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NIRS People: Interview Dr Chunming Lu

June 3rd, 2019

Dr Chunming Lu

Dr Chunming Lu

Chunming Lu’s lab in Beijing Normal University has published a paper titled “Shared neural representations of syntax during online dyadic communication” in NeuroImage. This is another fNIRS hyperscanning study within a month (check out another paper by Dr Xianchun Li and his group: http://www.alivelearn.net/?p=2172)! Lu’s lab studied the brain response of real time verbal communication between two people. We interviewed Dr Lu and his team and they are generous in sharing their experience about this study.

Congratulations on your recent paper in NeuroImage.

First of all, I would like to thank Dr. Xu Cui for giving me a chance to introduce our recent work at such a good place. Our work has benefited a lot from Dr. Cui’s pioneering work on fNIRS hyperscanning. His work has inspired a lot of studies in the past few years.

1. Reading your past publications I get a feel that you are very interested in studying the mechanism of human verbal communication. Why are you interested in this area?

My previous research mainly focused on the relationship between brain and language. I have been studying communication disorders such as stuttering for many years using fMRI. Stuttering is a common speech production disorder that affects about 1% of the general population. One of the major features of stuttering is that the typical symptoms of stuttering occurs more often in a dialog context. In a monolog or covert speech context, however, most people who stutter don’t stutter at all. Thus, it is suggested that social interaction play a key role in the relationship between brain and language. Since then I am keen to studying language (including verbal and nonverbal) communication in a social interaction context.

2. Since the experiment requires the participant to read, did the mouth or head motion introduce any artifact (noise) in the NIRS signal? If so, what did you do to remove/minimize it?

Speaking (and gesturing) will definitely introduce artifacts in the fNIRS signal. It is generally agreed that artifacts can only be avoided but can’t be removed. Fortunately, from our experiences, most of the artifacts resulted from movements can be avoided if you place the probes on the head appropriately.
But if artifacts do occur, what should we do? In our experiment, we have conducted a running-window procedure to identify the suspected artifacts. The percentage of time points that were contaminated by artifacts was less than 5%. We also conducted a simulation experiment by adding a variety of percent of artifacts to the signal (i.e., 5%, 10%, 15%, and 20%). The results showed that 15% artifacts would significantly decrease the detectability of the syntactic-related effect in our study. We also replaced the identified artifacts by means of the neighbor time points. The results of artifact-free data were the same as the raw data. These results indicate that data may not be significantly contaminated by artifacts when the artifacts are less than 5%.
There are several other good methods that can be used to deal with the artifacts issue. For instance, Dr. Cui has developed a nice method by calculating the correlation between the HBO and HBR signal. My colleague Dr. Haijing Niu also developed a software to do quality control, http://www.nitrc.org/projects/fcnirs.

3. This study is fairly big - you had 90 pairs of participants! What method did you use to recruit so many participants? Did you pay them? How long did it take to collect the data?

Thanks to BNU so that we could recruit so many participants ! Because psychology and cognitive neuroscience is one of the best disciplines of BNU, most students have interests and are willing to participant in psychological experiment. We also have some participants from other universities that are close to BNU.
Our participants were paid after the experiment. For each pair of the participants, the set up and performance of the experiment took about 1 hour in total.

4. You used wavelet transform coherence method to analyze the NIRS data. In your opinion, what is the advantage of wavelet over other methods?

I am aware that several different methods have been used to characterize interpersonal neural synchrony during social interaction. But so far WTC is one of the most popular methods among them, since its first application in Cui et al., 2012, NeuroImage.
WTC is my favorite because it provides both temporal and frequency information based on a relatively simple principle. During language communication, people usually produce 5-6 syllables per seconds, and they take turns in a dialog within a time window of less than 80-100 ms. Thus, a precise estimation on the temporal pattern is necessary for smooth communication. WTC allows us to take a close look at the temporally dynamic process of coordination during communication across many different frequency bands.
On the other hand, I also believe that there is no such a method that can address any issues. I would love to try different methods in different contexts.

5. How did you identify which frequency range/band to use to calculate the coherence?
What advice do you give to researchers who want to use fNIRS hyperscanning but have little experience?

Frequency selection has been considered as one of the key processes in fNIRS hyperscanning study. For studies that employed a traditional ER or BLOCK design, usually there is a priori hypothesis about the frequency of interest (FOI). For studies on naturalistic communication, however, so far there is no standard procedure for frequency selection.
During the past few years we have developed a procedure to address this issue (Zheng et al., 2018, HBM; Liu et al., 2019, NeuroImage). The idea is that the FOI should be defined based on a center and a range. The center should be a statistically strict threshold that determines the position of the frequency, whereas the range could be a relatively loose threshold that determines the width of the FOI. In Liu et al., 2019, the center was set as P < 0.0005 whereas the range was P < 0.05. All frequency ranges that survived this criteria were examined. In addition, the frequency ranges that totally overlapped among conditions were combined, whereas those differing in frequency position or range were considered independently.
Compared to EEG or fMRI, fNIRS is more friendly and easy to use. I believe that all people who have a good academic training are able to use it in their research.

6. Can you use one sentence to summarize your finding in this study?

A distinctive pattern of interpersonal neural synchronization underlies shared representations of syntax between interlocutors.

7. What is your plan for future research?
In the future, my research will continue to focus on the neural mechanism of language communication. We also have interests in how language is used in different social contexts, and how language is related to other aspects of cognition during social interactions.

Author: Xu Cui Categories: brain, nirs Tags:

Interview with Drs. Chenbo Wang and Xianchun Li

April 26th, 2019

A group of scientists in East China Normal University has published a paper earlier this month titled “Dynamic interpersonal neural synchronization underlying pain-induced cooperation in females” in HBM. They studied how pain affected cooperation between female students using fNIRS hyperscanning. We interviewed the authors Chenbo Wang and Xianchun Li and they are generous in sharing their experience about this study.

Chenbo Wang

Chenbo Wang

Xianchun Li

Xianchun Li

Congratulations on your recent paper in HBM.

1. What motivated you to study the effect of pain on cooperation? And why to study female only?
Response: Dr. Chenbo Wang is interested in how physical pain influences human cognition and social behaviors. In one of his previous studies, results indicated that acute pain promotes cooperative behavior in social interaction (Wang et al., 2018). On the other hand, Dr. Xianchun Li’s previous works provided an interpersonal neural mechanism of cooperative behavior (Cheng et al., 2015; Pan et al., 2017). Thus, it was a perfect collaboration between the two teams that allowed us to further elucidate the underlying neural basis of pain-induced cooperation.
Female participants only were recruited due to that in previous study, an increased effect of pain on cooperative behavior was observed only in females but not in males (Wang et al., 2018). Although gender differences in pain perception and prosocial behavior was well-documented, we realized that having also males measured would then have made it possible to check the measured results if they comply with this previous finding.

2. 33 pairs of participants is not a small number. How long did it take to collect the data?
Response: It took nearly five months to collect all the data, from the fall of 2016 to spring 2017. Before that, we spent two months to set up the experimental equipment.

3. Compared to 1 person experiment, what are the unique challenges in hyperscanning experiment?
Response: One challenge is related to the experimental design. The paradigm chosen for an experiment should be subtle to interpersonal interaction, either socially or mentally. It should be very cautious to interpret the meaning of the observed interpersonal neural synchronization. Another challenge is regarded to conducting the experiment. It should be verified that the two participants understand the task similarly and perform it on the same page.

4. Why did you not use fMRI hyperscanning?
Response: In our experiment, each pair of two participants sat face-to-face during the cooperative task, which ensured a real social interaction that they performed the task in each other’s presence. However, this real social interaction would be prevented if the two participants lay in two separate fMRI scanners.

5. What was participants’ reaction when they found this would be a “painful” experiment? Do you have any particular example?
Response: Before decided to participate, it was informed that they would receive by a half-chance a moderate pain induced by capsaicin cream. With this expectation, most participants controlled themselves well throughout the whole experiment, although they would feel a little bit annoying.

6. How long did it take to analyze the data?
Response: It took nearly six months to analyze the data.

7. Compared to single subject data analysis, what are the unique challenges in hyperscanning data analysis?
Response: Hyperscanning data analysis depends on an estimator to quantify brain-to-brain synchrony. The most frequently used method is wavelet transform coherence (WTC). Different with that of single subject data analysis, the amplitude of the complex coherence value from two participants is calculated as an index of interpersonal neural synchronization (INS). This method requires choosing a frequency band of interest in which the task-related brain-to-brain synchrony is expected to occur based on previous studies and visual inspection of the data. So how to choose the best and accurate frequency band and how to explain it are unique challenges in hyperscanning data analysis.

8. What software did you use to create figure 5? If you used in-house program, do you mind sharing it?
Response: We applied to an open-source software package called circularGraph, based on MATLAB® (The MathWorks Inc., Natick, Massachusetts) scripts. It can be downloaded for free at https://ww2.mathworks.cn/matlabcentral/fileexchange/48576-circulargraph

9. From the initial submission to the acceptance of the paper, how long did it take? Were the reviewers friendly?
Response: It took 2 months to get the first revision and in total 4 months to receive the notice of acceptance.
Both reviewers provided us with positive comments. One reviewer remarked our experimental paradigm (pain + cognitive task + fNIRS hyperscanning) as being novel. The other reviewer commented that this work could make good contributions to the field. The reviewers raised some concerns regarding to the experimental settings and the procedure, the approach of preprocessing, and the method of FDR correction, all of which significantly helped improve the manuscript. For example, we conducted additional artifact rejection procedures (PCA method) to remove flow oscillations or other global systematic components and reanalyzed our data.

10. Can you use one sentence to summarize your finding in this study?
Response: When a dyad in painful state performing a cooperation task, their cooperation rate was improved across time; simultaneously, the interpersonal neural synchronization (INS) occurred successively at bilateral prefrontal cortex and right parietal cortex, along with increased fronto-parietal associations.

11. What is your plan for future research?
Response: There are three lines of future research. First, to replicate this finding by using EEG-based hyperscanning technique, providing extra information with ERP components and frequency band of high temporal resolution. Second, as we proposed in the current study that individuals in pain was “motivated” to cooperate; we were particularly interested in how pain would modulate neural activities in brain regions including ventral striatum and medial prefrontal cortex in the motivation and reward system, with fMRI technique. Third, it is encouraged to critically compare the effect of acute pain with chronic pain, by using the identical cooperative task.

Author: Xu Cui Categories: brain, nirs, writing Tags:

6 experiments you should do with NIRS (vs fMRI)

February 19th, 2019

[last update: 2019-02-20]

Let’s be frank. Compared to fMRI, NIRS has a number of intrinsic weaknesses. The signal to noise ratio is lower, the spatial resolution is (10x) lower, it can’t measure the deep brain, and it only covers a portion of the brain surface. As a result, fMRI, when possible, is still the preferred method to elucidate brain mechanisms. So if we do use NIRS, we’d better do experiments fMRI can’t, or at least very difficult to, do.

Below are 6 experiments NIRS can do, but fMRI can’t (or difficult to do):

  1. Study of the mechanism of BOLD signal
    1. Study of the relationship between oxygenated and deoxygenated hemoglobin concentration
      For a given voxel, fMRI only gives you a single number at a time. The number (BOLD signal) depends on many factors. On the other hand, NIRS can give you two numbers, the concentrations of both oxygenated and deoxygenated hemoglobin. So if you ever want to study the relationship between them, we should use NIRS. For an example, please check out https://www.ncbi.nlm.nih.gov/pubmed/19945536
    2. Study of the fine temporal change of BOLD signal
      In most fMRI settings the temporal resolution of BOLD signal is low (about 2 seconds, or 0.5Hz). That means we only get a number every two seconds. If we want to know the rise and fall of the BOLD signal in much higher temporal resolution, e.g. 10Hz, then NIRS would be a good choice.
  2. Study in naturalistic settings
    If you ever participate in a fMRI study, you will feel the room far from friendly. The scanner is loud, and it looks scary. During the entire experiment, you are lonely inside the confined space. You can’t move, can’t talk (for most experiments), and feel guilty when you swallow saliva as the experimenter probably told you in advance that any motion might distort the signal. What you often see is much simple visuals (such as blocks and circles) on a computer screen. Do we live such a life in reality? On the contrary, NIRS is small, even portable, and can be used in a naturalistic environment.

    1. Sports and exercise
      As long as the optodes are attached to the head nicely to ensure proper signal collection, the participants can move freely. NIRS has been used in a number of sports or exercises studies such as running, squatting, biking, ping-pong, piano playing, and stretching etc. Be sure to check out the videos in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434677/

      Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430058/
    2. Face to face communication
      You might see other people’s face on a screen in fMRI, but with NIRS, you can see a real face. We humans are fundamentally social, and to study social behavior in a naturalistic environment is important. NIRS allows you to study face-to-face gaming (video or board), talking, brain storming, cooperation or competition, etc. For example, in one of our studies (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3254802/) participants played a video game together. my colleague Ning Liu et al has an experiment when two people play Jenga games (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782164/).
    3. Study in a moving environment
      Many NIRS devices are small enough so you can carry it in a car or even a backpack. That means you can study the driver’s brain when he is actually driving! An example would be https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671603/
  3. Hyperscanning
    fMRI can be used in hyperscanning studies, and in fact the first hyperscanning is done with fMRI by my former advisor Dr Montague (https://www.ncbi.nlm.nih.gov/pubmed/12202103). However, seeing it with first hand, I know hyperscanning with fMRI is technically challenging. You need two fMRI scanners, each costs a few million dollars; and you also need to synchronize them. On the other hand, many NIRS devices are able to do hyperscanning natually. For example, when we used ETG 4000, we simply use one patch on one participant and the other on another participant. It does not add complexity on the hardware part.
    To date there are a number of hyperscanning studies with NIRS. For a list, please refer to https://www.ncbi.nlm.nih.gov/pubmed/?term=(fnirs+OR+nirs)+hyperscanning. If you are patient enough to scroll to the last page, you will find our paper!
  4. Real time (feedback), brain computer interface
    Let’s say you want your participants to move a bar in a computer game mentally (brain computer interface), or you provide a real time neural feedback to your participants to improve cognitive function, in both cases you need the brain signal in real time.  fMRI can do this too, but first you will need to get the data in real time, and second, due to poorer time resolution (2s), there will be some lag. Many NIRS devices allows you to get the measured signal out without delay, and of course the temporal resolution of NIRS is typically much higher (e.g. 10Hz). An recent example can be found at https://www.ncbi.nlm.nih.gov/pubmed/30634177
    Some may argue that higher temporal resolution is not that useful because the BOLD signal is already slow (usually 4-6s from onset to peak). However, with finer temporal resolution, we can use algorithms (e.g. machine learning) to detect signal with much smaller delay. For example, in one of our studies the delay can be reduced by 50% (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978722/)
  5. Field studies, large scale studies
    You can’t carry an fMRI scanner around for sure, but with NIRS’ portability, you can collect neural data in the field. My colleague Dr Baker took a NIRS device to the rural places in Costa Rica, and scanned the local farmers. Check out his paper.

    In addition, since you may carry NIRS devices around, you can scan a lot of participants, e.g. a few hundred or even thousand. Imagine you are to study the brain and cognitive development of children in rural areas in China in a large scale (say 10,000 participants), then NIRS will become your top choice.
  6. Study on special participants
    Due to safety reason, the following people should not do fMRI experiments (source):

    1. with shrapnel or other metal or electronic implants in their bodies (such as pacemakers, aneurysm clips, surgical devices, metallic tattoos on the head, etc.)
    2. pregnant
    3. with a history of head trauma or fainting
    4. currently using sleeping aids, painkillers (including aspirin), or other agents known to affect brain function (for instance, antihistamines, decongestants, etc.)
    5. with major medical, neurological, or psychological disorders (including depression, generalized anxiety, panic attacks, AD(H)D, strokes, tumors, heart conditions, claustrophobia, etc.)

    In addition to this list, fMRI is also not friendly to infants (but still can be done in fMRI). In contrast, NIRS does not have issues with the participants in the list. Imagine you can do an experiment on claustrophobia with NIRS, but probably not with fMRI.

So, what is the ideal experiment with NIRS? Based on the above list, if one is to use hyperscanning to study the face-to-face communication when two claustrophobia participants are running together along a mountain trail, I would say it’s pretty hard for fMRI to catch on.
Author: Xu Cui Categories: brain, nirs Tags:

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:

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: