Looking for help: assembling a list of neuroscience methods intro papers

I’ve not yet found a satisfying neuroscience methods book to use as an introduction for students. I’ve therefore started a list of introductory papers. My goal is to have a complete list with good-to-read papers that introduce each method, show examples of their use in research, and discuss their weaknesses. Optimally, there will be a “very easy overview” paper, and then some additional, more in-depth papers for each method.

As of now, the list is far from perfect: for one, it is hopelessly incomplete, but I suspect this is more because of my ignorance of good papers than because of a lack of good papers. Besides, many of the papers here are too difficult for entry-level reading.

So, if you know any good methods papers that are suited for beginning students, please drop me a line via Email or on Twitter!

Hopefully, the list will be useful also for others. Missing links to papers will be inserted over time, and I’ll indicate the difficulty of each paper.

 

general

Donoghue, J. P. (2008). Bridging the Brain to the World: A Perspective on Neural Interface Systems. Neuron, 60(3), 511–521. http://doi.org/10.1016/j.neuron.2008.10.037

Taub, E., Uswatte, G., & Elbert, T. (2002). New treatments in neurorehabiliation founded on basic research. Nature Reviews Neuroscience, 3(3), 228–236. http://doi.org/10.1038/nrn754

King, M., Dablander, F., Jakob, L., Agan, M., Huber, F., Haslbeck, J., & Brecht, K. (2016). Registered Reports for Student Research. Journal of European Psychology Students, 7(1). http://doi.org/10.5334/jeps.401

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716–aac4716. http://doi.org/10.1126/science.aac4716

Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. http://doi.org/10.1038/nrn3475

 

EEG/MEG

Otten, L. J., & Rugg, M. D. (2004). Interpreting event-related brain potentials. In T. C. Handy (Ed.), Event-Related Potentials: A Methods Handbook (pp. 3–16). Cambridge: MIT Press. Retrieved from http://discovery.ucl.ac.uk/185452/

Lopes da Silva, F. (2004). Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. Magnetic Resonance Imaging, 22(10), 1533–1538. http://doi.org/10.1016/j.mri.2004.10.010

Lehmann, D., & Skrandies, W. (1984). Spatial analysis of evoked potentials in man—a review. Progress in Neurobiology, 23(3), 227–250. http://doi.org/10.1016/0301-0082(84)90003-0

Rush, S., & Driscoll, D. A. (1968). Current distribution in the brain from surface electrodes. Anesthesia & Analgesia, 47(6), 717–723.

Baillet, S., Mosher, J. C., & Leahy, R. M. (2001). Electromagnetic brain mapping. Signal Processing Magazine, IEEE, 18(6), 14–30.

 

EMG

Mcneil, C. J., Butler, J. E., Taylor, J. L., & Gandevia, S. C. (2013). Testing the excitability of human motoneurons. Frontiers in Human Neuroscience, 7, 152. http://doi.org/10.3389/fnhum.2013.00152

Zwarts, M. J., & Stegeman, D. F. (2003). Multichannel surface EMG: Basic aspects and clinical utility. Muscle & Nerve, 28(1), 1–17. http://doi.org/10.1002/mus.10358

 

fMRI

Heeger, D. J., Huk, A. C., Geisler, W. S., & Albrecht, D. G. (2000). Spikes versus BOLD: what does neuroimaging tell us about neuronal activity? Nature Neuroscience, 3(7), 631–633. http://doi.org/10.1038/76572

Logothetis, N. K., & Pfeuffer, J. (2004). On the nature of the BOLD fMRI contrast mechanism. Magnetic Resonance Imaging, 22(10), 1517–1531. http://doi.org/10.1016/j.mri.2004.10.018

Logothetis, N. K., & Wandell, B. A. (2004). Interpreting the BOLD Signal. Annual Review of Physiology, 66(1), 735–769. http://doi.org/10.1146/annurev.physiol.66.082602.092845

Orban, G. A., Van Essen, D., & Vanduffel, W. (2004). Comparative mapping of higher visual areas in monkeys and humans. Trends in Cognitive Sciences, 8(7), 315–324. http://doi.org/10.1016/j.tics.2004.05.009

Wandell, B. A., & Winawer, J. (2011). Imaging retinotopic maps in the human brain. Vision Research, 51, 718–737. http://doi.org/10.1016/j.visres.2010.08.004

O’Reilly, J. X., Woolrich, M. W., Behrens, T. E. J., Smith, S. M., & Johansen-Berg, H. (2012). Tools of the trade: psychophysiological interactions and functional connectivity. Social Cognitive and Affective Neuroscience, 7(5), 604–609. http://doi.org/10.1093/scan/nss055

 

TMS

Di Lazzaro, V., & Rothwell, J. C. (2014). Corticospinal activity evoked and modulated by non-invasive stimulation of the intact human motor cortex. The Journal of Physiology, 592(19), 4115–4128. http://doi.org/10.1113/jphysiol.2014.274316

Bestmann, S., & Krakauer, J. W. (2015). The uses and interpretations of the motor-evoked potential for understanding behaviour. Experimental Brain Research, 233(3), 679–689. http://doi.org/10.1007/s00221-014-4183-7

Bestmann, S., & Duque, J. (2015). Transcranial Magnetic Stimulation Decomposing the Processes Underlying Action Preparation. The Neuroscientist, 1073858415592594. http://doi.org/10.1177/1073858415592594

 

tA/DCS

Di Lazzaro, V., & Rothwell, J. C. (2014). Corticospinal activity evoked and modulated by non-invasive stimulation of the intact human motor cortex. The Journal of Physiology, 592(19), 4115–4128. http://doi.org/10.1113/jphysiol.2014.274316

Merrill, D. R., Bikson, M., & Jefferys, J. G. R. (2005). Electrical stimulation of excitable tissue: design of efficacious and safe protocols. Journal of Neuroscience Methods, 141(2), 171–198. http://doi.org/10.1016/j.jneumeth.2004.10.020

Fertonani, A., & Miniussi, C. (2016). Transcranial Electrical Stimulation: What We Know and Do Not Know About Mechanisms. The Neuroscientist. http://doi.org/10.1177/1073858416631966

 

measuring movement & using movement to infer cognition

Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9(4), 292–303. http://doi.org/10.1038/nrn2258

Franklin, D. W., & Wolpert, D. M. (2008). Specificity of Reflex Adaptation for Task-Relevant Variability. The Journal of Neuroscience, 28(52), 14165–14175. http://doi.org/10.1523/JNEUROSCI.4406-08.2008

Wolpert, D. M., & Landy, M. S. (2012). Motor control is decision-making. Current Opinion in Neurobiology. Retrieved from http://www.sciencedirect.com/science/article/pii/S0959438812000827

Song, J. H., & Nakayama, K. (2009). Hidden cognitive states revealed in choice reaching tasks. Trends in Cognitive Sciences, 13(8), 360–366.

 

invasive recordings in animals

Alivisatos, A. P., Andrews, A. M., Boyden, E. S., Chun, M., Church, G. M., Deisseroth, K., … Zhuang, X. (2013). Nanotools for Neuroscience and Brain Activity Mapping. ACS Nano, 7(3), 1850–1866. http://doi.org/10.1021/nn4012847

Donoghue, J. P. (2008). Bridging the Brain to the World: A Perspective on Neural Interface Systems. Neuron, 60(3), 511–521. http://doi.org/10.1016/j.neuron.2008.10.037

 

invasive recordings in humans (grids, epilepsy, BMIl)

Bensmaia, S. J., & Miller, L. E. (2014). Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nature Reviews Neuroscience, 15(5), 313–325. http://doi.org/10.1038/nrn3724

Hatsopoulos, N. G., & Donoghue, J. P. (2009). The Science of Neural Interface Systems. Annual Review of Neuroscience, 32(1), 249–266. http://doi.org/10.1146/annurev.neuro.051508.135241

 

cooling/lesioning

Lomber, S. G. (1999). The advantages and limitations of permanent or reversible deactivation techniques in the assessment of neural function. Journal of Neuroscience Methods, 86(2), 109–117. http://doi.org/10.1016/S0165-0270(98)00160-5

 

Calcium imaging

Alivisatos, A. P., Andrews, A. M., Boyden, E. S., Chun, M., Church, G. M., Deisseroth, K., … Zhuang, X. (2013). Nanotools for Neuroscience and Brain Activity Mapping. ACS Nano, 7(3), 1850–1866. http://doi.org/10.1021/nn4012847

 

optogenetics

 

animal models (mouse, worms, fish, monkey)

 

stats/methods

Myung, I. J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47(1), 90–100. http://doi.org/10.1016/S0022-2496(02)00028-7

Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177–190. http://doi.org/10.1016/j.jneumeth.2007.03.024

Pernet, C. R., Chauveau, N., Gaspar, C., & Rousselet, G. A. (2011). LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data. Computational Intelligence and Neuroscience, 2011, 1–11. http://doi.org/10.1155/2011/831409

Nakagawa, S., & Hauber, M. E. (2011). Great challenges with few subjects: Statistical strategies for neuroscientists. Neuroscience & Biobehavioral Reviews, 35(3), 462–473. http://doi.org/10.1016/j.neubiorev.2010.06.003

Cumming, G., Fidler, F., & Vaux, D. L. (2007). Error bars in experimental biology. The Journal of Cell Biology, 177(1), 7.

Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E.-J. (2011). Erroneous analyses of interactions in neuroscience: a problem of significance. Nat Neurosci, 14(9), 1105–1107. http://doi.org/10.1038/nn.2886

Kliegl, R., Wei, P., Dambacher, M., Yan, M., & Zhou, X. (2011). Experimental effects and individual differences in linear mixed models: estimating the relationship between spatial, object, and attraction effects in visual attention. Frontiers in Quantitative Psychology and Measurement, 1, 238. http://doi.org/10.3389/fpsyg.2010.00238

Osborne, J. W. (2013). Is data cleaning and the testing of assumptions relevant in the 21st century? Frontiers in Psychology, 4. http://doi.org/10.3389/fpsyg.2013.00370

Speelman, C. P., & McGann, M. (2013). How Mean is the Mean? Frontiers in Psychology, 4. http://doi.org/10.3389/fpsyg.2013.00451

Cumming, G. (2014). The New Statistics Why and How. Psychological Science, 25(1), 7–29. http://doi.org/10.1177/0956797613504966

Gelman, A., & Stern, H. (2006). The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American Statistician, 60(4), 328–331. http://doi.org/10.1198/000313006X152649

 

practical analysis/programming

Lacouture, Y., & Cousineau, D. (2008). How to use MATLAB to fit the ex‐Gaussian and other probability functions to a distribution of response times. Tutorials in Quantitative Methods for Psychology.

Urai, Anne, Prettier plots in Matlab, http://anneurai.net/2016/06/13/prettier-plots-in-matlab
 

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