Activation network improves spatiotemporal modelling of human brain communication processes
Xucheng Liu,
Ze Wang,
Shun Liu,
Lianggeng Gong,
Pedro A. Valdes Sosa,
Benjamin Becker,
Tzyy-Ping Jung,
Xi-jian Dai,
Feng Wan
Affiliations
Xucheng Liu
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China
Ze Wang
Macao Centre for Mathematical Sciences, and the Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China
Shun Liu
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China
Lianggeng Gong
Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China
Pedro A. Valdes Sosa
The Clinical Hospital of Chengdu Brain Sciences Institute. University of Electronic Sciences and Technology of China, Chengdu, 611731, China; Cuban Neuroscience Center, La Habana 10200, Cuba
Benjamin Becker
State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong 999077, China; Department of Psychology, The University of Hong Kong, Hong Kong 999077, China
Tzyy-Ping Jung
Department of Bioengineering, University of California at San Diego, La Jolla 92092, United States; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California at San Diego, La Jolla 92093, United States
Xi-jian Dai
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China; Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China; Corresponding authors at: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
Feng Wan
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China; Corresponding authors at: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated ''ground truth'' validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.
