Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

PhD Ed. Neuroscience: PHDC720-Experimental Methods in Cognitive Neuroscience

COURSE DESCRIPTION

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.

ejournal articles

Wong, A. L., Goldsmith, J., Forrence, A. D., Haith, A. M., & Krakauer, J. W. (2017). Reaction times can reflect habits rather than computationsELife6, 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 EEGPsychophysiology51(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 discriminationScientific Reports7(1), 3269. https://doi.org/10.1038/s41598-017-03348-x

Schweinberger, S. R., & Neumann, M. F. (2016). Repetition effects in human ERPs to facesCortex80, 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 directionsNeuroscience & Biobehavioral Reviews49, 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 neurofeedbackNature Reviews Neuroscience18(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 reviewNeuroImage172, 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 mappingNature Neuroscience6(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 strokeNeuroImage145, 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 sciencesWiley Interdisciplinary Reviews: Cognitive Science6(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. Nature453(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 Imaging00(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 subjectsProceedings of the National Academy of Sciences103(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 disorderNeuroscience Letters400(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 diseaseCurrent Opinion in Neurology22(4), 340–347. https://doi.org/10.1097/WCO.0b013e32832d93dd

Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methodsNeuroimage11(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 Neurology28(4), 313–322. https://doi.org/10.1097/WCO.0000000000000222

eBooks

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.

 

SOFTWARE AND ONLINE RESOURCES