USA ( ); New Zealand (1999); Germany ( ); Netherlands (2008-present) Ten Katestraat 13-iii 1053 BV, Amsterdam, the Netherlands

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1 April 2015 Demographic information Date of birth 24 May 1979 Citizenship Countries of residence Home address Internet USA USA ( ); New Zealand (1999); Germany ( ); Netherlands (2008-present) Ten Katestraat 13-iii 1053 BV, Amsterdam, the Netherlands home: mikexcohen.com lab: sincs.nl Scientific expertises Primary research tool: electrophysiology The electroencephalogram (EEG) measures the electrical activity of tens of thousands of neurons that form a functional ensemble. EEG provides a millisecond-resolution view into the rich cortical landscape of electrical activity, and is ideal for investigating the rapid and complex dynamics underlying rapidly changing human cognitive processes. Frontal theta and cognitive control Data analysis methods Cognitive control includes the ability to engage flexible and adaptive strategies to avoid making errors. One key finding is characterization of a cognitive-control-specific spatiotemporal pattern of oscillatory electrical brain activity, called midfrontal theta. See publications on page 4. Because the literature linking network oscillations to cognition and behavior is so nascent, the field has lacked sufficient data analysis methods. An important part of my research involves developing, evaluating, and teaching existing and novel data analysis techniques. See publications on page 5. Brain structure and behavior The architecture of the brain shapes its functioning, and thus cognition and behavior. Individual differences in brain structure are linked to behavioral characteristics, includings personality, cognitive control, and memory processing. See publications on page 5. Deep brain stimulation (DBS) DBS involves delivering electrical currents to the human brain via small implanted wires. DBS holds great promise for treating Parkinson s, major depression, and obsessive compulsive disorder. DBS also allows direct recordings of electrical activity in brain structures that otherwise cannot be recorded in humans. See publications on page 5. Academic history 2015 Staff scientist: Netherlands Institute for Neuroscience (Netherlands) 2010-present Senior researcher: University of Amsterdam; psychology dept. (Netherlands) 2012-present Guest researcher: University of California, San Francisco; radiology dept. (USA) Research assistant professor: University of Arizona; physiology dept. (USA) Post-doctoral researcher: University of Arizona; psychology dept. (USA) Post-doctoral researcher: University of Amsterdam; psychology (Netherlands) Visiting scientist: Epilepsy clinic, University of Bonn (Germany) PhD student: University of California, Davis; psychology dept. (USA) Bachelor s student: Carnegie Mellon University; psychology dept. (USA) (page 1 of 6)

2 Scientific output H-index Number of citations Peer-reviewed articles Monograph books Editorial experience Invited lectures Research funding Google Scholar: 41 Web of Knowledge: (Google Scholar) 3799 (Web of Knowledge, excluding self-citations) 79 listed on pubmed.com (38 first-author; 7 senior-author) Year See pages 4-6 for selected list of most relevant publications Analyzing Neural Time Series Data: Theory and Practice (MIT Press, 2014) Fundamentals of time-frequency analyses in Matlab/Octave (sinc(x)press, 2014) Editorial board member of NeuroImage Editoral board member of Cognitive, Affective, and Behavioral Neurosciences Guest editor for Frontiers in Human Neuroscience Ad-hoc reviewer for many journals (I review ~4 manuscripts/month) Generally 4-5 invited colloquia at other universities per year (e.g., 2013: UC Davis, Surrey Uni., Dusseldorf Uni., Oldenburg Uni.) Talks and poster presentations at several conferences each year (e.g., 2013: ABIM, ENP, SPR, SfN) VIDI (NWO; Dutch science funding agency) 800,000 for research on oscillations, cognitive control, and brain structure HFSP (human frontiers in science program) Post-doctoral funding for research on oscillations and cognitive control NRSA (National Institute for Drug Abuse) Pre-doctoral funding for research on reward learning DAAD (German academic exchange service) Funding for research on implanted EEG in epilepsy patients in Bonn APF COGDOP dissertation award ($3000 top prize) Bay Area Emotion Consortium (National Institute for Mental Health) Pre-doctoral training grant for research on cognition, emotion, and health Number citations Citation count by year. January Organization of meetings Discussions and opinions in cognitive neuroscience (2 days) This meeting resulted in a special issue in Frontiers in Human Neuroscience Amsterdam Brain Connectivity (2 days) ( (page 2 of 6)

3 Mentoring and supervising Current PhD students PhD co-promotor PhD committee member Irene van de Vijver (Uni of Amsterdam; 2015) Joram van Driel (Uni of Amsterdam; 2015) Anderson Mora Cortes (Uni of Amsterdam; expected 2016) Helga Harsay (Uni of Amsterdam; 2014) Hanneke van Dijk (Donders institute; 2011) Cigir Kalfaoglu (Uni of Sheffield; 2012) Marijn van Wingerden (Uni of Amsterdam; 2013) Zar Zavala (Oxford University; 2015) Research masters students (<1 year) Tara van Viegen (2014/5) Leon Retteig (2014) Hanneke Hartigh-van IJzeren (2013) Berno Bucker (2012) Rudy van den Brink (2012) Marlies Vissers ( ) Katerina Georgeopolou (2011) William Steel (2011) Wouter Boekel (2010) Teaching Cognitive Electrophysiology Methods Brain Organization and Cognition Advanced Cognitive Neuroscience Guest lectures This 8-week master s level course covers basic and advanced topics in analyzing electrical brain data. It is both math- and programming-intensive. I designed this course because no other similar courses existed. Student evaluations: (none available for 2012), 4.8 out of 5 (2013) This 8-week master s level course covers advanced topics in brain anatomy, function, computation, and cognition. Student evaluations: 4.13 out of 5 (2014) This 8-week master s level course covers most main topics in cognitive neuroscience, and includes several guest lectures and discussions. Student evaluations: 4.5 out of 5 (2010), 6.1 of out 7 (2011), 6.5 out of 7 (2012) Note: All lectures are 2 hours long and can be at bachelor s or master s level Methods in cognitive neuroscience Brain oscillations and cognition Oscillations and cognitive control Cross-frequency-coupling: Methods, theories, and data Major collaborators Netherlands University of Amsterdam: Ridderinkhof, Guerts, Slagter, Donner, Lamme Netherlands Institute for Neuroscience: Roelfsema, Heimel Academic Medical Center: Denys, Mazaheri Groningen University: van Rijn USA Australia University of Arizona: Allen, Gothard University of California, San Francisco: Nagarajan Brown University: Frank University of New Mexico: Cavanagh New Castle University: Karayanidis (page 3 of 6)

4 Publications about midfrontal theta and cognitive control Cohen, M. X (2014). A neural microcircuit for cognitive conflict detection and signaling. Trends in Neurosciences, 37(9), Cohen, M. X, & van Gaal, S. (2014).Subthreshold muscle twitches dissociate oscillatory neural signatures of conflicts from errors. NeuroImage, 86, Cohen, M. X, & Ridderinkhof, K. R. (2013). EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing. PloS One, 8(2), e Cohen, M. X, & Donner, T. H. (2013). Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology, 110, Cohen, M. X, & van Gaal, S. (2013). Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors. Cerebral Cortex, 23(5), Mansfield, E. L., Karayanidis, F., & Cohen, M. X (2012). Switch-related and general preparation processes in task-switching: evidence from multivariate pattern classification of EEG data. Journal of Neuroscience, 32(50), Van Driel, J., Ridderinkhof, K. R., & Cohen, M. X (2012). Not all errors are alike: theta and alpha EEG dynamics relate to differences in error-processing dynamics. Journal of Neuroscience, 32(47), Van Gaal, S., de Lange, F. P., & Cohen, M. X (2012). The role of consciousness in cognitive control and decision making. Frontiers in Human Neuroscience, 6, 121. Nigbur, R., Cohen, M. X, Ridderinkhof, K. R., & Stürmer, B. (2012). Theta dynamics reveal domain-specific control over stimulus and response conflict. Journal of Cognitive Neuroscience, 24(5), Cohen, M. X (2011). Error-related medial frontal theta activity predicts cingulate-related structural connectivity. NeuroImage, 55(3), Cohen, M. X, & Cavanagh, J. F. (2011). Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Frontiers in Psychology, 2, 30. Cavanagh, J. F., Cohen, M. X, & Allen, J. J. B. (2009). Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. Journal of Neuroscience, 29(1), Cohen, M. X, van Gaal, S., Ridderinkhof, K. R., & Lamme, V. A. F. (2009). Unconscious errors enhance prefrontal-occipital oscillatory synchrony. Frontiers in Human Neuroscience, 3, 54. Cavanagh, J. F., Wiecki, T. V., Cohen, M. X, Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14(11), (page 4 of 6)

5 Publications about EEG analysis methods Cohen, M. X (2014). Fluctuations in oscillation frequency control spike timing and coordinate neural networks. Journal of Neuroscience. 34 Cohen, M. X (2014). Effects of time lag and frequency matching on phase-based connectivity. Journal of Neuroscience Methods. Cohen, M. X (2014). Comparison of different spatial transformations applied to EEG data: A case study of error processing. Int J psychophysiology. Cohen, M. X (2011). It s about Time. Frontiers in Human Neuroscience, 5, 2. Allen, J. J. B., & Cohen, M. X (2010). Deconstructing the resting state: exploring the temporal dynamics of frontal alpha asymmetry as an endophenotype for depression. Frontiers in Human Neuroscience, 4, 232. Cohen, M. X (2008). Assessing transient cross-frequency coupling in EEG data. Journal of Neuroscience Methods, 168(2), Publications about brain structure and function Vissers, M. E., Cohen, M. X, & Geurts, H. M. (2012). Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neuroscience and Biobehavioral Reviews, 36(1). Cohen, M. X (2011). Hippocampal-prefrontal connectivity predicts midfrontal oscillations and long-term memory performance. Current Biology: 21(22), Cohen, M. X, Schoene-Bake, J.-C., Elger, C. E., & Weber, B. (2009). Connectivity-based segregation of the human striatum predicts personality characteristics. Nature Neuroscience, 12(1), Cohen, M. X, Elger, C. E., & Weber, B. (2008). Amygdala tractography predicts functional connectivity and learning during feedback-guided decision-making. NeuroImage, 39(3), Publications about deep brain stimulation (DBS) Cohen, M. X, Bour, L., Mantione, M., Figee, M., Vink, M., Tijssen, M. A. J., Denys, D. (2012). Topdown-directed synchrony from medial frontal cortex to nucleus accumbens during reward anticipation. Human Brain Mapping, 33(1), Cohen, M. X, Axmacher, N., Lenartz, D., Elger, C. E., Sturm, V., & Schlaepfer, T. E. (2009). Good vibrations: cross-frequency coupling in the human nucleus accumbens during reward processing. Journal of Cognitive Neuroscience, 21(5), Cohen, M. X, Axmacher, N., Lenartz, D., Elger, C. E., Sturm, V., & Schlaepfer, T. E. (2009). Nuclei accumbens phase synchrony predicts decision-making reversals following negative feedback. Journal of Neuroscience, 29(23), Schlaepfer, T. E., Cohen, M. X, Frick, C., Kosel, M., Brodesser, D., Axmacher, N., Sturm, V. (2008). Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression. Neuropsychopharmacology, 33(2), (page 5 of 6)

6 Publications about reward learning and modeling Van de Vijver, I., & Cohen, M. X, Ridderinkhof, K. R. (2014). Aging affects medial but not anterior frontal learning-related theta oscillations. Neurobiology of Aging, 35(3), Cohen, M. X, Wilmes, K., & Vijver, I. van de. (2011). Cortical electrophysiological network dynamics of feedback learning. Trends in Cognitive Sciences, 15(12), Van de Vijver, I., Ridderinkhof, K. R., & Cohen, M. X (2011). Frontal oscillatory dynamics predict feedback learning and action adjustment. Journal of Cognitive Neuroscience, 23(12), Cohen, M. X, Elger, C. E., & Fell, J. (2009). Oscillatory activity and phase-amplitude coupling in the human medial frontal cortex during decision making. Journal of Cognitive Neuroscience, 21(2), Cohen, M. X, & Frank, M. J. (2009). Neurocomputational models of basal ganglia function in learning, memory and choice. Behavioural Brain Research, 199(1), Cohen, M. X (2008). Neurocomputational mechanisms of reinforcement-guided learning in humans: a review. Cognitive, Affective & Behavioral Neuroscience, 8(2), Cohen, M. X (2007). Individual differences and the neural representations of reward expectation and reward prediction error. Social Cognitive and Affective Neuroscience, 2(1), Cohen, M. X, & Ranganath, C. (2007). Reinforcement learning signals predict future decisions. Journal of Neuroscience, 27(2), Cohen, M. X, Krohn-Grimberghe, A., Elger, C. E., & Weber, B. (2007). Dopamine gene predicts the brain s response to dopaminergic drug. European Journal of Neuroscience, 26(12), 3652 Cohen, M. X, & Ranganath, C. (2005). Behavioral and neural predictors of upcoming decisions. Cognitive, Affective & Behavioral Neuroscience, 5(2), Cohen, M. X, Young, J., Baek, J.-M., Kessler, C., & Ranganath, C. (2005). Individual differences in extraversion and dopamine genetics predict neural reward responses. Cognitive Brain Research, 25(3), (page 6 of 6)