讲座题目：Planning invasiveand non-invasive brain network disorder treatments using computational models

主讲人：Marcus Kaiser 教授

主持人：范明霞 

开始时间：2019年04月23日下午14:00

讲座地址：中北校区理科大楼A207会议室

主办单位：上海市磁共振重点实验室

报告人简介：

    Marcus Kaiser is leader of Neuroinformatics UKrepresenting more than 600 researchers in the field (http://www.neuroinformatics.org.uk/),Chair of the Neuroinformatics SpecialInterest Group of the British Neuroscience Association, Chair of the NHS CHAINTechnology Sub-group on Computational Neurology, and panel member of the MRCNeuroscience and Mental Health board. After studying biology andcomputer science, he obtained his PhD, funded by a fellowship from the GermanNational Academic Foundation, from Jacobs University Bremen in 2005. In 2016,he was elected Fellow of the Royal Society of Biology. He is on the editorialboards of Network Neuroscience (MIT Press) and Royal Society Open Science, andauthor of the first review on connectomics (1,800+ citations since 2004). Researchinterests are understanding the link between structure and function by modellingbrain development, neural dynamics, and therapeutic interventions (see http://www.dynamic-connectome.org/).

报告内容简介：

    Their work on connectomics over the last 15 years has shown asmall-world, modular, and hub architecture of brain networks. Small-worldfeatures enable the brain to rapidly integrate and bind information while themodular architecture, present at different hierarchical levels, allows separateprocessing of various kinds of information (e.g. visual or auditory) whilepreventing wide-scale spreading of activation. Hub nodes play critical roles ininformation processing and are involved in many brain diseases.

After discussing the organisation ofbrain networks, He will show how connectivity in combination with machinelearning and computer simulations can identify the progression towards dementiabefore the onset of symptoms informing interventions that can delay diseaseprogression. For epilepsy patients, connectome-based simulations can also be used to predictthe outcome of surgical interventions as well as alternative target regions.While these models rely on the network between brain regions, we also developedmodels of tissue within a brain region (http://www.vertexsimulator.org).Such models can observe the effects of invasive or non-invasive electricalbrain stimulation.

I will finally outline how these models could, in the future, informinvasive interventions, such as optogentic stimulation in epilepsy patients (http://www.cando.ac.uk)or non-invasive interventions using electrical, magnetic or focused ultrasoundstimulation.