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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