# Personally Identifiable Brains

The idea of Personally Identifiable Brain (PIB) has been in my mind for a long time. This was largely inspired by Poldrack’s MyConnectome Project. You can find the structural and functional imaging data of my own brain from here (updating). The newly-proposed Brain Imaging Data Structure (BIDS) was used for preparing the files.

# Structural and functional MRI brain scans of (young) ME

It has been a long time since I planed to share the imaging data of my own brain. The idea is inspired by Poldrack’s MyConnectome project. As a neuroscientist, we usually scan our own brains. Although each single dataset is much smaller (compared to MyConnetome), many a little make a mickle. If these datasets are collected and shared, it would be a unique resource for better understanding of human brain, in particular of the neuroscientists’ brain and their academic life (as well as many other individual differences).

# Parcellation display with the same color using freesurfer

Two methods for displaying ROI analysis results with brain parcellation in FreeSurfer.

1. Solution #1 using R. The R code can be found HERE. After running the code, a configration file (like THIS) can be obtained. Then, by following the instructions at the end of the R code, you can have the display with tksurfer.

# WGCNA Background

WGCNA是Weighted Gene Co-expression Network Analysis的简称，其从网络连接的角度出发，考查基因之间的交互。该方法的提出背景是通过微阵列（microarray）实验可以获取的信息远多于仅得到一组差异表达的基因（differentially expressed genes）。基于微阵列microarray数据，我们可以通过计算基因表达模式（gene expression profiles）之间的相关来考查不同基因之间的交互。采用WGCNA方法，可以从数千基因的表达水平数据中识别可能具有临床价值的基因模块（gene modules），并最终采用模块内连接（intramodular connectivity）和基因-特质相关（gene significance）来发现某些疾病通路的关键基因，用于进一步验证。

# 3D Print Your Brain from MRI

Do you think it would be cool to have a 3D printed brain of yourself? With standard anatomical MRI scanning and some 3D surface reconstraction methods, you can make it. Here are some useful links for making this dream come true.

# An Excel problem in scatter plot and solutions

Can you imagine using Excel without plot or formulas? Recently I came this kind of problem when using the Excel 2016 on my office PC: Excel functions like SUM() did not work, and I could not get any plot as expected. A bunch of questions flash across my mind. Is it a bug of the new version of Excel? Is my Excel corrupt or is this due to some malicious virus? This might also be caused by the different language using in the system. After a few days’ worry and search, here is the solutions to the problem I had, which might be helpful for others.

# Switching from Octopress to Hexo

Now, I am a reseach staff at a new institute. New campus, new air, new office, new computer. Everything is completelt new to me. To go on with my blog, I decided to switch (from Octopress) to Hexo, which is ‘A fast, simple & powerful blog framework’. So, March on, soldier!

# Bayes Factor (简述贝叶斯因子) [2]

Bayes factor的几个应用途径

1. 作为一种统计推断的方法，Bayes factor首先可以用来代替p值（一般根据p<0.05来决定拒绝虚无假设（null hypothesis, H0），接受备择假设（alternative hypothesis, H1））来确定备择假设是否可靠。这里的H0和H1即两个不同的模型，计算Bayes factor（）。同样，如果计算得到的Bayes factor大于3，即数据x支持H1的概率是数据x支持H0的3倍以上，则被认为有足够的证据说明模型H1的正确性（类似根据p<0.05做出的结论）。

2. 采用p值做统计推断时，一个常识是不可以简单地使用p>0.05作为H0成立的证据，即研究者不可以简单地做接受H0的结论（因为导致不显著的原因除了“H0成立”之外，还有比如样本不足，不够敏感等的影响；因此，如果非要做结论，一般需要结合power或effect size的信息来辅助进行）。而Bayes factor在这种场景中却派上了用场。如果统计结果显示上面计算得到的Bayes factor小于1/3，甚至更小，研究者就有足够的信心来接受H0模型。因此，Bayes factor可以方便研究者确定“没有结果”的可靠性，用于理论检验和构建。

3. 个人认为Bayes factor还可以在另一个地方派上用场，即关于大样本研究中发现的效应值小（small effect size）的问题。随着数据采集条件的完善，行为神经科学中大样本的数据不断普及。同时研究者也发现，在大样本的数据统计中，0.2左右的效应量变得异常普遍，于是一些没有相关经验的审稿人（reviewer）便通常会提出类似“the amount of variance that impulsivity accounted for was a mere 2%”的问题，并质疑结果的可靠性。这个时候reviewer一般会要求做分半，进一步验证结果的可靠性。Bayes factor提供了另一个角度来展示结论的可靠性。以我自己的研究为例（Kong et al., PLOS ONE, 2014），我们发现被试在核磁扫描中头动（In-scanner head motion）的大小和被试的自我控制特质（Self-control impulsivity）存在显著关联，但是相关系数只有0.14（p=0.001），这时，可以计算该分析的Bayes factor发现BF = 9.1，因为大于3，可以认为有足够的证据确信头功和被试的自我控制特质之间存在的关联。

• Kong X-Z, Zhen Z, Li X, Lu H-h, Wang R, Liu, L, He, Y, Zang, Y-f, Liu, J. (2014) Individual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging. PLoS ONE 9(8): e104989. doi:10.1371/journal.pone.0104989.

# Bayes Factor (简述贝叶斯因子) [1]

Bayes factor是什么？

Bayes factor（贝叶斯因子）被用来描述一个理论优于另一个理论的相对确证性（ the relative evidence for one theory over another ）(Dienes, 2014)，采用数学符号表示即

• Julian et al. (2015). Place recognition and heading retrieval are mediated by dissociable cognitive systems in mice. Proc.Natl.Acad.Sci.U.S.A. www.pnas.org/cgi/doi/10.1073/pnas.1424194112

• Dienes Z. (2014). Using Bayes to get the most out of non-significant results. Front.Psychol. 5:781. doi:10.3389/fpsyg.2014.00781

• Jeffreys, H. (1939/1961). The Theory of Probability, 1st/3rd Edn. Oxford, England: Oxford University Press.

# 一种基于单被试MRI脑影像量化脑区间连接的新方法

1. 计算脑中每个体素的局部形态学特征；
2. 脑区分割，并估计每个脑区形态学特征的分布；
3. 通过估计两两脑区的形态学特征的分布来量化两两脑区间的关系，即脑区连接。

X.-z. Kong et al. / Journal of Neuroscience Methods 237 (2014) 103–107 105

# PyMVPA安装

Scipy安装方法类似，主要涉及如下命令：

# pip install --user pymvpa2


Successfully installed pymvpa2
Cleaning up...


unable to execute 'swig': No such file or directory
error: command 'swig' failed with exit status 1


# 投稿经历: A Paper On Head Motion And Impulsivity

Kong X-z, Zhen Z, Li X, Lu H-h, Wang R, et al. (2014) Individual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging. PLoS ONE 9(8): e104989. doi:10.1371/journal.pone.0104989

• 过程中，虽然收到reject会有些失望，但是不断有来自外界的意见也会感到很踏实，每次都根据意见修改，每次都有所提高。
• 投稿过程中推荐reviewer至关重要，尤其对于neuroimage以下的这种中等杂志。
• 如果有机会回复reviewer的意见，要尽力做到100%满足reviewer的意见。
• 回复reviewer意见和提交修改稿要安排好时间，尽量在20~30天时完成重新提交

Congrats on a really interesting paper on head motion and impulsivity. (Even more surprising is that it was not in a high impact journal!)