Oscillatory Activity and Phase–Amplitude Coupling in the Human Medial Frontal Cortex during Decision Making Michael X Cohen 1,2 , Christian E. Elger 1 , and Juergen Fell 1 Abstract & Electroencephalogram oscillations recorded both within and over the medial frontal cortex have been linked to a range of cognitive functions, including positive and negative feed- back processing. Medial frontal oscillatory characteristics dur- ing decision making remain largely unknown. Here, we examined oscillatory activity of the human medial frontal cortex recorded while subjects played a competitive decision-making game. Distinct patterns of power and cross-trial phase coherence in multiple frequency bands were observed during different decision-related processes (e.g., feedback anticipation vs. feedback processing). Decision and feedback processing were accompanied by a broadband increase in cross-trial phase co- herence at around 220 msec, and dynamic fluctuations in power. Feedback anticipation was accompanied by a shift in the power spectrum from relatively lower (delta and theta) to higher (alpha and beta) power. Power and cross-trial phase coherence were greater following losses compared to wins in theta, alpha, and beta frequency bands, but were greater fol- lowing wins compared to losses in the delta band. Finally, we found that oscillation power in alpha and beta frequency bands were synchronized with the phase of delta and theta oscil- lations (‘‘phase–amplitude coupling’’). This synchronization differed between losses and wins, suggesting that phase– amplitude coupling might reflect a mechanism of feedback valence coding in the medial frontal cortex. Our findings link medial frontal oscillations to decision making, with relations among activity in different frequency bands suggesting a phase- utilizing coding of feedback valence information. & INTRODUCTION Decision making is a critical function of the brain and is supported by a network of neural structures that relies on functioning in the medial frontal cortex (Kennerley, Walton, Behrens, Buckley, & Rushworth, 2006; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004; Bush et al., 2002). Indeed, lesions to the rat or nonhuman pri- mate medial frontal cortex impair animals’ ability to adapt reward-seeking behavior flexibly according to changes in the environment (Rushworth, Buckley, Behrens, Walton, & Bannerman, 2007; Kennerley et al., 2006; Walton, Bannerman, Alterescu, & Rushworth, 2003; Walton, Bannerman, & Rushworth, 2002). Al- though the neurocognitive mechanisms of decision making are receiving increasing attention in cognitive neuroscience (Rushworth et al., 2007; Volz, Schubotz, & von Cramon, 2006; Ridderinkhof et al., 2004; Montague & Berns, 2002), much remains unknown about even the basic neural processes involved in simple decision- making situations. Electroencephalography (EEG), which measures the sum of dendritic activity and has a high temporal resolution, offers a way to examine the time course of neural events, including oscillation power and cross-trial phase coherence. Researchers who study EEG correlates of human decision making typically focus on the feedback-related negativity (FRN), a relatively nega- tive potential beginning around 200 msec following negative compared to positive feedback, that is maxi- mal over fronto-central scalp electrodes (Cohen, Elger, & Ranganath, 2007; Cohen & Ranganath, 2007; Frank, Woroch, & Curran, 2005; Holroyd, Nieuwenhuis, Yeung, & Cohen, 2003; Holroyd & Coles, 2002; Miltner, Braun, & Coles, 1997). The FRN is thought to reflect the engage- ment of a medial frontal network that uses positive and negative feedback to adjust future behavior toward more optimal or reward-maximizing levels (Nieuwenhuis, Holroyd, Mol, & Coles, 2004; Holroyd & Coles, 2002). EEG data may comprise both brief bursts of neural activity as well as ongoing oscillations. Oscillations in EEG are driven by rhythmic, synchronized dendritic activity in populations of neurons. When creating stimulus-locked event-related potentials (ERPs), oscillations survive aver- aging only when those oscillations are phase-locked to the stimulus. Although the traditional view has been that non-phase-locked oscillations reflect background neural processes, a growing body of research demonstrates that these oscillations can yield novel insights into neurocog- nitive processes, beyond what can be gleaned from ERPs (Fell et al., 2004; Freeman, Holmes, Burke, & Vanhatalo, 1 University of Bonn, Germany, 2 University of California, Davis D 2008 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 21:2, pp. 390–402