GraphVar beta 0.62 out now

Submitted by Johann Kruschwitz on
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GraphVar

UPDATES GraphVar beta 0.62
DONWLOAD HERE

1. Added two NEW "dynamic" graph measures!
- nodal flexibility and nodal promiscuity coefficient which are based on changing community assignments in an ordered multislice matrix (as in Braun et al., 2015: Dynamic reconfiguration of frontal brain networks during executive cognition in humans)

2. Added new "regular" graph metrics:

Error Scrubbing

Submitted by joana on
Dear all, I've been working with DPARSF to preprocess resting-state fMRI data. I got an error for a few of my subjects when I applied scrubbing (FD Power, threshold: 0.5, Linear Interpolation) - Please see the error below. Do you know why this happened?

Fw: 第十三届fMRI培训班通知-2015（北京）

Submitted by YAN Chao-Gan on

FW:

近十年，功能神经影像技术的发展日新月异，已成为研究认知和临床脑疾病的重要手段。利用不同模态的神经影像新技术（如静息态和任务态fMRI，弥散磁共振成像DTI，脑神经网络技术等），临床研究人员可以获得脑结构、功能、代谢等多方面的信息。神经影像新技术在认知科学和神经/精神科学领域的研究和应用已经越来越广泛，为理解重大脑疾病如精神分裂症、抑郁症、阿尔茨海默病、儿童多动症、癫痫、中风、脑外伤、药物成瘾等的病理生理学机制，以及探索重要的临床问题如早期诊断、药物治疗机制等提供重要帮助。但值得注意的是，神经影像学研究属于典型的交叉学科，需要研究者具备扎实的多学科背景知识和系统深入的学习训练，才能在该领域有所成就。

本次培训班的宗旨是重点让初步接触神经影像学领域的学员们，系统学习不同模态神经影像技术的基本原理、实验设计、数据采集、数据预处理、影像分析、结果解释等多方面的知识。对有一定经验的学员们，本培训班也提供了极好的机会加深认识，帮助了解神经影像技术在国内外的最新进展和动态，为进一步做出高水平的研究工作打下坚实基础。本培训班将结合实例，重点介绍静息态fMRI、任务态fMRI、DTI和脑神经网络技术等在认知研究和临床实验设计所需要的基本要素以及数据处理相关的统计方法。

本次培训班主要包含三方面内容：

ReDo Nuisance Covariates Regression

Submitted by Sharon Chen on
Dear Prof. Yan, Thanks for developing such a helpful toolkit. Currently, after I went through the whole processing and extracted some interesting ROI time course, may I redo the step of "Nuisance covariate regression " for removing some specific signal attributed by some ROI signal? then, which "starting Directory Name" I should use? please don't tell me that I should redo the whole processing from the beginning. you know that "Reorient Fun/T1" and "New Segment+DARTEL" are really time consuming. thanks for your response. Best, Sharon
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