Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-10-09 Cooperative journals: 《心理科学进展》
Abstract： Spatial navigation is an essential aspect of daily life that exhibits significant individual differences. The decline in spatial navigation is considered a critical early behavioral manifestation of various brain disorders, particularly Alzheimer's disease (AD). However, the biological and environmental origins of such differences remain poorly defined. In this study, we conducted a multi-scale review of the latest research on spatial navigation to explore the formation mechanisms of individual differences.We summarized the multi-level individual differences in spatial navigation from a measurement perspective, including personal long-term experience or learning in real environments, virtual reality technology, and online games and big data. We then reviewed and discussed the formation mechanisms from both genetic and environmental factors. In terms of genetic factors, we found that the heritability of spatial ability was approximately 60%. Several candidate genes, including Bcl-2, S100B, and APOE and a few other genes, were proposed to affect spatial navigation behaviors. The mechanism of action studies gradually shifted from the biological perspective to the brain mechanism perspective. The hippocampus, retrosplenial cortex (RSC), and parahippocampal place area (PPA) were identified as important brain regions where genetic factors act on spatial navigation. However, the complete neurogenetic pathway model has not been established yet.Regarding environmental exposure, cultural background, living environment, early life experience, navigation software use, and lifestyle were found to shape individuals' spatial navigation ability. However, the environmental associations were relatively superficial. Related studies mostly focused on the structure and function of the hippocampus, and further investigation of its mechanism of action, particularly the brain mechanism, is still lacking.To overcome these limitations, we propose a gene/environment-brain-behavior model to map the links between genetic and environmental factors and individual differences in spatial navigation. Future research could be developed in three directions. Firstly, genome-wide association studies (GWAS) can be used to comprehensively reveal the key genetic variations influencing spatial navigation ability. Bioinformatics methods, such as polygenic score or polygenic risk score, genetic correlation, and enrichment analysis, can explore the key pathways of related genetic factors. Secondly, gene-environment interaction studies can reveal the complex pathways among genetics, environment, cognition, and behavior, and big data can help make it possible. Finally, brain imaging genetics research can correlate genetics, brain imaging, cognition, and behavior. Through international multicenter collaborations and cohort databases, spatial navigation-related imaging metrics can be correlated with multimodal genetic information to comprehensively reveal key genes and genetic mechanisms affecting brain networks of spatial navigation.In conclusion, integrative analysis of multi-omics and clinical data would be promising for future studies concerning the complex pathways of spatial navigation. Results will help us understand the development patterns of spatial navigation and further explore the potential clinical applications relevant to brain diseases. Key words
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： The system for representing space/places is one of the core knowledge systems in the human brain. Spatial navigation is also emerging as a potential cost-effective cognitive biomarker to detect Alzheimer’s disease (AD) in the preclinical stages. Existing studies have revealed multiple regions across the brain that are specific for different cognitive components of spatial navigation, such as the scene selective areas located in the parahippocampal gyrus and the retrosplenial cortex. Recently, it has been suggested that a non-aggregate network process involving multiple interacting brain regions could better characterize the neural basis of spatial navigation. But little is known about how these regions work together as a network (referred to as navigation network) to support flexible navigation behaviors. This work presents a conceptual framework for research to explore the brain network basis of spatial navigation. Various cutting-edge techniques including multimodal brain imaging, brain network modeling, big data analysis and brain stimulation are integrated for this purpose. Accordingly, three different studies are described as following.Study 1 focuses on localization, modeling and analyses of the network for spatial navigation. Specifically, a large-scale neuroimaging meta-analysis which integrates thousands of functional activations in relevant tasks will be used to localize brain regions important for spatial navigation. Then, multimodal brain networks will be modeled based on data of different imaging modalities, including structural magnetic resonance imaging (MRI), diffusion MRI, and resting-state and task functional MRI. Next, graph-theorical techniques will be used to investigate the topological properties such as modularity and hub distribution of the networks. We are also interested in the interactions of these different networks. Study 2 aims to identify influencing factors of spatial navigation. Using a public database of large samples (e.g., UK Biobank), association analyses will be conducted between the network properties and a large number of genetic and environmental variables including early life experience factors (e.g., birthplace, family economic status, and home street layouts) and genetic variants. Integrative approaches including multivariate analysis (e.g., partial least squares, PLS), genome-wide association analysis (GWAS) and genetic functional analysis will be applied. Genetic-environmental interactions will also be investigated. Based on results of this project, in Study 3 we focus on the stimulation of the navigation network. We are particularly interested in the hub regions within this network. Specifically, direct electrical stimulation and stereoelectroencephalography (SEEG) will be combined. This work plan to recruit a group of patients with medically refractory epilepsy who has undergone SEEG electrode implantation due to clinical needs. Stimulation targets will be determined according to the network modeling results of the present project and previous studies (e.g., hippocampus and retrosplenial cortex). With SEEG and direct electrical stimulation, we will explore the stimulation effects of different targeting regions on navigation network and behavioral performance. Effects of various stimulation parameters will also be explored. In sum, this project integrates interdisciplinary research techniques and computational methods, and aims to establish a brain network model for spatial navigation, reveal important factors that influence the development of this network, and explore the underlying mechanisms of such influences. Results of the present work would provide new insights into understanding the neural network basis of spatial navigation in humans. The established network model and the potential stimulation mechanisms would provide new data and perspectives for studying brain disorders of cognitive impairments such as AD, and help develop new methods for early diagnosis and precise treatment of relevant disorders. In addition, results of this project would spur new theoretical thinking for study of spatial navigation as well as other cognitive functions, which in turn would facilitate new research questions and hypotheses on human cognition and relevant disorders.
Subjects: Psychology >> Statistics in Psychology submitted time 2018-05-08
Abstract： Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0 and the H1, it is not “violently biased” against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers’ statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open.
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