Frontal midline theta reflects anxiety and cognitive control: Meta-analytic evidence

Highlights

• The midcingulate cortex is involved in adaptively regulating behavior.

• This domain general process is common to negative affect and cognitive control.

• Frontal-midline theta band EEG signals reflect these adaptations to uncertainty.

• Three meta analyses support the domain general nature of frontal-midline theta.

Abstract

Evidence from imaging and anatomical studies suggests that the midcingulate cortex (MCC) is a dynamic hub lying at the interface of affect and cognition. In particular, this neural system appears to integrate information about conflict and punishment in order to optimize behavior in the face of action-outcome uncertainty. In a series of meta-analyses, we show how recent human electrophysiological research provides compelling evidence that frontal-midline theta signals reflecting MCC activity are moderated by anxiety and predict adaptive behavioral adjustments. These findings underscore the importance of frontal theta activity to a broad spectrum of control operations. We argue that frontal-midline theta provides a neurophysiologically plausible mechanism for optimally adjusting behavior to uncertainty, a hallmark of situations that elicit anxiety and demand cognitive control. These observations compel a new perspective on the mechanisms guiding motivated learning and behavior and provide a framework for understanding the role of the MCC in temperament and psychopathology.

Introduction

The rostral cingulate cortex, the thick belt of cortex encircling the genu and body of the corpus callosum (Fig. 1A), plays a central role in neuroscientific models of emotion and cognition (Etkin et al., 2011, Lindquist et al., 2012, Pessoa, 2008, Shenhav et al., 2013). Work to understand these two basic domains has profoundly influenced contemporary perspectives on more complex psychological phenomena, including psychopathology, pain, social processes, and the nature of executive control (Behrens et al., 2009, Etkin et al., 2011, Grupe and Nitschke, 2013, Iannetti et al., 2013). There is a growing consensus that the dorsal region of the rostral cingulate, the midcingulate cortex (MCC), is sensitive to both the elicitation of negative affect and the need for cognitive control, suggesting that the MCC implements a common, domain general process (Botvinick, 2007, Etkin et al., 2011, Pereira et al., 2010, Pessoa, 2008). Indeed, a recent meta-analysis of functional imaging studies demonstrates that the elicitation of both negative affect and cognitive control are associated with activation of an overlapping region in the anterior MCC (Fig. 1B) (Shackman et al., 2011). This overlap is consistent with anatomical evidence suggesting that the MCC represents a hub where information about pain, threat, and other more abstract forms of potential punishment can be synthesized and used to modulate regions involved in expressing fear and anxiety, executing goal-directed behaviors, and biasing the focus of selective attention (Fig. 1C and D) (Shackman et al., 2011).

Despite this progress, the functional significance of activity in the rostral cingulate remains incompletely understood. The objective of the present review is to highlight recent advances in understanding the adaptive control system that have been made using electrophysiological measures indicative of MCC activity. A key focus will be on investigations characterized by frontal midline theta (FMΘ) signals: ∼4–8 Hz oscillations recorded from sensors on the scalp overlying the MCC. Using meta-analytic techniques to synthesize the human electrophysiology literature, we provide evidence that anxious individuals show larger FMΘ control signals and that larger control signals are, in turn, associated with a more cautious or inhibited response set following errors and punishment. Collectively these observations support the idea that FMΘ reflects a common mechanism, a lingua franca, for implementing adaptive control in a variety of contexts involving uncertainty about actions and their motivationally-significant potential outcomes (Cavanagh et al., 2012b). More broadly, they provide a neurobiologically-grounded framework for conceptualizing the mechanisms that confer increased risk for the development of anxiety and other psychiatric disorders.

On the basis of brain imaging and anatomical evidence, it has been hypothesized that MCC activity reflects control processes that optimize responses made in the face of uncertainties about instrumental actions and their potentially aversive outcomes (Fig. 1E) (Shackman et al., 2011), a perspective that we term The Adaptive Control Hypothesis (TACH). Put simply, TACH suggests that anxiety and negative affect tend to involve the same processes described by cognitive control theories in order to solve similar problems (see also Grupe and Nitschke, 2013). We suggest that this is a domain-general function of the MCC.

Control processes are engaged when automatic or habitual responses are insufficient to support goal-directed behavior (Botvinick et al., 2001, Norman and Shallice, 1986, Shenhav et al., 2013). This occurs when there is uncertainty about the optimal course of action (e.g., probabilistic learning), when potential actions are associated with the possibility of error or punishment, or when there is competition between alternative courses of action (e.g., flee/freeze, go/no-go). These features are hallmarks of dangerous environments, as in studies of fear, anxiety, and pain (Choi et al., 2010, Steenland et al., 2012). Not surprisingly, optimal instrumental behavior in threatening environments has long been thought to require control processes to monitor risk and generate the biasing signals required to resolve response uncertainty and avoid potentially catastrophic actions (Dehaene et al., 1998, Gray and McNaughton, 2000, Norman and Shallice, 1986, Rushworth and Behrens, 2008). Importantly, a growing body of behavioral and biological evidence indicates that errors, like punishments and other kinds of control prompts, are experienced as unpleasant and facilitate avoidance, reinforcing the possibility that MCC makes a similar contribution to ‘cognitive’ and ‘affective’ control (Dreisbach and Fischer, 2012, Kool et al., 2010, Lindström et al., 2013, Schouppe et al., 2012).

Neuronal control signals generated within the MCC propagate to the scalp, where they can be measured using well-established electroencephalographic (EEG) techniques. In particular, MCC-related control processes are reflected in a variety of event-related potential (ERP) components elicited by novel information, conflicting stimulus-response requirements, punishing feedback, and the realization of errors (Fig. 2A). For example, the presentation of cue arrays associated with conflicting response options (as in the Eriksen flanker task) elicits the N2, a negative potential that peaks approximately 300 ms after the onset of conflicting cue arrays (for a review, see: Folstein and Van Petten, 2008). Likewise, unexpected punishment elicits a similar signal, the feedback-related negativity (FRN) (for a review, see: Walsh and Anderson, 2011). ERP control signals can also be elicited by endogenous activities (i.e., internal error signals), as with the error-related negativity (ERN), a negative potential peaking approximately 80 ms after the commission of an error (for a review, see: Gehring et al., 2012). While the cerebral generators of these scalp-recorded signals remains a matter of active research, and likely includes contributions from other brain regions (Bonini et al., 2014, Cohen et al., 2008, Emeric et al., 2010), a variety of evidence implicates the MCC as a key generator, including EEG source estimation (Gehring et al., 2012, Van Noordt and Segalowitz, 2012, Walsh and Anderson, 2012), EEG-informed fMRI (Becker et al., 2014, Debener et al., 2005, Edwards et al., 2012, Hauser et al., 2014, Huster et al., 2011), MEG (Doñamayor et al., 2011), and invasive recordings in humans and monkeys (Cohen et al., 2008, Gemba et al., 1986, Tsujimoto et al., 2010, Tsujimoto et al., 2006, Wang et al., 2005, Womelsdorf et al., 2010a, Womelsdorf et al., 2010b).

Although data derived using these ERP components have played a crucial role in the development of formal models of cognitive control and reinforcement learning (Holroyd and Coles, 2002, Yeung et al., 2004), there are theoretical and methodological advantages to focusing on the spectral characteristics of these signals rather than the separate ERP components. Spectral methods decompose complex signals into different contributions of frequency, power and phase angle over time, each of which can differentially contribute to information representation. Spectral decomposition has revealed that the ERN (errors), FRN (punishment), and N2 (conflict) share a common signature in the theta band (Cavanagh et al., 2009, Luu et al., 2004, Luu et al., 2003, Trujillo and Allen, 2007, Yordanova et al., 2004) (Fig. 2B and C). It was recently proposed that this family of theta signals reflect canonical phase-locked activities that are used for the temporal organization of distributed neuronal ensembles (Cavanagh et al., 2012b). Neural reactions to conflict, punishment, and error manifest as variations of these obligatory theta band phase dynamics, particularly via power increases (Cohen and Donner, 2013). In the context of this common spectral perspective, we refer to this collection of control-sensitive EEG signals as FMΘ. While these ERP components are partially dissociable, emphasizing their common dominant FMΘ processes offers an appropriately broad methodological and theoretical perspective.