Multi-source brain computing with systematic fusion for smart health

Abstract With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and pathology, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to modeling content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experiment design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social-cyber-physical spaces. Experimental results have demonstrated the efficacy of proposed brain computing with systematic fusion.