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To beginner: task-based fNIRS study design (2)

March 19th, 2018

This is a guest post by Ning Liu from Stanford University.


A basic block design includes two conditions: task condition and control condition. The two conditions present alternatively, thus is also called ‘AB block’ (Fig. 1A). This design assumes that the two conditions can be cognitively added, implying no interactions among the cognitive components of a task. A subtraction comparison strategy then can be used in the data analysis to assess the brain regions involved in the performance of the task. Although, in most cases, this assumption is invalid, many people still use it because it usually produces robust and reproducible results. A block design task often includes more than five epochs per condition, and each epoch lasts 10 to 30 seconds. As an example, we used the following block-designed emotional face task in one of our studies: R-F-S-F-S-F-S-F-S-F-S-F-S-R, where R, F and S represent rest, fearful face and scrambled face epochs respectively. Each task epoch (both F and S) lasts 20 s, and each rest epoch lasts 30 s. (Liu, et al. 2015)
Event-related design (Fig.1B) allows the order of conditions present randomly and the time intervals between stimuli vary. This design is more naturalistic and allows for detecting transient variations in hemodynamic responses (HRF). However, event-related design usually needs more number of stimuli in order to enhance the statistical power, and the experiments are often longer than blocked designs.
Mixed design (Fig. 1C) combines block and event-related designs. It alternates two conditions (task and control) as in a block design. Within a block, the interstimulus interval (ISI) varies as in an event-related design. It allows for extracting brain regions either exhibiting transient neural activity (item-related information processing) or sustained neural activity (task-related information processing). However, it involves more assumptions than other designs, and the estimation of the HRF is poorer than event-related design.

experiment design

Fig. 1. Experiment design (A)Block design (B)Event design (C)Mixed design (ref Edson et al. 2006)

References:
Amaro E Jr, Barker GJ, “Study design in fMRI: basic principle”, Brain and Cognition, 2006, 60(3):220-32.
Liu N, Cui X, Bryant DM., Glover GH, Reiss AL, “Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy”, Biomedical Optics Express, 2015, 6(3): 1074-89. doi: 10.1364/BOE.6.001074.

Author: Xu Cui Categories: brain, nirs Tags:

浅谈近红外脑成像(fNIRS)任务态实验设计(二)

March 19th, 2018

本文作者是斯坦福大学刘宁

传统的组块设计(block design)通常包括两种组块: 任务组块和控制组块,又被称为“AB block”设计(如图1A)。这种设计基于一个假设: 两种组块条件下的意识态是线性叠加的,它们之间没有相互作用。因此可以用两种条件下的意识态相减。 尽管这种假设往往是不成立的,但是这种实验设计通常可以得到比较强的信号,易于寻找被任务激活的脑区,并得到比较稳健的实验结果,所以这种设计还是一直得到广泛使用。常用的组块设计,每种条件包括六个以上的实验组块(epoch), 一个实验组块持续10到30秒时间。例如,我们用过下面这种组块设计的面部表情实验: R-F-S-F-S-F-S-F-S-F-S-F-S-R。其中, R 代表安静状态组块,F代表恐惧的面部表情组块,S代表模糊化的面部表情组块。每个刺激组块(both F and S)持续20秒,安静组块(R) 持续30秒(Liu, et al. 2015)。

事件设计(event-related design, 如图1B)能够呈现单个的刺激而不是以组块的形式呈现,单个的刺激之间有长短变化的时间间隔,设计更加灵活,可以提供比组块设计更多的信息,例如血流动力学响应函数(HRF)的信息,因此也经常被使用。但为了得到较稳定的结果需要刺激重复次数较多,因此事件设计总的实验时间通常较长。

另一种比较常见的设计是混合设计(mixed design,如图1C),就是把组块设计和事件设计结合起来,既有任务组块和控制组块的区分,单个刺激之间的时间间隔又是长短变化的。这种设计的优点是既易于得到被任务激活的脑区,又能得到HRF的信息。缺点是需要基于更多的假设,对HRF的估算也较事件设计差一些。

experiment design

Fig. 1. 任务设计示意图。(A) 组块设计; (B)事件设计; (C)混合设计。(此图转自Edson et al. 2006)

Author: Xu Cui Categories: brain, nirs Tags:

To beginner: task-based fNIRS study design (1)

March 10th, 2018

This is a guest post by Ning Liu from Stanford University.

For someone who has no imaging background and just began to use fNIRS, s/he might be surprised to find out that the imaging study design is so different with study designs in other fields. For instance, task-based imaging studies usually involve bunch of repeated stimuli. This is quite different with what we usually do in a naturalistic environment in our daily life. Why not use just one stimulus? This is due to the difficulty in detecting signal changes against a much stronger background physiological noise. Thus, in order to measure brain actives, one needs to carefully design the experiment and use some clever method to analysis the data.

For task-based study, there are two types of basic design. One is called block-design and the other event-design. It worth noticing that they have something in common — the same stimulus needs to be repeated many times, and the detected signal changes are statistically tested for significance.

Another aspect that beginners usually ignore is the design of control condition. A typical task design usually contains epochs or events of interest along with control epochs or events, such that a cognitive subtraction (for instance) can be performed and resulting in robust and reproducible results.

Author: Xu Cui Categories: brain, nirs Tags:

浅谈近红外脑成像(fNIRS)任务态实验设计(一)

March 10th, 2018

本文作者是斯坦福大学刘宁

对于一个近红外初学者,特别是没有任何脑成像背景的初学者,他们往往没有想到脑成像的实验设计和别的学科的实验设计,诸如行为科学或者生物实验等有很大不同。通过一些文献阅读或者相关培训,初学者可能注意到脑成像的任务态实验设计往往有很多重复的环节。那么,为什么脑成像的实验需要这样的设计?究其根本原因,其实是因为现有的无侵入式脑成像技术,无论是核磁共振脑功能成像(fMRI) 还是近红外脑功能成像(fNIRS), 其直接测量到的信号变化与噪音相比都太小(~1%量级)。换言之,巨大的生理噪音掩盖了信号的变化。因此,为了能测量到与大脑活动相关的信号,具体到近红外成像中就是测量到氧合血红蛋白和脱氧血红蛋白的浓度变化,就需要巧妙的设计实验和分析数据。

对于任务态实验,有两种基本的实验设计:一种是采用组块设计(block design),另一种是采用事件设计(event design)。然而,无论哪种,它们的共同点是:实验中多次重复同一个任务/操作,数据处理时通过建立模型,用统计的办法找出具有统计意义的相关活跃脑区。

任务态的实验设计中常常被初学者忽略的另一个重要环节就是对照条件(control condition)的设计。刺激任务除了设计能激活相关脑区的任务以外,一般还应该设计一个好的对照条件,进而可以通过两种条件下脑信号的对比,找出真正与任务相关的脑区。

Author: Xu Cui Categories: brain, nirs Tags:

近红外成像入门培训 —— 小型私人辅导

February 23rd, 2018

代朋友发布这个培训信息。有兴趣直接联系 fnirs_studio@outlook.com

————————————————
近红外成像入门培训 —— 小型私人辅导
专门针对初学者,不需要任何近红外成像经验和背景。 近红外专家用最浅显易懂
的语言准确解释近红外成像原理及应用。专门针对各种背景的初学者量身定做,深
入浅出,帮助初学者快速入门。授课老师拥有近红外成像博士学位(美),有多年
近红外成像实践经验,并具有丰富教学经验,曾经成功教导过各种学术背景人士,
包括医生(精神科、儿科、脑外科 等)、大学本科生、研究生、以及高中生等,得
到一致好评。
基本培训单元包括:
(1) 近红外脑功能成像技术原理及理论基础
(2) 数据采集基本流程
(3) 单人扫描实验设计及数据处理
(4) 双人同步扫描(超扫描)实验设计及数据处理
培训内容可根据具体需求做适当调整。咨询详情,请联系
:fnirs_studio@outlook.com
培训老师: 刘宁,生物医学工程博士(近红外成像方向),现任斯坦福大学助理
研究员。有十年以上近红外成像领域科研经验。对近红外脑功能成像有深入研究和
丰富实践经验。主导过的科研项目包括:单人和双人,成人和儿童,正常人和特殊
人群 脑功能成像。

Author: Xu Cui Categories: nirs Tags:

How to track new NIRS publications and grants?

October 27th, 2017

Near infrared spectroscopy (NIRS) is an increasingly popular technology in brain imaging research. Compared to fMRI, NIRS can be used for more naturalistic experiments, including face to face communication, natural body movements, sports, infants, and is well suited for real-time applications.

If we count the number of NIRS publications using keyword (NIRS OR fNIRS) AND brain, we find the number increases steadily over the past 10 years:

With this trend, it’s expected that we will reach a speed of 1 new paper every day in the year of 2018 or 2019. This is a fast field, and it’s nearly impossible for us to search (NIRS OR fNIRS) AND brain every day or even every week. For a student who does not follow literature proactively, he might know of a paper a year after its publication.

The easiest way to solve this problem is to use an app to notify us of the new publications. The one I use is called Stork . All I need to do is to enter a few keywords I am interested in, e.g.:

(NIRS OR fNIRS) AND brain

But what if I want to follow more specific fields, such as hyperscanning, or comparison with fMRI etc? We can enter the following keywords:
(NIRS OR fNIRS) AND brain AND hyperscanning
(NIRS OR fNIRS) AND brain AND fMRI
(NIRS OR fNIRS) AND brain AND "deep learning"
(NIRS OR fNIRS) AND brain AND social

What if I want to follow some scientists in the field? We may enter their names, such as:
David Boas
Allan Reiss (NIRS or fNIRS)

After we setup the keywords, Stork, like a diligent assistant, will search for us every day. If she finds something, she will send an email to us, listing the new publications. A recent example is shown below:

fmri nirs

Papers
The Temporal Muscle of the Head Can Cause Artifacts in Optical Imaging Studies with Functional Near-Infrared Spectroscopy. by Martin Schecklmann, Alexander Mann, …, Florian B Haeussinger (2017) Front Hum Neurosci (impact factor: 3.6) Free full text

nirs brain

Papers
Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features. by Rihui Li, Thomas Potter, Weitian Huang, Yingchun Zhang (2017) Front Hum Neurosci (impact factor: 3.6)Free full text

Not only does Stork search scientific publications for you, she also searches NIH grant database (this is an advanced feature). That means whenever some researchers receive a grant on NIRS, you will know it. You will know what is his research direction in the next few years, how much money he received etc. This feature is mostly useful for professors and senior graduate students and postdocs.

The link of Stork is: storkapp.me and it’s very easy to use.

Stork

Stork

Author: Xu Cui Categories: nirs, stork Tags:

What is reality?

September 9th, 2017

David Eagleman was my co-advisor during my Ph.D study in Baylor College of Medicine. He produced a documentary movie last year with BBS addressing an important question “What is reality?” based on advances in neuroscience. I personally find it eye-opening and provoke deeper thinking of our own existence.

Here is the video:

Author: Xu Cui Categories: brain Tags:

3 ways to check NIRS data quality

August 31st, 2017

Before performing any data analysis, we should check the data quality first. Below are 3 ways to do so.

1. Visual check of the time series

The best pattern detector is our eyes and brains! In many cases, if we visually see the data, we know what is wrong. You may use the method and program in this post to plot the time courses of all channels (not just one).

Visual data quality check of NIRS time courses

Visual data quality check of NIRS time courses

In the plot above, all 48 channels are plotted together (the y-axis). We can clearly see two types of noise:

  1. The spikes which occur in most channels after time point 7000. These spikes are caused by head motion.
  2. The high noise level in the “red” channels (channels 39, 34, 31). This is more evident if we plot the variance of each channel (figure below). As we can easily see, the variance of channels 39, 34 and 31 is much higher than other channels.

    Variance vs Channel

    Variance vs Channel

2. Existence of the “heart-beat” band

If the NIRS signal was acquired well, then the heart beating signal should be captured, leaving a bright brand in the frequency ~1Hz in the wavelet transform plot, just like the left plot in the figure below (the band close to period 8). If there is no such band, it does not necessarily mean the signal is trash, but you need to be cautious. To use the wavelet transform toolbox, please download here: http://www.alivelearn.net/?p=1561


NIRS wavelet

NIRS wavelet

Example:
figure;wt(hbo(:,1))

3. Correlation between hbo and hbr

The third way is to check the correlation between hbo and hbr. They are supposed to have negative correlation, at least in young healthy subjects. If not, or if they have perfect negative correlation (-1), then they might contain too much noise. We have a separate article on this method. Please check out http://www.alivelearn.net/?p=1767

Correlation between oxy and deoxy-Hb

Correlation between oxy and deoxy-Hb

Below is the scripts used for the 3 methods.

[hbo,hbr,mark]=readHitachData('SA06_MES_Probe1.csv');

figure;plotTraces(hbr,1:52,mark)

figure;wt(hbo(:,1))

for ii=1:52; wt(hbo(:,ii)); pause; end

[badchannels] = checkDataQuality(hbo,hbr);

Do you have other ways to check data quality? Please let me know!

Author: Xu Cui Categories: brain, nirs Tags:

[培训信息] 第一届近红外脑功能数据处理培训班 NIRS training course in China

August 23rd, 2017

重庆思影科技有限公司将于2017年10月16日– 2017年10月18日(周一至周三)举办第一届近红外脑功能数据处理培训班(详见课表安排)。欢迎大家前来咨询。

培训班依然坚持小班教学,手把手带教的教学模式,争取使每一位参加培训的学员能够在近红外脑功能数据处理方法上取得进步。

1、培训简介

功能性近红外光谱技术(functionalnear-infrared spectroscopy, fNIRS)技术是一项利用近红外光穿过皮层组织时的衰减程度定量化地测量大脑局部氧合血红蛋白和脱氧血红蛋白浓度变化的技术。该技术相对fMRI和PET等技术具有更高的时间分辨率(最高可达数十Hz)、便捷性高等优点;相对EEG/ERP技术具有相对更高的空间分辨率。该技术自问世以来,在基础研究、工程与临床实践中有广泛的应用,而且其普及性仍在增长。使用该技术的专业领域涉及心理学、医学、神经科学、脑机接口、运动科学和教育学等。

fNIRS信号的数据处理与fMRI和EEG/ERP等相比既有很多相同点也有很多其独有的方法。准确掌握fNIRS相关数据处理技能对于我们设计fNIRS实验、分析fNIRS数据至关重要。由于fNIRS技术专业性较强,需要系统的培训才能掌握,因此,我们拟举办fNIRS信号数据处理分析培训班,本培训班将在实验设计、数据分析方法原理以及常用开源软件操作等方面对学员展开系统培训,旨在帮助刚刚接触fNIRS技术的人员,如心理、运动科学、生物医学工程等专业研究生,精神、神经内外科、康复科、儿科等医生或临床科研人员及快速了解本领域及掌握fNIRS实验设计及编程、数据处理及分析的相关方法。

2、培训对象

本次培训班面向的对象是一些希望利用近红外技术进行科研和临床研究的医生、研究人员等,为了使数据处理不再成为脑科学研究的拦路虎,培训班实行小范围的理论与实践相结合,授课、操作、指导及问题解决一体化,最终达到独立操作。

培训内容主要包括:fNIRS基本原理及实验设计,SPM 操作及批处理流程,多人脑间功能连接数据分析原理,Homer软件数据处理基础,Homer软件数据处理进阶。

注:如方便,请于会议开始前一天到达会场(9:00 - 20:00)熟悉场地及安装软件、拷贝资料等事宜。

课程安排:

时间

课程名

主要内容

第一天

胡志善

10月16日

上午

fNIRS基本原理及实验设计

-fNRIS成像原理简介

-Block与Event实验设计及其变式

-fNIRS原始数据结构简介

-fNIRS设备信号调试技巧(根据授课进度穿插)

下午

SPM 操作及批处理流程

-NIRS SPM GUI界面介绍

-数据转换(data conversion)

-通过GUI界面逐步处理过程(含数据转换conversion、选择GLM所需参数、滤波filter、去漂移detrending等)

-通过脚本文件编写批处理程序

-数据结果的解释

第二天

张明明

10月17号

上午

多人脑间功能连接

数据分析原理

-傅里叶变换及小波分析原理

-小波相干分析原理

-格兰杰因果分析原理

下午

多人脑间功能连接

数据分析操作

-小波相干分析个体操作

-小波相干分析批处理操作

-格兰杰因果分析操作

第三天

王乾东

10月18日

上午

Homer软件数据处理基础

-NIRS一般数据处理步骤介绍

-Homer软件介绍

-通过Homer软件的GUI界面处理数据

下午

Homer软件数据处理进阶

-Matlab编程快速入门

-了解Homer处理的数据的结构与含义

-编写Matlab脚本更加自由地处理NIRS数据

4、培训人数

为保证培训质量,此次培训限定人数20人左右,报名敬请从速。

5、培训地点

重庆市渝中区青年路38号重庆国贸中心2004#,具体见会议指南。

6、培训费用

所有参会人员2000/人(含资料费、培训费和午餐费,交通及住宿费自理)。

7、报名方式

请将报名回执发送至:syfmri@163.com。

8、缴费方式

银行转账或者支付宝(18580429226,户名:杨晓飞),不接受现场缴费,谢绝录像,主办方提供发票。

9、联系方式

联系人:彭庭烨。电话:023-63084468/15123187262。

10、备注

请各位培训学员自带笔记本电脑(windows 64位系统、i3、4G内存等基本配置);学员自己有数据的可以带3-5例进行现场处理;并在10月1日前进行缴费及将回执表发给彭小姐,便于培训安排。

报名回执表

单位名称

(发票抬头)

姓名

性别

QQ

电话号码

科室/专业

缴费方式

□转帐  □支付宝 (请选择在□打√)

银行信息

户名:重庆思影科技有限公司

账号:123909127710102

开户银行:招商银行重庆分行渝中支行

汇款备注

第一届近红外脑功能数据处理培训班

注:请完整填写回执表后回传给我们,以便给你发送确认函,谢谢支持!

11、在线支持服务

参加培训学员将得到在线技术支持服务,伴随参加培训班的学员共同成长。

12、培训人员简介:

胡志善,澳门大学博士研究生。在心理科学进展及JECP各发文一篇。专注于使用fNIRS进行认知神经方面的研究,有近5年fNIRS的实验设计、执行及数据分析经验。能够熟练运用Python进行fNIRS的实验编程,使用NIRSport 及 CW6 等设备进行认知方面的研究,已完成决策、说谎、执行功能、数字计算、语言、运动等认知功能等多项实验;能够熟练运用NIRS SPM 及 Homer2 进行数据分析,并能够熟练运用MATLAB进行数据的批处理,代码风格良好。

张明明,博士研究生。东南大学神经信息工程专业在读博士研究生。目前研究方向为近红外超扫描(hyperscanning)技术支持下的多人社会交互行为研究,擅长多人社会互动中的近红外数据分析,做过多个近红外实验具有丰富数据分析经验。已在相关领域学术杂志期刊发表学术论文若干,其中SCI收录学术论文2篇。

王乾东,北京大学前沿交叉学科研究院在读博士研究生。目前研究主要采用近红外、眼动、多导生理和行为技术探究自闭症儿童的认知发展。能够熟练运用Matlab进行近红外和眼动的实验编程及数据处理。目前正进行一项多模态的实验(同时采集近红外、眼动和多道生理仪的数据)。已有多篇文章发表在国外SCI以及国内权威和一级心理学期刊上。

Author: Xu Cui Categories: nirs Tags:

A few recent NIH grants awarded related to NIRS (2017-07-05)

July 5th, 2017
The following email was sent from Stork to me. Stork is an easy-to-use app to alert me of new scientific publications and NIH grants based on my own keywords. Below are a few grants awarded in the NIRS field.
David Boas
Awarded Grants
Enabling
widespread use of high resolution imaging of oxygen in the brain
by
David A Boas (2017) NIH Grants Awarded (Amount: $288,619) Duration: 2017-07-01 to
2018-06-30

fmri nirs

Awarded Grants
Mechanisms
of Interpersonal Social Communication: Dual-Brain fNIRS Investigation

by Joy Hirsch (2017) NIH Grants Awarded (Amount: $416,250) Duration: 2017-06-01 to
2018-05-31

Multimodal
Neuroimaging of Cigarette Smoking
by Yunjie Tong (2017) NIH Grants
Awarded (Amount:
$137,928)
Duration: 2017-06-19 to 2017-11-30

nirs brain

Awarded Grants
Neural
Mechanisms for Social Interactions and Eye Contact in ASD
by Joy
Hirsch (2017) NIH Grants Awarded (Amount: $640,560) Duration: 2017-07-01 to 2018-06-30

Free
Surfer Development, Maintenance, and Hardening
by Bruce Fischl (2017)
NIH Grants Awarded (Amount: $523,203) Duration: 2017-07-01 to 2018-06-30

Coherent
hemodynamics spectroscopy for cerebral autoregulation and blood flow
by
Sergio Fantini (2017) NIH Grants Awarded (Amount: $517,756) Duration: 2017-05-01 to
2018-04-30

Training
in Drug Abuse and Brain Imaging
by Scott E Lukas (2017) NIH
Grants Awarded (Amount:
$256,451)
Duration: 2017-07-01 to 2018-06-30

Author: Xu Cui Categories: brain, nirs Tags: