基于高场强结构与功能磁共振数据的功能脑区分割及脑功能连接分析研究

MRI techniques have been widely adopted to investigate the human brain functional anatomy and connectivity patterns in studies of the normal brain and brain disorders. In such studies, it is essential to accurately and robustly identify the brain structures of interest and obtain high-quality imaging data with sufficiently high spatial resolution and contrasts. Aiming to explore fine grained functional anatomy and functional connectivity patterns of specific brain structures, the project will collect 7T and 3T MRI structural and functional brain imaging data, develop multimodality image registration algorithms and image segment and partition algorithms based on supervised and semi-supervised machine learning techniques, and validate these methods by segmenting and partitioning hippocampus based on 7T MRI data. The results obtained based on 7T MRI data will also be used as prior information to segment and partition the same brain structure based on 3T MRI data. We will not only develop a suite of multi-modal brain image registration and segmentation methods, generally applicable to studies of other brain structures, but also produce a statistical atlas of the hippocampus and its subfields with a high spatial resolution and fine grained functional connection patterns.

基于磁共振脑影像数据刻画人脑的功能解剖结构及其连接模式已成为研究脑与脑疾病的一种重要手段，而精确定位感兴趣脑区与获取高分辨率影像数据对于精细刻画其功能解剖结构与连接模式至关重要。本项目以探索特定脑区的精细功能解剖结构及其功能连接模式为研究目标，将采集7T与3T磁共振结构与功能脑影像数据，发展多模态磁共振影像配准方法及基于监督与半监督机器学习的特定脑区自动分割与子结构划分技术，建立基于7T结构与功能脑影像数据的海马区及其功能子区的统计图谱与功能连接模式，并在此基础上通过半监督学习方法建立基于3T结构与功能脑影像数据的相应统计图谱与功能连接模式。本研究项目的实施将不仅建立一系列具有独创性的多模态脑影像配准、分割方法,为细分其他脑区提供计算工具及方法，而且将产生具有高空间分辨率的海马及其子区的统计图谱与功能连接模式图谱。

本项目以探索特定脑区的精细功能解剖结构及其功能连接模式为研究目标，采集了7T与3T磁共振结构与功能脑影像数据，发展了基于监督与半监督机器学习的特定脑区自动分割与子结构划分技术，建立了基于结构与功能脑影像数据的海马区与杏仁核区及其功能子区的统计功能连接模式。