近紅外會議分享：NIRSTORM
NIRSTORM: a Brainstorm plugin dedicated to fNIRS statistical analysis, 3D reconstructions and optimal probe design
NIRSTORM is a plugin dedicated for fNIRS data analysis, built upon Brainstorm, an internationally recognized software for EEG/MEG processing, featuring advanced databasing, visualization, signal processing, source localization and statistical analysis methods. The purpose of this webinar is to introduce NIRSTORM as a user-friendly and fully complete environment dedicated to fNIRS statistical analysis. The first section will introduce NIRSTORM database, data importation and classical channel-space fNIRS processing (band pass filtering, Modified Beer-Lambert Law, motion correction and window averaging) as well as more recently added General Linear Model based statistical analyses (auto- regressive/precoloring model, mixed-effect group level analysis) to allow statistics of the hemodynamic response in the channel space or along the cortical surface after 3D reconstruction. We will then present the most advanced NIRSTORM features, such as the integration of MCXLab software [Fang and Boas Opt. Express 2009] to estimate light sensitivity profiles within anatomical head models, our method allowing personalized optimal montage design targeting a predefined brain region [Machado et al JNS-Methods 2018, Sc. Report 2021] and advanced 3D reconstructions using weighted Minimum Norm and Maximum Entropy on the Mean [Cai et al, submitted]
NIRSTORM 是一個專門用于 fNIRS 數(shù)據(jù)分析的插件，基于腦電圖/腦磁圖處理軟件Brainstorm，具有先進(jìn)的數(shù)據(jù)庫建立、可視化、信號處理、源定位和統(tǒng)計(jì)分析方法。這次網(wǎng)絡(luò)研討會的目的是介紹 NIRSTORM 作為一個用戶友好和完整的環(huán)境，致力于 fNIRS 的統(tǒng)計(jì)分析。第一部分將介紹 NIRSTORM 數(shù)據(jù)庫、數(shù)據(jù)輸入和經(jīng)典的通道空間 fNIRS 處理(帶通濾波、修正的 Beer-Lambert 定律、運(yùn)動校正和窗口平均) ，以及最近增加的基于一般線性模型的統(tǒng)計(jì)分析(自回歸/預(yù)著色模型、混合效應(yīng)組水平分析) ，以統(tǒng)計(jì)通道空間或皮層表面在3 d 重建后的血流動力學(xué)反應(yīng)。然后我們將介紹最先進(jìn)的 NIRSTORM 特性，例如集成 MCXLab 軟件[Fang and Boas Opt. Express 2009]在解剖頭部模型中估計(jì)光敏感度剖面，我們的方法允許針對預(yù)定義的大腦區(qū)域進(jìn)行個性化的最佳蒙太奇設(shè)計(jì)[ Machado 等人 JNS-Methods 2018，sc. Report 2021]和使用加權(quán)最小范數(shù)和平均最大熵的高級3D 重建[ Cai 等人提交]
2021年4月23日 20:30 北京，上海
注冊地址：https://us02web.zoom.us/webinar/register/WN_-VkVKIgkTbqaur9wmv9tDg?timezone_id=Asia%2FShanghai（左下角閱讀原文）
文章來源郵件訂閱，翻譯僅限學(xué)習(xí)分享使用，可能存在錯誤，敬請諒解，不用于商業(yè)行為，若有侵權(quán)及疑問，請后臺留言！