  
Monoamine Neurotransmitters ◽  
Reward Circuitry ◽  
High Doses ◽  
The Brain

Depression, a major worldwide mental disorder, leads to massive disability and can result in death. The PFC-NAc-VTA neuro circuit is related to emotional, neurovegetative, and cognitive functions, which emerge as a circuit-level framework for understanding reward deficits in depression. Neurotransmitters, which are widely distributed in different brain regions, are important detected targets for the evaluation of depression. Shuganheweitang (SGHWT) is a popular prescription in clinical therapy for depression. In order to investigate its possible pharmacodynamics and anti-depressive mechanism, the complex plant material was separated into different fractions. These in low and high doses, along with low and high doses of SGHWT were tested in animal behavior tests. The low and high doses of SGHWT were more effective than the various fractions, which indicate the importance of synergistic function in traditional Chinese medicine. Furthermore, amino acid (GABA, Glu) and monoamine neurotransmitters (DA, 5-HT, NA, 5-HIAA) in the PFC-NAc-VTA neuro circuit were investigated by UPLC-MS/MS. The level trend of DA and 5-HT were consistent in the PFC-NAc-VTA neuro circuit, whereas 5-HIAA was decreased in the PFC, Glu was decreased in the PFC and VTA, and NA and GABA were decreased in the NAc. The results indicate that the pathogenesis of depression is associated with dysfunction of the PFC-NAc-VTA neural circuit, mainly through the neural projection effects of neurotransmitters associated with various brain regions in the neural circuit. PCA and OPLS-DA score plots demonstrated the similarities of individuals within each group and the differences among the groups. In this study, SGHWT could regulate the concentration level of different neurotransmitters in the PFC-NAc-VTA neuro circuit to improve the depression, which benefitted from the recognition of the brain reward circuitry in mood disorders.

scholarly journals HIV Infection Is Associated with Attenuated Frontostriatal Intrinsic Connectivity: A Preliminary Study

2015 ◽  
Vol 21 (3) ◽  
pp. 203-213 ◽  
Author(s):  
Jonathan C. Ipser ◽  
Gregory G. Brown ◽  
Amanda Bischoff-Grethe ◽  
Colm G. Connolly ◽  
Ronald J. Ellis ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Regions ◽  
Group Differences ◽  
Hiv Rna ◽  
Functional Connections ◽  
Intrinsic Connectivity ◽  
Dorsolateral Prefrontal ◽  
Independent Replication ◽  
Subcortical Regions ◽  
The Brain

AbstractHIV-associated cognitive impairments are prevalent, and are consistent with injury to both frontal cortical and subcortical regions of the brain. The current study aimed to assess the association of HIV infection with functional connections within the frontostriatal network, circuitry hypothesized to be highly vulnerable to HIV infection. Fifteen HIV-positive and 15 demographically matched control participants underwent 6 min of resting-state functional magnetic resonance imaging (RS-fMRI). Multivariate group comparisons of age-adjusted estimates of connectivity within the frontostriatal network were derived from BOLD data for dorsolateral prefrontal cortex (DLPFC), dorsal caudate and mediodorsal thalamic regions of interest. Whole-brain comparisons of group differences in frontostriatal connectivity were conducted, as were pairwise tests of connectivity associations with measures of global cognitive functioning and clinical and immunological characteristics (nadir and current CD4 count, duration of HIV infection, plasma HIV RNA). HIV – associated reductions in connectivity were observed between the DLPFC and the dorsal caudate, particularly in younger participants (<50 years, N=9). Seropositive participants also demonstrated reductions in dorsal caudate connectivity to frontal and parietal brain regions previously demonstrated to be functionally connected to the DLPFC. Cognitive impairment, but none of the assessed clinical/immunological variables, was also associated with reduced frontostriatal connectivity. In conclusion, our data indicate that HIV is associated with attenuated intrinsic frontostriatal connectivity. Intrinsic connectivity of this network may therefore serve as a marker of the deleterious effects of HIV infection on the brain, possibly via HIV-associated dopaminergic abnormalities. These findings warrant independent replication in larger studies. (JINS, 2015, 21, 1–11)

Beyond the Reward Pathway: Coding Reward Magnitude and Error in the Rat Subthalamic Nucleus

2009 ◽  
Vol 102 (4) ◽  
pp. 2526-2537 ◽  
Author(s):  
Sylvie Lardeux ◽  
Remy Pernaud ◽  
Dany Paleressompoulle ◽  
Christelle Baunez
Keyword(s):  
Subthalamic Nucleus ◽  
Firing Pattern ◽  
Sucrose Solution ◽  
Critical Role ◽  
The Other ◽  
Reward Circuitry ◽  
Reward Delivery ◽  
Reward Pathway ◽  
Subthalamic Neurons ◽  
The Brain

It was recently shown that subthalamic nucleus (STN) lesions affect motivation for food, cocaine, and alcohol, differentially, according to either the nature of the reward or the preference for it. The STN may thus code a reward according to its value. Here, we investigated how the firing of subthalamic neurons is modulated during expectation of a predicted reward between two possibilities (4 or 32% sucrose solution). The firing pattern of neurons responding to predictive cues and to reward delivery indicates that STN neurons can be divided into subpopulations responding specifically to one reward and less or giving no response to the other. In addition, some neurons (“oops” neurons) specifically encode errors as they respond only during error trials. These results reveal that the STN plays a critical role in ascertaining the value of the reward and seems to encode that value differently depending on the magnitude of the reward. These data highlight the importance of the STN in the reward circuitry of the brain.

Finite Difference Time Domain Simulations of Hybrid Neurotransducers Based Optical Microlasers

2020 ◽  
Author(s):  
Maurizio Manzo ◽  
Omar Cavazos
Keyword(s):  
Finite Difference ◽  
Chronic Traumatic Encephalopathy ◽  
Numerical Technique ◽  
Well Being ◽  
Infrared Light ◽  
Neuron Activity ◽  
Living Matter ◽  
Brain Functions ◽  
Laser Modes ◽  
The Brain

Abstract Different pathologies such as Alzheimer’s, Parkinson’s, Wilson’s diseases, and chronic traumatic encephalopathy due to blasts and impacts affect the brain functions altering the neuronal electrical activity. An important aspect of the brain study is the use of non-invasive, non-surgical methodologies that are suitable to the well-being of the patients. Only a portion of the electromagnetic field can be detected by applying sensors outside the scalp; in addition, surgery is often involved if sensors are applied in the subcutaneous region of the skull. Optical techniques applied to biomedical research and diagnostics have been spread during the last decades. For example, near infrared light (NIR) of spectral range goes from 800 nm to 1300 nm, it is harmless radiation for the living tissue, and can penetrate the living matter in depth as, it turns out that most of the living matter is transparent to the NIR light. Optical microlasers have been recently proposed as neurotransducers for minimally invasive neuron activity detection for the next generation of brain-computer interface (BCI) systems. They are lightweight, require low power consumption and exhibit low latency. This novel sensor that can be made of biocompatible material is coupled with a voltage sensitive dye; the fluorescence of the dye, which is excited by an external light source, is used to generate optical (laser) modes. Any variation in the neurons’ membrane electric potential via evanescent field’s perturbation turn affect the shifting of these laser modes. In order to reduce the energy required to power these devices and to improve their optical emission, metal nanoparticles can be coupled in order to use their plasmonic effect. In this paper, finite-difference timedomain (FDTD) numerical technique is used to analyze the performances on a dye-doped microlaser. Purcell effect and resonant wavelengths are observed.

scholarly journals Dynamic prospect theory - two core economic decision theories coexist in the gambling behavior of monkeys

2021 ◽  
Author(s):  
Agnieszka Tymula ◽  
Yuri Imaizumi ◽  
Takashi Kawai ◽  
Jun Kunimatsu ◽  
Masayuki Matsumoto ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prospect Theory ◽  
Weighting Function ◽  
Gambling Behavior ◽  
Theory Model ◽  
Probability Weighting ◽  
Choice Data ◽  
Mathematical Functions ◽  
Weighting Functions ◽  
Dynamic Function

Research in behavioral economics and reinforcement learning has given rise to two influential theories describing human economic choice under uncertainty. The first, prospect theory, assumes that decision-makers use static mathematical functions, utility and probability weighting, to calculate the values of alternatives. The second, reinforcement learning theory, posits that dynamic mathematical functions update the values of alternatives based on experience through reward prediction error (RPE). To date, these theories have been examined in isolation without reference to one another. Therefore, it remains unclear whether RPE affects a decision-maker's utility and/or probability weighting functions, or whether these functions are indeed static as in prospect theory. Here, we propose a dynamic prospect theory model that combines prospect theory and RPE, and test this combined model using choice data on gambling behavior of captive macaques. We found that under standard prospect theory, monkeys, like humans, had a concave utility function. Unlike humans, monkeys exhibited a concave, rather than inverse-S shaped, probability weighting function. Our dynamic prospect theory model revealed that probability distortions, not the utility of rewards, solely and systematically varied with RPE: after a positive RPE, the estimated probability weighting functions became more concave, suggesting more optimistic belief about receiving rewards and over-weighted subjective probabilities at all probability levels. Thus, the probability perceptions in laboratory monkeys are not static even after extensive training, and are governed by a dynamic function well captured by the algorithmic feature of reinforcement learning. This novel evidence supports combining these two major theories to capture choice behavior under uncertainty.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Leveling up: improving power in fMRI by moving beyond cluster-level inference

2021 ◽  
Author(s):  
Stephanie Noble ◽  
Mandy Mejia ◽  
Andrew Zalesky ◽  
Dustin Scheinost
Keyword(s):  
Average Effect ◽  
Brain Areas ◽  
Whole Brain ◽  
Typical Sample ◽  
Human Connectome Project ◽  
Inference Network ◽  
The Brain ◽  
Cluster Level ◽  
Human Neuroimaging ◽  
Adequate Power

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power (β =80%) to detect an average effect, with almost double the power for network- compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

scholarly journals Mathematical Modelling of Nitric Oxide Release Caused by Exocytosis and Determination of a Stellate Neuron Activity Function in Rat Brain

2007 ◽  
Vol 12 (3) ◽  
pp. 399-408 ◽  
Author(s):  
A. Oleinick ◽  
C. Amatore ◽  
O. Klymenko ◽  
I. Svir
Keyword(s):  
Mathematical Modelling ◽  
Neuron Activity ◽  
Nitric Oxide Release ◽  
Numerical Fitting ◽  
No Release ◽  
Initial Hypothesis ◽  
Gaussian Decomposition ◽  
The Brain ◽  
Theoretical Results

In this work we report the results of the mathematical modelling of NO◦ -release by neurons considering a series of Gaussian bursts, together with its transport in the brain by diffusion. Our analysis relies on the NO◦ -release from a neuron monitored before, during and after its patch-clamp stimulation as detected by an ultramicroelectrode introduced into a slice of living rat’s brain. The parameters of the neuron activity function have been obtained by numerical fitting of experimental data with simulated theoretical results. Within our initial hypothesis about the Gaussian decomposition of NO◦ -release that allowed drawing qualitative and quantitative conclusions about the considered neuron activity function. It is noted that since the activity function can be readily modified this signal processing may be adapted to the treatment of other and maybe more physiologically relevant hypotheses.

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