The functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG) from infants (ages 4–11 months of age) and adults while they observed three types of actions: (a) reaching for an object; (b) walking; and (c) object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity) were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e., independent walking), suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e., reaching). These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network.
The hippocampus is a key brain region that participates in a range of cognitive and affective functions, and is involved in the etiopathogenesis of numerous neuropsychiatric disorders. The structural complexity and functional diversity of the hippocampus suggest the existence of structural and functional subdivisions within this structure. For the first time, we parcellated the human hippocampus with two independent data sets, each of which consisted of 198 T1‐weighted structural magnetic resonance imaging (sMRI) images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel‐to‐voxel linear correlation approach, as well as a multivariate masked independent component analysis approach. We subsequently interrogated the relationship between the GMV covariance patterns and the functional connectivity patterns of the hippocampal subregions using sMRI and resting‐state functional MRI (fMRI) data from the same participants. Seven distinct GMV covariance‐based subregions were identified for bilateral hippocampi, with robust reproducibility across the two data sets. We further demonstrated that the structural covariance patterns of the hippocampal subregions had a correspondence with the intrinsic functional connectivity patterns of these subregions. Together, our results provide a topographical configuration of the hippocampus with converging structural and functional support. The resulting subregions may improve our understanding of the hippocampal connectivity and functions at a subregional level, which provides useful parcellations and masks for future neuroscience and clinical research on the structural and/or functional connectivity of the hippocampus.
Analytic epidemiology is a transdisciplinary study on the cognitive, theoretical, and mathematical models of COVID-19 and other contagious diseases. It is recognized that analytic epidemiology may be better studied by big data explorations at the macro level rather than merely biological analyses at the micro level in order to not loss the forest for the trees. This paper presents a basic research on analytic epidemiology underpinned by sciences of cognition, computer, big data, information, AI, mathematics, epidemiology, and systems. It introduces a novel Causal Probability Theory (CPT) for explaining the Dynamic Pandemic Transmission Model (DPTM) of analytic epidemiology. It reveals how the fundamental reproductive rate (R0) may be rigorously calibrated based on big data of COVID-19. A theoretical framework of analytic epidemiology is developed to elaborating the insights of pandemic mechanisms in general and COVID-19 in particular. Robust and accurate predictions on key attributes of COVID-19, including R0(t), forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.