nirs2img, create an image file from NIRS data

October 2nd, 2019

I was asked where to get nirs2img script. Here it is. The download link is at the bottom of this article.

nirs2img is to create an image file from the input data. Then the
image file can be viewed by any fmri image viewing programs such as
xjview. This function requires function mni2cor and spm

function nirs2img(imgFileName, mni, value, doInterp, doXjview, bilateral)

imgFileName: the file name to be saved, e.g. ‘testnirs.img’
mni: Nx3 matrix, each row a coordinate in mni space
value: Nx1 matrix, each row is the value corresponding to mni
doInterp: 1 or 0 , whether or not do linear interpolation to
smooth data.
doXjview: 1 or 0, whether or not to view the generated image by xjview
now
bilateral: 1 or 0, whether or not the input mni is bilateral or not. If
bilateral, the first half points are considered as left side. There is no
interpolation between left and right side. (This argument is
useless if doInterp is 0)

output:
an image file whose name is specified by input

If you have mni points of probes (instead of channels), you may need to
convert first. Use function probe2channel

This function will write to a image file which can be viewed by xjview.
In xjview, you need to check render view.

Example:
nirs2img(’nirs_test.img’, mni, value, 1, 1, 0);

Xu Cui
2009/06/11
last update: 2009/07/06: have an option that left and right do not
interpolate

nirs2img.m
mni2cor.m

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

Phrase frequency analysis on fNIRS hyperscanning with Stork Big Analysis

September 29th, 2019

In the previous blog, we have used Stork’s big analysis to analyze the literature of fNIRS. At that time, Stork only analyzed the frequency of a single word (number indicates the number of occurences):

near-infrared (1945)
spectroscopy (1868)
cerebral (1850)
brain (1322)
oxygenation (801)
functional (775)
imaging (558)
prefrontal (541)
infrared (498)
cortex (469)
oxygen (467)
blood (413)
monitoring (402)
activation (391)
optical (367)
fnirs (364)
patients (364)
activity (342)

Some of the words, such as spectroscopy, brain, functional, imaging etc, are not very meaningful by itself. Maybe they are part of phrases which carry more information than a single word does.

Today, Stork Big Analysis has updated and now it can analyze frequency of phrases with 2, 3, and 4 words. Below is the result with the same keywords (fnirs OR nirs OR "near infrared") brain:

Top phrase (2 words):

cerebral oxygenation (362)
infrared spectroscopy (344)
prefrontal cortex (262)
oxygen saturation (243)
cerebral blood (218)
cerebral oxygen (201)
blood flow (184)
brain injury (128)
preterm infants (127)
cerebral hemodynamics (102)
cerebral oximetry (98)
regional cerebral (96)
tissue oxygenation (95)
brain activity (90)
working memory (85)

Top phrase (3 words):

cerebral blood flow (134)
cerebral oxygen saturation (129)
traumatic brain injury (71)
cerebral blood volume (57)
regional cerebral oxygen (53)
verbal fluency task (43)
prefrontal cortex activity (38)
cerebral tissue oxygenation (37)
tissue oxygen saturation (36)
prefrontal cortex activation (35)
magnetic resonance imaging (32)
diffuse optical tomography (31)
transcranial magnetic stimulation (30)
positron emission tomography (26)
cerebral tissue oxygen (25)
hypothermic circulatory arrest (23)
transcranial direct current (22)

Top phrase (4 words):

regional cerebral oxygen saturation (52)
cerebral tissue oxygen saturation (24)
transcranial direct current stimulation (21)
deep hypothermic circulatory arrest (15)
functional magnetic resonance imaging (15)
cerebral tissue oxygenation index (12)
repetitive transcranial magnetic stimulation (12)
attention deficit hyperactivity disorder (11)
regional cerebral blood flow (9)
cerebral oxygen saturation measured (9)
cerebral blood flow velocity (8)
severe traumatic brain injury (8)
cerebral regional oxygen saturation (7)
cerebral oxygen saturation monitoring (7)
coronary artery bypass grafting (7)
red blood cell transfusion (6)
hypoplastic left heart syndrome (6)
cerebral blood flow autoregulation (6)
mild traumatic brain injury (5)
anodal transcranial direct current (5)
resting state functional connectivity (5)
mixed venous oxygen saturation (5)

We see some meaningful phrases show up. A single word such as “blood” now has a clearer meaning (e.g. “cerebral blood flow”). We also discovered a few top phrases which we can’t find with a single word analysis, such as “verbal fluency task”. This is a popular task used in fNIRS studies but I have not realized it is that popular! “Traumatic brain injury” is another example, showing the popular application of fNIRS in diagnose this condition.

More information about Big Analysis can be found at this page. (中文版)

Author: Xu Cui Categories: brain, nirs, stork, web Tags:

NIRS_SPM 批处理成像的一个小改进

September 23rd, 2019

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

前些天 在用 NIRS_SPM批处理 做fNIRS脑图时,有几组数据总是报错(大约占总数的三分之一左右),总是说无法生成图像。

??? Error using ==> image
Error using ==> image
Image CData can not be complex

Error in ==> imagesc at 19
hh = image(varargin{1},'CDataMapping','scaled');

Error in ==> activation_map_batch at 365
imagesc(stat_brain);

开始以为是前面几步处理时出了什么问题,于是认认真真从头又做一遍,问题照旧。焦头烂额几天,发现做插值(interpolation)之前一切都是美好的,问题就是在插值以后生成了 复数的interp_var!没办法,只好调出NIRS_SPM 里的批处理函数(activative_map_batch.m),一步一步跟进追踪。Activative_map_batch.m里面 是这么计算interp_var的:

nch = length(chs{kk}); % # of channels
mtx_var = zeros(nch);
for aa = 1:nch
for bb = 1:nch
mtx_var(aa,bb) = var(chs{kk}(aa), chs{kk}(bb));
end
end
[V_X D_X] = eig(mtx_var);
tmp = D_X.^(1/2) * V_X' * B{kk};
interp_var = [interp_var sum(tmp.^2,1)];

问题就出在eig函数那里。Eig函数是用来求本征值和本征向量的,但是在运行某些数据时,会生成一些很小很小的本征值,通常远小于别的本征值十个数量级以上,非常接近于零。我的情况下生成的本征值就是1e-20 量级。 这些奇怪的值(特别是负数的极小值)导致了下面一步开平方出现了复数(complex number),从而报错提示不能使用图形生成函数imagesc。这些值的产生通常是因为做数字近似的时候产生的误差(artifact of numerical approximation)。找到原因,解决办法就很简单了,把这些极小值用零替换就好了。改进后的程序是这样的:

nch = length(chs{kk}); % # of channels
mtx_var = zeros(nch);
for aa = 1:nch
for bb = 1:nch
mtx_var(aa,bb) = var(chs{kk}(aa), chs{kk}(bb));
end
end
[V_X D_X] = eig(mtx_var);

% round very small engenvalues to zero <--by NL
% very small engenvalues result from eig function
% usually is an artifact of numerical approximation. simply
% round them to zero here.
[D_X_err_I1, D_X_err_J1] = find(D_X <0);
disp(D_X(D_X_err_I1, D_X_err_J1));
D_X(D_X_err_I1, D_X_err_J1) = 0;
[D_X_err_I2, D_X_err_J2] = find(D_X>0 & D_X<1e-15);
disp(D_X(D_X_err_I2, D_X_err_J2));
D_X(D_X_err_I2, D_X_err_J2) = 0;
% #

tmp = D_X.^(1/2) * V_X' * B{kk};
interp_var = [interp_var sum(tmp.^2,1)];

本文作者:刘宁博士

如果需要改进后的批程序,请发电子邮件给本文作者刘宁博士: fnirs_studio@outlook.com

Author: Xu Cui Categories: brain, nirs Tags:

fNIRS overview by Stork’s Big Analysis

August 8th, 2019

What’s the trend of fNIRS in brain research? Is the field growing or dying? Which country and which institute are the most productive? Who are the experts in the field and how can I contact them for collaboration? Which brain region(s) are mostly studied? Which disorder appears most frequently in the publications?

These are important questions to ask. However, even to a reseacher who works in the field (brain research with fNIRS), the answers may not readily available. Fortunately, Stork team’s new advanced feature “Big Analysis” makes it very easy to get the answers. As a matter a fact, it only required me to enter the keyword, and then it downloaded and analyzed about 6,500 publications and generated beautiful reports in less than 3 minutes.

Below is a screenshot of the report using keyword: (fnirs OR nirs  OR “near infrared”) brain

Stork's Big Analysis for fnirs Brain

Stork Big Analysis for fnirs Brain

Now we see, fNIRS research on the brain is growing steadily over the past 20 years, with recent growth ~10%. US, Japan, Germany and China are the top countries in this field. National Insitute of Radiological Sciences, UCL and Harvard are top institutes. The top diseases studied are Stroke, Brain Injury, Schizophrenia and Stress. NeuroImage, Adv Exp Med Biol, J Biomed Opt are top journals where the research is published. The mostly studied brain region is the frontal lobe. Some experts in the field are shown too, such as Hongjie Dai, David Boas, Allan Reiss, and Ilias Tachtsidis etc. Stork also shows the author network graph. It’s an interactive graph and it’s fun to drag things around to find out who are connected to who.

Most of the experts’ emails are available. That means when I want to contact them for collaboration, or invite them to review a paper, I don’t have to spend time in digging their emails.

The “Big Analysis” tool can be purchased through Stork website. The description can be found at here.

Author: Xu Cui Categories: brain, nirs, stork, web Tags:

Are the two balls of the same color?

June 12th, 2019

Look at the two balls below. What colors are they?

Blue and Green balls?

Blue and Green balls?

To me, the left ball is definitely blue, and the right one is green.

If you see the same way, Haha, you (your brain) is tricked! I used photoshop to remove the background purple and yellow colors, then here is the result:

Same color?

Same color?

The two balls are of the same color!

This is a good example to show that:

  1. Our brain (and us) is easily tricked
  2. Color is a construct of our brain. Put it in a different way: we color the world we see!
  3. The so called “reality” does not exist.
Source: This illusion was made by Professor David G. Novick from The University of Texas at El Paso. He published this illusion in his twitter.
Author: Xu Cui Categories: brain, writing Tags:

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:

Fix narginchk Error using SPM

May 7th, 2019

Today I downloaded SPM 12 latest version, and when I tried to start it complained:

Error using narginchk (line 10)
too many input arguments

This error is caused by the conflict of multiple narginchk functions. To find out where they are, type

>> which -all narginchk
C:\Users\Xu Cui\Dropbox\spm12\external\fieldtrip\compat\matlablt2011b\narginchk.m
built-in (C:\MATLAB\R2015b\toolbox\matlab\lang\narginchk)

Then I simply renamed the first narginchk.m to a different name (e.g. _narginchk.m), and now it works.

Author: Xu Cui Categories: brain, fmri Tags:

How to fix bluetooth mouse not working under windows 10?

May 3rd, 2019

I am using a Dell laptop with Windows 10. It’s been working great, but occasionally my bluetooth mouse won’t work. Sometimes I need to re-install the driver, sometimes I need to restart the computer. But it does not always resume working.

Below is a method which solved my problem (it’s a youtube video):

In a nutshell, you need to disable “fast boot” in power options, and delete the blooth device in device manager, and then shut down (not restart) your computer, and then start your computer. Hope it works for you.

Author: Xu Cui Categories: life 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: