Reminder - Today P/L002 1-2PM The Human Connectome: Linking Brain Network Features to Healthy and Pathological Information Processing Monday 9 May 2016, 1.00PM to 14:00 *Speaker: *Professor Marcus Kaiser, Newcastle University Synopsis Our work on connectomics over the last 15 years has shown a small-world, modular, and hub architecture of brain networks [1,2]. Small-world features enable the brain to rapidly integrate and bind information while the modular architecture, present at different hierarchical levels, allows separate processing of various kinds of information (e.g. visual or auditory) while preventing wide-scale spreading of activation [3]. Hub nodes play critical roles in information processing and are involved in many brain diseases [4]. Nonetheless, general observations of human brain connectivity, or of patients at the group-level, have so far had little impact on understanding cognition, or deficiencies in cognition, in individual subjects. As a result, human connectome information is not used as a biomarker for diagnosis or a predictor of the most suitable treatment strategy. After discussing the organisation of brain networks, we will show how connectivity can be used to determine the disease type of individual dementia patients. An important aspect of these brain networks is their spatial organisation in terms of the length of fibre tracts and the location of brain regions [5]. However, simply observing connectivity is insufficient as small changes in network organisation might lead to large changes in network behaviour (dynamics) [6]. We therefore show how simulations can be applied to predict regions that are involved in neural processes. For epilepsy, simulations show us which regions are involved [7], which treatment approach should be used, and whether surgical intervention will be successful or not. We conclude with the role of simulations in understanding the developmental origin of diseases as determining these origins will again inform diagnosis and treatment ( http://www.greenbrainproject.org/ ). These are first steps towards using connectome-based computer simulations as a tool to understand normal and pathological processing in individuals. Developing models that are based on anatomical information will be crucial to define the most suitable intervention [8]. [1] Martin, *Kaiser*, Andras, Young. Is the Brain a Scale-free Network? SfN Abstract, 2001. [2] Sporns, Chialvo, *Kaiser*, Hilgetag. Trends in Cognitive Science, 2004. [3] *Kaiser* et al. New Journal of Physics, 2007. [4] *Kaiser* et al. European Journal of Neuroscience, 2007. [5] *Kaiser* et al. PLOS Computational Biology, 2006. [6] *Kaiser*. Frontiers in Human Neuroscience, 2013. [7] Hutchings, Han, Keller, Weber, Taylor, *Kaiser*. PLOS Computational Biology, 2015. [8] Wang, Hutchings, *Kaiser*. Computational Modelling of Neurostimulation in Brain Diseases. Progress in Brain Research, 2015. *Biography* *Marcus Kaiser* studied biology and computer science at the Ruhr-University Bochum and the Distance University Hagen finishing with a master degree in 2002. He obtained his PhD, funded by a fellowship from the German National Merit Foundation, from Jacobs University Bremen in 2005. He is initiator and co-director of the Wellcome Trust PhD programme in Systems Neuroscience and leader of the UK INCF Special Interest Group in Image-based Neuroinformatics. He authored the first major review (Trends in Cognitive Sciences, 2004; cited 1,300+ times) and more than 50 other publications in the field of brain connectivity. Research interests are understanding the link between structure and function by modelling brain development, neural dynamics, and therapeutic interventions (see http://www.dynamic-connectome.org/ ). *Location: *PL002 -- Miss Helen Fagan Graduate Admissions and Demonstrator Coordinator ******************************************** Department of Electronics University of York Heslington York YO10 5DD Telephone: 01904 324485 Fax: 01904 323224 Email: helen.fagan(a)york.ac.uk *Follow us on:* Facebook (https://www.facebook.com/electronicsyork) Twitter (https://twitter.com/ElectronicsYork) Weibo (http://www.weibo.com/ElectronicsYork <http://www.weibo.com/ElectronicsYork?is_hot=1>) Pinterest (https://uk.pinterest.com/deptelecyork/) EMAIL DISCLAIMER http://www.york.ac.uk/docs/disclaimer/email.htm <http://www.elec.york.ac.uk/EqualityDiversity/award1.png>