Establishing Method Of Macaque Brainnetome Atlas And Its Application In Frontal Pole Based On Diffusion Magnetic Resonance Imaging

Brain atlas is one of the most important tools for the research of frontiers sciences.Constructing high-quality brain atlas could not only greatly promote the study of structure-function relationships,neuroanatomical studies,clinical medical studies,but also provide important enlightenment for neuromorphic computing,intelligent technology,and chip design.Diffusion tensor imaging is a r ecently developed and non-invasive imaging technique.The construction of brain atlas based on diffusion tensor imaging has been a very important research direction in brain science.As one of the excellent models of human brain,the research of monkey brain has been of great interest to more and more scientists.At present,however,the systematic research on monkey brain mapping based on diffusion tensor imaging technology is still very limited,and the publicly available tools for the parcellation of macaque monkey brain are also very rare.The thesis,in view of this situation,systematically investigated the mapping and its methods of macaque monkey brain atlas based on anatomical connectivit,and then developed a software toolbox for the parcellation of macaque brain,and used it to parcellate multiple macaque brain regions.First,based on diffusion tensor imaging at macroscale,we integrated and optimized a variety of image processing algorithms.1)Automated and robust algorithms for macaque monkey brain extraction are required for neuroimaging research,involving high-throughput analysis and large population in particular.In this study,we developed a multi-atlas segmentation based protocol for automatically extracting brain tissues from structural magnetic resonance imaging data.We evaluated the performance by comparing the results against manual segmentations.In this part,the main contributions of our work lies in two aspects: we used two independent datasets(24 and 30 subjects,respectively)for testing the generalizability of the developed pipeline;we proposed two label fusion strategies from magnetic resonance imaging using multi-atlas segmentation methods.2)Our algorithm process integrates advanced registration algorithm to make the whole framework more suitable for the image registration of macaque monkey brain.3)We integrated a statistical framework of principal component analysis,and then optimized the algorithm.Finally,we proposed a verification scheme for the parcellation of the monkey brain at the individual diffusion level.Second,we developed a software toolbox,named Monkeycbp,which could completely realize the parcellation pipeline of macaque brain segmentation,and all the code was released to the public.The toolbox provides two modes of operation,including the command line version and the graphical user interface version.The command line version supports the parallel computation for the parcellation of multiple brain regions.The graphical interface version dedicates to the parcellation of single brain regions,and is very convenient for the experimenters to adjust configuration parameters.Based on two batches of macaque monkey data,we tested and verified the effectiveness and stability of the Monkey CBP.The toolbox is highly automated and easy to operate.Before the experiment,users just need to complete some basic configuration,and then the program will be executed automatically.In addition,Monkey CBP supports multi-core CPU parallel computation and GPUs acceleration,which is high efficient.Finally,based on the Monkey CBP and a set of macaque data(8 subjects),we systematically mapped the macaque frontal pole cortex atlas with the help of high-performance computing clusters.We parcellated the macaque frontal pol e cortex of into 8 subregions,and constructed the fingerprint of each subregion.Meanwhile,we compared the results of anatomical connectivities and tracer injection to evaluate the consistency of the two results of different techniques.Then,based on the anatomical connections of each subregion,we optimized the hierarchical clustering algorithm,and used it to explore the hierarchical modularity across the subregions of frontal pole cortex.Subsequently,we investigated and explored the connections between the subregions of macaque frontal pole cortex and default mode network,social-interaction network,and metacognition.We found that the dorsolateral frontal pole cortex was mainly connected to regions of the default-mode network.The ventral frontal pole cortex was mainly connected to regions of the social-interaction network.The dorsal frontal pole cortex was mainly connected to the metacognitive brain networks.These results may increase our understanding of the macaque brain anatomy and circuitry,and benefit the frontal pole cortex related clinical research.