{"id":174,"date":"2025-04-02T22:57:44","date_gmt":"2025-04-02T14:57:44","guid":{"rendered":"http:\/\/106.52.84.14\/?p=174"},"modified":"2025-04-02T22:57:44","modified_gmt":"2025-04-02T14:57:44","slug":"19-11-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%e4%bc%9a%e5%90%8e%e6%80%bb%e7%bb%93","status":"publish","type":"post","link":"https:\/\/www.mics-ai.com\/index.php\/2025\/04\/02\/19-11-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%e4%bc%9a%e5%90%8e%e6%80%bb%e7%bb%93\/","title":{"rendered":"[#19-11] MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u4f1a\u540e\u603b\u7ed3"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821\u5f20\u5bd2\u535a\u58eb2019\u5e7407\u670802\u65e5MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u6210\u529f\u4e3e\u529e\uff01<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"http:\/\/mp.weixin.qq.com\/s?__biz=MzU1ODc4Mjg1Mw==&amp;mid=2247483832&amp;idx=1&amp;sn=30e0eff812575c4b09482cfff5f60a9d&amp;chksm=fc200152cb578844db1ed01e5b36a53fc1ba39aad25429cc8f9070e2ea4fa7dd9e6bdab3291a&amp;scene=21#wechat_redirect\" target=\"_blank\" rel=\"noreferrer noopener\">[#19-11] MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\uff1a\u5f20\u5bd2<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\uff08Medical Imaging Computing 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network analysis\u3011\uff09\u6765\u964d\u4f4e\u566a\u58f0\u5bf9\u4e8e\u52a8\u6001\u529f\u80fd\u8fde\u63a5\u4f30\u8ba1\u7684\u5f71\u54cd\u3002<\/li>\n\n\n\n<li>\u95ee\u98982\uff1a\u76ee\u524d\u57fa\u4e8e\u529f\u80fd\u78c1\u5171\u632f\u7684\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u5bf9\u4e8e\u4e0d\u540c\u4e2d\u5fc3\u6570\u636e\u7684\u9c81\u68d2\u6027\u5982\u4f55\uff1f\u5efa\u8bae\u63d0\u4f9b\u4e00\u4e2a\u591a\u4e2d\u5fc3\u5206\u7c7b\u7814\u7a76\u9009\u9879\uff0c\u6bd4\u5982\u91c7\u7528leave-center-out cross validation\uff1f<br>\u5f20\u5bd2\uff1a\u8fd9\u662f\u5f88\u597d\u7684\u5efa\u8bae\u3002\u76ee\u524d\u5f88\u591a\u6570\u636e\u96c6\u4e3a\u4e86\u589e\u52a0\u6837\u672c\u91cf\uff0c\u90fd\u91c7\u7528\u591a\u4e2d\u5fc3\u5171\u540c\u91c7\u96c6\u3002\u5373\u4f7f\u662f\u540c\u4e00\u4e2d\u5fc3\uff0c\u4e5f\u53ef\u80fd\u4e3a\u4e86\u5c3d\u53ef\u80fd\u591a\u7eb3\u5165\u88ab\u8bd5\uff0c\u91c7\u7528\u4e86\u4e0d\u540c\u53c2\u6570\u91c7\u96c6\u5230\u7684\u6570\u636e\u3002\u4e00\u4e2a\u65b9\u6cd5\u5728\u4e0d\u540c\u4e2d\u5fc3\u6570\u636e\u4e0a\u5177\u6709\u9c81\u68d2\u6027\u6211\u4e2a\u4eba\u8ba4\u4e3a\u662f\u4e00\u4e2a\u6311\u6218\uff0c\u8fd9\u4e0d\u4ec5\u4ec5\u662f\u5bf9\u4e8e\u673a\u5668\u5b66\u4e60\u662f\u6311\u6218\uff0c\u5bf9\u4e8e\u4efb\u4f55\u5176\u4ed6\u7684fMRI\u65b9\u6cd5\u548c\u7814\u7a76\u90fd\u4e00\u6837\u662f\u4e0d\u786e\u5b9a\u7684\u56e0\u7d20\u3002\u4e4b\u524d\u7684\u5bf9\u4e8e\u57fa\u4e8e\u591a\u4e2d\u5fc3\u6570\u636e\u7684\u8111\u7f51\u7edc\u5206\u7c7b\u7684\u505a\u6cd5\u6709\u5f88\u591a\uff0c\u5e76\u6ca1\u6709\u907f\u800c\u4e0d\u8c08\u3002\u6bd4\u5982\uff0c\u91c7\u7528\u4e00\u4e2a\u4e2d\u5fc3\u6570\u636e\uff08\u53ef\u80fd\u66f4\u591a\u6837\u672c\uff09\u8bad\u7ec3\uff0c\u7528\u53e6\u4e00\u4e2a\u4e2d\u5fc3\u7684\u201c\u72ec\u7acb\u91c7\u96c6\u201d\u5230\u7684\uff08\u76f8\u5bf9\u5c0f\u6837\u672c\uff09\u6570\u636e\u9a8c\u8bc1\u3002\u4e5f\u6bd4\u5982\u9884\u5148\u628a\u201c\u4e2d\u5fc3\u201d\u7684\u6548\u5e94\u4ecefeature\u4e2d\u56de\u5f52\u6389\uff0c\u7528\u56de\u5f52\u6389\u4e2d\u5fc3\u6548\u5e94\u7684\u66f4\u65b0\u540e\u7684features\u7ee7\u7eed\u505a\u5206\u7c7b\u3002\u5728\u673a\u5668\u5b66\u4e60\u91cc\u8fd8\u6709\u4e13\u95e8\u7684\u201c\u8fc1\u79fb\u5b66\u4e60(transfer learning\u6216\u8005adaptive learning)\u201d\u6765\u89e3\u51b3\u8fd9\u7c7b\u95ee\u9898\u3002\u4ece\u6570\u636e\u5c42\u9762\uff0c\u6211\u4eec\u53ef\u4ee5\u5c3d\u53ef\u80fd\u7528\u540c\u6837\u626b\u63cf\u53c2\u6570\uff0c\u91c7\u53d6\u66f4\u4e25\u683c\u8d28\u63a7\uff08\u6bd4\u5982\u4e0d\u540c\u4e2d\u5fc3\u91c7\u53d6\u540c\u4e00\u6c34\u6a21\u9a8c\u8bc1\u4fe1\u566a\u6bd4\uff0c\u5982ADNI\u6570\u636e\u96c6\uff09\uff0c\u91c7\u53d6data harmonization\u7684\u65b9\u6cd5\u4f7f\u5f97\u6570\u636e\u540c\u8d28\u5316\u3002\u6211\u4eec\u76ee\u524d\u7684\u6d4b\u8bd5\u4e2d\uff0c\u8f6f\u4ef6\u5bf9\u591a\u4e2d\u5fc3\u6570\u636e\u7684\u8868\u73b0\u4e0d\u9519\uff0c\u591a\u4e2d\u5fc3\u5bf9\u5206\u7c7b\u7ed3\u679c\u4e0d\u592a\u6709\u5f71\u54cd\u3002\u6211\u4eec\u4f1a\u5728\u5c06\u6765\u63d0\u4f9b\u4e00\u4e2a\u4e2d\u5fc3cross validation\u7684\u9009\u9879\uff0cleave the whole data from one center out \u540c\u65f6\u5229\u7528\u5176\u4ed6\u4e2d\u5fc3\u6570\u636e\u505atraining (leave-center-out cross validation)\u3002<\/li>\n\n\n\n<li>\u95ee\u98983\uff1a\u60a8\u63d0\u5230\u7684\u4e2a\u4f53\u5316\u662f\u5728\u524d\u5904\u7406\u75c5\u4eba\u529f\u80fd\u6838\u78c1\u914d\u51c6\u4e2a\u4f53\u7684mri\u7ed3\u6784\u50cf\uff0c\u8fd8\u662f\u6807\u51c6\u7ed3\u6784\u6a21\u677f\uff1f<br>\u5f20\u5bd2\uff1a\u4e2a\u4f53\u5316\u662f\u6307\u4e2a\u4f53\u5316\u8bca\u65ad\u3002\u8fd9\u4e2a\u6211\u6ca1\u6709\u8bb2\u592a\u6e05\u695a\u3002\u8fd9\u91cc\u8865\u5145\u4e00\u4e0b\u3002\u6211\u8bf4\u7684\u4e2a\u4f53\u5316\u8bca\u65ad\u662findividualized diagnosis\uff0c\u662f\u76f8\u6bd4\u4e8e\u4f20\u7edf\u7684 (\u57fa\u4e8e\u7edf\u8ba1\u7684) group-wise comparison\u800c\u8a00\u7684\u3002\u5728\u4e34\u5e8a\u7814\u7a76\u4e2d\uff0c\u5f80\u5f80\u9700\u8981\u5bf9\u67d0\u4e00\u4e2a\u88ab\u8bd5\u6216\u60a3\u8005\u8fdb\u884c\u8bca\u65ad\uff0c\u8fd9\u5c31\u662f\u4e2a\u4f53\u5316\u8bca\u65ad\u3002\u6bd4\u5982\uff0c\u7ed9\u4e00\u4e2a\u8111\u80bf\u7624\u75c5\u4eba\u7684MRI\u5f71\u50cf\uff0c\u5f71\u50cf\u79d1\u5927\u592b\u9700\u8981\u636e\u6b64\u7ed9\u51fa\u5224\u65ad\uff0c\u5982\u8be5\u60a3\u8005\u662f\u5426\u662f\u8111\u819c\u7624\u8fd8\u662f\u80f6\u8d28\u7624\uff0c\u662f\u9ad8\u7ea7\u522b\u8fd8\u662f\u4f4e\u7ea7\u522b\u80f6\u8d28\u7624\u3002\u533b\u751f\u4f9d\u8d56\u81ea\u5df1\u7684\u7ecf\u9a8c\u548c\u4e4b\u524d\u7684\u6848\u4f8b\u505a\u51fa\u81ea\u5df1\u7684\u5224\u65ad\uff0c\u8fd9\u548c\u673a\u5668\u5b66\u4e60\u7684\u76ee\u6807\u662f\u4e00\u81f4\u7684\u3002\u5728\u57fa\u4e8e\u8111\u7f51\u7edc\u7684\u75be\u75c5\u5206\u7c7b\u4e2d\uff0c\u5206\u7c7b\u5668\u6839\u636e\u6d4b\u8bd5\u6570\u636e\u9884\u5148\u8bad\u7ec3\u597d\uff0c\u65b0\u6765\u7684\u6d4b\u8bd5\u6837\u672c\u76f4\u63a5\u901a\u8fc7\u8fd9\u4e2a\u5df2\u7ecf\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u5668\u5f97\u5230\u5206\u7c7b\u548c\u8bca\u65ad\u7ed3\u679c\u3002\u76f8\u6bd4\u800c\u8a00\uff0c\u4f20\u7edf\u7684\u57fa\u4e8e\u7edf\u8ba1\u7684\u7ec4\u95f4\u6bd4\u8f83\u5fc5\u987b\u8981\u6536\u96c6\u4e24\u7ec4\u4eba\u624d\u80fd\u7ed9\u51fa\u4e00\u4e2a\u7edf\u8ba1\u68c0\u9a8c\u7684\u7ed3\u679c\uff08\u5982p\u503c\uff09\uff0c\u8fd9\u5c31\u4e0d\u662f\u4e2a\u4f53\u5316\u8bca\u65ad\u3002\u60a8\u63d0\u5230\u7684\u524d\u5904\u7406\uff08\u9884\u5904\u7406\uff09\u9636\u6bb5\u91c7\u7528\u529f\u80fdfMRI\u914d\u51c6\u4e2a\u4f53MRI\u7ed3\u6784\u50cf\u7684\u6b65\u9aa4\u5e76\u4e0d\u662f\u4e2a\u4f53\u5316\u7684\u610f\u601d\u3002\u8fd9\u4e00\u9884\u5904\u7406\u6b65\u9aa4\u662f\u5fc5\u987b\u7684\uff0c\u56e0\u4e3a\u4e2a\u4f53\u7ed3\u6784\u50cf\u8fd8\u9700\u8981\u548c\u6807\u51c6\u7ed3\u6784\u6a21\u677f\u505a\u914d\u51c6\u3002\u6709\u4e9bfMRI\u7814\u7a76\u6216\u8005fMRI\u9884\u5904\u7406\u8f6f\u4ef6\u5728\u505a\u9884\u5904\u7406\u7684\u65f6\u5019\u53ef\u4ee5\u5148\u8ba1\u7b97metric map\uff0c\u6bd4\u5982\u9759\u606f\u6001fMRI\u7684\u79cd\u5b50\u70b9\u529f\u80fd\u8fde\u63a5map\uff0c\u7136\u540e\u518d\u628a\u8fd9\u4e2amap\u914d\u51c6\u5230\u6807\u51c6\u7a7a\u95f4\u8fdb\u884c\u9010\u4f53\u7d20\u5206\u6790\uff0c\u6216\u8005\u628a\u6807\u51c6\u7a7a\u95f4\u7684atlas\u53cd\u53d8\u6362\u5230\u4e2a\u4f53\u7a7a\u95f4\u63d0\u53d6ROI\u7684feature\uff0c\u8fd9\u7c7b\u65b9\u6cd5\u548c\u6211\u8bf4\u7684\u4e2a\u4f53\u5316\u8bca\u65ad\u4e0d\u662f\u4e00\u4e2a\u5c42\u9762\u7684\u6982\u5ff5\u3002<\/li>\n\n\n\n<li>\u95ee\u98984\uff1a\u8bf7\u95ee\u8f6f\u4ef6\u5305\u5f97\u5230\u7684\u9ad8\u9636\u8111\u7f51\u7edc\u53ef\u4ee5\u4f5c\u4e3a\u4e2d\u95f4\u7ed3\u679c\u8f93\u51fa\u5417\uff1f\u80fd\u4e0d\u80fd\u62ff\u8fd9\u4e2a\u8111\u7f51\u7edc\u53bb\u7ed3\u5408\u81ea\u5df1\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u53bb\u505a\u5206\u7c7b\uff1f<br>\u5f20\u5bd2\uff1a\u6211\u4eec\u5df2\u7ecf\u51b3\u5b9a\u5728v1.1\u7248\u672c\uff08\u4eca\u65e5\u4f1a\u516c\u5e03\uff09\u4e2d\u4f53\u73b0\u201c\u7075\u6d3b\u7684\u5206\u6790 (flexibility in analysis)\u201d\u8fd9\u4e00\u6982\u5ff5\u3002\u6bd4\u5982\uff0c\u6211\u4eec\u4f1a\u5c06\u6240\u6709\u53c2\u6570\u7ec4\u5408\u4e0b\u6bcf\u4e00\u4e2a\u88ab\u8bd5\u7684\u8111\u7f51\u7edc\u81ea\u52a8\u7684\u5b58\u50a8\u4e0b\u6765\uff0c\u65b9\u4fbf\u7814\u7a76\u8005\u91c7\u7528\u5176\u4ed6\u540e\u5904\u7406\u65b9\u6cd5\u6216\u8005\u5176\u4ed6\u8f6f\u4ef6\u5206\u6790\u8fd9\u4e9b\u8111\u7f51\u7edc\u3002\u518d\u6bd4\u5982\u6211\u4eec\u4f1a\u63d0\u4f9b\u547d\u4ee4\u884c\u63a5\u53e3\uff0c\u5c06\u7528\u6237\u81ea\u5df1\u5206\u6790\u5f97\u5230\u7684\u8111\u7f51\u7edc\u6216\u8005\u8111\u7279\u5f81\u8f93\u5165BrainNetClass\u8f6f\u4ef6\u91cc\u8fdb\u884c\u540e\u7eed\u7684\u7279\u5f81\u63d0\u53d6\uff0c\u7279\u5f81\u9009\u62e9\uff0c\u5206\u7c7b\uff0c\u6a21\u578b\u9a8c\u8bc1\uff0c\u7ed3\u679c\u8f93\u51fa\u3002<\/li>\n\n\n\n<li>\u95ee\u98985\uff1a\u8111\u7f51\u7edc\u7684ground truth\u4e0d\u77e5\u9053\uff0c\u5982\u4f55\u8bc4\u4ef7\u5176\u597d\u574f\uff1f<br>\u5f20\u5bd2\uff1a\u6ca1\u91d1\u6807\u51c6\u9a8c\u8bc1\u662f\u8111\u7f51\u7edc\u7814\u7a76\u7684\u95ee\u9898\u4e4b\u4e00\u3002\u4f46\u662f\uff0c\u6211\u4eec\u4ecd\u7136\u80fd\u901a\u8fc7\u5f88\u591a\u65b9\u6cd5\u6765\u9a8c\u8bc1\u6211\u4eec\u6784\u5efa\u7684\u8111\u7f51\u7edc\u662f\u4e0d\u662f\u201c\u597d\u201d\u3002\u8bc4\u4ef7\u8fd9\u4e2a\u201c\u597d\u201d\u7684\u6307\u6807\u6709\u5f88\u591a\uff0c\u6bd4\u5982test-retest reliability\uff08\u91cd\u6d4b\u53ef\u6d4b\u4fe1\u5ea6\uff09\uff0c\u6bd4\u5982reproducibility\uff08\u5728\u4e00\u4e2a\u6570\u636e\u4e0a\u80fd\u7528\uff0c\u5728\u53e6\u4e00\u4e2a\u6570\u636e\u4e0a\u4e5f\u53ef\u4ee5\u4e48\uff09\uff0c\u518d\u6bd4\u5982\u5b83\u7684\u7ed3\u6784\u66f4\u7b26\u5408\u4e00\u4e9b\u751f\u7406\u5047\u8bbe\uff08\u5982\u7a00\u758f\u6027\uff0c\u6a21\u5757\u5316\uff09\u3002\u6b64\u5916\uff0c\u4e5f\u6709\u4e00\u4e9b\u7ecf\u5e38\u4f7f\u7528\u7684\u8bc4\u4ef7\u65b9\u6cd5\uff0c\u6bd4\u5982\u5728\u57fa\u4e8e\u8111\u7f51\u7edc\u7684\u75be\u75c5\u8bca\u65ad\u4e2d\uff0c\u80fd\u591f\u5f97\u5230\u66f4\u597d\u7684\u5206\u7c7b\u6548\u679c\uff0c\u5206\u7c7b\u6b63\u786e\u7387\u66f4\u9ad8\uff0c\u8fd9\u6837\u7684\u8111\u7f51\u7edc\u5c31\u6bd4\u8f83\u597d\u3002<\/li>\n\n\n\n<li>\u95ee\u98986\uff1a\u5173\u4e8e\u6ed1\u52a8\u7a97\u7684\u5927\u5c0f\u5982\u4f55\u9009\u53d6\uff0c\u4f9d\u636e\u6807\u51c6\u662f\u4ec0\u4e48\uff1f<br>\u5f20\u5bd2\uff1a\u6ed1\u52a8\u7a97\u957f\u5ea6\u76ee\u524d\u6709\u75281\u5206\u949f\u4ee5\u5185\uff08\u537330-60\u79d2\uff09\u7684\uff0c\u4e5f\u6709\u7528\u8d85\u8fc71\u5206\u949f\u7684\uff08\u5982100\u79d2\uff09\u3002\u7a97\u957f\u592a\u957f\u5219\u5bfc\u81f4\u5f97\u5230\u7684\u52a8\u6001\u529f\u80fd\u8fde\u63a5\u4e0d\u201c\u52a8\u6001\u201d\uff0c\u592a\u77ed\u5219\u5bfc\u81f4\u566a\u58f0\u5f71\u54cd\u8fc7\u5927\u3002\u7efc\u5408\u6765\u8bf4\uff0c\u4e4b\u524d\u6709NeuroImage\u6587\u7ae0\u8ba8\u8bba\u8fc7\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u8bf4\u76841\u5206\u949f\u4e5f\u662f\u4ed6\u4eec\u5b9a\u51fa\u6765\u7684\u3002\u4f46\u8981\u6ce8\u610f\uff0c\u8fd9\u4e9b\u6587\u7ae0\u57fa\u672c\u4e0a\u6839\u636e\u6a21\u62df\u6570\u636e\u6765\u786e\u5b9a\u7684\uff0c\u4f46\u662f\u5b9e\u9645\u4e0afMRI\u6570\u636e\u6ca1\u6709\u6240\u8c13\u7a97\u957f\u91d1\u6807\u51c6\u3002\u5177\u4f53\u95ee\u9898\u5e94\u8be5\u5177\u4f53\u5206\u6790\uff0c\u5bf9\u6211\u6765\u8bf4\uff0c\u6211\u8ba4\u4e3a\u4e0d\u540c\u6570\u636e\u751a\u81f3\u53ef\u4ee5\u91c7\u7528\u4e0d\u540c\u7684\u7a97\u957f\u3002\u6211\u4eec\u7684dHOFC\u91cd\u6d4b\u4fe1\u5ea6\u7814\u7a76\u53d1\u73b0\uff0c\u7a97\u957f\u9009\u62e920\uff0c40\uff0c50\u4e2a\u65f6\u95f4\u70b9\uff08\u5373\u5f53TR=2\u79d2\u65f640\uff0c80\uff0c100\u79d2\u957f\u5ea6\uff09\u65f6\uff0c20\u4e2a\u65f6\u95f4\u70b9\u4fe1\u5ea6\u6700\u597d (Zhang, H., Chen, X., Zhang, Y., Shen, D., 2017. Test-retest reliability of \u201chigh-order\u201d functional connectivity in young healthy adults. Frontiers in Neuroscience, 11:439)\u3002\u7136\u800c\uff0c\u5982\u679c\u4ee5\u5206\u7c7b\u6b63\u786e\u7387\u4f5c\u4e3a\u8bc4\u5224\u6307\u6807\uff0c\u6211\u4eec\u4f7f\u752870\u4e2a\u65f6\u95f4\u70b9\uff08ADNI\u6570\u636e\uff0cTR=3s\uff0c\u89c1Chen, X., Zhang, H., Gao, Y., Wee, C.Y., Li, G., Shen, D., Alzheimer&#8217;s Disease Neuroimaging, I., 2016. High-order resting-state functional connectivity network for MCI classification. Hum Brain Mapp 37, 3282-3296\uff09\u3002\u7efc\u4e0a\uff0c\u6211\u8ba4\u4e3a\u5e94\u5f53\u5177\u4f53\u95ee\u9898\u5177\u4f53\u5206\u6790\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u8bbe\u7f6e\u4e0d\u540c\u7684\u7a97\u957f\uff0c\u5206\u522b\u6784\u5efa\u4e0d\u540c\u7684\u7f51\u7edc\uff0c\u7136\u540e\u5c06\u5176\u5408\u5e76\u7528\u4e8e\u5206\u7c7b\uff0c\u4ece\u800c\u89c4\u907f\u4f7f\u7528\u5355\u4e00\u7a97\u957f\u7684\u95ee\u9898\u3002<\/li>\n\n\n\n<li>\u95ee\u98987\uff1a\u75c5\u6001\u7684\u8bdd\u5f80\u5f80\u529f\u80fd\u8fde\u63a5\u4f4e\u503c\u66f4\u53d7\u5230\u5173\u6ce8\uff0c\u53ef\u662f\u8bb2\u5ea7\u63d0\u5230\u7684\u65b9\u6cd5\u88ab\u7a00\u758f\u6389\u4e86\uff0c\u8fde\u63a5\u7cfb\u6570\u4e3a0\u4e86\uff0c\u4ee4\u4eba\u8d28\u7591\u5de5\u5177\u5728\u8bca\u65ad\u75be\u75c5\u4e2d\u7684\u4f5c\u7528\uff0c\u5982\u4f55\u89e3\u91ca\uff1f<br>\u5f20\u5bd2\uff1a\u5e94\u8be5\u8bf4\uff0c\u75c5\u4eba\u7684\u529f\u80fd\u8fde\u63a5\u6709\u6240\u53d8\u5316\uff0c\u4f46\u4e0d\u4e00\u5b9a\u662f\u53d8\u4f4e\uff0c\u4e5f\u6709\u53d8\u9ad8\u7684\u53ef\u80fd\u6027\u3002\u800c\u4e14\uff0c\u75c5\u4eba\u548c\u6b63\u5e38\u4eba\u6709\u5dee\u5f02\u7684\u529f\u80fd\u8fde\u63a5\u4e0d\u4e00\u5b9a\u662f\u5fae\u5f31\u7684\u529f\u80fd\u8fde\u63a5\uff0c\u6709\u53ef\u80fd\u67d0\u4e00\u8fde\u63a5\u6b63\u5e38\u4eba\u5f88\u5f3a\u800c\u75c5\u4eba\u53d8\u5f31\u4e86\uff08\u4f46\u4ecd\u7136\u5f88\u5f3a\uff09\u3002\u53e6\u5916\uff0c\u57fa\u4e8e\u7a00\u758f\u8868\u5f81\u7684\u7f51\u7edc\u6784\u5efa\u65b9\u6cd5\u4e0d\u4e00\u5b9a\u4f1a\u628a\u5f31\u7684\u529f\u80fd\u8fde\u63a5\u7f6e0\u3002\u4ece\u6211\u4eec\u7ed9\u51fa\u7684\u4f8b\u5b50\u770b\u51fa\u6709\u5f88\u591a\u8fde\u63a5\u662f\u5fae\u5f31\u7684\u4f46\u4e0d\u662f0\u3002\u5373\u4f7f\u662f\u6211\u4eec\u4e00\u822c\u8ba4\u4e3a\u7a00\u758f\u8868\u5f81\u4f1a\u5e26\u6765\u5f88\u591a0\uff0c\u8fd9\u4e5f\u662f\u6211\u4eec\u8ba4\u4e3a\u7a00\u758f\u8868\u5f81\u7684\u7f3a\u70b9\u4e4b\u4e00\u3002\u6211\u4eec\u91c7\u7528\u66f4\u52a0\u7b26\u5408\u751f\u7406\u5047\u8bbe\u7684\u57fa\u4e8e\u7a00\u758f\u8868\u5f81\u7684\u7f51\u7edc\u6784\u5efa\u65b9\u6cd5\uff0c\u901a\u8fc7\u4fee\u6539\u7ea6\u675f\u9879\uff0c\u4f7f\u5f97\u751f\u6210\u7684\u7f51\u7edc\u5177\u6709\u7b26\u5408\u751f\u7406\u5047\u8bbe\u3002\u6bd4\u5982\u5728\u6ee1\u8db3\u7a00\u758f\u6027\u7684\u540c\u65f6\uff0c\u5177\u6709\u7ed3\u6784\u5316\u7279\u5f81\uff08\u6a21\u5757\u5316\u5c5e\u6027\uff09\u3002\u5177\u4f53\u5e94\u7528\u8868\u660e\uff0cBrainNetClass\u5bf9\u4e8e\u75be\u75c5\u8bca\u65ad\u6765\u8bf4\u8fd8\u662f\u5f88\u6210\u529f\u7684\u3002\u4f46\u662f\uff0c\u4e5f\u4e0d\u662f\u8bf4\u4efb\u4f55\u7814\u7a76\u90fd\u5fc5\u987b\u4f7f\u7528\u57fa\u4e8e\u7a00\u758f\u8868\u5f81\u7684\u7f51\u7edc\u6784\u5efa\u3002\u5728\u8bb2\u8bfe\u4e2d\uff0c\u6211\u63d0\u5230\u5982\u679c\u6570\u636e\u566a\u58f0\u6bd4\u8f83\u5927\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e8e\u7a00\u758f\u8868\u5f81\u7684\u7f51\u7edc\uff0c\u8fd9\u662f\u56e0\u4e3a\u5b83\u80fd\u591f\u53bb\u566a\u3002\u6211\u4e5f\u8bf4\u4e86\u5982\u679c\u6211\u4eec\u8ba4\u4e3a\u4fdd\u6301\u66f4\u591a\u7684\u8fde\u63a5\uff08\u4e0d\u90a3\u4e48\u7a00\u758f\uff09\u5bf9\u4e8e\u5f53\u524d\u95ee\u9898\u66f4\u91cd\u8981\uff0c\u6211\u63a8\u8350\u4f7f\u7528pairwise\u7684\u65b9\u6cd5\uff0c\u6bd4\u5982tHOFC\uff0caHOFC\u548cdHOFC\u3002<\/li>\n\n\n\n<li>\u95ee\u98988\uff1a\u5728\u78c1\u5171\u632f\u5206\u6790\u4e2d\u57fa\u4e8e\u76f8\u4f4d\u7684\u8fde\u63a5\u662f\u4e0d\u662f\u4e0d\u5e38\u7528\uff1f<br>\u5f20\u5bd2\uff1a\u76ee\u524d\u8f6f\u4ef6\u4e0d\u652f\u6301\u8fd9\u79cd\u5206\u6790\u65b9\u6cd5\u3002\u4f46\u662f\u5982\u679c\u4f60\u7528\u8fd9\u79cd\u65b9\u6cd5\u6784\u5efa\u4e86\u7f51\u7edc\uff08\u6bd4\u5982coherence-based FC\uff09\uff0c\u4e5f\u53ef\u4ee5\u7528\u57fa\u4e8e\u547d\u4ee4\u884c\u7684\u6a21\u5f0f\u91c7\u7528BrainNetClass\u8fdb\u884c\u5206\u7c7b\u3002\u4f46\u662f\u76ee\u524d\u6765\u8bf4\u7528\u57fa\u4e8e\u76f8\u4f4d\u7684\u8fde\u63a5\u6784\u5efa\u7f51\u7edc\u7684\u6587\u7ae0\u4e0d\u662f\u7279\u522b\u591a\u3002<\/li>\n\n\n\n<li>\u95ee\u98989\uff1a\u5728\u5b9e\u9a8c\u4e2dcorrelation of correlation\u4e0esparse correlation\u7684performance\u54ea\u79cd\u4f1a\u66f4\u597d\uff1f<br>\u5f20\u5bd2\uff1a\u8fd9\u4e2a\u95ee\u9898\u5f88\u91cd\u8981\u3002\u4e00\u53e5\u8bdd\u6765\u8bf4\uff0c\u6ca1\u6709\u56fa\u5b9a\u7684\u63a8\u8350\u3002\u4e0d\u540c\u7684\u95ee\u9898\uff0c\u4e0d\u540c\u7684\u6570\u636e\u5e94\u8be5\u4f7f\u7528\u4e0d\u540c\u65b9\u6cd5\u3002\u6bd4\u5982\uff0c\u57fa\u4e8ecorrelation of correlation\u7684\u9ad8\u9636\u7f51\u7edc\u53ef\u80fd\u66f4\u597d\u7684\u8865\u5145\u4f20\u7edf\u7684\u4f4e\u9636\u7f51\u7edc\uff0c\u5982\u679c\u4f4e\u9636\u7f51\u7edc\u505a\u4e0d\u51fa\u597d\u7684\u7ed3\u679c\uff0c\u4e0d\u59a8\u5c1d\u8bd5\u7528\u9ad8\u9636\u7f51\u7edc\u6765\u505a\u3002\u57fa\u4e8edHOFC\u7684\u7f51\u7edc\u6bd4\u8f83\u9002\u7528\u4e8e\u7cbe\u795e\u75be\u75c5\u7814\u7a76\u6216\u8005\u4e24\u7ec4\u4e4b\u524d\u6ca1\u6709\u5f88\u5f3a\u7684\u9759\u6001\u529f\u80fd\u8fde\u63a5\u7684\u5dee\u5f02\u7684\u7814\u7a76\u3002\u800c\u6240\u6709\u7684\u9ad8\u9636\u7f51\u7edc\u7814\u7a76\u90fd\u53ef\u4ee5\u751f\u6210\u6bd4\u8f83\u5bc6\u7684\u7f51\u7edc\uff0c\u5982\u679c\u57fa\u4e8e\u7a00\u758f\u7684\u7f51\u7edc\u5ffd\u7565\u4e86\u592a\u591a\u8fde\u63a5\uff0c\u4e0d\u59a8\u7528\u9ad8\u9636\u7f51\u7edc\u3002\u5f53\u6570\u636e\u6bd4\u8f83\u566a\u6216\u8005\u4eba\u548c\u4eba\u4e4b\u95f4\u5dee\u5f02\u5f88\u5927\u65f6\uff0c\u53ef\u4ee5\u7528sparse representation\u7684\u65b9\u6cd5\u6784\u5efa\u7f51\u7edc\u3002\u5177\u4f53\u7684\u65b9\u6cd5\u63a8\u8350\uff0c\u8bf7\u770b\u6211\u4eec\u7684\u6587\u7ae0\uff08Zhou, Z., Chen, X., Zhang, Y., Qiao, L., Yu, R., Pan, G., Zhang, H., Shen, D., 2019. Brain network construction and classification toolbox (BrainNetClass). arXiv:1906.09908\uff09\u4e2d\u8ba8\u8bba\u90e8\u5206\u7684\u4e00\u4e2a\u5c0f\u8282\uff1aAlgorithm choosing determination\u3002<\/li>\n\n\n\n<li>\u95ee\u989810\uff1a\u8fd9\u4e48\u591a\u65b9\u6cd5\uff0c\u80fd\u5426\u4e3e\u4f8b\u5176\u4e2d\u4e00\u4e2a\uff0c\u8bf4\u660e\u5f53\u65f6\u4ea7\u751f\u8fd9\u4e2a\u65b9\u6cd5\u7684\u601d\u8def\uff1f<br>\u5f20\u5bd2\uff1a\u8bf7\u5230\u6211\u4eec\u7684\u8f6f\u4ef6\u4e3b\u9875\u4e0a\u770b\u9644\u5f55\u7684\u53c2\u8003\u6587\u732e\u3002\u6bcf\u4e2a\u65b9\u6cd5\u7684\u63d0\u51fa\uff0c\u5b9e\u9645\u4e0a\u90fd\u662f\u6709\u5176\u7279\u522b\u7684\u8003\u91cf\u3002\u6bd4\u5982\uff0c\u57fa\u4e8e\u62d3\u6251\u7ed3\u6784\u7684\u9ad8\u9636\u7f51\u7edc(tHOFC)\u7684\u63d0\u51fa\uff0c\u5c31\u662f\u56e0\u4e3a\u4f20\u7edf\u7684\u57fa\u4e8eBOLD\u4fe1\u53f7\u7684\u529f\u80fd\u8fde\u63a5\u5728\u6709\u4e9b\u75be\u75c5\u7684\u7814\u7a76\u4e0a\u4e0d\u80fd\u5f97\u5230\u5f88\u597d\u7684\u7ed3\u679c\uff0c\u9700\u8981\u6211\u4eec\u91cd\u65b0\u5b9a\u4e49\u4ec0\u4e48\u662f\u529f\u80fd\u8fde\u63a5\u3002\u529f\u80fd\u8fde\u63a5\u53ef\u4ee5\u662f\u4e24\u4e2a\u8111\u533a\u65f6\u95f4\u6d3b\u52a8\u7684\u4e00\u81f4\u6027\uff0c\u4e5f\u53ef\u4ee5\u662f\u4e24\u4e2a\u8111\u533a\u5176\u4ed6\u65b9\u9762\u5c5e\u6027\u7684\u4e00\u81f4\u6027\u3002\u81f3\u4e8e\u5176\u4ed6\u65b9\u9762\u7684\u5c5e\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u4e00\u4e2a\u8111\u533a\u5230\u5176\u4ed6\u6240\u6709\u8111\u533a\u4e4b\u95f4\u7684\u529f\u80fd\u8fde\u63a5\u7279\u5f81\u6765\u5b9a\u4e49\u3002\u6bd4\u5982\uff0c\u57fa\u4e8e\u52a8\u6001\u529f\u80fd\u8fde\u63a5\u7684\u9ad8\u9636\u7f51\u7edc(dHOFC)\u7684\u63d0\u51fa\uff0c\u5c31\u662f\u56e0\u4e3a\u4f20\u7edf\u7684\u57fa\u4e8e\u52a8\u6001\u529f\u80fd\u8fde\u63a5\u7684\u65b9\u6cd5\u5927\u90e8\u5206\u53ea\u80fd\u627e\u51fa\u5c11\u6570\u51e0\u4e2a\u201c\u72b6\u6001\u201d\uff0c\u4ee5\u53ca\u72b6\u6001\u6709\u5173\u7684\u5c11\u6570\u6307\u6807\u5982\u201c\u51fa\u73b0\u9891\u7387\u201d\uff0c\u8fd9\u4e9b\u7279\u5f81\u5bf9\u4e8e\u5206\u7c7b\u6765\u8bf4\u8fdc\u8fdc\u4e0d\u591f\uff0c\u800c\u4e14\u592a\u591a\u4fe1\u606f\u5728\u8fd9\u4e00\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\u88ab\u6254\u6389\u4e86\u3002\u6211\u4eec\u8ba4\u4e3a\u52a8\u6001\u529f\u80fd\u8fde\u63a5\u53ef\u80fd\u662f\u5927\u8111\u7684\u4e00\u4e9badaptation\u6216\u8005\u662f\u52a8\u6001\u7684control\uff0c\u4e24\u4e2a\u8111\u533a\u4e4b\u95f4\u8fd9\u79cd\u52a8\u6001\u7684\u5173\u7cfb\u5f88\u53ef\u80fd\u548c\u5176\u4ed6\u4e24\u4e2a\u8111\u533a\u4e4b\u95f4\u7684\u52a8\u6001\u5173\u7cfb\u6709\u8054\u7cfb\uff0c\u8fd9\u79cd\u8054\u7cfb\u53ef\u4ee5\u7528\u7c7b\u4f3cBOLD\u4fe1\u53f7\u76f8\u5173\u7684\u65b9\u6cd5\u523b\u753b\uff0c\u5373\u8ba1\u7b97dynamic 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Class\u4e3b\u8981\u662f\u4e3a\u4e34\u5e8a\u670d\u52a1\u7684\u5417\uff1f<br>\u5f20\u5bd2\uff1aBrainNetClass\u7684\u63d0\u51fa\u662f\u4e3a\u4e86\u89e3\u51b3\u76ee\u524d\u4e34\u5e8a\u533b\u751f\u624b\u4e2d\u6709\u5927\u91cf\u6570\u636e\u5e76\u4e14\u5e0c\u671b\u80fd\u591f\u8bad\u7ec3\u5206\u7c7b\u5668\u8fdb\u884c\u4e2a\u4f53\u5316\u8bca\u65ad\uff0c\u4f46\u662f\u4ed6\u4eec\u5bf9\u673a\u5668\u5b66\u4e60\u548c\u7f16\u7a0b\u53c8\u4e0d\u592a\u719f\u6089\u8fd9\u4e00\u77db\u76fe\u3002\u6b64\u5916\uff0c\u4e34\u5e8a\u533b\u751f\u5bf9\u4e8e\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u53ef\u80fd\u66f4\u80fd\u591f\u4ece\u4e34\u5e8a\u548c\u751f\u7406\u7684\u89d2\u5ea6\u7ed9\u4e88\u89e3\u91ca\u548c\u7406\u89e3\uff0c\u8fd9\u5bf9\u4e8e\u8111\u7f51\u7edc\u5728\u4e34\u5e8a\u7814\u7a76\u4e2d\u7684\u5e94\u7528\u5177\u6709\u91cd\u8981\u4ef7\u503c\u3002\u57fa\u4e8e\u8ba1\u7b97\u673a\u8f85\u52a9\u7684\u5206\u7c7b\u7814\u7a76\u5176\u5b9e\u4e0d\u4ec5\u4ec5\u80fd\u7ed9\u51fa\u4e00\u4e9b\u6570\u5b57\u5316\u7684\u8bc4\u4ef7\u7ed3\u679c\u5982\u6b63\u786e\u7387\uff0c\u654f\u611f\u5ea6\uff0c\u7279\u5f02\u5ea6\u7b49\uff0c\u6211\u4eec\u66f4\u9700\u8981\u7684\u662f\uff0c\u600e\u4e48\u6837\u53bb\u7406\u89e3\u8fd9\u4e2a\u5206\u7c7b\u6a21\u578b\uff0c\u4ec0\u4e48\u6837\u7684\u7279\u5f81\u80fd\u591f\u5bf9\u5206\u7c7b\u6709\u5e2e\u52a9\u7b49\u7b49\u3002\u8be5\u5de5\u5177\u7bb1\u63d0\u4f9b\u4e86\u4e00\u6574\u5957\u6807\u51c6\u800c\u53c8\u4e25\u8c28\u7684\u57fa\u4e8e\u8111\u7f51\u7edc\u7684\u75be\u75c5\u4e2a\u4f53\u5206\u7c7b\u548c\u8bca\u65ad\u65b9\u6cd5\uff0c\u5728\u7279\u5f81\u63d0\u53d6\u3001\u9009\u62e9\u548c\u964d\u7ef4\uff0c\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u6a21\u578b\u8bc4\u4f30\u7b49\u591a\u4e2a\u6b65\u9aa4\u63d0\u4f9b\u6807\u51c6\u7684\uff0c\u88ab\u5927\u90e8\u5206\u7814\u7a76\u8005\u6240\u63a5\u53d7\u7684\u89e3\u51b3\u65b9\u6848\u5e76\u5141\u8bb8\u9ad8\u9636\u7528\u6237\u8fdb\u884c\u4e2a\u6027\u5316\u5b9a\u5236\u3002\u8f6f\u4ef6\u6700\u7ec8\u4f1a\u7ed9\u51fa\u4e30\u5bcc\u7684\u7ed3\u679c\u62a5\u544a\u548c\u6a21\u578b\u8bc4\u5206\uff08\u5305\u62ecROC\u66f2\u7ebf\uff0c\u53c2\u6570\u654f\u611f\u5ea6\u6d4b\u8bd5\uff0c\u6a21\u578b\u9c81\u68d2\u6027\u6d4b\u8bd5\uff0c\u6709\u7528\u7279\u5f81\u62a5\u544a\u7b49\uff09\u3002\u65b9\u4fbf\u795e\u7ecf\u79d1\u5b66\u3001\u4e34\u5e8a\u533b\u751f\u6216\u8005\u5176\u4ed6\u9886\u57df\u7684\u7814\u7a76\u8005\u4f7f\u7528\uff0c\u7814\u7a76\u8005\u4e0d\u9700\u8981\u592a\u591a\u673a\u5668\u5b66\u4e60\u7684\u80cc\u666f\u77e5\u8bc6\u4e5f\u80fd\u8f7b\u677e\u638c\u63e1\u3002<\/li>\n\n\n\n<li>\u95ee\u989812\uff1a\u6709\u6ca1\u6709\u5173\u4e8e\u8fd9\u4e2a\u65b9\u5411\u7684\u76f8\u5173\u7684\u8bba\u6587\u6253\u5305\uff1f\u65b9\u4fbf\u5f00\u653e\u5417\uff1f<br>\u5f20\u5bd2\uff1a\u5728\u8f6f\u4ef6\u4e3b\u9875\uff08https:\/\/github.com\/zzstefan\/BrainNetClass\uff09\u548c\u8f6f\u4ef6\u6587\u7ae0\uff08http:\/\/arxiv.org\/abs\/1906.09908\uff09\u4e2d\u6709\u8be6\u7ec6\u7684\u76f8\u5173\u8bba\u6587\u4ecb\u7ecd\u3002\u611f\u5174\u8da3\u7684\u7814\u7a76\u8005\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e24\u4e2a\u7f51\u7ad9\u7684reference list\u4e0b\u8f7d\u76f8\u5e94\u7684\u6587\u7ae0\u3002<\/li>\n\n\n\n<li>\u95ee\u989813\uff1a\u8fd9\u4e2a\u8f6f\u4ef6\u9002\u7528\u5c0f\u6837\u672c\u88ab\u8bd5\u5417\uff1f\u5c0f\u6837\u672c\u89c4\u6a21\u4e00\u822c\u591a\u5927\uff1f<br>\u5f20\u5bd2\uff1a\u9002\u7528\u5c0f\u6837\u672c\u3002\u4e00\u822c\u6765\u8bf4\u5982\u679c\u80fd\u670930-50\u6bcf\u7ec4\u5373\u53ef\u3002\u5f53\u7136\uff0c\u6837\u672c\u91cf\u8d8a\u5927\u8d8a\u597d\u3002\u673a\u5668\u5b66\u4e60\u5bf9\u4e8e\u6837\u672c\u91cf\u8981\u6c42\u662f\u6bd4\u8f83\u9ad8\u7684\uff0c\u6837\u672c\u91cf\u8d8a\u9ad8\uff0c\u7ed3\u679c\u53ef\u91cd\u590d\u6027\u548c\u63a8\u5e7f\u6027\u8d8a\u597d\u3002BrainNetClass\u4f7f\u7528SVM\u5206\u7c7b\u5668\uff0c\u5bf9\u4e8e\u5c0f\u6837\u672c\u95ee\u9898\u4e5f\u8fd8\u662f\u5f88\u9c81\u68d2\u7684\u3002\u5982\u679c\u662f\u5c0f\u6837\u672c\uff0c\u63a8\u8350\u7528\u7559\u4e00\u6cd5\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/li>\n\n\n\n<li>\u95ee\u989814\uff1a\u6700\u4f73\u7684\u65f6\u95f4\u7a97\u662f\u5982\u4f55\u786e\u5b9a\u7684\uff1f<br>\u5f20\u5bd2\uff1a\u6839\u636e\u524d\u4eba\u7ecf\u9a8c\uff0c\u7528TR\u7ed3\u5408\u9884\u5904\u7406\u6ee4\u6ce2\u53c2\u6570\u6765\u5b9a\u3002\u5bf9\u4e8eBrainNetClass\uff0c\u6839\u636ecross validation\u7684\u51c6\u786e\u7387\u6765\u5b9a\u3002\u65f6\u95f4\u7a97\u662f\u4e00\u4e2a\u53ef\u4ee5\u8fdb\u884c\u4f18\u5316\u7684\u53c2\u6570\uff0c\u5728\u8ba1\u7b97\u7684\u8fc7\u7a0b\u4e2d\u81ea\u52a8\u4f18\u5316\u3002<\/li>\n\n\n\n<li>\u95ee\u989815\uff1a\u7a00\u758f\u8868\u793a\u7684\u7f51\u7edc\u662f\u5bf9\u79f0\u7684\u5417\uff1f<br>\u5f20\u5bd2\uff1a\u4e0d\u662f\u5bf9\u79f0\u7684\u3002\u53ef\u4ee5\u5728\u540e\u671f\u5904\u7406\u65f6\u5bf9\u79f0\u5316\uff1a(W + W\u2019) \/ 2.<\/li>\n\n\n\n<li>\u95ee\u989816\uff1a\u7a00\u758f\u8868\u793a\u662f\u4e0d\u662f\u504f\u76f8\u5173\u7684\u4e00\u79cd\uff1f\u672c\u8d28\u4e0a\u8bf4\u7528\u7a00\u758f\u8868\u793a\u6784\u5efa\u504f\u76f8\u5173\u7f51\u7edc\uff1f\u8fd9\u6837\u7684\u7279\u5f81\u8868\u793a\u662f\u4e00\u4e2a\u4f18\u7684\u8868\u793a\u6cd5\u5417\uff1f<br>\u5f20\u5bd2\uff1a\u662f\u7684\u3002\u7a00\u758f\u8868\u793a\u53ef\u4ee5\u7406\u89e3\u6210\u4e00\u79cd\u504f\u76f8\u5173\u3002\u5b9e\u9645\u4e0a\uff0cself-representation\u662f\u4e00\u79cd\u89e3\u504f\u76f8\u5173\u7684\u65b9\u6cd5\u3002\u504f\u76f8\u5173\u662f\u4e00\u79cd\u7efc\u5408\u8861\u91cf\u591a\u4e2a\u8111\u533a\u5173\u7cfb\u7684\u65b9\u6cd5\uff0c\u5b83\u5728\u4e24\u4e2a\u8111\u533a\u8ba1\u7b97\u76f8\u5173\u7684\u57fa\u7840\u4e0a\uff0c\u6392\u9664\u4e86\u5176\u4ed6\u8111\u533a\u7684\u5f71\u54cd\u3002\u52a0\u5165L1norm\u4ee5\u540e\uff0c\u8fd9\u4e2a\u95ee\u9898\u5c31\u53d8\u6210\u4e86sparse representation\uff08\u7a00\u758f\u8868\u793a\uff09\u4e86\u3002\u5177\u4f53\u7684motivation\u53ef\u4ee5\u53c2\u8003\u6587\u7ae0Qiao, L., Zhang, H., Kim, M., Teng, S., Zhang, L., Shen, D., 2016. Estimating functional brain networks by incorporating a modularity prior. 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