Archive for the ‘brain’ Category

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 ‘’ 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

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, thus its temporal resolution is 100ms. Some other device, such as NIRScout, has sampling rate from 2.5 – 62.5 Hz (from, 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)


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)

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:


March 19th, 2018


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:


March 10th, 2018


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


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
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
(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
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.:


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 "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

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

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: and it’s very easy to use.



Author: Xu Cui Categories: nirs, stork Tags: