Monday \u2013 August 8, 2011 \u2013 4:00pm<\/p>\n
Dr. Jonas Richiardi <\/strong> Medical Image Processing Laboratory 44 Cummington St. Room 401<\/p>\n Abstract: This talk will focus on an emerging technique for analysing brain networks: connectivity-based decoding. This is an interesting tool for neuroimagers and provides complementary information to both activation-based decoding and qualitative analysis in terms of graph-theoretic properties and graph topology. It is applicable to both brain state decoding and clinical applications such as diagnosis. After a whole-brain regional connectivity graph has been established, the problem can be cast as a weighted graph classification task. We will show that the graphs of interest form a restricted class of graphs whose properties prevent the application of classical graph matching techniques to elicit a useful distance or dissimilarity between graphs, and advocate for the use of modern graph embedding methods. We will present several vector space representations of graphs that are suitable for the class of graphs of interest, and discuss experimental results on cognitive tasks, ageing populations, and clinical populations.<\/p>\n","protected":false},"excerpt":{"rendered":" August 1st, 2011 Monday \u2013 August 8, 2011 \u2013 4:00pm Dr. Jonas Richiardi Brain decoding with functional connectivity patterns: a graph embedding approach Medical Image Processing Laboratory Ecole Polytechnique Federale de Lausanne Switzerland 44 Cummington St. Room 401 Abstract: Whole-brain connectivity information is becoming increasingly popular with neuroscientists and neuroimagers alike, and for good reasons: […]<\/p>\n","protected":false},"author":15420,"featured_media":0,"parent":0,"menu_order":179,"comment_status":"closed","ping_status":"closed","template":"page-templates\/no-sidebars.php","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/pages\/497"}],"collection":[{"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/users\/15420"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/comments?post=497"}],"version-history":[{"count":1,"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/pages\/497\/revisions"}],"predecessor-version":[{"id":498,"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/pages\/497\/revisions\/498"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/bravi\/wp-json\/wp\/v2\/media?parent=497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nBrain decoding with functional connectivity patterns: a graph embedding approach <\/strong><\/p>\n
\nEcole Polytechnique Federale de Lausanne
\nSwitzerland <\/strong><\/p>\n
\nWhole-brain connectivity information is becoming increasingly popular with neuroscientists and neuroimagers alike, and for good reasons: it provides complementary information to statistical activation maps, and enables fundamental insights into the network organization of the brain in terms of information flow, resilience, efficiency, or modularity. Furthermore, it is now gaining importance for clinical applications.<\/p>\n