Erroneous analyses widely exist in neuroscience (and beyond)

Today Neuroskeptic posted a new blog entry: "Neuroscience Fails Stats 101?", which introduced a recently published paper:

S.Nieuwenhuis, B.U.Forstmann, and E-J Wagenmakers, Erroneous analyses of interactions in neuroscience: a problem of significance, Nature Neuroscience, vol. 14, no. 9, 2011
The paper mainly discusses the significant tests. However, I'd to say, when people apply machine learning techniques to neuroscience data (e.g. EEG, fMRI), erroneous analyses (even logically wrong) also exist. Sometimes the erroneous analyses are not explicitly, but more harmful.

One example is the application of ICA on the EEG/MEG/fMRI data. A key assumption of ICA is the independence or uncorrelation of "sources". This assumption is obviously violated in these neuroscience data. But some people seem to be too brave when using ICA to do analysis.

I am not saying using ICA to analyze neuroscience data is wrong. My point is: people should be more careful when using it:

(1) First, you should deeply understand ICA. You need to read enough classical papers, or even carefully read a book (e.g. A.Hyvarinen's book: independent component analysis).

I saw some people only read one or two papers and then jumped to the "ICA-analysis" job. Due to the availability of various ICA toolboxes for neuroscience, some people even didn't read any paper, and even could not correctly write the basic ICA model (really!).

It's very dangerous. This is because ICA is a complicated model and unfortunately, neuroscience is a more complicated field (probably the most complicated field in science). In the world there is nobody that have exact knowledge on the "sources" of EEG/MEG/fMRI data. As a result, people don't know whether the ICA separation is successful. This is different to other fields, where people can easily know whether their ICA is successful. For example, when people use ICA to separate speech signals, they can listen the separated signals to know whether the ICA separation is successful or not.  But in neuroscience, you CAN NOT.  We still lacks of much knowledge on these "sources" of EEG/MEG/fMRI data. This requires the analyzers to deeply understand the mathematical tools they are using: the sensitivity, the robustness, the all kinds of possibility of failure, etc. 

It has been observed that ICA can split a signal emitted from an active brain area into two or more "independent sources". It has been observed that ICA only provides a temporal-averaged spatial distribution. It has also been observed that ICA fails when several brain activity are coupled. However, all these warnings are ignored by those brave people.

(2) Be careful when using two or more advanced machine learning analysis (e.g. ICA separation in a domain and then ICA separation in another domain, ICA followed by another exploring data analysis, etc). Due to the inconsistency of ICA models and neuroscience data, errors always exist. However, we don't have any knowledge on the errors from ICA. So, the errors from ICA is unpredictable, and such errors can also be unpredictably amplified when we use another advanced machine learning algorithm after ICA. The same goes to the use of other advanced algorithms successively.

In summary, ICA is a tiger, and to control it, the controller needs to be very skilled; otherwise, the controller will be seriously harmed by it.


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Nepenthes. x dyeriana.
This nepenthes was gaven by my friend, Bob, as a gift. It is a rare hybrid. Photo was taken by my friend Luo.

When Bayes Meets Big Data

In the June Issue of The ISBA Bulletin, Michael Jordan wrote an article titled "The Era of Big Data". The article discussed the possibility and challenges to apply Bayesian techniques to Big Data (e.g. terabytes, petabytes, exabytes and zettabytes). Michael pointed out several advantages of Bayes over non-Bayes, which I quote here:

(1) Analyses of Big Data often have an exploratory flavor rather than a confirmatory flavor. Some of the concerns over family-wise error rates that bedevil classical approaches to exploratory data analysis are mitigated in the Bayesian framework.

(2) In the sciences, Big Data problems often arise in the context of “standard models,” which are often already formulated in probabilistic terms. That is, significant prior knowledge is often present and directly amenable to Bayesian inference.

(3) Consider a company wishing to offer personalized services to tens of millions of users. Large amounts of data will have been collected for some users, but for most users there will be little or no data. Such situations cry out for Bayesian hierarchical modeling.

(4) The growing field of Bayesian nonparametrics provides tools for dealing with situations in which phenomena continue to emerge as data are collected. For example, Bayesian nonparametrics not only provides probability models that yield power-law distributions, but it provides inferential machinery that incorporate these distributions.

Based on my experience on compressed sensing, I feel that Bayes provides a more flexible way to exploit structured sparsity. Such power gained from Bayes cannot be gained from non-Bayes methods. However, Bayes is computationally demanding. So, combining Bayes and non-Bayes is my research theme in compressed sensing. This is why I wrote the two papers:

Z.Zhang, B.D.Rao, Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors, ICASSP 2011

Z. Zhang, B.D.Rao, Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity, ICML 2011 Workshop on Structured Sparsity

Above Pictures: Nepenthes. jamban (growing in my patio)
This rare species was discovered in the island of Sumatra in Indonesian in 2005. The pitchers have a unique toilet shape, so the plant was affectionately called jamban, which means toilet in Indonesian.

Open problems in Bayesian statistics

Mike Jordan wrote a report on his interesting survey on 50 statisticians by asking them what are the open problems in Bayesian statistics. Here is his report:  http://members.bayesian.org/sites/default/files/fm/bulletins/1103.pdf

The top open problems in his report are as follows:

No.1. Model selection and hypothesis testing.

No.2. Computation and statistics.

No.3. Bayesian/frequentist relationships

No.4. Priors

No.5. Nonparametrics and semiparametrics

Andew Gelman made excellent comments on these problems. Here is the link: http://statisticsforum.wordpress.com/2011/04/28/what-are-the-open-problems-in-bayesian-statistics/