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fNIRS中基线的做法和注意事项 (Baseline in fNIRS)

November 5th, 2018

This article is a guest post by Rui Chen. An English version can be downloaded here

对于信号的分析,不论是脑电的数据分析还是近红外的数据分析,基本上都会遇到一个参考基线的问题,这是很多童鞋和老师会疑惑的一个关键点。为什么要使用基线以及如何正确的使用。

在本文中,小编主要介绍近红外的基线问题,对于脑电数据分析中的基线在小编的脑电数据分析系列文章中会进行完整的阐述。

目前我们使用的近红外所使用的光源发射方式,大多数是采用连续波长,对于完全采集绝对路径的信号是无法进行采样,所以在当前所评估的血红蛋白的指标中,总是对相对于另一个时间点的变化进行测量评估,例如,如果您研究响应刺激的氧合变化,您实际上正在测量在呈现刺激之前的短时间与之间的响应变化。刺激前的时期通常被称为基线。

什么是基线以及如何使用它?

对于那些刚刚开始使用NIRS技术的科研人员来说,有些不清楚测量结果是什么意思。测量的数据相对于什么,为了解释这一点,我们来说说基线的起源。

最常用的NIRS是“连续波”CW-NIRS,它基于修正的Beer-Lambert定律,使用连续发射光源,近红外光进入大脑组织有可能发生散射(改变其方向)又可能被吸收的光学特性来研究大脑活动水平。散射和吸收都是CW-NIRS提供相对测量的原因。

当把手指放在红色激光指示器前时,散射效果清晰可见。你会看到你的整个手指亮起来,而不只是一条直线,光散射在组织中。因此,如果您直接测量手指上的激光,则根本没有吸收,也不会通过激光指示器接收100%的发射光。

我们可以假设这种在所有方向上的散射是恒定的,所以如果我们测量接收光的变化,那么光从光源到接收器的路径中的吸收肯定是会发生改变。

现在使用近红外光来对大脑的研究,利用了近红外光在大脑组织中血红蛋白对光的特性,近红外光进入大脑组织大部分的吸收是由血红蛋白引起的,这就是为什么我们可以利用NIRS进行测量的原因。然而,光也会被其他组织(例如脂肪组织,毛发,皮肤、颅骨、脑积液等无关组织)吸收,但这是无法避免的。所以,我们假设在测量期间这种吸收是恒定的,并且接收光的任何变化都是由血红蛋白吸收的变化引起的。既然血红蛋白发生了变化,那么就会存在“起始状态”,对于CW-NIRS使用探测器接收到近红外光的变化,根据Mod Beer-Lambert定律来计算血红蛋白浓度的变化,那么,对于这个变化来说总是相对于“起始状态”或基线的。

CW-NIRS只能测量浓度的变化,因此无法提供起始浓度(通常设置为任意零点)。但是,它可以提供由任何任务或研究的状态引起的基线变化(例如收缩肌肉或增加大脑某部分的活动)。这种变化以毫摩尔每升(mmol/liter or mM)定量。有可能是正值,意味着来自任务或研究状态的激活;也有可能是负值,意味着自任务或研究状态的相对浓度降低或抑制。

如何使用基线?

想要获取较好的基线可能比理论状态下获取更困难。我们可以在数据中使用任意一个时间点并将其设置为零(通过采集到所有数据点中减去该值),或者可以在特定时间(例如开始的静息态1分钟)内获取基线得到该平均值然后通过采集到的所有数据点减去该值。如果时间条件下允许,在测量结束时记录另一个基线也是极好的。如果结束时的基线与初始基线没有显着差异,则可以对数据质量有足够的把握。

基线不应该做的

不要比较组之间的基线平均值。平均值是任意的,在可能状态下设置为零。在基线和任务之间不要有连续的刺激。因此,在fNIRS中,在每个刺激之前通常存在可以用作基线的rest时间。

总结

这期的文章中仅仅只是从理论状态上说明了使用基线的重要性,这也是只针对近红外的基线,当然在脑电数据中也是存在刺激前的基线校正,总之,在使用近红外技术对大脑研究时,尽可能的将基线的设定是在较平稳的状态下,这同时也是判断实验设计好坏的根据之一。

对于基线的数学公式,可参考一下这篇文章

This article is a guest post by Rui Chen. An English version can be downloaded here

Author: Xu Cui Categories: nirs Tags:

BOLD5000, A public fMRI dataset of 5000 images

September 11th, 2018

Official website and download
Full text paper link

Good news for brain imaging researchers. There is a new dataset available for you to play with.

BOLD5000 is a large-scale, slow event-related fMRI dataset collected on 4 subjects, each observing 5,254 images over 15 scanning sessions. The images are selected from three computer vision datasets.

  1. 1,000 images from Scene Images (with scene categories based on SUN categories)
  2. 2,000 images from the COCO dataset
  3. 1,916 images from the ImageNet dataset

BOLD5000 image data

BOLD5000 image data

Author: Xu Cui Categories: brain, web Tags:

Google Dataset search, a great tool for fNIRS and fMRI?

September 6th, 2018

Google just launched a new search engine: Google Dataset search. With this app, scientists can search public datasets published in scientific journals (and possibly other sources). According to Google, “Dataset Search enables users to find datasets stored across thousands of repositories on the Web, making these datasets universally accessible and useful.”

I searched ‘fNIRS’ and it returned 30+ results. See figure below. I clicked the first one, fNIRS/EEG/EOG classification, and it shows some meta information (e.g. the source and authors). Then I clicked the ‘zenodo.org’ website and did see the download link of the MAT file.

google dataset search

google dataset search

I also tried to search ‘fMRI’. The number of datasets for fMRI is much larger than that for fNIRS.

Currently the number of datasets indexed by Google is still limited, but I expect it will grow rapidly and become a very useful tool for scientists and anybody who want to play with data.

Link: Google Dataset search

Author: Xu Cui Categories: nirs, web Tags:

fNIRS 2018

August 29th, 2018

fNIRS 2010 conference will be held during October 5-8, 2018 in Tokyo, Japan. You may find more information at http://fnirs2018.org/.

The early registration deadline is 2018-09-05.

Author: Xu Cui Categories: nirs Tags:

Temporal resolution of CW fNIRS devices

August 10th, 2018

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

Temporal resolution provides information on the distance of time between the acquisitions of two images (data) of the same area. It is the reciprocal of sampling rate (or acquisition rate) of an fNIRS device. For some devices, sampling rate is a fixed number; for some other ones, sampling rate may depend on number of sources or detectors to use. Why is that? It is because they have different instrumental design. For those with unfixed sampling rate, multiple sources time-share an optical detector by means of a multiplexing circuit that turns the sources on and off in sequence, so that only one source within the detector range is on at any given time. The NIRx system, for instance, are using this type of design. For those with fixed sampling rate, they usually use low frequency modulated light source to provide the excitation light, thus one detector can ‘see’ only one source.

For instance, Hitachi ETG4000 system has sampling rate of 10Hz (from http://www.hitachi.com/businesses/healthcare/products-support/opt/etg4000/contents2.html), thus its temporal resolution is 100ms. Some other device, such as NIRScout, has sampling rate from 2.5 – 62.5 Hz (from https://nirx.net/nirscout/), thus its temporal resolution is 16 - 400ms. Why the sampling rate is changing from 2.5 – 62.5 Hz? That’s because users can choose different number of sources and detectors in their configuration. The more number of sources and detectors to use, the smaller the sampling rate. The following table is from a review article (Scholkmann, et al., 2014) on NeuroImaging volume 85 (2104), a special issue of functional near-infrared spectroscopy. It summarizes the specifications of some popular commercially available fNIRS devices, mainly focused on continuous wave devices.

Time resolution of NIRS devices

Time resolution of NIRS devices (click to enlarge, F. Scholkmann et al. / NeuroImage 85 (2014) 6–27)

本文作者为斯坦福大学刘宁。她提供NIRS培训服务。

Author: Xu Cui Categories: nirs Tags:

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: