Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject''s learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.     

Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression

BackgroundAnterior cingulate cortex (ACC) and striatum are part of the emotional neural circuitry implicated in major depressive disorder (MDD). Music is often used for emotion regulation, and pleasurable music listening activates the dopaminergic system in the brain, including the ACC. The present study uses functional MRI (fMRI) and an emotional nonmusical and musical stimuli paradigm to examine how neural processing of emotionally provocative auditory stimuli is altered within the ACC and striatum in depression.MethodNineteen MDD and 20 never-depressed (ND) control participants listened to standardized positive and negative emotional musical and nonmusical stimuli during fMRI scanning and gave subjective ratings of valence and arousal following scanning.ResultsND participants exhibited greater activation to positive versus negative stimuli in ventral ACC. When compared with ND participants, MDD participants showed a different pattern of activation in ACC. In the rostral part of the ACC, ND participants showed greater activation for positive information, while MDD participants showed greater activation to negative information. In dorsal ACC, the pattern of activation distinguished between the types of stimuli, with ND participants showing greater activation to music compared to nonmusical stimuli, while MDD participants showed greater activation to nonmusical stimuli, with the greatest response to negative nonmusical stimuli. No group differences were found in striatum.ConclusionsThese results suggest that people with depression may process emotional auditory stimuli differently based on both the type of stimulation and the emotional content of that stimulation. This raises the possibility that music may be useful in retraining ACC function, potentially leading to more effective and targeted treatments.     

A Simple Mathematical Model Inspired by the Purkinje Cells: From Delayed Travelling Waves to Fractional Diffusion

Recently, several experiments have demonstrated the existence of fractional diffusion in the neuronal transmission occurring in the Purkinje cells, whose malfunctioning is known to be related to the lack of voluntary coordination and the appearance of tremors. Also, a classical mathematical feature is that (fractional) parabolic equations possess smoothing effects, in contrast with the case of hyperbolic equations, which typically exhibit shocks and discontinuities. In this paper, we show how a simple toy-model of a highly ramified structure, somehow inspired by that of the Purkinje cells, may produce a fractional diffusion via the superposition of travelling waves that solve a hyperbolic equation. This could suggest that the high ramification of the Purkinje cells might have provided an evolutionary advantage of “smoothing” the transmission of signals and avoiding shock propagations (at the price of slowing a bit such transmission). Although an experimental confirmation of the possibility of such evolutionary advantage goes well beyond the goals of this paper, we think that it is intriguing, as a mathematical counterpart, to consider the time fractional diffusion as arising from the superposition of delayed travelling waves in highly ramified transmission media. The case of a travelling concave parabola with sufficiently small curvature is explicitly computed. The new link that we propose between time fractional diffusion and hyperbolic equation also provides a novelty with respect to the usual paradigm relating time fractional diffusion with parabolic equations in the limit. This paper is written in such a way as to be of interest to both biologists and mathematician alike. In order to accomplish this aim, both complete explanations of the objects considered and detailed lists of references are provided.     

A Bayesian Model of Category-Specific Emotional Brain Responses

Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories—fear, anger, disgust, sadness, or happiness—is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.     

A Model of Sequence-Dependent Protein Diffusion Along DNA

We introduce a probabilistic model for protein sliding motion along DNA during the search of a target sequence. The model accounts for possible effects due to sequence-dependent interaction between the nonspecific DNA and the protein. Hydrogen bonds formed at the target site are used as the main sequence-dependent interaction between protein and DNA. The resulting dynamical properties and the possibility of an experimental verification are discussed in details. We show that, while at large times the process reaches a linear diffusion regime, it initially displays a sub-diffusive behavior. The sub-diffusive regime can last sufficiently long to be of biological interest.     

Effects of AKAP5 Pro100Leu Genotype on Working Memory for Emotional Stimuli

Recent investigations addressing the role of the synaptic multiadaptor molecule AKAP5 in human emotion and behavior suggest that the AKAP5 Pro100Leu polymorphism (rs2230491) contributes to individual differences in affective control. Carriers of the less common Leu allele show a higher control of anger as indicated by behavioral measures and dACC brain response on emotional distracters when compared to Pro homozygotes. In the current fMRI study we used an emotional working memory task according to the n-back scheme with neutral and negative emotional faces as target stimuli. Pro homozygotes showed a performance advantage at the behavioral level and exhibited enhanced activation of the amygdala and fusiform face area during working memory for emotional faces. On the other hand, Leu carriers exhibited increased activation of the dACC during performance of the 2-back condition. Our results suggest that AKAP5 Pro100Leu effects on emotion processing might be task-dependent with Pro homozygotes showing lower control of emotional interference, but more efficient processing of task-relevant emotional stimuli.     

Reflection of the Emotional Significance of Visual Stimuli in the Characteristics of Evoked EEG Potentials

Healthy subjects (n = 88) were asked to passively visualize positive and passive emotiogenic visual stimuli and also stimuli with a neutral emotional content. Images of the International Affective Picture System (IAPS) were used. Amplitude/time characteristics of the components of evoked EEG potentials (EPs), P1, N1, P2, N2, and P3 and topographic distribution of the latter components were analyzed. The latencies, amplitudes, and topography of the EP waves induced by presentation of positive and negative stimuli were found to be different from the respective values for the EPs induced by neutral stimuli. The level and pattern of these differences typical of different EP components were dissimilar and depended on the sign of the emotions. Specificities related to the valency of an identified stimulus were observed within nearly all stages of processing of visual signals, for the negative stimuli, beginning from an early stage of sensory analysis corresponding to the development of wave Р1. The latencies of components Р1 in the case of presentation of emotiogenic negative stimuli and those of components N1, N2, and Р3 in the case of presentation of the stimuli of both valencies were shorter than the latencies observed at neutral stimuli. The amplitude of component N2 at perception of positive stimuli was, on average, lower, while the Р3 amplitude at perception of all emotiogenic stimuli was higher than in the case of presentation of neutral stimuli. The time dynamics of topographic peculiarities of processing of emotiogenic information were complicated. Activation of the left hemisphere was observed during the earliest stages of perception, while the right hemisphere was activated within the intermediate stages. Generalized activation of the cortex after the action of negative signals and dominance of the left hemisphere under conditions of presentation of positive stimuli were observed only within the final stages. As is supposed, emotiogenic stimuli possess a greater biological significance than neutral ones, and this is why the former attract visual attention first; they more intensely activate the respective cortical zones, and the corresponding visual information is processed more rapidly. The observed effects were more clearly expressed in the case of action of negative stimuli; these effects involved more extensive cortical zones. These facts are indicative of the higher intensity of activating influences of negative emotiogenic stimuli on neutral systems of the higher CNS structures.     

A Combinatorial Model of Malware Diffusion via Bluetooth Connections

We outline here the mathematical expression of a diffusion model for cellphones malware transmitted through Bluetooth channels. In particular, we provide the deterministic formula underlying the proposed infection model, in its equivalent recursive (simple but computationally heavy) and closed form (more complex but efficiently computable) expression.     

Personality,Attentional Biases towards Emotional Faces and Symptoms of Mental Disorders in an Adolescent Sample

Objective

To investigate the role of personality factors and attentional biases towards emotional faces, in establishing concurrent and prospective risk for mental disorder diagnosis in adolescence.

Method

Data were obtained as part of the IMAGEN study, conducted across 8 European sites, with a community sample of 2257 adolescents. At 14 years, participants completed an emotional variant of the dot-probe task, as well two personality measures, namely the Substance Use Risk Profile Scale and the revised NEO Personality Inventory. At 14 and 16 years, participants and their parents were interviewed to determine symptoms of mental disorders.

Results

Personality traits were general and specific risk indicators for mental disorders at 14 years. Increased specificity was obtained when investigating the likelihood of mental disorders over a 2-year period, with the Substance Use Risk Profile Scale showing incremental validity over the NEO Personality Inventory. Attentional biases to emotional faces did not characterise or predict mental disorders examined in the current sample.

Discussion

Personality traits can indicate concurrent and prospective risk for mental disorders in a community youth sample, and identify at-risk youth beyond the impact of baseline symptoms. This study does not support the hypothesis that attentional biases mediate the relationship between personality and psychopathology in a community sample. Task and sample characteristics that contribute to differing results among studies are discussed.     

一个关于害虫管理的时滞脉冲生态传染病模型

本文建立了一个通过脉冲投放含有感染病毒的染病者的阶段结构SI传染病模型。并且作出了相应的数学和生物的研究．获得了当脉冲释放率大于一个关键值μ^＊时全局吸引的害虫根除的周期解．当易感害虫存在时,可以通过染病者的脉冲释放量把易感害虫控制在经济危害水平（EIL）以下．     

A Gamma-Knife-Enabled Mouse Model of Cerebral Single-Hemisphere Delayed Radiation Necrosis

Purpose

To develop a Gamma Knife-based mouse model of late time-to-onset, cerebral radiation necrosis (RN) with serial evaluation by magnetic resonance imaging (MRI) and histology.

Methods and Materials

Mice were irradiated with the Leksell Gamma Knife^® (GK) Perfexion^TM (Elekta AB; Stockholm, Sweden) with total single-hemispheric radiation doses (TRD) of 45- to 60-Gy, delivered in one to three fractions. RN was measured using T2-weighted MR images, while confirmation of tissue damage was assessed histologically by hematoxylin & eosin, trichrome, and PTAH staining.

Results

MRI measurements demonstrate that TRD is a more important determinant of both time-to-onset and progression of RN than fractionation. The development of RN is significantly slower in mice irradiated with 45-Gy than 50- or 60-Gy, where RN development is similar. Irradiated mouse brains demonstrate all of the pathologic features observed clinically in patients with confirmed RN. A semi-quantitative (0 to 3) histologic grading system, capturing both the extent and severity of injury, is described and illustrated. Tissue damage, as assessed by a histologic score, correlates well with total necrotic volume measured by MRI (correlation coefficient = 0.948, with p<0.0001), and with post-irradiation time (correlation coefficient = 0.508, with p<0.0001).

Conclusions

Following GK irradiation, mice develop late time-to-onset cerebral RN histology mirroring clinical observations. MR imaging provides reliable quantification of the necrotic volume that correlates well with histologic score. This mouse model of RN will provide a platform for mechanism of action studies, the identification of imaging biomarkers of RN, and the development of clinical studies for improved mitigation and neuroprotection.     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread

Gliomas are primary brain tumours arising from the glial cells of the nervous system. The diffuse nature of spread, coupled with proximity to critical brain structures, makes treatment a challenge. Pathological analysis confirms that the extent of glioma spread exceeds the extent of the grossly visible mass, seen on conventional magnetic resonance imaging (MRI) scans. Gliomas show faster spread along white matter tracts than in grey matter, leading to irregular patterns of spread. We propose a mathematical model based on Diffusion Tensor Imaging, a new MRI imaging technique that offers a methodology to delineate the major white matter tracts in the brain. We apply the anisotropic diffusion model of Painter and Hillen (J Thoer Biol 323:25–39, 2013) to data from 10 patients with gliomas. Moreover, we compare the anisotropic model to the state-of-the-art Proliferation–Infiltration (PI) model of Swanson et al. (Cell Prolif 33:317–329, 2000). We find that the anisotropic model offers a slight improvement over the standard PI model. For tumours with low anisotropy, the predictions of the two models are virtually identical, but for patients whose tumours show higher anisotropy, the results differ. We also suggest using the data from the contralateral hemisphere to further improve the model fit. Finally, we discuss the potential use of this model in clinical treatment planning.     

A Delayed Nonlinear PBPK Model for Genistein Dosimetry in Rats

Genistein is an endocrine-active compound (EAC) found in soy products. It has been linked to beneficial effects such as mammary tumor growth suppression and adverse endocrine-related effects such as reduced birth weight in rats and humans. In its conjugated form, genistein is excreted in the bile, which is a significant factor in its pharmacokinetics. Experimental data suggest that genistein induces a concentration-dependent suppression of biliary excretion. In this article, we describe a physiologically based pharmacokinetic (PBPK) model that focuses on biliary excretion with the goal of accurately simulating the observed suppression. The mathematical model is a system of nonlinear differential equations with state-dependent delay to describe biliary excretion. The model was analyzed to examine local existence and uniqueness of a solution to the equations. Furthermore, unknown parameters were estimated, and the mathematical model was compared against published experimental data. This research was supported by the American Chemistry Council (formerly the Chemical Manufacturers Association, CMA Agreement Reference Number 9121).     

Delayed Feedback Model of Axonal Length Sensing

A fundamental question in cell biology is how the sizes of cells and organelles are regulated at various stages of development. Size homeostasis is particularly challenging for neurons, whose axons can extend from hundreds of microns to meters (in humans). Recently, a molecular-motor-based mechanism for axonal length sensing has been proposed, in which axonal length is encoded by the frequency of an oscillating retrograde signal. In this article, we develop a mathematical model of this length-sensing mechanism in which advection-diffusion equations for bidirectional motor transport are coupled to a chemical signaling network. We show that chemical oscillations emerge due to delayed negative feedback via a Hopf bifurcation, resulting in a frequency that is a monotonically decreasing function of axonal length. Knockdown of either kinesin or dynein causes an increase in the oscillation frequency, suggesting that the length-sensing mechanism would produce longer axons, which is consistent with experimental findings. One major prediction of the model is that fluctuations in the transport of molecular motors lead to a reduction in the reliability of the frequency-encoding mechanism for long axons.