Multimodal Approaches to Investigating Neural Dynamics in Cognition and Related Clinical Conditions: Integrating EEG, MEG, and fMRI Data

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Background

The neural mechanisms of cognitive functions have not yet been fully understood. Studies have revealed that many psychiatric disorders underlie altered and dysfunctional cognitive functioning. A better understanding of human neural dynamics can lead to discoveries on how neurons communicate and interact with each other, the role of nonlinear phenomena in neural computation, and neural mechanisms underlying executive functions (including attentional control, cognitive inhibition, inhibitory control, working memory, and decision-making) in healthy individuals as well as in those with psychiatric disorders (e.g. attention deficit disorder, depression, anxiety, schizophrenia, bipolar disorder, or OCD) will have enormous implications. Many factors, such as cortical structure, neurochemical concentration, connectivity across different brain regions, and electrical gradients within the brain, are involved in creating and modulating neuronal dynamics. Therefore, the study of such complex dynamics necessitates the simultaneous usage of several brain imaging technologies, termed multimodal approaches. These approaches provide complementary data that enable the investigation of brain activities from different aspects. Electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are among the more common and popular imaging methods due to their non-invasiveness, ease of recording, and relatively lower cost. Integration of these approaches provides higher temporal and spatial information and enables scientists to design more realistic models of neural dynamics. However, the execution of multimodal approaches and the computational processing of their data or recordings are usually associated with several challenges. For example, the simultaneous recordings of EEG and fMRI are rare as they need some special consideration to avoid damage to the EEG device when it is placed in an MRI room with a high-intensity magnetic field or to reduce magnetic noises on electrical records. Further, aligning or harmonizing the data from two different methods poses a challenge due to huge variations in temporal and spatial resolutions conferred by these methods.

The aims of this research topic are:
• To investigate how different brain imaging technologies, such as EEG, MEG, and fMRI, complement each other in the study of human neural dynamics, and what are the advantages and limitations of each technique.
• To explore the challenges associated with the simultaneous usage of several brain imaging technologies, and how they can be addressed to improve the quality and reliability of multimodal data.
• To gain a more comprehensive understanding of the role of nonlinear phenomena in neural computation, and how they contribute to executive functions (including attention control, cognitive inhibition, inhibitory control, working memory, and decision-making) and their deficits in psychiatric disorders.
• To study how the integration of multimodal approaches in neuroscience research can help us better understand neural and/or psychiatric disorders in humans, such as attention deficit disorder, depression, anxiety, schizophrenia, bipolar disorder, or OCD, and develop more effective diagnostic and therapeutic strategies.
• To investigate how we can develop more realistic models of human neural dynamics by incorporating data from multiple brain imaging techniques, and what are the potential applications of these models in neuroengineering and brain-machine interfaces.
• To discuss recent advances in multimodal brain imaging technology

This research topic will include both review and research articles that cover the following themes or any other topic that addresses the above list of goals:
• Multimodal analysis of neural synchronization/desynchronization in executive functions (including attention control, cognitive inhibition, inhibitory control, working memory, and decision-making)
• Computational models of human executive functions and their related psychiatric disorders using multimodal recordings
• The effect of different modalities in extracting more accurate networks of brain components involved in human executive functions
• The study of nonlinear phenomena in human neural dynamics involved in executive functions using multimodal approaches
• Novel developments in human multimodal brain imaging techniques and algorithms
• Role of multimodal approaches in early detection or diagnosis of brain diseases or disorders related to deficits of executive functions
• Multimodal approaches to understanding the neural mechanisms underlying cognitive functions
• Investigating the role of cross-modal brain oscillations in cognitive flexibility and its disruption in neurological and/or psychiatric disorders related to deficits of executive functions
• Linking brain oscillations across modalities: implications for understanding neural network dynamics in executive functions and their deficits
• Providing a review of findings related to probable challenges in multimodal approaches and possible solutions in human studies.

Keywords: Multimodal brain imaging, Multimodal recordings, Multimodal analysis, EEG-fMRI, MEG-fMRI, Nonlinear dynamics, Brain disorders and diseases, Synchronization/desynchronization, Brain oscillation, Neural computation, Neural networks, Computational models, Cross-modal integration and attention, Human Data, Executive functions, Attention control, Working memory, Cognitive inhibition, Inhibitory control, Decision-making

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