Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects …
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront …
J Tao, Z Yu, R Zhang, F Gao - Applied Soft Computing, 2021 - Elsevier
Neural network prediction and data processing have been widely used in chemical industry, however, there exist many disturbance variables that will affect the system output, and …
Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject …
Background Classic psychedelics, such as psilocybin and LSD, and other serotonin 2A receptor (5-HT 2AR) agonists evoke acute alterations in perception and cognition. Altered …
There is significant interest in adopting surface-and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to perform …
P Wei, R Bao, Y Fan - Plos one, 2022 - journals.plos.org
Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets …
Group-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are …
The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or …