Systematic Fusion of Multi-Source Cognitive Networks With Graph Learning - A Study on Fronto-Parietal Network

Cognitive tasks induce fluctuations in the functional connectivity between brain regions which constitute cognitive networks in the human brain. Although several cognitive networks have been identified, consensus still cannot be achieved on the precise borders and distribution of involved brain regions for each network, due to the multifarious use of diverse brain atlases in different studies. To address the problem, the current study proposed a novel approach to generate a fused cognitive network with the optimal performance in discriminating cognitive states by using graph learning, following the synthesization of one cognitive network defined by different brain atlases, and the construction of a hierarchical framework comprised of one main version and other supplementary versions of the specific cognitive network. As a result, the proposed method demonstrated better results compared with other machine learning methods for recognizing cognitive states, which was revealed by analyzing an fMRI dataset related to the mental arithmetic task. Our findings suggest that the fused cognitive network provides the potential to develop new mind decoding approaches

Web Intelligence meets Brain Informatics: Towards the future of artificial intelligence in the connected world

Understanding human intelligence, especially brain intelligence, is the cornerstone of reaching the ultimate AI. In this paper, we briefly review the historical interactions between AI and brain science, and look towards the future vision of AI in the connected world. In particular, we introduce two rapidly developing fields in Web Intelligence (WI, AI in the Connected World) and Brain Informatics (BI, the brain/mind-centric study and the application of brain-machine intelligence), and combine them to accelerate the arrival of a human–level AI society. Furthermore, combining these two fields by connecting AI and brain science with big data, creates a new vision from the systematic brain-machine intelligence research to new AI industry chain in the connected social-cyber-physical-thinking spaces

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