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Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Introduction:
We aim to highlight the utility of this model in the analysis of the psycho-behavioral implications of family cancer, presenting the scientific literature that used Leventhal’s model as the theoretical framework of approach.
Material and methods:
A systematic search was performed in six databases (EBSCO, ScienceDirect, PubMed Central, ProQuest, Scopus, and Web of Science) with empirical studies published between 2006 and 2015 in English with regard to the Common Sense Model of Self-Regulation (CSMR) and familial/hereditary cancer. The key words used were: illness representations, common sense model, self regulatory model, familial/hereditary/genetic cancer, genetic cancer counseling. The selection of studies followed the PRISMA-P guidelines (Moher et al., 2009; Shamseer et al., 2015), which suggest a three-stage procedure.
Results:
Individuals create their own cognitive and emotional representation of the disease when their health is threatened, being influenced by the presence of a family history of cancer, causing them to adopt or not a salutogenetic behavior. Disease representations, particularly the cognitive ones, can be predictors of responses to health threats that determine different health behaviors. Age, family history of cancer, and worrying about the disease are factors associated with undergoing screening. No consensus has been reached as to which factors act as predictors of compliance with cancer screening programs.
Conclusions:
This model can generate interventions that are conceptually clear as well as useful in regulating the individuals’ behaviors by reducing the risk of developing the disease and by managing as favorably as possible health and/or disease.
The relevance for in vitro three-dimensional (3D) tissue culture of skin has been present for almost a century. From using skin biopsies in organ culture, to vascularized organotypic full-thickness reconstructed human skin equivalents, in vitro tissue regeneration of 3D skin has reached a golden era. However, the reconstruction of 3D skin still has room to grow and develop. The need for reproducible methodology, physiological structures and tissue architecture, and perfusable vasculature are only recently becoming a reality, though the addition of more complex structures such as glands and tactile corpuscles require advanced technologies. In this review, we will discuss the current methodology for biofabrication of 3D skin models and highlight the advantages and disadvantages of the existing systems as well as emphasize how new techniques can aid in the production of a truly physiologically relevant skin construct for preclinical innovation.