IEEE WCCI2024 Special Session CFP for " Integrated Understanding of Structural and Functional Brain Networks and Cognitive Function: Mathematical Modeling and Neuroimaging Data-Driven Approaches"

1. Aim and Scope: 

Cognitive function in the brain is an emergent phenomenon, arising from the interaction of the brain's comprehensive neural activities. To grasp this emergent functionality, research integrating the structural and functional networks of the brain as well as the emergence of cognitive functions is needed. Therefore, a collaborative approach combining mathematical modeling and neuroimaging is essential.

The mathematical modeling of neural networks encompasses various levels exploring a wide range of properties, spanning the dynamics of neuron membrane potentials, firing characteristics of neuron populations, and even functional-level properties in cognitive functions such as cognitive neurodynamics. 

Functional magnetic resonance imaging (fMRI) is the most widely used method for research based on neuroimaging data. However, the blood oxygenation level-dependent (BOLD) signal captured by fMRI reflects blood flow, indirectly reflecting the firing activity of neuron populations with a maximum time resolution of only a few Hz. Neural activity contains a wide range of frequency components, reaching around 100 Hz, making it crucial to recognize the importance of high time-resolution neuroimaging methods such as electroencephalography (EEG) and magnetoencephalography (MEG) for multi-scale temporal analysis. Additionally, data from behavioral methods such as pupillometry reflect deep brain neural activity that cannot be captured by EEG/MEG, further emphasizing the significance of neuroimaging. Consequently, current neuroimaging research is rapidly advancing to include a broad range of areas spanning various frequency domains, including deep brain regions.

In this special session, we aim to gather research findings in mathematical modeling and neuroimaging, offering a platform for presenting research on the integration of the brain's structural and functional networks, and the emergent cognitive functions derived from them.

2. List of Topics:

This special session welcomes papers related or relevant to all aspects utilizing a mathematical modeling and neuroimaging data-driven approach to understanding cognitive function. We also encourage interdisciplinary contributions from other areas at the boundaries of the proposed scope. Topics include, but are not limited to:

• Bifurcation analysis in mathematical modeling of neural network models, ranging from abstract rate-coding models to physiological spiking neural networks.

• Spatiotemporal neural activity in mathematical modeling of neural network models, featuring phenomena such as synchronization, chaos, and chimera states.

• Functionality of neural activity through mathematical modeling, including spiking neural networks, reservoir computing, and other physiological neural network models.

• Capturing structural and functional brain networks using neuroimaging data, such as EEG/MEG/fMRI, and behavioral data. 

3. Organizers: 

Sou Nobukawa (Department of Computer Science, Chiba Institute of Technology), Keiichiro Inagaki (Department of Artificial Intelligence and Robotics, Chubu University) , Teijiro Isokawa (Graduate School of Engineering, University of Hyogo)

4. Important dates

Paper Submission: January 15, 2024

Notification of Acceptance: March 15, 2024

Final Paper Submission: May 01, 2024

IEEE WCCI 2024, Yokohama, Japan. June 30 - July 05, 2024 

The special session was successfully conducted!