Résultats de recherche pour « Brain Networks »

Abstract It is taken for granted that the brain is composed of a set of networks. But what is a brain network? How should we characterize them? Adding to Anderson's (2014) persuasive anti-modular framework, I propose that (1) networks do not have a single, unique function; (2) networks are highly overlapping and not disjoint; and (3) tasks reconfigure networks found at rest.

Brain network analysis has become an important approach to understanding brain function. Given the human brain's complexity and dynamic nature, traditional brain networks based on temporal synchronization may not capture all the nuances of brain activity. The emerging trend is to construct brain networks from regional information, creating pathways that connect regional profiles to network-level insights to better understand brain function. In this study, we constructed structural networks based on regional brain structural information, specifically gray matter volume (GMV), and functional networks based on regional brain activity, measured by brain entropy (BEN). We compared these newly constructed networks with traditional networks: functional connectivity networks (FCN) based on temporal synchronization, structural connectivity networks (SCN) derived from fiber tracking, and MEG-derived functional networks. Our results reveal that GMV network (GMVN) and BEN network (BENN) show correlations with these networks but also exhibit distinct differences. Furthermore, we conducted connectome gradient analyses, uncovering meaningful brain function distribution patterns in both GMVN and BENN. Finally, we used GMVN, BENN, and traditional FCN to predict cognitive and emotional scores. The results showed that BENN, in the resting state, provided the best prediction of both cognitive and emotional scores. This study systematically evaluates the relationship between brain networks constructed from regional brain information and existing networks, as well as their ability to predict behavioral phenotypes. It demonstrates that networks built from regional information capture aspects of brain activity that traditional networks cannot represent and may even provide superior predictive power for behavioral phenotypes. This opens a new path for understanding brain function from regional to network-level information and lays the foundation for future applications in brain development, individual differences, and clinical research.

Summary A recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (Richiardi et al., 2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.