猕猴脑网络组图谱:包含分区、连接和组织学的多层面全新大脑地图

The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary research.However,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been made.In this study,we created a new whole-brain map,the Macaque Brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data.The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections.The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images.As a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure.The resulting resource includes:(1)the thoroughly delineated Macaque Brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(Brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single macaque.MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function.Therefore,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Decoding human brain functions: Multi-modal, multi-scale insights

Unraveling the intricate relationship between the structure and function of the human brain remains a central and unresolved question in neuroscience.Ethical considerations impose significant constraints on invasive techniques in human neuroscience research.Consequently,knowledge about human brain function often relies on animal models to provide valuable discoveries and insights.However,caution is warranted,as findings from animal studies may not always be directly translatable to humans,especially when investigating higher cognitive functions.

SSDBA:the stretch shrink distance based algorithm for link prediction in social networks

In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided into two categories:similarity-based and learning-based methods.The learning-based methods have higher accuracy,but their time complexities are too high for complex networks.However,the similarity-based methods have the advantage of low time consumption,so improving their accuracy becomes a key issue.In this paper,we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm(SSDBA),In SSDBA,we first detect communities of a social network and identify active nodes based on community average threshold(CAT)and node average threshold(NAT)in each community.Second,we propose the stretch shrink distance(SSD)model to iteratively calculate the changes of distances between active nodes and their local neighbors.Finally,we make predictions when these links'distances tend to converge.Furthermore,extensive parameters learning have been carried out in experiments.We compare our SSDBA with other popular approaches.Experimental results validate the effectiveness and efficiency of proposed algorithm.