Frontal theta as a mechanism for cognitive control

Abstract

Recent advancements in cognitive neuroscience have afforded a description of neural responses in terms of latent algorithmic operations. However, the adoption of this approach to human scalp electroencephalography (EEG) has been more limited, despite the ability of this methodology to quantify canonical neuronal processes. Here, we provide evidence that theta band activities over the midfrontal cortex appear to reflect a common computation used for realizing the need for cognitive control. Moreover, by virtue of inherent properties of field oscillations, these theta band processes may be used to communicate this need and subsequently implement such control across disparate brain regions. Thus, frontal theta is a compelling candidate mechanism by which emergent processes, such as 'cognitive control', may be biophysically realized.

Keywords: ERP; cognitive control; computational modeling; frontal cortex; prediction error; theta.

Figure 1. A variety of eliciting events is associated with a similar neuroelectrical signature on the scalp

(A) Traditional event-related potential (ERP) components in the time-domain. N2: an ERP component elicited by novelty or stimulus/response conflict. Feedback Related Negativity (FRN): A similar N2-like component elicited by external feedback signaling that one’s actions were incorrect or yielded a loss. Correct-Related Negativity (CRN): a small, obligatory component evoked by motor responses even when these are correct according to the task, and enhanced by response conflict. Error Related Negativity (ERN): A massive ERP component evoked by motor commission errors. While these ERP components (i.e., peaks and troughs in the signal locked to particular external events and averaged across trials) are related to learning and adaptive control, they represent a small fraction of ongoing neural dynamics. (B) Time-frequency plots show richer spectral dynamics of event-related neuroelectrical activity which allow one to study power following particular events without requiring signals to be phased-locked. Here, significant increases in power to novelty, conflict, punishment and error are outlined in black, revealing a common theta-band feature. (C) Scalp topography of event-related theta activity. The distribution of theta power bursts is consistently maximal over the frontal midline. Data and statistical tests from [9].

Figure 2. Theta as a biophysical mechanism for organizing local and distal neurocomputational functions

(A) In humans, mid-frontal theta evoked by errors (here, the ERN) has been localized to MCC on the basis of dipole source modeling (red) and concurrent hemodynamic activity (blue). (B) Theta activity recorded from the rostral cingulate sulcus in rhesus macaques. Recordings were made in a region (shown in red) during performance of an anti-saccade task. Increased theta power on anti vs. pro saccade trials (blue > red traces) was associated with stronger spike-field coupling within the theta rhythm, demonstrating how MCC theta provides a temporal window for coincident neural activities that contribute to adaptive control. (C) Mid-frontal theta is thought to reflect the synchronization of goal-relevant information around critical decision points, such as action selection. In this example, theta activities co-ordinate inputs across cortical areas (arrows), particularly at the trough of the oscillation (grey bars). Action selection is likely to be executed when these sources of choice-relevant information (context, reward, memory, etc.) are successfully integrated (solid arrows). (D) Theta band phase consistency is thought to reflect the instantiation of transient functional networks (purple and green traces). For instance, inter-site theta band phase consistency following signals of the need for control have been observed between sources modeled in mid-cingulate cortex (MCC), lateral PFC (lPFC), motor areas, and sensory (i.e. extrastriate visual) cortex. Theta activity may also implement communications between MCC and the basal ganglia (BG). (a) reproduced from [31] with permission from the Society for Neuroscience; (b) reproduced from [35] with permission from the Proceedings of the National Academy of Sciences and [98] with permission from Cell press; (c) reproduced from [42] with permission from the authors.

Figure 3. Theta band phase consistency between mid-frontal and lateral sites is transiently increased following events that indicate a need for control

Eleven separate studies (A–K) have replicated the finding of theta-band phase synchrony between mid-frontal sites and varied cortical areas, including lateral prefrontal cortex (presumably for goal or attention reorientation), motor cortex (presumably to alter motor threshold), and sensory cortices (presumably to boost sensory gain). Legend: error feedback is punishment; there have been no studies of cue-locked error signals or feedback-locked conflict. Citations: A[14], B[15], C[16] D[17], E[18], F[19] G[20] H[21], I[22], J[23], K[24].

Figure 4. Algorithmic models of learning and decision making, and their potential relationships to theta band signals reflecting the need for control

(A) Reinforcement (reward and punishment) learning can be modeled by a variety of similar algorithmic approaches. Shown here is a cartoon example of Q-Learning[99] during a probabilistic learning task[100]. The difference between expected and actual reward is calculated as a reward prediction error conveying whether events are better or worse than expected. These reward prediction errors are then used to adjust future expectations, scaled by a learning rate. (B) A common model of two-alternative forced choice is the Drift Diffusion Model (DDM), shown here[93]. Black lines indicate the accumulated evidence trace (drift rates) for one decision option over another across multiple example trials that grow towards one of two boundaries (decision thresholds), defining when a decision is made. (C) Punishment-induced FRN/FMθ power correlates with the prediction error (shown in (A))[60]. While many investigations have found stronger relationships between FRN/FMθ and worse-than-expected outcomes, more detailed investigations have revealed that even better-than-expected outcomes can also linearly relate to FRN/FMθ power, suggesting that much of this relationship is predicated on the need for change rather than the valence of the feedback per se. However, punishment may be associated with an overall larger response (i.e. higher intercept)[60,82] (D) Response conflict is not only greater during difficult perceptual-performance tasks (such as the Stroop, flanker or Simon task) but also as a function of uncertainty when choosing options with probabilistically different reinforcement rates[100]. This type of uncertainty can be quantified by estimating, for example, the Q-values in (A) as belief distributions with means (expected value) but also variance (estimation uncertainty). During dynamic foraging, the degree of theta response to high uncertainty can predict exploration. [60] (E) Stimulus-induced conflict not only signals a need for increased control (larger N2/FMθ), but this theta signal is related to a transiently increased decision threshold on a trial-by-trial level, effectively linking conflict-induced theta power to enhanced response caution (longer RT, more accurate at avoiding mistakes)[94].