Cognitive and Computational Neuroscience
Quantifying Mindfulness Based on Functional Brain Network
Mindfulness meditation, which is defined as nonjudgmental observation of one's current experiences such as emotion, thoughts and sensations inside and around them, is a key practice to promote our human well-being. However, it is difficult for novices to evaluate how correctly their meditation is performed. To overcome this issue, we aim to characterize the brain state during meditation based on fMRI data. Recent studies have revealed that meditation could affect brain plasticity. Furthermore, the brain activation and deactivation patterns have been found to differ between different meditation styles. However, the functional network structures of the various meditation styles have not yet been established because of the heterogeneity of conditions across studies or the diversity of meditation practices. Here we propose a novel data-driven approach to find the specific functional network organization associated with meditation using an evolutionary optimization algorithm. Moreover, the network structure derived by our method is used to quantify how well the practitioners can meditate.
Topological data analysis on neuroimaging data
Most of cognitive neuscience studies have mainly taken the hypothesis-driven approach, however, neuroimaging data is extremely high-dimensional, that is why the data-driven approach would be effective. Topological data analysis (TDA), which visualize and analyze features of dataset focusing on the 'shape' of the dataset by representing it in forms of networks or graphs, is recently applied to various fields of data analysis. We apply TDA framework to cognitive neuscience study instead of using hypothesis testing to enable data-driven knowledge discovery. The network topology of the neuroimaging dataset and its relations with the behavioral and psychological metrics will give new insights on human cognition and behavior. We also aim to develop new TDA method to emphasize them.
Development of Mindful Driving System
Distracted driving is a major cause of traffic accidents and injuries. We aim to promote 'mindful driving' by detecting and quantifying the degree of driver's distraction while driving, using a multimodal measurement of human behavior (e.g., operations of steering wheel, acceleration and deceleration pedals) and biological information including brain activity, heartbeat and so on. The prediction model for driver's distraction are modeled through machine learning on these multimodal data and then implemented on the vehicle.