This course explores current tools and research protocols that address contemporary issues in cognitive and educational neuroscience to study the human brain and behavior in healthy and clinical populations. To investigate brain function, experimental research methods and neuroimaging tools will be covered in detail and applied; this includes recent emerging tools that offer exciting opportunities to explore brain function under natural conditions. Students will acquire the skills and the in-depth knowledge involved in behavioral testing and brain imaging (e.g. implementation, uses, limitations, design, resolution, sensitivity, equipment, data type, acquisition, reconstruction, inferences) so they will be able to understand what one can (or cannot) do with each tool, and how to use them. Students will develop the skills to help them appreciate the burgeoning literature in applied cognitive neuroscience, along with its methods and protocols. Ultimately, they will be able to identify the right tools, along with testing and designing appropriate procedures for research in the field of brain and behavior.
Wong, A. L., Goldsmith, J., Forrence, A. D., Haith, A. M., & Krakauer, J. W. (2017). Reaction times can reflect habits rather than computations. ELife, 6, e28075. https://doi.org/10.7554/eLife.28075
Jackson, A. F., & Bolger, D. J. (2014). The neurophysiological bases of EEG and EEG measurement: A review for the rest of us: Neurophysiological bases of EEG. Psychophysiology, 51(11), 1061–1071. https://doi.org/10.1111/psyp.12283
Retter, T. L., & Rossion, B. (2017). Visual adaptation reveals an objective electrophysiological measure of high-level individual face discrimination. Scientific Reports, 7(1), 3269. https://doi.org/10.1038/s41598-017-03348-x
Schweinberger, S. R., & Neumann, M. F. (2016). Repetition effects in human ERPs to faces. Cortex, 80, 141–153. https://doi.org/10.1016/j.cortex.2015.11.001
Khanna, A., Pascual-Leone, A., Michel, C. M., & Farzan, F. (2015). Microstates in resting-state EEG: Current status and future directions. Neuroscience & Biobehavioral Reviews, 49, 105–113. https://doi.org/10.1016/j.neubiorev.2014.12.010
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., Birbaumer, N., & Sulzer, J. (2017). Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience, 18(2), 86–100. https://doi.org/10.1038/nrn.2016.164
Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R., & Raz, A. (2018). Neurofeedback with fMRI: A critical systematic review. NeuroImage, 172, 786–807. https://doi.org/10.1016/j.neuroimage.2017.12.071
Bates, E., Wilson, S. M., Saygin, A. P., Dick, F., Sereno, M. I., Knight, R. T., & Dronkers, N. F. (2003). Voxel-based lesion–symptom mapping. Nature Neuroscience, 6(5), 448–450. https://doi.org/10.1038/nn1050
Price, C. J., Hope, T. M., & Seghier, M. L. (2017). Ten problems and solutions when predicting individual outcome from lesion site after stroke. NeuroImage, 145, 200–208. https://doi.org/10.1016/j.neuroimage.2016.08.006
Wilcox, T., & Biondi, M. (2015). Fnirs in the developmental sciences: Fnirs in the developmental sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 6(3), 263–283. https://doi.org/10.1002/wcs.1343
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878. https://doi.org/10.1038/nature06976
Thulborn, K. R., & Davis, D. (2001). Quality assurance for clinical fmri. Current Protocols in Magnetic Resonance Imaging, 00(1), A6.2.1-A6.2.4. https://doi.org/10.1002/0471142719.mia0602s00
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences, 103(37), 13848–13853. https://doi.org/10.1073/pnas.0601417103
Tian, L., Jiang, T., Wang, Y., Zang, Y., He, Y., Liang, M., Sui, M., Cao, Q., Hu, S., Peng, M., & Zhuo, Y. (2006). Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neuroscience Letters, 400(1–2), 39–43. https://doi.org/10.1016/j.neulet.2006.02.022
Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease: Current Opinion in Neurology, 22(4), 340–347. https://doi.org/10.1097/WCO.0b013e32832d93dd
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. Neuroimage, 11(6), 805-821.
Weiskopf, N., Mohammadi, S., Lutti, A., & Callaghan, M. F. (2015). Advances in MRI-based computational neuroanatomy: From morphometry to in-vivo histology. Current Opinion in Neurology, 28(4), 313–322. https://doi.org/10.1097/WCO.0000000000000222
Toga, A. W., & Mazziotta, J. C. (Eds.). (2002). Brain mapping: The methods (2nd ed). Academic Press.
Blake, R., & Sekuler, R. (2006). Perception (5. ed., internat. ed). McGraw-Hill.
Huettel, S. A., Song, A. W., & McCarthy, G. (2008). Functional magnetic resonance imaging (2nd ed). Sinauer Associates.
Matlab
CogLab
CogLab: The Online Cognition Lab
Behavioral Demonstrations
Cognition Laboratory Experiments
Brain Resources
Statistical Parametric Mapping
Neuronline
Harvard Whole Brain Atlas: www.med.harvard.edu/AANLIB/home.html