Frontiers in Neuroscience

Publications in this journal

• Article: Brain control of olfaction via top-down regulation on adult neurogenesis

  Livio Oboti, Jean-Claude Platel
  Frontiers in Neuroscience 08/2012;
• Article: Iowa Gambling Task: There is more to consider than long-term outcome. Using a linear equation model to disentangle the impact of outcome and frequency of gains and losses.

  Horstmann A, Villringer A, Neumann J
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  ABSTRACT: The Iowa Gambling Task (IGT) has been widely used to assess differences in decision-making under uncertainty. Recently, several studies have shown that healthy subjects do not meet the basic predictions of the task (i.e. prefer options with positive long-term outcome), hence questioning its basic assumptions. Since choice options are characterized by gain and net loss frequency in addition to long-term outcome, we hypothesized that a combination of features rather than a single feature would influence participants’ choices. Offering an alternative way of modeling IGT data, we propose to use a system of linear equations to estimate weights that quantify the influence of each individual feature on decision-making in the IGT. With our proposed model it is possible to disentangle and quantify the impact of each of these features. Results from 119 healthy young subjects suggest that choice behavior is predominantly influenced by gain and loss frequency. Subjects preferred choices associated with high frequency gains to those with low frequency gains, regardless of long-term outcome. However, among options with low frequency gains, subjects learned to distinguish between choices that led to advantageous and disadvantageous long-term consequences. This is reflected in the relationship between the weights for gain frequency (highest), loss frequency (intermediate) and long-term outcome (lowest). Further, cluster analysis of estimated feature weights revealed subgroups of participants with distinct weight patterns and associated advantageous decision behavior. However, subjects in general do not learn to solely base their preference for particular decks on expected long-term outcome. Consequently, long-term outcome alone is not able to drive choice behavior on the IGT. In sum, our model facilitates a more focused conclusion about the factors guiding decision-making in the IGT. In addition, differences between clinical groups can be assessed for each factor individually.
  Frontiers in Neuroscience 04/2012;
• Article: Molecular Coevolution of Neuropeptides Gonadotropin-Releasing Hormone and Kisspeptin with their Cognate G Protein-Coupled Receptors.

  Dong-Kyu Kim, Eun Bee Cho, Mi Jin Moon, Sumi Park, Jong-Ik Hwang, Jean-Luc Do Rego, Hubert Vaudry, Jae Young Seong
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  ABSTRACT: The neuropeptides gonadotropin-releasing hormone (GnRH) and kisspeptin (KiSS), and their receptors gonadotropin-releasing hormone receptor (GnRHR) and kisspeptin receptor (KiSSR) play key roles in vertebrate reproduction. Multiple paralogous isoforms of these genes have been identified in various vertebrate species. Two rounds of genome duplication in early vertebrates likely contributed to the generation of these paralogous genes. Genome synteny and phylogenetic analyses in a variety of vertebrate species have provided insights into the evolutionary origin of and relationship between paralogous genes. The paralogous forms of these neuropeptides and their receptors have coevolved to retain high selectivity of the ligand-receptor interaction. These paralogous forms have become subfunctionalized, neofunctionalized, or dysfunctionalized during evolution. This article reviews the evolutionary mechanism of GnRH/GnRHR and KiSS/KiSSR, and the fate of the duplicated paralogs in vertebrates.
  Frontiers in Neuroscience 01/2012; 6:3.
• Article: Unsupervised adaptation of brain-machine interface decoders.

  Tayfun Gürel, Carsten Mehring
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  ABSTRACT: The performance of neural decoders can degrade over time due to non-stationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI) require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator) instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i) calibration phases interrupt the autonomous operation of the BMI and (ii) between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e., without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories toward targets at random orientations on a 2-dimensional computer screen. For initially unknown and non-stationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error-signals instead or in conjunction with the proposed unsupervised adaptation.
  Frontiers in Neuroscience 01/2012; 6:164.
• Source

  Available from: PubMed Central

  Article: Role of a redox-based methylation switch in mRNA life cycle (pre- and post-transcriptional maturation) and protein turnover: implications in neurological disorders.

  Malav S Trivedi, Richard C Deth
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  ABSTRACT: Homeostatic synaptic scaling in response to neuronal stimulus or activation, and due to changes in cellular niche, is an important phenomenon for memory consolidation, retrieval, and other similar cognitive functions (Turrigiano and Nelson, 2004). Neurological disorders and cognitive disabilities in autism, Rett syndrome, schizophrenia, dementia, etc., are strongly correlated to alterations in protein expression (both synaptic and cytoplasmic; Cajigas et al., 2010). This correlation suggests that efficient temporal regulation of synaptic protein expression is important for synaptic plasticity. In addition, equilibrium between mRNA processing, protein translation, and protein turnover is a critical sensor/trigger for recording synaptic information, normal cognition, and behavior (Cajigas et al., 2010). Thus a regulatory switch, which controls the lifespan, maturation, and processing of mRNA, might influence cognition and adaptive behavior. Here, we propose a two part novel hypothesis that methylation might act as this suggested coordinating switch to critically regulate mRNA maturation at (1) the pre-transcription level, by regulating precursor-RNA processing into mRNA, via other non-coding RNAs and their influence on splicing phenomenon, and (2) the post-transcription level by modulating the regulatory functions of ribonucleoproteins and RNA binding proteins in mRNA translation, dendritic translocation as well as protein synthesis and synaptic turnover. DNA methylation changes are well recognized and highly correlated to gene expression levels as well as, learning and memory; however, RNA methylation changes are recently characterized and yet their functional implications are not established. This review article provides some insight on the intriguing consequences of changes in methylation levels on mRNA life-cycle. We also suggest that, since methylation is under the control of glutathione anti-oxidant levels (Lertratanangkoon et al., 1997), the redox status of neurons might be the central regulatory switch for methylation-based changes in mRNA processing, protein expression, and turnover. Lastly, we also describe experimental methods and techniques which might help researchers to evaluate the suggested hypothesis.
  Frontiers in Neuroscience 01/2012; 6:92.
• Source

  Available from: PubMed Central

  Article: Experience and abstract reasoning in learning backward induction.

  Daniel R Hawes, Alexander Vostroknutov, Aldo Rustichini
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  ABSTRACT: Backward induction is a benchmark of game theoretic rationality, yet surprisingly little is known as to how humans discover and initially learn to apply this abstract solution concept in experimental settings. We use behavioral and functional magnetic resonance imaging (fMRI) data to study the way in which subjects playing in a sequential game of perfect information learn the optimal backward induction strategy for the game. Experimental data from our two studies support two main findings: First, subjects converge to a common process of recursive inference similar to the backward induction procedure for solving the game. The process is recursive because earlier insights and conclusions are used as inputs in later steps of the inference. This process is matched by a similar pattern in brain activation, which also proceeds backward, following the prediction error: brain activity initially codes the responses to losses in final positions; in later trials this activity shifts to the starting position. Second, the learning process is not exclusively cognitive, but instead combines experience-based learning and abstract reasoning. Critical experiences leading to the adoption of an improved solution strategy appear to be stimulated by brain activity in the reward system. This indicates that the negative affect induced by initial failures facilitates the switch to a different method of solving the problem. Abstract reasoning is combined with this response, and is expressed by activation in the ventrolateral prefrontal cortex. Differences in brain activation match differences in performance between subjects who show different learning speeds.
  Frontiers in Neuroscience 01/2012; 6:23.