Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

The genomic estimated breeding value (GEBV) represents an animal's genetic merit calculated using a combination of phenotypes, pedigree, and genomic information through a procedure known as single-step genomic best linear unbiased prediction (ssGBLUP). The accuracy of a GEBV reflects how closely it correlates with the true breeding value. However, calculating accuracies is not computationally feasible for large datasets with genomic information. In this context, methods for approximating accuracies have been proposed and implemented into genetic evaluations. This study aimed to compare 2 algorithms to approximate accuracies for ssGBLUP. In algorithm 1, genomic contributions are based on the diagonal of the genomic relationship matrix (G), combined with contributions from animal records and pedigrees. In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data for this study were provided by the American Angus Association and included datasets of growth, carcass, and marbling traits. Genotypes were available for 1,235,930 animals, and the pedigree had 12,492,581 animals. We showed that algorithm 2 is better suited for approximating accuracies, as its approximations closely matched the exact accuracy values obtained from the inverse of the mixed model equations.

Continuous-Time Model Identification of the Subglottal System.

Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment.

Depth recordings of the mouse homologue of the Reward Positivity.

We recently advanced a rodent homologue for the reward-specific, event-related potential component observed in humans known as the Reward Positivity. We sought to determine the cortical source of this signal in mice to further test the nature of this homology. While similar reward-related cortical signals have been identified in rats, these recordings were all performed in cingulate gyrus. Given the value-dependent nature of this event, we hypothesized that more ventral prelimbic and infralimbic areas also contribute important variance to this signal. Depth probes assessed local field activity in 29 mice (15 males) while they completed multiple sessions of a probabilistic reinforcement learning task. Using a priori regions of interest, we demonstrated that the depth of recording in the cortical midline significantly correlated with the size of reward-evoked delta band spectral activity as well as the single trial correlation between delta power and reward prediction error. These findings provide important verification of the validity of this translational biomarker of reward responsiveness, learning, and valuation.

Characterization of deconditioning-update on fear memory attenuation.

Fear memory expression can be attenuated by updating the footshock perception during the plastic state induced by retrieval, from a strong unconditioned stimulus to a very weak one through deconditioning. In this process, the original fear association of the conditioned stimulus with the footshock is substituted by an innocuous stimulus and the animals no longer express a fear response. In the present study, we explore the boundaries of this deconditioning-update strategy by the characterization of this phenomenon. We found that there is an optimal mismatch between the footshock intensity delivered in the training and in the reactivation. Likewise, we characterized the temporal window that the protocol is efficient in hindering fear response. Our findings contribute to a better understanding of the limits in which deconditioning acts in attenuating fear memory, so that an optimized protocol using this strategy can be planned in order to deal with emotional disorders.

Grounding Context in Embodied Cognitive Robotics.

Biological agents are context-dependent systems that exhibit behavioral flexibility. The internal and external information agents process, their actions, and emotions are all grounded in the context within which they are situated. However, in the field of cognitive robotics, the concept of context is far from being clear with most studies making little to no reference to it. The aim of this paper is to provide an interpretation of the notion of context and its core elements based on different studies in natural agents, and how these core contextual elements have been modeled in cognitive robotics, to introduce a new hypothesis about the interactions between these contextual elements. Here, global context is categorized as agent-related, environmental, and task-related context. The interaction of their core elements, allows agents to first select self-relevant tasks depending on their current needs, or for learning and mastering their environment through exploration. Second, to perform a task and continuously monitor its performance. Third, to abandon a task in case its execution is not going as expected. Here, the monitoring of prediction error, the difference between sensorimotor predictions and incoming sensory information, is at the core of behavioral flexibility during situated action cycles. Additionally, monitoring prediction error dynamics and its comparison with the expected reduction rate should indicate the agent its overall performance on executing the task. Sensitivity to performance evokes emotions that function as the driving element for autonomous behavior which, at the same time, depends on the processing of the interacting core elements. Taking all these into account, an interactionist model of contexts and their core elements is proposed. The model is embodied, affective, and situated, by means of the processing of the agent-related and environmental core contextual elements. Additionally, it is grounded in the processing of the task-related context and the associated situated action cycles during task execution. Finally, the model proposed here aims to guide how artificial agents should process the core contextual elements of the agent-related and environmental context to give rise to the task-related context, allowing agents to autonomously select a task, its planning, execution, and monitoring for behavioral flexibility.

Temporal prediction error triggers amygdala-dependent memory updating in appetitive operant conditioning in rats.

Reinforcement learning theories postulate that prediction error, i.e., a discrepancy between the actual and expected outcomes, drives reconsolidation and new learning, inducing an updating of the initial memory. Pavlovian studies have shown that prediction error detection is a fundamental mechanism in triggering amygdala-dependent memory updating, where the temporal relationship between stimuli plays a critical role. However, in contrast to the well-established findings in aversive situations (e.g., fear conditioning), only few studies exist on prediction error in appetitive operant conditioning, and even less with regard to the role of temporal parameters. To explore if temporal prediction error in an appetitive operant paradigm could generate an updating and consequent reconsolidation and/or new learning of temporal association, we ran four experiments in adult male rats. Experiment 1 verified whether an unexpected delay in the time of reward's availability (i.e., a negative temporal prediction error) in a single session produces an updating in long-term memory of temporal expectancy in an appetitive operant conditioning. Experiment 2 showed that negative prediction errors, either due to the temporal change or through reward omission, increased in the basolateral amygdala nucleus (BLA) the activation of a protein that is critical for memory formation. Experiment 3 revealed that the presence of a protein synthesis inhibitor (anisomycin) in the BLA during the session when the reward was delayed (Error session) affected the temporal updating. Finally, Experiment 4 showed that anisomycin, when infused immediately after the Error session, interfered with the long-term memory of the temporal updating. Together, our study demonstrated an involvement of BLA after a change in temporal and reward contingencies, and in the resulting updating in long-term memory in appetitive operant conditioning.

Different dimensions of the prediction error as a decisive factor for the triggering of the reconsolidation process.

The reconsolidation process is the mechanism by which strength and/or content of consolidated memories are updated. Prediction error (PE) is the difference between the prediction made and current events. It is proposed as a necessary condition to trigger the reconsolidation process. Here we analyzed deeply the role of the PE in the associative memory reconsolidation in the crab Neohelice granulata. An incongruence between the learned temporal relationship between conditioned and unconditioned stimuli (CS-US) was enough to trigger the reconsolidation process. Moreover, after a partial reinforced training, a PE of 50% opened the possibility to labilize the consolidated memory with a reminder which included or not the US. Further, during an extinction training a small PE in the first interval between CSs was enough to trigger reconsolidation. Overall, we highlighted the relation between training history and different reactivation possibilities to recruit the process responsible of memory updating.

Dissociable patterns of abnormal frontal cortical activation during anticipation of an uncertain reward or loss in bipolar versus major depression.

OBJECTIVES: Recent research has found abnormalities in reward-related neural activation in bipolar disorder (BD), during both manic and euthymic phases. However, reward-related neural activation in currently depressed individuals with BD and that in currently depressed individuals with major depressive disorder (MDD) have yet to be directly compared. Here, we studied these groups, examining the neural activation elicited during a guessing task in fronto-striatal regions identified by previous studies. METHODS: We evaluated neural activation during a reward task using fMRI in two groups of depressed individuals, one with bipolar I disorder (BD-I) (n = 23) and one with MDD (n = 40), with similar levels of illness severity, and a group of healthy individuals (n = 37). RESULTS: Reward expectancy-related activation in the anterior cingulate cortex was observed in the healthy individuals, but was significantly reduced in depressed patients (BD-I and MDD together). Anticipation-related activation was increased in the left ventrolateral prefrontal cortex in the BD-I depressed group compared with the other two groups. There were no significant differences in prediction error-related activation in the ventral striatum across the three groups. CONCLUSIONS: The findings extend previous research which has identified dysfunction within the ventrolateral prefrontal cortex in BD, and show that abnormally elevated activity in this region during anticipation of either reward or loss may distinguish depressed individuals with BD-I from those with MDD. Altered activation of the anterior cingulate cortex during reward expectancy characterizes both types of depression. These findings have important implications for identifying both common and distinct properties of the neural circuitry underlying BD-I and MDD.