Fwd: 邀请专家讲座（Dr. Gary Hui Zhang, UCL）

[Gaolang Gong]

Just a reminder for the talk of this morning. Please attend it on time. -Gaolang

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From: 认知神经科学与学习国家重点实验室 <key...@bnu.edu.cn>
Date: Thu, Jul 4, 2013 at 4:55 PM
Subject: 邀请专家讲座（Dr. Gary Hui Zhang, UCL）
To:

各位老师：

您好！由龚高浪老师邀请了University College London的Dr. Gary Hui Zhang来实验室讲座，欢迎感兴趣的老师和同学参加！

报告人：Dr. Gary Hui Zhang

报告时间：7月11日上午10:30

报告地点：脑成像中心308会议室（大会议室）

报告题目：In vivo MR imaging of neurite morphology: the technique and its applications

内容摘要：

The quantification of neurite morphology in live subjects is an outstanding challenge in neuroimaging.  Neurites, including both dendrites and axons, are the cellular building blocks that constitute the complex computational circuitries of the brain.  Quantifying neurite morphology, e.g., in terms of its density and patterns of branching, is recognized as vital for developing our fundamental understanding of the structural basis of brain function both in normal populations and in populations with brain disorders.

Our group has recently developed the first MRI technique to map key morphological features of neurites in live subjects.  The technique, which we call Neurite Orientation Dispersion and Density Imaging (NODDI), has been designed from ground up to be amenable to a broad range of applications.  It has modest demand for imaging time  and does not require specialized imaging hardwares or softwares.  This talk will give an overview of the NODDI technique and present a number of recent applications, including brain development and epilepsy.

报告人简介：

Dr Gary Hui Zhang is a Lecturer (Assistant Professor) in Medical Image Computing within Department of Computer Science and Centre for Medical Image Computing at University College London.  He has broad interest in computational approaches for understanding the structure and function of the brain.  His particular expertise is in developing quantitative imaging biomarkers for quantifying brain tissue at both the macroscopic and microscopic scales.  He is best known for developing DTI-TK, the top-ranked spatial normalization tool for diffusion MRI data.  His most recent work focuses on advancing the next-generation imaging techniques that can access tissue microstructure much more directly than previously possible and be readily used in clinical and neuroscience studies.

此致

敬礼！

 

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认知神经科学与学习国家重点实验室

2013年7月4日