{"id":269,"date":"2025-04-02T23:33:45","date_gmt":"2025-04-02T15:33:45","guid":{"rendered":"http:\/\/106.52.84.14\/?p=269"},"modified":"2025-04-02T23:33:45","modified_gmt":"2025-04-02T15:33:45","slug":"19-16-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9a%e8%83%a1%e6%ad%a3%e7%8f%b2","status":"publish","type":"post","link":"https:\/\/www.mics-ai.com\/index.php\/2025\/04\/02\/19-16-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9a%e8%83%a1%e6%ad%a3%e7%8f%b2\/","title":{"rendered":"[#19-16] MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\uff1a\u80e1\u6b63\u73f2"},"content":{"rendered":"\n
\u5609\u5bbe<\/strong><\/td>\u80e1\u6b63\u73f2\uff08\u6d59\u6c5f\u5de5\u4e1a\u5927\u5b66\uff09<\/td><\/tr>
\u65f6\u95f4<\/strong><\/td>2019\u5e7410\u670822\u65e5\uff08\u661f\u671f\u4e8c\uff09\u665a\u4e0a 20:00\uff08\u5317\u4eac\u65f6\u95f4\uff09<\/td><\/tr>
\u9898\u76ee<\/strong><\/td>Unified Model Selection Approach Based on Minimum Description Length  Principle in Granger Causality Analysis<\/td><\/tr>
\u4e3b\u6301<\/strong><\/td>\u8c22\u7acb\uff08\u6d59\u6c5f\u5927\u5b66\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n
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\u62a5\u544a\u6458\u8981<\/strong><\/p>\n\n\n\n

Granger causality analysis (GCA) provides a powerful tool for uncovering the patterns of brain connectivity mechanism using neuroimaing techniques. Conventional GCA applies two different mathematical theories in a two-stage scheme: (1) the Bayesian or Akaike information criterion for the predictors associated with history information; (2) F-statistics for evaluating the relative effects of exogenous variables. While specifying endogenous and exogenous effects are essentially the same model selection problem, this could produce different benchmark in the two stages and therefore degrade the performance of GCA. In this course, we present a unified model selection approach based on the minimum description length (MDL) principle for GCA in the context of the general regression model paradigm. Compared with conventional methods, our approach emphasize that a single mathematical theory should be hold throughout the GCA process. First, the MDL principle help to select an appropriate model being suitable to the specific noise level that is present. The MDL principle then serves as a single mathematical framework for specifying endogenous and exogenous effects. Under this framework, all candidate models within the model space might be compared freely in the context of the description length, without the need for an intermediate model. We illustrate its advantages over conventional two-stage GCA approach in a 3-node network and a 5-node network synthetic experiments. The unified model selection approach is capable of identifying the actual connectivity while avoiding the false influences of noise. More importantly, the proposed approach produced more consistent results in a challenge fMRI dataset for causality investigation, mental calculation network under visual and auditory stimulus, respectively. The proposed approach has potential to accommodate other Granger causality representations in other function space. The comparison between different GC representations in different function spaces can also be naturally deal with in the framework.<\/p>\n\n\n\n

\u5609\u5bbe\u7b80\u4ecb<\/strong><\/p>\n\n\n\n

\u80e1\u6b63\u73f2\uff0c2005\u5e74\u6d59\u6c5f\u5927\u5b66\u7269\u7406\u7cfb\u51dd\u805a\u6001\u7269\u7406\u535a\u58eb\u6bd5\u4e1a\uff0c\u9999\u6e2f\u79d1\u6280\u5927\u5b66\u7535\u5b50\u4e0e\u8ba1\u7b97\u673a\u7cfb\uff0c\u7f8e\u56fdRochester Institute of Technology \u8ba1\u7b97\u4e0e\u4fe1\u606f\u79d1\u5b66\u7cfb\u535a\u58eb\u540e\uff0c\u73b0\u4e3a\u6d59\u6c5f\u5de5\u4e1a\u5927\u5b66\u7406\u5b66\u9662\u6559\u6388\uff0c\u4e3b\u8981\u4ece\u4e8b\u6570\u5b57\u4fe1\u53f7\u5904\u7406\u4e0e\u56fe\u50cf\u5206\u6790\u5de5\u4f5c\u3002\u5728\u76f8\u5173\u9886\u57df\u53d1\u8868SCI\u8bba\u6587\u4e09\u5341\u4f59\u7bc7\uff0c\u4e3b\u6301\u6216\u53c2\u4e0e\u5404\u7c7b\u7701\u90e8\u7ea7\u79d1\u7814\u9879\u76ee\u5341\u4f59\u9879\u3002\u7533\u8bf7\u56fd\u5bb6\u4e13\u52295 \u9879\u3002\u66fe\u83b7\u9999\u6e2f\u79d1\u6280\u5927\u5b66\u201cProductivity Reward\u201d\u3001\u201c\u6d59\u6c5f\u7701\u94b1\u6c5f\u4eba\u624d\u8ba1\u5212\u201d\u3001\u201c\u6d59\u6c5f\u7701\u79d1\u6280\u8fdb\u6b65\u4e8c\u7b49\u5956\u201d\u3002<\/p>\n\n\n\n

\u7279\u522b\u611f\u8c22\u672c\u6b21Webinar\u4e3b\u8981\u7ec4\u7ec7\u8005\uff1a<\/strong>
\u8c22\u7acb\uff08\u6d59\u6c5f\u5927\u5b66\uff09<\/strong>
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\u6d3b\u52a8\u987b\u77e5<\/strong><\/p>\n\n\n\n

1. MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u4f9d\u6258\u5728\u7ebf\u76f4\u64ad\u5e73\u53f0\u8fdb\u884c\uff0c\u542c\u4f17\u8bf7\u70b9\u51fb\u76f4\u64ad\u94fe\u63a5https:\/\/live.polyv.cn\/watch\/400484\uff08\u6ce8\uff1a\u8be5\u94fe\u63a5\u4e3a10\u670822\u65e5\u62a5\u544a\u94fe\u63a5\uff0c\u8be5\u94fe\u63a5\u6bcf\u671f\u4f1a\u548c\u8bb2\u8005\u4fe1\u606f\u4e00\u8d77\u66f4\u65b0\uff09\u53c2\u52a0\u6d3b\u52a8\uff0c\u652f\u6301\u5b89\u88c5Windows\u7cfb\u7edf\u7684\u7535\u8111\u3001MAC\u7535\u8111\u3001\u624b\u673a\u7b49\u8bbe\u5907\uff1b\u624b\u673a\u5ba2\u6237\u7aef\u4e5f\u53ef\u76f4\u63a5\u626b\u63cf\u4e8c\u7ef4\u7801\u8fdb\u5165\u76f4\u64ad\uff1b<\/p>\n\n\n

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\u53ef\u76f4\u63a5\u626b\u63cf\u4e8c\u7ef4\u7801\u8fdb\u516510\u670822\u65e5\u76f4\u64ad<\/p>\n\n\n\n

2. \u6d3b\u52a8\u5f00\u59cb\u524d5\u5206\u949f\u5de6\u53f3\uff0c\u8bb2\u8005\u4f1a\u5f00\u542f\u76f4\u64ad\uff0c\u6d3b\u52a8\u65f6\u8bb2\u8005\u4f1a\u4e0a\u4f20PPT\u6216\u5171\u4eab\u5c4f\u5e55\uff0c\u542c\u4f17\u53ef\u4ee5\u770b\u5230Slides\uff0c\u542c\u5230\u8bb2\u8005\u7684\u8bed\u97f3\uff0c\u5e76\u901a\u8fc7\u804a\u5929\u529f\u80fd\u4e0e\u8bb2\u8005\u4ea4\u4e92\uff1b<\/p>\n\n\n\n

3. \u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u8bf7\u4e0d\u8981\u8bf4\u65e0\u5173\u8bdd\u8bed\uff0c\u4ee5\u514d\u5f71\u54cd\u6d3b\u52a8\u6b63\u5e38\u8fdb\u884c\uff1b\u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u5982\u51fa\u73b0\u542c\u4e0d\u5230\u6216\u770b\u4e0d\u5230\u89c6\u9891\u7b49\u95ee\u9898\uff0c\u5efa\u8bae\u9000\u51fa\u518d\u91cd\u65b0\u8fdb\u5165\uff0c\u4e00\u822c\u90fd\u80fd\u89e3\u51b3\u95ee\u9898\uff1b\u5efa\u8bae\u901a\u8fc7\u901f\u5ea6\u8f83\u5feb\u7684\u7f51\u7edc\u53c2\u52a0\u6d3b\u52a8\uff0c\u4f18\u5148\u91c7\u7528\u6709\u7ebf\u7f51\u7edc\u8fde\u63a5\uff1b<\/p>\n\n\n\n

4. \u540e\u7eed\u6bcf\u671f\u8bb2\u5ea7\u4fe1\u606f\uff0c\u4f1a\u901a\u8fc7MICS\u5fae\u4fe1\u516c\u4f17\u53f7\u201c\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\u201d\uff08\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\u5173\u6ce8MICS\u5fae\u4fe1\u516c\u4f17\u53f7\uff09\u6216MICS QQ\u7fa4\uff08\u7fa4\u53f7\uff1a641894878\uff09\u8fdb\u884c\u901a\u77e5\uff08\u6ce8\uff1a\u7533\u8bf7\u52a0\u5165MICS QQ\u7fa4\u65f6\u9700\u9a8c\u8bc1\u59d3\u540d\u3001\u5355\u4f4d\u548c\u8eab\u4efd\uff0c\u7f3a\u4e00\u4e0d\u53ef\uff1b\u5165\u7fa4\u540e\uff0c\u8bf7\u5b9e\u540d\uff0c\u59d3\u540d\u8eab\u4efd\u5355\u4f4d\uff1b\u8eab\u4efd\uff1a\u5b66\u6821\u53ca\u79d1\u7814\u5355\u4f4d\u4eba\u5458T\uff0c\u4f01\u4e1a\u7814\u53d1I\uff0c\u535a\u58ebD\uff0c\u7855\u58ebM\uff09<\/p>\n\n\n

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\u201c\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\u201d\u5fae\u4fe1\u516c\u4f17\u53f7<\/p>\n\n\n\n

MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u7684\u6a21\u5f0f\u548c\u7ec4\u7ec7\u65b9\u5f0f\u501f\u9274\u4e86\u5f88\u591aVALSE\u7684\u7ecf\u9a8c\uff0c\u4eceVALSE\u5f97\u5230\u4e86\u5f88\u591a\u7684\u542f\u53d1\uff0c\u5728\u6b64\u5bf9VALSE\u7ec4\u59d4\u4f1a\u8868\u793a\u8877\u5fc3\u7684\u611f\u8c22\uff0c\u4e5f\u795d\u613fMICS\u548cVALSE\u8d8a\u529e\u8d8a\u597d\uff01<\/p>\n\n\n\n

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[#18-01] \u6c88\u5b9a\u521a-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n\n\n\n

[#19-01] \u5f20\u9053\u5f3a-\u5357\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-02] \u9648\u65b0\u5efa-\u82cf\u5dde\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-03] \u5b59\u5251-\u897f\u5b89\u4ea4\u901a\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-04] \u5f90\u519b-\u5357\u4eac\u4fe1\u606f\u5de5\u7a0b\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-05] \u8bb8\u71d5-\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-06] \u502a\u4e1c-\u6df1\u5733\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-07] \u738b\u6d2a\u51ef-\u5927\u8fde\u7406\u5de5\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-08] \u5e84\u5413\u6d77-\u590d\u65e6\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-09] \u53f2\u9896\u6b22-\u5357\u4eac\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-10] \u859b\u5fe0-\u4e0a\u6d77\u8054\u5f71\u667a\u80fd\u533b\u7597\u79d1\u6280\u6709\u9650\u516c\u53f8<\/a><\/p>\n\n\n\n

[#19-11] \u5f20\u5bd2-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n\n\n\n

[#19-12] \u79e6\u749f-\u9999\u6e2f\u7406\u5de5\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-13] \u9ec4\u6653\u857e-\u5bbe\u5dde\u5dde\u7acb\u5927\u5b66<\/a><\/p>\n\n\n\n

[#19-14] \u5434\u4e39-\u6d59\u6c5f\u5927\u5b66<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

\u5609\u5bbe \u80e1\u6b63\u73f2\uff08\u6d59\u6c5f\u5de5\u4e1a\u5927\u5b66\uff09 \u65f6\u95f4 20…<\/p>\n","protected":false},"author":1,"featured_media":270,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-269","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-4"],"_links":{"self":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/269","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/comments?post=269"}],"version-history":[{"count":0,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/269\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/media\/270"}],"wp:attachment":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/media?parent=269"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/categories?post=269"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/tags?post=269"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}