In the Bullmore lab meetings recently I’ve mentioned a few papers that I’d seen on twitter and I promised to email them around. Here is that email (but as a blog post for posterity!)
Please note that these are absolutely not the only papers I’ve thought were interesting, just a non-random selection that I think others may want to know about
Genetic analyses of depression
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression
Wray, N. R., & Sullivan, P. F. (2017). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. bioRxiv. https://doi.org/10.1101/167577
Genome-wide association study of depression phenotypes in UK Biobank (n = 322,580) identifies the enrichment of variants in excitatory synaptic pathways
Howard, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., … McIntosh, A. M. (2017). Genome-wide association study of depression phenotypes in UK Biobank (n = 322,580) identifies the enrichment of variants in excitatory synaptic pathways. bioRxiv. https://doi.org/10.1101/168732
While I’m here talking about UK Biobank I want to share this resource: https://biobankengine.stanford.edu. I haven’t explored it much, but it seems pretty fantastic!
Problems with candidate gene lists for Schizophrenia
This paper is still in press (and the fact that the title is not formatted properly unreasonably annoys me) but I think it’s an important one to consider if we’re going to better understand the genetics of schizophrenia.
Johnson, E. C., Border, R., Melroy-Greif, W. E., de Leeuw, C., Ehringer, M. A., Keller, M. C., & al., et. (2017). Full title: No evidence that schizophrenia candidate genes are more associated with schizophrenia than non-candidate genes. Biological Psychiatry, 13(0), 537–551. https://doi.org/10.1016/j.biopsych.2017.06.033
No evidence that schizophrenia candidate genes are more associated with schizophrenia than non-candidate genes https://t.co/BeeERrh212— PGC Consortium (@PGCgenetics) July 13, 2017
There’s also this slightly older review paper that seems worth checking out:
Farrell, M. S., Werge, T., Sklar, P., Owen, M. J., Ophoff, R. A., O’Donovan, M. C., … Sullivan, P. F. (2015). Evaluating historical candidate genes for schizophrenia. Mol Psychiatry, 20(5), 555–562. https://doi.org/10.1038/mp.2015.16 PubMed
Glial contributions to Schizophrenia
I’m a big fan of glial cells (particularly oligodendrocytes) so I wanted to like this paper which showed abnormal myelination in mice that had glial progenitor cells made from patients with childhood-onset schizophrenia.
Windrem, M. S., Osipovitch, M., Liu, Z., Bates, J., Chandler-Militello, D., Zou, L., … Goldman, S. A. (2017). Human iPSC Glial Mouse Chimeras Reveal Glial Contributions to Schizophrenia. Cell Stem Cell, 21(2), 195–208.e6. https://doi.org/10.1016/j.stem.2017.06.012
But I can’t like it because I can’t download it! Gaaaaah! If someone finds the PDF please do share it back with me .
There is a nice STAT write up though, which gives an overview.
Some fun stats papers
I love the idea behind this paper on a multiverse analysis. But I also didn’t know that it already had a name: sensitivity analysis and statisticians have been doing them forever…
Steegan, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency through a Multiverse Analysis. Perspectives on Psychological Science, 11(5), 702–712. https://doi.org/10.1177/1745691616658637
Simon White also recommended this book chapter for anyone looking for a reference on the rule of thumb AIC parameters:
- Δ AIC 0-2 : no evidence
- Δ AIC 2-6 : some evidence
- Δ AIC 6-10 : strong evidence
- Δ AIC 10+ : very strong evidence
Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Ecological Modelling (Vol. 172). Retrieved from http://www.springer.com/gb/book/9780387953649
Artificial intelligence and neuroscience
I spoke at an event at the British Library about the Life, work and legacy of Alan Turing and read this paper to prepare. I liked it a lot!
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2), 245–258. https://doi.org/10.1016/j.neuron.2017.06.011
Our new Perspective article in Neuron, on how neuroscience and AI can help and influence each other: https://t.co/Ic8ZJ7dRlE— Demis Hassabis (@demishassabis) July 22, 2017
In lab meeting last week we rather adored this quote from a write up of the Brain Connectivity Workshop in Cambridge in 2003.
Finally, no report on a spring meeting in England would be complete without some comment on the notoriously capricious weather. Overseas participants were perhaps pleasantly surprised, on the evening of the first day, to find themselves enjoying a glass or two of Pimm’s (an English summer fruit punch) in a warm and sunny Cambridge garden. By the evening of the second day, however, cold and squally conditions threatened, but did not deter, the workshop’s collective voyage, in a flotilla of open punts, down the river Cam to dinner in a local restaurant: a subset of punters even opted for the hazardous return trip upstream after dinner, past the backs of some of the older colleges under low cloud late at night.
Bullmore, E., Harrison, L., Lee, L., Mechelli, A., & Friston, K. (2004). Brain Connectivity Workshop, Cambridge UK, May 2003. Neuroinformatics, 2(2), 123–126. https://doi.org/10.1385/NI:2:2:123