Can a mouse understand the crypto market?
Neurons in the mouse brain correlate with cryptocurrency price: a cautionary tale
Nowadays it is pretty much accepted that in animals with a nervous system, neural activity leads to behaviour. This framework is very useful to ultimately find a satisfying explanation of how and why animals behave, as it implies that there is a causal relationship between neuronal spiking and muscle and gland activity. In order to get closer to this causation, a common approach in neuroscience is to find correlations between behavioural variables and neuronal activity. Dr. Meijer's manuscript "Neurons in the mouse brain correlate with cryptocurrency price: a cautionary tale"  serves as a proof of concept that neuroscientists need to be careful about the statistical tests they use when looking for these correlations.
In this work, the author considers two recent datasets containing signals that display slow continuous trends over time: neuronal spiking activity from 40,100 neurons, and Bitcoin and Ethereum prices. When testing for correlations between the activity of individual neurons and the simultaneous fluctuations of cryptocurrency prices, he finds that over two thirds of the neurons correlated significantly, and that classical corrective conservative methods still result in one third of the neurons showing correlation. In order to estimate the true false discovery rate of these type of comparissons, the author tested two statistical methods shown to work for simulated data . He shows that also for this large-scale dataset, both the session permutation and the linear shift method manage to reduce the number of correlated neurons to statistically-acceptable levels. Additionaly, the author goes on to show that it is the slow time constant of the crypto prices that are the root for the initial correlations. This work serves as an example for how mislead scientists can be if proper statistical tests are not applied in order to avoid "nonsense correlations" with neuronal data, and it aims to increase awareness about this problem in the neuroscience community.
This rigorous and yet entertaining work can now be added to the collection of cautionary tales that include a dead salmon understanding human emotions  and rat cortical neurons predicting stock market prices . At the very least, it can be a piece of advice for Elon Musk to wait for more evidence before merging two of his new recent interests.
 Meijer, Guido. (2021). Neurons in the Mouse Brain Correlate with Cryptocurrency Price: A Cautionary Tale. PsyArXiv, ver. 3 peer-reviewed and recommended by Peer Community in Circuit Neuroscience. https://doi.org/10.31234/osf.io/fa4wz.
 K. D. Harris. (2020). Nonsense correlations in neuroscience. bioRxiv. 402719. https://doi.org/10.1101/2020.11.29.402719
 T. Marzullo, C. Miller, and D. Kipke. (2016). Stock Market Behavior Predicted by Rat Neurons2. Annals of Improbable Research 12, 401. PDF Link
Hernando Martinez Vergara (2021) Can a mouse understand the crypto market?. Peer Community in Neuroscience, 100002. 10.24072/pci.cneuro.100002
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.31234/osf.io/fa4wz
Version of the preprint: 1
Author's Reply, 20 Aug 2021
Decision by Hernando Martinez Vergara, 09 Jul 2021
Dear Dr. Meijer,
Thanks for submitting your preprint titled "Neurons in the mouse brain correlate with cryptocurrency price:
a cautionary tale" (https://doi.org/10.31234/osf.io/fa4wz) to PCI C.Neuro.
I have received three different reviews of your manuscript. The general view is that the paper is well written, the methodology is properly employed and explained, and the conclusions derived from the work are well stated and supported by the analysis. Using an open-source large-scale state-of-the-art resource to draw attention to 'non-sense correlations' in neuroscience, exemplified using modern finantial instruments, is a great idea. However, in order for this piece to add significant value to the existing literature, I agree with the reviewers that additional works need to be done to address their points:
- "The underlying cause of the results are, however, not explicitly clarified and could be further looked into by testing the hypothesis whether using signals with temporal autocorrelations that have different time constants still maintains significant correlations."
- "with 60 s long time bins, almost never used in neuroscience studies". The preprint would benefit from adding examples about when these time bins are used and which behavioural correlates have such time constants. Additionally, I would be curious to see how the interplay between the time constant and different time bins affects the results.
Looking forward for your resubmission,
Hernando M. Vergara