A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.

How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data

Introduction

Volume conduction models of the human head are used in various neuroscience fields, such as for source reconstruction in EEG and MEG, and for modeling the effects of brain stimulation. Numerous studies have quantified the accuracy and sensitivity of volume conduction models by analyzing the effects of the geometrical and electrical features of the head model, the sensor model, the source model, and the numerical method. Most studies are based on simulations as it is hard to obtain sufficiently detailed measurements to compare to models. The recording of stereotactic EEG during electric stimulation mapping provides an opportunity for such empirical validation.

Methods

In the study presented here, we used the potential distribution of volume-conducted artifacts that are due to cortical stimulation to evaluate the accuracy of finite element method (FEM) volume conduction models. We adopted a widely used strategy for numerical comparison, i.e., we fixed the geometrical description of the head model and the mathematical method to perform simulations, and we gradually altered the head models, by increasing the level of detail of the conductivity profile. We compared the simulated potentials at different levels of refinement with the measured potentials in three epilepsy patients.

Results

Our results show that increasing the level of detail of the volume conduction head model only marginally improves the accuracy of the simulated potentials when compared to in-vivo sEEG measurements. The mismatch between measured and simulated potentials is, throughout all patients and models, maximally 40 microvolts (i.e., 10% relative error) in 80% of the stimulation-recording combination pairs and it is modulated by the distance between recording and stimulating electrodes.

Discussion

Our study suggests that commonly used strategies used to validate volume conduction models based solely on simulations might give an overly optimistic idea about volume conduction model accuracy. We recommend more empirical validations to be performed to identify those factors in volume conduction models that have the highest impact on the accuracy of simulated potentials. We share the dataset to allow researchers to further investigate the mismatch between measurements and FEM models and to contribute to improving volume conduction models.

Long-Term Projections of Cancer Incidence and Mortality in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020-2054: An Empirical Validation Approach

PURPOSE

The aim of this study was to project new cancer cases/deaths forward to 2054, and decompose changes in cancer cases/deaths to assess the impact of demographic transitions on cancer burden.

METHODS

We collected data on cancer cases/deaths up to 2019, empirically validated the projection performance of multiple statistical models, and selected optimal models by applying time series cross-validation.

RESULTS

We showed an increasing number of new cancer cases but decreasing number of cancer deaths in both genders, with a large burden attributed to population aging. We observed the increasing incidence rates in most cancer sites but reducing rates in some infection-associated cancers, including stomach and liver cancers. Colorectal and lung cancers were projected to remain as leading cancer burdens of both incidence and mortality in Japan over 2020-2054, while prostate and female breast cancers would be the leading incidence burdens among men and women, respectively.

CONCLUSIONS

Findings from decomposition analysis require more supportive interventions for reducing mortality and improving the quality of life of Japanese elders. We emphasize the important role of governments and policymakers in reforming policies for controlling cancer risk factors, including oncogenic infections. The rapid increase and continued presence of those cancer burdens associated with modifiable risk factors warrant greater efforts in cancer control programs, specifically in enhancing cancer screening and controlling cancer risk factors in Japan.

Conceptual Integration and Empirical Validation of a Unified Taxonomy: Quantitative Data Analysis for Virtual Learning Environments

Specific classes of cyberspaces emphasize different types of digital transactions given the user’s context, thus making it essential to take into account what these environments can afford. In this way, we can portray the niches of technological use as ecologies of particular possibilities and compare how they differ between distinct spheres of human life. The present research is focused on describing the conceptual integration of a taxonomic crossover between Virtual Learning Environments and Educational Affordances of Technology, while also performing empirical testing and determining the psychometric properties in a scale regarding the aforementioned taxonomy. The study sample consisted of 320 students in the departments of foreign languages from three different universities in Sonora (northwestern region of Mexico). Students were given a questionnaire of 21 items organized into four subscales with a Likert-type response option to measure the notions concerning their usage of Virtual Learning Environments. Internal consistency procedures and confirmatory factor analysis by means of Cronbach’s alpha and Structural modeling support the derived factorial structure, which contains Cyber-Communications, Virtual Behavior Settings, Virtual Communities, and Availability and Access to Connectivity. This structure traces the environmental properties perceived by learners in a virtual environment. Results sustain the initial conceptual construction regarding the proposed taxonomy, conclude that the ‘Virtual Learning Environments Questionnaire’ demonstrates adequate psychometric properties, and validate it as a fitting measure to assess the perceived psychological experience of students in a digital educational setting.

Development of a Multiple-Choice Test for Novice Anesthesia Residents to Evaluate Knowledge Related to Management of General Anesthesia for Urgent Cesarean Delivery

BACKGROUND

Teaching trainees the knowledge and skills to perform general anesthesia (GA) for cesarean delivery (CD) requires innovative strategies, as they may never manage such cases in training. We used a multistage design process to create a criterion-referenced multiple-choice test as an assessment tool to evaluate CA1's knowledge related to this scenario.

METHODS

Three faculty created 33 questions, categorized as: (1) physiologic changes of pregnancy (PCP), (2) pharmacology (PHA), (3) anesthetic implications of pregnancy (AIP), and (4) crisis resource management principles (CRM). A Delphi process (3 rounds) provided content validation. In round 1, experts (n = 15) ranked questions on a 7-point Likert scale. Questions ranked ≥ 5 in importance by ≥ 70% of experts were retained. Five questions were eliminated, several were revised, and 1 added. In round 2, consensus (N = 14) was reached in all except 7 questions. In round 3 (N = 14), all questions stabilized. A pilot test of the 29-question instrument evaluating internal consistency, reliability, convergent validity, and item analysis was conducted with the July CA1 classes at our institution after a lecture on GA for CD (n = 26, "instructed group") and another institution with no lecture (n = 26, "uninstructed group"), CA2s (N = 17), and attendings (N = 10).

RESULTS

Acceptable internal consistency and reliability was demonstrated (ρ = 0.67). Convergent validity coefficients between the CA1 uninstructed and instructed group suggested theoretical meaningfulness of the 4 sub-scales: PCP correlated at 0.29 with PHA, 0.35 with CRM, and 0.25 with AIP. PHA correlated with CRM and AIP at 0.23 and 0.28, respectively. The correlation between CRM and AIP was 0.29.

CONCLUSION

The test produces moderately reliable scores to assess CA1s' knowledge related to GA for urgent CD.

Proposed Core Competencies and Empirical Validation Procedure in Competency Modeling: Confirmation and Classification

Competency models provide insight into key skills which are common to many positions in an organization. Moreover, there is a range of competencies that is used by many companies. Researchers have developed core competency terminology to underline their cross-organizational value. The article presents a theoretical model of core competencies consisting of two main higher-order competencies called performance and entrepreneurship. Each of them consists of three elements: the performance competency includes cooperation, organization of work and goal orientation, while entrepreneurship includes innovativeness, calculated risk-taking and pro-activeness. However, there is lack of empirical validation of competency concepts in organizations and this would seem crucial for obtaining reliable results from organizational research. We propose a two-step empirical validation procedure: (1) confirmation factor analysis, and (2) classification of employees. The sample consisted of 636 respondents (M = 44.5; SD = 15.1). Participants were administered a questionnaire developed for the study purpose. The reliability, measured by Cronbach's alpha, ranged from 0.60 to 0.83 for six scales. Next, we tested the model using a confirmatory factor analysis. The two separate, single models of performance and entrepreneurial orientations fit quite well to the data, while a complex model based on the two single concepts needs further research. In the classification of employees based on the two higher order competencies we obtained four main groups of employees. Their profiles relate to those found in the literature, including so-called niche finders and top performers. Some proposal for organizations is discussed.