Exploring the Brain Information Processing Mechanisms from Functional Connectivity to Translational Applications.

Exploring information processing mechanisms in the human brain is of significant importance to the development of artificial intelligence and translational study. In particular, essential functions of the brain, ranging from perception to thinking, are studied, with the evolution of analytical strategies from a single aspect such as a single cognitive function or experiment to the increasing demands on the multi-aspect integration. Here we introduce a systematic approach to realize an integrated understanding of the brain mechanisms with respect to cognitive functions and brain activity patterns. Our approach is driven by a conceptual brain model, performs systematic experimental design and evidential type inference that are further integrated into the method of evidence combination and fusion computing, and realizes never-ending learning. It allows comparisons among various mechanisms on a specific brain-related disease by means of machine learning. We evaluate its ability from the brain functional connectivity perspective, which has become an analytical tool for exploring information processing of connected nodes between different functional interacting brain regions, and for revealing hidden relationships that link connectivity abnormalities to mental disorders. Results show that the potential relationships on clinical signs–cognitive functions–brain activity patterns have important implications for both cognitive assessment and personalized rehabilitation.