Hyperscanning 2.0 Analyses of Multimodal Neuroimag.. (Hyperscanning 2.0)
Hyperscanning 2.0 Analyses of Multimodal Neuroimaging Data: Concept, Methods and Applications
Date du début: 1 juil. 2014,
Date de fin: 31 déc. 2016
"Classical ""Hyperscanning"" is concerned with determining brain interactions of two subjects by analyzing correlations of their brain signals. We here define ""Hyperscanning 2.0"" as a broader concept, which refers to the analysis of (arbitrarily) coupled brain activity in multiple datasets that are temporally synchronized. With this definition, applications of Hyperscanning 2.0 range from the analysis of brain responses to repeated complex stimulation to the determination of interactions between different types of brain signals of multiple subjects in multi-modal recordings. In this research proposal, we will use Hyperscanning 2.0 for detecting specific multi-modal brain signatures of visual attention and attentional shifts during movie viewing, as well as of emotional valence in clinical populations suffering from Major Depressive Disorder (MDD). To this end, we will collect and analyze respective electroencephalography (EEG) and functional magentic resonance imaging (fMRI) data.Many of the computational tools needed for carrying out Hyperscanning 2.0 analyses are yet to be developed. With this project we will provide a blind source separation (BSS) framework for optimally extracting latent (not directly observable) brain processes with well-defined similarities/couplings from multiple neuroimaging datasets. Within this framework we will implement two algorithms, canonical and hyper source power correlation analysis (cSPoC and HyperSPoC), which will for the first time enable the optimal treatment of an important class of neurophysiological features - namely brain oscillations - in a Hyperscanning setting. Since our algorithms will be capable of isolating brain activity of interest in the absense of external trigger information, they will enable the study of attention and emotion in more realistic scenarios than previous approaches and thereby promise to contribute to a better understanding of these cognitive functions in healthy subjects and patients in the future."
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