Cortical-limbic circuit dynamics of approach-avoidance conflict in humans

Domain: biology
Published: 2026-03-12
Editorial outcome: minor_revision

Abstract
Choosing to approach or avoid is common in everyday life and excessive avoidance is a cardinal feature of anxiety disorders. We use intracranial EEG to define a prefrontal-limbic circuit supporting approach and avoidance. Presurgical epilepsy patients (n=20) performed an approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where patients trade off rewards against losses from ghost attack. During approach, theta power increases across a limbic circuit including the hippocampus, amygdala, orbitofrontal cortex and anterior cingulate cortex, which drops during avoidance. Theta connectivity between this circuit and lateral prefrontal cortex increases during approach and falls during avoidance. Network connectivity tracks how long patients approach, with enhanced synchronicity extending approach times. During imminent threat, the system switches to sustained increase in high-frequency activity in the lateral prefrontal cortex. The results provide evidence of a distributed prefrontal-limbic circuit, mediated by theta oscillations and high frequency activity, underlying approach-avoidance conflict in humans.

Introduction
Anxiety is a complex response to ambiguous and potentially negative future states. Anxiety comprises threat appraisal and uncertainty management, among other constituents, and dysfunction in any of these domains could result in persistent or pathological anxiety. A key behavioral paradigm for investigating both clinical and normative anxiety is approach-avoidance conflict. Approach-avoidance conflict describes the daily situations where a single choice entails potential gains and losses simultaneously, which reliably induces anxiety in everyday life 1,2. For example, consider the decision to approach someone new while attending a party. The same action could result in the positive outcome of a nice conversation or a new friend, or a negative outcome such as social rejection or embarrassment. While anxiety in this context is normative, when avoidance becomes excessive (never speaking to anyone new, or avoiding social gatherings altogether) it becomes maladaptive1,3,4. Approach-avoidance decisions are dysregulated in generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), and agoraphobia. Specifically, in clinical disorders there is maladaptive avoidance of potentially aversive cues at the cost of large rewards2,5,6 and this can be disambiguated from adaptive avoidance, such as avoiding cues that are likely to result in large costs, with low probability of reward (e.g., avoiding speaking to a known rude or unkind person at a party). Understanding the cortical and limbic neural circuits that regulate healthy and adaptive approach-avoidance conflict is key to understanding the neural underpinnings of both normal and excessive clinical anxiety.

There is a rich history defining the limbic system, long known to underly aspects of emotional behavior7–9, including approach-avoidance10–12 . In 1937, Papez described brain regions, particularly the hippocampus and anterior cingulate, associated with emotional control13 and coined the term the limbic system based on Broca’s anatomical description of the ‘limbic lobe’ encircling the corpus callosum14. McClean added the amygdala to the limbic system in response to reports of abnormal behavior in monkeys with bilateral amygdala lesions15,16. Based on the disordered emotional and social behavior of patient Phineas Gage17, the orbitofrontal cortex completed the modern definition of the limbic system. Given the complexity and extent of the limbic system, we interrogated the role of all these regions with a focus on theta oscillations known to be involved in network communication in a host of human behaviors. We further hypothesized that the engagement of the lateral prefrontal cortex, specifically the middle frontal gyrus (MFG), in approach-avoidance would be distinct from limbic regions2,18. The MFG has been associated with cognitive control and emotion regulation, and nonhuman primates (NHPs) research has reported evidence that the prefrontal cortex is part of a cognitive circuit exerting top-down control on the limbic circuit19. Here, we use intracranial EEG (iEEG) to delineate a prefrontal-limbic system engaged in approach-avoidance including the middle frontal gyrus (MFG), orbitofrontal cortex (OFC), amygdala, anterior cingulate cortex (ACC) and hippocampus (HC) in humans.

Elements of this network have been explored in both rodents, nonhuman primates, and humans. Approach-avoidance conflict tasks, such as the elevated plus maze or the open field test have been used to study the neural circuits underlying anxiety in rodents2,20,21. In these tasks, there is a conflict between the positive drive to explore new areas versus the negative drive to avoid exposed areas. Using a variety of approaches, researchers have identified pairs of prefrontal-limbic regions, mediated by theta-band power, associated with avoidance behavior on these tasks22–24. Researchers have also found that a subset of ventral hippocampal (vHC) neurons projecting to medial PFC (mPFC) show increased theta synchrony in anxiogenic contexts25. Optogenetically stimulating these neurons at 4-12Hz increases both synchrony between the vHC and the mPFC and increases avoidance behavior in rodents24. Similarly, optical activation of rodent basolateral amygdala neurons that terminate in the mPFC increases avoidance in the elevated plus maze, open field test, and other anxiogenic tasks, while inhibition of these neurons increases approach behavior26. Other studies report bidirectional mPFC-amygdala circuitry, implicating the mPFC in top-down control of amygdala-mediated anxiety behaviors in rodents21,25. However, the human homologue of the rodent mPFC is still debated27 and the massive expansion of human prefrontal cortex limits a direct translation of rodent to human findings.

Similar regions are implicated in human approach-avoidance using non-invasive imaging and neuropsychology. Bilateral hippocampal lesioned patients showed increased approach behavior28, and a magnetoencephalograph study found that hippocampal-PFC theta synchrony increased with threat29. Some fMRI studies of human approach-avoidance conflict report activations in the amygdala30, but others do not31–33. It is hypothesized that the amygdala responds equally to appetitive and aversive cues and may be missed in standard fMRI contrasts2. Other studies using fMRI have found activations in orbitofrontal cortex, anterior cingulate and dorsolateral prefrontal cortex that correlate with task parameters like increasing threat, reward, and conflict, though there is variation between different tasks2,32–35. Separately, researchers report that EEG midline frontal theta, increases with approach-avoidance conflict similar to midline frontal theta elevations in tasks requiring cognitive control36. Together, this work suggests that both prefrontal and limbic regions are engaged in approach-avoidance behavior. However, the real-time circuit dynamics of approach-avoidance has yet to be characterized in humans.

Here we test for the existence of such a prefrontal-limbic network underlying approach-avoidance by examining 1) task-dependent modulations in theta power in middle frontal gyrus and limbic regions including the hippocampus, amygdala, OFC and anterior cingulate; 2) elevated theta band coherence across these regions; and 3) dynamic changes in this circuit in response to increases in threat and reward. To test these hypotheses, we collected intracranial electrode recordings across this circuit in twenty epileptic patients during presurgical assessment while they performed a continuous-choice, approach-avoidance conflict task based on the arcade game Pac-Man.

In this work, we find that theta power is elevated during the approach period compared to the avoidance period in each of the limbic regions, but not in the MFG. Theta connectivity between this limbic circuit and lateral prefrontal cortex increases during approach and falls during avoidance. Furthermore, theta synchrony across limbic regions and MFG correlates with how long participants approach in a given trial. Finally, we find that high frequency activity in the middle frontal gyrus tracks threat when the participant is under direct attack by the Ghost.

Results
Behavioral Results: We designed an approach-avoidance conflict task based on the arcade game Pac-Man (See Figure 1a). Briefly, on each conflict trial (80% of trials), Pac-Man, the Ghost, and dots are placed along a single corridor that runs horizontally across the screen. The participant controls the Pac-Man and they are instructed to collect as many dots as they can while avoiding the Ghost, which is moving back and forth at a constant speed at the opposite side of the corridor. The participant makes a single button press, using the left or right arrow keys, to initiate movement along the corridor at a constant speed. By moving towards the center of the corridor, they can collect up to 5 dots, resulting in points, which varied in either small (10 points) or large (20 point) sizes and were placed in pseudorandom configurations between Pac-Man and the Ghost. Importantly, at any point in the trial, they could also choose to move away from the center of the corridor (and, thus, away from both the dots and the Ghost) where they could exit to the next trial at the end of the corridor. Critically, the distance between the Pac-Man and the Ghost directly determined the probability the Ghost would initiate an attack. During an Attack, if Pac-Man was “caught” by the Ghost, they lost one of their three lives and all the points acquired on that trial. Please see the Methods for the full description of the task. 20% of trials were conflict-free, where the participant was free to collect the reward without threat of the Ghost.

We validated that the task was inducing approach-avoidance trade-offs by assessing the behavior in a sample of 191 participants recruited from the online platform Prolific (see Methods for details). Across the twelve blocks of twenty trials each, participants collected an average of 3.83 +/ 0.34 dots per trial, demonstrating that they often left some potential reward in the trial (max 5 dots) to maintain a greater distance from the ghost (See Figure 1b). Participants on average turned away from the ghost when they were 35.3+/ 3.4 units away from the ghost (corresponding to ~17% of the length of the corridor), though there was across-subject variability in how close participants were willing to get to the ghost to receive more reward (See Figure 1c). We also hypothesized that participants would make risk/reward trade-offs tolerating more risk to get large rewards. We tested this by running a linear mixed effects model, where a two-level factor specifying if the last reward was large or small was used to predict the distance to the ghost at the time of the decision to avoid. As predicted, when the last reward in the corridor was large, participants were willing to move closer to the ghost to collect the large reward (Estimate : 0.11, 95% CI [0.09-0.13], P+ > 0.99, BF > 100, ESS = 14861.4; see Figure 1d, see Table 1 for model details; see Source Data File). We did not find evidence that the self-reported sex of the participant altered how close participants were willing to get to the ghost (Estimate : -0.04, 95% CI [-0.16-0.08], ESS = 1052), nor had any interaction with reward size (Estimate : 0.03, 95% CI [-0.02-0.18], ESS = 15356). Since approach-avoidance conflict is often used as a behavioral model of anxiety, we asked participants to report their emotions during the trial. Participants were given a set of emotions (anger, anxiety, boredom, disinterest, excitement, frustration, hope, stress, and suspense) as well as an option to write their own. Participants could report multiple emotions. A majority of participants reported experiencing anxiety, stress, and suspense during the task, supporting face-validity of the task (Fig 1e).

Twenty patients with seizure- and artifact-free intracranial electrodes in the amygdala, hippocampus, OFC, ACC, and MFG (Fig 1f; see methods and supplementary data for patient details) performed the task. Behavioral performance across the twenty patients was comparable to the performance in our online sample (See Figure 1 b-e). Electrodes near seizure foci were removed, and epochs where seizure activity spread beyond the local seizure onset zone were excluded. This left a total of 115 amygdala contacts, 174 hippocampal contacts, 144 OFC contacts, 69 anterior cingulate contacts, and 129 MFG contacts that were included in our analyses. While some patients did not have contacts in all five regions, there was always at least 12 patients with coverage in each possible pair of our five regions, allowing us to probe the neural dynamics across all sets of regions (See Supplemental Figure 1 and Supplemental Table 1 for individual patient electrode coverage and demographics).

Theta power is locally modulated by approach-avoidance decisions in limbic regions: We hypothesized there would be task-dependent theta power modulations within our regions of interest. In each region, we calculated time-frequency plots time-locked to the patients’ choice to stop approaching and turn back to exit the trial. If the participant turned multiple times, we selected the final timepoint as the decision to avoid. We observed a similar pattern in the hippocampus, amygdala, OFC, and ACC, where low-frequency power (between 3-8Hz) is elevated during the approach period of the trial but drops after the decision to avoid and exit the trial (See Figure 2a).

We next tested if the approach period correlated with greater theta power compared to the avoidance period across the different regions. We excluded the initial period of the trial where the Pac-Man was stationary to best compare the approach activity to avoidance activity. On average, patients began moving 1.02+/- 0.46 seconds after trial onset and approached both the threat (Ghost) and reward (dots) for 1.39 +/- 0.22 seconds. We therefore averaged theta power during the final 1.5 seconds of approach and first 1.5 seconds of avoidance in each electrode and patient, on trials without a Ghost Attack. We estimated a Bayesian mixed effects model of theta power using their approach/avoidance decisions with hierarchically grouped random effects of patient and electrode (See Methods and Table 1 for details). We found that theta power was elevated during approach compared to avoidance in conflict trials within the hippocampus (See Figure 2a; Conflict-Trial Estimate : 0.24, 95% CI [0.10-0.37], P+ > 0.99, BF > 4, ESS = 3838.9, Ratio of Sig. Electrodes = 72/115), amygdala (Conflict-Trial Estimate : 0.16, 95% CI [0.07-0.26], P+ > 0.99, BF > 3, ESS = 5903.8, Ratio of Sig. Electrodes = 59/101), OFC (Conflict-Trial Estimate : 0.21, 95% CI [0.13-0.29], P+ > 0.99, BF > 100, ESS = 4243.2, Ratio of Sig. Electrodes = 127/167) and ACC (Conflict-Trial Estimate : 0.26, 95% CI [0.14-0.38], P+ > 0.99, BF > 18, ESS = 7222.9, Ratio of Sig. Electrodes = 50/73). In the subset of control trials that were conflict-free, i.e. where patients collected reward without threat of the ghost, we ran an equivalent model, where the approach period (e.g., 1.5 seconds before the decision to turn around, usually made after all rewards were collected) was compared again the 1.5 seconds after the decision to turn. We did not find a significant difference in theta power between the approach and return windows (Hippocampus Estimate : 0.08, 95% CI [0.08-0.25], P+ = 0.84, BF = 0.07, ESS = 2778.1, Ratio of Sig. Electrodes = 72/115; Amygdala Estimate : 0.11, 95% CI [-0.02-0.22], P+ = 0.96, BF = 0.14, ESS = 4697.1, Ratio of Sig. Electrodes = 38/101; OFC Estimate : 0.13, 95% CI [-.01-0.27], P+ = 0.97, BF > 0.21, ESS = 4350.9, Ratio of Sig. Electrodes = 71/167; ACC Estimate : 0.12, 95% CI [-0.070.31], P+ = 0.90, BF > 0.11, ESS = 7437.9, Ratio of Sig. Electrodes = 24/73), indicating that the effects were not likely driven by purely spatial or reward-only behavior (See Supplemental Figure 2 for TFRs of the conflict-free trials, see Source Data File for model estimates).

We then tested for theta power modulations in the MFG, implicated in previous fMRI and nonhuman-primate studies of approach-avoidance but not considered to be part of the limbic system18,19,34. We did not find evidence for increased theta power in the approach period compared to the avoidance period in the MFG (Conflict-Trial Estimate : 0.06, 95% CI [-0.08-0.19], P+ = 0.82, BF = 0.05, ESS = 4642.4, Ratio of Sig. Electrodes = 31/143; Conflict-Free Estimate : -0.06, 95% CI [-0.21-0.11], P+ = 0.24, BF = 0.05, ESS = 4166.8, Ratio of Sig. Electrodes = 11/143; see Supplemental Figure 3 for an analysis of the brief increase in low frequency power from an event-related potential at the beginning of the approach period in the MFG contacts). We next tested for the existence of theta oscillations in our MFG contacts, to assess if the absence of theta power changes between approach and avoidance windows was due to a lack of theta oscillations in this region, or due to inconsistency across patients or power stability across the trial. We used the FOOOF (Fitting Oscillations and One-Over-F) algorithm—which decomposes the power spectrum into its aperiodic (1/f) and oscillatory components—to these contacts and found evidence of theta oscillations in the MFG in each patient in at least one contact37. Therefore, the MFG did have evidence of theta oscillations during our task. However, the dynamics of the theta oscillations did not differ between approach and avoidance periods and were inconsistent across contacts and patients (see Supplemental Figure 4). Additionally, the MFG was characterized by an elevation of high-frequency activity (70-150 Hz) around the time of choice not apparent in other regions (See Figure 2c,d).

Limbic regions are connected via theta band activity during approach-avoidance decision-making: We hypothesized that the modulations of local theta power were accompanied by increased connectivity between these regions. We calculated the imaginary part of coherence, which removes the interactions due to potentially spurious volume conduction effects38, between theta activity in pairs of electrodes from different regions. We compared the true imaginary coherence to a shuffled distribution where trial labels in one region were shuffled 1000 times. Each time point was compared to the shuffled distribution and then FDR-corrected for the number of time points. Pairs of electrodes were marked as having significant theta-band coherence if there was a time window of at least 100ms of significant elevated coherence compared to the null distribution (See Figure 3a and supplement for analyses of additional results across different thresholds). We also calculated two alternative coherence measures, pairwise phase consistency and the debiased estimator of the weighted phase lag index that confirmed our results were robust across different estimates of coherence.

We found more electrode pairs demonstrated coherence within the theta band than would be predicted by chance across each of the limbic regions and with the middle frontal gyrus (See Figure 3b). We calculated the percentage of electrode pairs that showed elevated coherence in the theta band and averaged this percentage across our 20 patients. In all regions, on average, at least one-third of the pairs showed elevated coherence (See Supplementary Table 2), which is higher than the percentage expected by chance. This was comparable across all three measures of connectivity.

We then tested if the consistent limbic theta power elevations were indicative of elevated theta coherence between limbic regions, as compared to the MFG. To test this, we ran a Bayesian mixed effects model within each region to predict the average theta coherence during the approach window (1500ms preceding the avoidance decision), using a four-level factor representing the remaining regions as the predictor. This analysis can be thought of as a frequentist ANOVA; however, because the entire model is estimated at once, pairwise differences in coherence strength can be assessed directly without the need for post-hoc tests. We included patients and electrodes as random effects (see Methods and Table 1 for details). To avoid inflating our estimates, we tested these models across all electrode pairs, including those that did not show elevated theta coherence in our previous analysis.

Evidence for elevated limbic-to-limbic connectivity compared to limbic-to-MFG connectivity: As predicted, we found that the amygdala exhibited higher theta coherence with the hippocampus compared to the MFG, with an estimated connectivity strength ratio of 1.49 (95% credible interval [1.29, 1.75], P+ > 0.99, ESS = 7582.3; See Figure 3c). This indicates that amygdala-hippocampus coherence was ~1.5x stronger than amygdala-MFG coherence during the approach. Similarly, we found that amygdala-OFC coherence and amygdala-ACC coherence were both stronger than amygdala-MFG coherence, with a connectivity strength ratio of 1.22 (95% CI [1.07, 1.36] , P+ > 0.99 , ESS =6426.1; see Supplemental Figure 5) and 1.14 (95% CI [1.03, 1.28], P+ > 0.99, ESS = 11399.5; see Supplemental Figure 5), respectively. We found similar results in the hippocampus (See Figure 3c; Supplemental Figure 5; Supplemental Table 3); hippocampus coherence with the amygdala was 1.41 (95% CI [1.23, 1.65], P+ > 0.99, ESS = 5789.6) times greater than its theta coherence with the MFG, and 1.14 (95% CI [1.03, 1.27], P+ > 0.99 , ESS = 5719.2) times greater with the OFC compared to the MFG. Finally, we found that within the OFC, there is stronger OFC-ACC connectivity compared to OFC-MFG connectivity, with a connectivity strength ratio of 1.2 (95% CI [1.07-1.36], P+ > 0.99, ESS = 5742.9). These results provide evidence that the amygdala and hippocampus are more strongly connected to other limbic regions via theta compared to the MFG (See Supplemental Figure 5 for all regional comparisons using Imaginary Coherence; see Supplemental Figure 6 for results using Pairwise Phase Consistency and Phase Lag Index; see Supplemental Table 3 for full model results across the three connectivity metrics; see Supplemental Table 4 for posterior predictive checks across connectivity models; see Source Data File for model estimates).

Evidence against elevated limbic-to-limbic connectivity compared to limbic-to-MFG connectivity: Within the ACC, we see stronger theta coherence with the MFG compared to the amygdala and hippocampus, where ACC-amygdala coherence was only 0.76 (95% CI [0.65, 0.88], P+ < 0.01 , ESS = 4476.4) times the ACC-MFG coherence and ACC-hippocampus was only 0.77 (95% CI [0.65, 0.90], P+ < 0.01 , ESS = 3670.9) times ACC-MFG coherence (See Figure 3c, Supplemental Figure 5 and Supplementary Table 3 for details). Similarly, the ACC showed preferential connectivity with the OFC compared to the subcortical limbic regions (See Figure 3c, Supplemental Figure 5 and Supplementary Table 3 for details), meaning that, while the ACC shared similar theta power modulations as the subcortical limbic regions, the ACC was preferentially connected to other prefrontal regions compared to the two subcortical limbic regions. Furthermore, while the OFC had preferential connectivity with the ACC compared to the MFG, there was no difference in connectivity strength between OFC-hippocampus and OFC-MFG connectivity, nor any difference between OFC-amygdala and OFC-MFG connectivity (See Figure 3c, Supplementary Figure 5, Supplementary Figure 6, Supplementary Table 3-4 for details). Thus, instead of finding evidence for increased limbic-to-limbic connectivity, we found that the OFC had a more distributed connectivity pattern, while the ACC was more strongly connected to the OFC and MFG. Contrary to our initial hypothesis, the prefrontal regions, including the MFG, shared elevated theta coherence, indicating the connectivity was not limited to the limbic regions.

Connectivity within the MFG: We examined the connectivity patterns within the MFG. We found that the MFG also had preferential connectivity with the other prefrontal regions (OFC, ACC) compared to subcortical regions (See Figure 3c, Supplemental Figure 5 and Supplementary Table 3 for details), matching the profile we found in the ACC.

Theta coherence increases over the approach period and falls during avoidance: Next, we examined dynamic task-related changes in this circuit. Rodent models report that theta synchrony between the hippocampus and mPFC increases in anxiogenic versus safe contexts25. We hypothesized that theta synchrony across the prefrontal-limbic network would increase across the approach period as patients increased both the level of reward, threat and conflict.

We tested this by using time to predict theta coherence in the two seconds preceding the choice to avoid (see Methods and Table 1 for details). We averaged the connectivity values by region pair (e.g., OFC-ACC, Amyg.-Hipp., etc.) within patients and within 100ms time bins. We again used Bayesian mixed-effects models with a random effect of patient and electrode pair and limited electrode pairs to only those that showed significant levels of theta coherence in either the approach or avoidance periods to ensure we were capturing fluctuations in true coherence as opposed to random noise. We found that average connectivity values increased during the time the patient approaches (Estimate: 0.09, 95% CI [0.05-0.14], P+ > 0.99, BF > 9, ESS = 7680.4, Ratio of Sig. Electrodes = 1782/2816; See Figure 3d-f, Source Data File). While local theta power increases might potentially inflate connectivity estimates, this is unlikely the case because theta power – even though initially elevated – drops or remains constant at the end of the approach period, corresponding to when coherence across the network is highest (See Figure 2d). We confirmed this result using two other connectivity metrics (Pairwise Phase Consistency, Phase Lag Index) and across four different time windows ranging from 1.5-2 seconds (Supplementary Table 5).

We next included an interaction term between time and region pairs to identify which region pairs were driving this effect. We found that the following pairs demonstrated strong time-coherence modulations: amygdala-MFG, amygdala-hippocampus, hippocampus-MFG, hippocampus-OFC, ACC-OFC, and ACC-MFG (See Figure 3g; Source Data File). Next, we tested if theta coherence would fall, remain constant, or continue to rise during the avoidance period. We hypothesized the coherence would fall when retreating from the ghost, indicating that theta coherence across the circuit is highest during the portion of the trial with the highest levels of risk and conflict. As hypothesized, we found that theta coherence drops after the patients began avoiding and moved to exit the trial (Estimate: -0.11, 95% CI [-0.16 - -0.06], P+ < 0.01, BF > 14, ESS = 3520.3, Ratio of Sig. Electrodes = 1824/2816; see Figure d-f; see Supplemental Figure 5; Source Data File), and this effect was driven by the following pairs: amygdala-MFG, amygdala-ACC, hippocampus-ACC, hippocampus-OFC, ACCOFC, ACC-MFG, and OFC-MFG; See Figure 3g; Source Data File). As before, we found that the drop in theta coherence during avoidance was consistent across both alternate connectivity measures and multiple timepoints (See Supplementary Table 5).

Amygdala and lateral prefrontal activity drive theta oscillations in the OFC and ACC: We next assessed the directionality of the prefrontal-limbic circuit using two independent methods, State-Space spectral Granger causality and cross-correlation analysis25,39. We calculated the net spectral Granger causality between all electrode pairs with significant levels of theta-band coherence. We implemented the Granger analysis in a pairwise manner to limit the electrodes to those with elevated theta coherence, as well as to better match our control analysis, which used cross-correlation (see Supplemental Tables 7-8 for estimates of results across different thresholds). As there may be bidirectional information flow, we identified which signals dominated the information flow by calculating the difference between the Granger values calculated in both directions. We next subtracted the spectral Granger values calculated from the reversed time-series, as noise can inflate connectivity estimates if the signal-to-noise-ratio SNR increases or decreases over time40. This final net Granger score was compared to a permuted distribution where trial labels from one electrode were shuffled 1000 times. By comparing the absolute value of the true net Granger score to the permuted null distribution, we calculated permuted p values and then FDR-corrected these values for the number of timepoints. If more than 50 timepoints (500ms) were significant at p < .05, then that electrode pair was included in our subsequent analyses (see Methods and Table 1 for details).

When net Granger scores were higher than chance, we tested for a bias in directionality between our regions. We turned to our linear mixed-effects models to account for shared variance within electrodes and patients. Specifically, we modeled the net Granger scores during the approach window using an intercept-only model and random effects of electrode and patient. From these models, we report the Probability Positive (P+), which represents the proportion of posterior samples where the effect is greater than zero, as a measure of directional certainty, and we consider P+ > 0.95 as strong evidence for directionality. Since we did not have strong a priori hypotheses about the directionality in this circuit, we also validated these analyses by conducting a cross-correlation analysis on the theta-band passed signals for each pair of cohering electrodes (see Methods and Table 1 for details). We used similar linear mixed effects models to assess the P+ of the lead and lag times resulting from the cross-correlation analysis. Results were only interpreted if they resulted in significant P+ values using both independent methods, which relied on different calculations of theta power (Morlet Wavelets vs Bandpass filtering) and slightly different time windows (the cross-correlation analysis excluded timepoints where the patient was still at the beginning of the trial, while the Granger analysis included these timepoints). Details of both analyses can be found in the Methods.

While we found evidence of bidirectional information flow in many individual pairs of electrodes, we found four region pairs with consistent directionality across our patients. First, we found that theta oscillations in the Amygdala drove theta in both the OFC (P + = 0.97, ESS = 4891; P + = 0.96, ESS = 7542) and ACC (P + = 0.97, ESS = 5788; P + = 0.96, ESS = 7783). Second, we found that the Middle Frontal Gyrus also led theta oscillations in the OFC (P + = 0.99, ESS = 5216; P + = 0.96, ESS = 10048) and ACC (P + = 0.99, ESS = 7776, P + = 0.97, ESS = 8239), meaning that both the OFC and ACC were being driven by amygdal and lateral prefrontal contributions. We did not find consistent evidence of directionality between any of the other region pairs (See Supplemental Table 7 for ratios of significant electrodes, results for other regions, and results for the cross-correlation analysis).

Pairwise synchrony during approach behavior correlates with choice to avoid: We hypothesized that theta synchrony across the prefrontal-limbic circuit during approach would correlate with the time spent approaching given rodent data suggesting a role for theta in approach-avoidance24,25. Coherence measures were not reliable at the trial level, and we instead calculated the Pearson correlation between the amplitude envelopes of the theta signals in each electrode pair during the approach period, which is a common method for assessing functional connectivity between regions41. We tested this using Bayesian mixed effects models with random effects of patient and electrode in the set of electrode pairs that showed significantly high theta coherence. Times were log-transformed and scaled and synchrony scaled before estimating the model (see Methods and Table 1 for details). As hypothesized, the model found an overall significant correlation between approach times and theta synchrony (Estimate: 0.07, 95% CI [0.04-0.09], P+ > 0.99, BF > 100, ESS = 2816.6), with many individual region pairs contributing to this effect (See Figure 4 ce; see Supplemental Table 6 for full results; see Supplemental Figure 8 for results across different theta coherence thresholds; see Supplemental Table 9 for posterior predictive checks; see Source Data File for model estimates). In particular, theta synchrony between the OFC and all other region pairs correlated with longer approach times.

These data provide evidence that a distributed prefrontal-limbic theta circuit is recruited during approach-avoidance conflict, with increased theta coherence associated with longer approach times.

We hypothesized that theta power was providing a mechanism for network communication and next tested if local neuronal firing42,43, as approximated by high-frequency activity (HFA), also correlated with approach times. We used the same high theta coherence electrodes and calculated the Pearson correlation of the high-frequency activity during approach between the electrodes. Times were also log-transformed and scaled and synchrony scaled before estimating the model (see Methods and Table 1 for details). While the model did not find an overall significant correlation between approach times and HFA synchrony (Estimate: -0.01, 95% CI [-0.04-0.01], P+ = 0.1, BF = 0.01, ESS = 2159.4), we did find a more restricted set of region pairs that correlated with approach time (See Figure 4 c-e; see Supplemental Table 6 for full results; see Supplemental Figure 8 for results across different theta coherence thresholds; see Supplemental Table 9 for posterior predictive checks; see Source Data File for model estimates). Specifically, we found that OFC-MFG and OFCACC high-frequency synchrony correlated with longer approach times. We also found a negative correlation between hippocampus-MFG high-frequency synchrony and approach times indicating that the more similar the high-frequency activity in the hippocampus and MFG, makes it more likely the patient was to turn and avoid attack earlier.

Network circuit reorganization after decision to avoid: We provide evidence of a theta-mediated circuit across cortico-limbic regions which increases in connectivity strength as reward, threat, and conflict increases. Additionally, we have found that synchrony in both the theta band and HFA in these regions correlates with approach times and that the OFC connects the prefrontal regions with the hippocampus and amygdala. Next, we aimed to understand how this system changes as threat becomes more proximal. It has been proposed that separate neural circuits and corresponding behaviors respond to distal threats (anxiety-like behaviors) compared to proximal threats (fear-like behaviors)44,45. We hypothesized that we would see the prefrontal-limbic circuit adapt as threat increases. We tested this by investigating the special case of avoidance induced by a Ghost Attack (see Figure 5a for a trial schematic of these events). During an attack, there were two possible outcomes. First, the ghost could initiate a ‘Chase’, where the ghost’s speed remained constant in the direction of Pac-Man. In this case, as long as the patient turned to avoid, they had a chance to successfully escape. Second, the ghost could initiate a ‘Strike’, where the ghost’s speed was increased such that even if the patient responded appropriately and fled the attack, they would be caught by the ghost and lose a life. ‘Chase’ versus’ ‘Strike’ trials were counterbalanced, while the advent of an attack was determined by the patient’s behavior in the trial. All attack trials were removed before assessing regional theta power modulations to ensure that the effects described in Figure 2 were not driven by this subset of trials.

We time-locked to ghost attack initiation and found similar drops in theta power in the OFC, anterior cingulate, hippocampus, and amygdala as observed during distal-threat avoidance, despite these trials being excluded in those earlier analyses (See Figure 5f, 5g, Source Data File). We next turned to the MFG, where calculating the average TFR showed a striking increase in the HFA after the ghost initiates an attack (see Figure 5b). However, a Bayesian mixed effects model predicting HFA in the 1 second after the attack compared to the second before the attack found that this increase was driven by only a subset of electrodes (39/127 total MFG contacts). Given that the effect within this population of electrodes was strong enough to dominate our TFRs (See Figure 5b), we assessed if these electrodes were anatomically distinct. We found that the majority of the attack-activated electrodes were localized to the right hemisphere (84%; See Figure 5c). When we included hemisphere as an interaction in our HFA model before and after ghost attack, we found a significant interaction between hemisphere and attack, where right MFG contacts resulted in a significant elevation of HFA in the second after an attack began (estimate: -0.16 95% CI [-0.29 - -0.04], P+ < .01, BF > 1.75, ESS = 6782.9; See Figure 5d; Source Data File). To characterize what was driving HFA during the attack, we limited our analyses to the contacts in the right MFG (See Table 1 for model details). We hypothesized that HFA would correlate with the level of threat, where activation would decrease if the patient realized they could successfully escape. To test this hypothesis, we ran a Bayesian mixed effects model where time was used to predict HFA on the trial level (See Table 1 for model details). We included an interaction term for trial type (chase or strike trials), where we predicted chase trials to have a negative relationship between time and HFA as patients realized they would make a successful escape. As predicted, we found a main effect of trial type, where chase trials decreased HFA compared to strike trials (Estimate: -0.16 95% CI [-0.21 - 0.12] ], P+ < .01, BF > 100, ESS = 13160.1; see Source Data File) and a significant interaction with time where chase trials showed dropping HFA as patients neared the end of the trial (estimate: 0.12 95% CI [-0.17 - -0.08], P+ < .01, BF > 100, ESS = 13324.6; See Figure 5e; see Source Data File). We interpret these results as evidence that high-frequency activity in the right MFG tracks the perceived threat of the ghost. Given that ghost attacks were designed to motivate the behavior on attack-free trials, we could not investigate the connectivity between the MFG during these trials as there were only an average of 58 +/-13.4 trials (across both chase and strike trials) in the patients.

Methods
Patients: We recorded intracranial signals from twenty (10 female) adult patients (mean age 27.25 +/- 12.04) who underwent intraoperative neurosurgical treatment for pharmacoresistant epilepsy. All patients were implanted with depth electrodes to localize the seizure onset zone for eventual surgical resection. We selected patients with implantations in any of the following regions: hippocampus, amygdala, OFC, anterior cingulate, and middle frontal gyrus. The electrode placement was dictated solely by the patient’s clinical team. See Supplemental Figure 1 for patient specific electrode implantation and see Supplemental Figure 9 for a characterization of their self-reported depression and anxiety symptoms. Sex was self-reported and not considered in the study design due to opportunistic sampling strategy. The recordings took place at the following hospitals: Loma Linda University Medical Center (n = 6), Barnes-Jewish Hospital in St. Louis (n = 12), and the St. Louis Children’s Hospital (n=2). We additionally collected 191 participants from the online recruitment platform, Prolific, using the ‘Representative sample’ option, which balances the sample with regard to demographics like sex, based on census data from the US. The online participants were paid a set amount for their participation in the research, along with a bonus that correlated with task performance. Participants were given thorough instructions along with a short quiz to ensure comprehension of the task. All patients provided written informed consent as part of the research protocol approved by each hospital’s Institutional Review Board and by the University of California, Berkeley. Patients were tested when they were fully alert and willing to participate.

Behavioral Task: On each trial, Pac-Man, the Ghost, and dots are placed along a single corridor that runs horizontally across the screen. The participant controls the Pac-Man and they are instructed to collect as many dots as they can while avoiding the Ghost, which is moving back and forth at a constant speed at the opposite side of the corridor. The participant makes a single button press, using the left or right arrow keys, to initiate movement along the corridor at a constant speed. By moving towards the center of the corridor, they can collect up to 5 dots, resulting in points, which varied in either small (10 points) or large (20 point) sizes and were placed in pseudorandom configurations between Pac-Man and the Ghost. Importantly, at any point in the trial, they could also choose to move away from the center of the corridor (and, thus, away from both the dots and the Ghost) where they could exit to the next trial at the end of the corridor. If Pac-Man was “caught” by the Ghost, they lost one of their three lives, they also lost all the points acquired on that trial, and the Ghost capture was accompanied by a “death animation” used in the arcade game (e.g., a loud sound and the Pac-Man shape rotates and disappears). The Ghost’s starting direction and location, the distance between the dots and Pac-Man, and Pac-Man’s starting location are counterbalanced across trials.

The Ghost had three states, which we term “Pace”, “Chase”, and “Strike. The default state was “Pace”, where the Ghost moves in a set path back and forth at the end of the corridor. The end of the path was a jittered distance away from the last (5th) dot. In this state, the Ghost moved at the same speed at the PacMan and did not deviate from its path to capture the Pac-Man. The remaining two states, which we jointly call a “Ghost Attack”, were designed to motivate behavior on a typical trial (See Results for Details). On these Attack trials, the Ghost would begin moving towards the Pac-Man. If the Ghost was in a “Chase” state, it still moved at its constant speed, meaning that if the participant responded quickly, they could turn and safely exit the trial. If the Ghost entered the “Attack” state, the speed was increased such that even if the participant quickly tried to exit the trial, they would be caught by the Ghost. Please see below for a visual description of the Attack trials.

Critically, the probability of a Ghost Attack was directly determined by a beta distribution over the distance between Pac-Man and the Ghost, such that Attacks when the Pac-Man was far away from the Ghost were rare, while Attacks when Pac-Man was close were common. Whether the Ghost Attack was a Chase or Strike was randomized. Participants were directly instructed that the distance to the Ghost determined the probability of an attack. Furthermore, they were instructed that, to get the highest possible score, they would need to “balance the risks of being caught by the ghost and the rewards of getting as many dots as possible.” 20% of trials are conflict-free, where the participant is free to collect the 5 dots without threat of the Ghost. On these trials, the Ghost was not present on the corridor, though participants were still instructed that they had to return to exit the trial at the side of the corridor on which they began the trial.

Additionally, the game itself was composed of 240 trials. The task was played as a series of minigames, which end when either 1) Pac-Man loses all three lives or 2) a block of twenty trials are completed. At the end of each minigame, the score was reset and the patient was prompted to begin a new game. Direction, reward, location of the ghost, location of Pac-Man, and the ghost’s starting direction are counterbalanced across trials. Patients were shown and read instructions prior to playing a short practice and were explicitly told that 1) the closer they got to the ghost the higher the chance of attack and 2) to get the highest possible score, they would need to “balance the risks of being caught by the ghost and the rewards of getting as many dots as possible”. A full experimental run typically lasted approximately 20-25 minutes. Stimulus presentation was operated by JavaScript running in a Chrome browser.

Data acquisition: Electrophysiological data were recorded using BCI2000, an open-source software, at each clinical site76. This system synchronized the Pac-Man task with LFPs, eye tracking, and behavior (Pac-Man movement) in a single data stream which supports easy data pooling across sites and data sharing with the community77. The sampling rate at Barnes-Jewish Hospital was 2000Hz, while at Loma Linda the sampling rate was 512Hz.

Anatomical reconstructions: We used an anatomical data processing pipeline to localize electrodes from a pre-implantation MRI and a post-implantation CT scan78. The MRI and CT images were aligned to a common coordinate system and fused with each other using a rigid body transformation. We then compensated for brain shifts caused by the implantation surgery. A hull of the patient brain was generated using the Freesurfer analysis suite79. Electrodes were then classified by a neurologist according to the anatomical location within each patient’s anatomical space. Only electrodes confirmed to be in the regions of interest were included in the analysis. For illustration purposes only, we converted patient-space electrodes into Montreal Neurological Institute (MNI) coordinates using volume-based normalization. Figure 1f shows all the electrodes used in the analysis and was created using the python module nilearn80.

Data preprocessing: Offline, continuous data were low-pass filtered at 150Hz, and notched filtered at 60Hz and its harmonics up to 120Hz to remove line noise. Electrodes were re-referenced using a bipolar reference scheme. We visually identified and removed channels with excessive noise or with poor contact and each dataset was visually inspected by a neurologist to remove electrodes exhibiting epileptic spiking activity and epochs where spiking spread from the primary epileptic site. Data were epoched based on the time point of interest, either trial onset, turnaround choice, or trial end. Oscillatory and high-frequency activity was extracted at these timepoints using Morlet wavelets where the power of 80 log-spaced frequencies ranging from 1-150 was calculated using an increasing number of cycles, specifically 80 log-spaced cycles ranging from 2-30. Data preprocessing was carried out in Python using the MNE package81.

Power estimates: Theta-band and high-frequency power estimates were extracted from the TFR for all statistical analyses. Power was log-transformed and each specific frequency was z-scored using an ITI baseline. Each frequency between 3-8Hz was then averaged, resulting in a theta-band power estimate. For the high-frequency activity estimate, we followed a similar approach but averaged the individual frequencies in 20Hz overlapping sub-bands (e.g., 70-90Hz, 80-100Hz, … 130-150Hz) before averaging the power in each sub-band for a final estimate of high-frequency activity. We used a sub-band approach to minimize the effect of 1/f power scaling, which biases the power estimates to the lower frequencies in a large band, such as estimated from 70-150Hz41. As the two hospitals had different sampling rates (2000Hz and 512Hz) the data were downsampled to common rate (20Hz) after power was estimated.

For the cross-correlation analysis, we needed both power and phase information within the theta-band. We therefore recalculated theta by bandpass filtering between 3-8Hz using a zero-phase FIR filter with a Hamming window on all electrode pairs with elevated theta coherence. Data were z-scored to the ITI, time-locked to movement-onset, detrended, and only data during approach were included in this analysis.

Connectivity Measures - Coherence & Synchrony: Data epoched to the turnaround choice were extracted between the 2500 milliseconds before and after the turnaround using Python-MNE81. Coherence measures were computed over time but across trials for every combination of electrodes across the regions of interest using the package MNE-connectivity. Data were resampled to 100Hz to speed computation time. Spectrum estimation was done using Morlet Wavelets using the same parameters as above, but only within the theta-band (3-8Hz). Three different coherence measures were calculated to ensure our results were not biased by the choice of metric: imaginary coherence, pairwise phase consistency, and the debiased estimator of the squared, weighted Phase Lag Index. To identify electrodes with a significantly elevated level of theta coherence, the true coherence metrics were compared, timepoint-by-timepoint, to a shuffled null distribution where the trials in one region were shuffled 1000 times. P values were calculated across timepoints and FDR-corrected to account for multiple comparisons. Electrode pairs were included in subsequent analyses if there were 100 milliseconds with significantly elevated coherence. Note, for imaginary coherence we calculated the absolute value of the imaginary coherence before calculating the P values.

As trial-level estimates of coherence were unstable, when testing for associations with trial-level behavior we used the correlation between two electrodes’ power envelopes. For this analysis, we time-locked theta power to the final decision to avoid and filtered out the beginning of the trial when Pac-Man was stationary. We then calculated the Pearson correlation trial-by-trial for every combination of electrodes across the regions of interest. We applied this approach for both the theta-band power and for high-frequency activity estimates.

Spectral Granger Analysis: Spectral Granger causality (GC) quantifies the directionality of connectivity within specific frequency bands. Specifically, we used a State-Space formulation of Granger Causality as implemented in Python-MNE81, which can be thought of as a more versatile and robust formulation to previous parametric approaches39. To improve computational efficiency, data were resampled to 100 Hz. Frequency estimation between 3–8 Hz was performed using Morlet wavelets with the same parameters as above. Spectral Granger causality scores were computed using 12 lags, which in spectral State-Space GC acts as a spectral smoothing parameter across the frequencies. Granger values were then averaged across the 3–8 Hz frequency range. Following the approach of Haufe and colleagues40 we additionally contrasted the scores with net GC scores calculated on the reversed-times series, as flipping the direction of the signal should similarly flip the estimate of directed connectivity. Thus, our final net spectral Granger scores were calculated as:

(1) Net Granger(signal_a, signal_b) = GC(a -> b) - GC(b -> a) - GC(a -> b)_reversed - GC(b -> a)_reversed

Net Granger scores were calculated for each timepoint in the final 2500ms before the decision to avoid in all electrode pairs with significant levels of theta coherence. Only timepoints in the last 1500ms of approach were analyzed to avoid edge artifacts. These timepoints were compared to a permuted null distribution where trial labels from one region were shuffled 1000 times. The absolute value of the Net Granger scores were used to calculate permuted p values compared to this distribution. We then FDR-corrected for the number of timepoints and included electrode pairs in our subsequent analysis with 500ms of significantly elevated net Granger scores. The net Granger scores from these electrode pairs were the fed into our directionality tests (see below).

Cross-correlation Analysis: To further validate our directionality findings, we additionally conducted a cross-correlation analysis within the theta-band. After bandpass filtering in the theta-band (see Power Estimation for details), we then concatenated all approach windows across the trials into one sequence per pair of electrodes. We next calculated a cross-correlation function with 25 lags corresponding to 250ms on the concatenated theta signal. We then created a permuted distribution by shuffling the trial labels 200 times, while keeping the trial lengths constant to ensure that the discontinuities in the true concatenated signal were equivalent in each of the permuted samples. To assess if there was a significant correlation between two electrodes, we compared the correlation value from the true pair to the permuted distribution. If the correlation was in the top 5% of the permuted distribution, its best lag was included in our directionality test (see below).

Bayesian Linear Mixed Effects Models: Effects were assessed using Bayesian mixed effects models that always included hierarchically grouped random effects of patient and electrodes. The benefit of these models is they provide a single test of our hypotheses, eliminating the need to correct for the number of electrodes or develop second-level tests of how results varied across patients, while appropriately accounting for the correlations within electrodes and patients. This hierarchical random effects structure meant fitting models at times with over 3000 random effect levels. Bayesian models fit with MCMC sampling allowed us to appropriately and effectively fit this complex model structure.

Across all the Bayesian mixed effects models, we used a standard set of weakly informative priors. For intercepts, the prior was set to the normal distribution, centered on 0, with a standard deviation of 5. For beta coefficients, the prior was set to the normal distribution, centered on 0, with a standard deviation of 2. For the standard deviation of the random effects, we used an exponential distribution with a positive arrival rate of 1. Finally, we set a prior on the correlation between random effects using the Lewandowski-Kurowicka-Joe distribution with the shape parameter of 2. All models used 4 chains and were initially fit using 5000 iterations of which 1000 were used as warmup. If there were indications that the models did not converge, we increased the number of iterations to a max of 10000 iterations, of which 5000 were warmup. Rhat metrics, effective sample size and trace plots were used to confirm model convergence and high resolution; all Rhat values for estimates were equal to or below 1.01 and all effects of interest had Rhat values equal to 1.0. Effective samples were all greater than 1000. Posterior predictive checks on the mean and standard deviation were completed across all models (See Supplemental Table 3, 4 & 7). All models were fit using the package “brms” in R82.

Directionality Analyses: To assess whether net Granger causality values or cross-correlation time exhibited a consistent directional trend across patients, we employed a hierarchical Bayesian model using the brms package in R. The model estimated the central tendency of net Granger values while accounting for patient- and electrode-level variability. We used weakly informative priors (see above for details). To quantify the probability that net Granger values or time lags were consistently positive across patients, we computed the Probability Positive (P+), defined as the proportion of posterior samples where the estimated effect exceeded zero. This metric provides an intuitive, probabilistic measure of directional certainty, with P+ values closer to 1 indicating strong evidence for consistent directionality.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary of the Article:\nThis study investigates the neural circuit dynamics underlying approach-avoidance conflict in humans using intracranial\nEEG (iEEG). Participants performed a Pac-Man-inspired task where they balanced reward collection against the threat of\nghost attacks. The authors identified a distributed prefrontal-limbic circuit mediated by theta oscillations (3–8 Hz) in the\nhippocampus, amygdala, orbitofrontal cortex (OFC), and anterior cingulate cortex (ACC), with theta power increasing during\napproach and dropping during avoidance. Theta-band connectivity within this circuit correlated with longer approach times,\nwhile high-frequency activity (HFA) in the middle frontal gyrus (MFG) tracked imminent threat. The findings highlight the role\nof theta synchrony in coordinating approach-avoidance behavior and suggest distinct contributions of limbic and prefrontal\nregions to conflict processing.\nComments and Suggestions for Improvement:\nAnxiety and Uncertainty Context:The introduction and discussion could better contextualize anxiety. Anxiety is a very\ncomplex symptom that involves several areas related to the anticipation of adverse or uncertain events. While the avoidance\nof these situations can be damaging or excessively persistent—making it a key component of anxiety—there are also other\ncritical components, such as:The perception or management of uncertainty in situations where avoidance is not possible.\nAlterations in learning and the acquisition of statistical contingencies in the environment, where, again, uncertainty reflects a\nkey aspect.In this context, the introduction and discussion should better frame these aspects. In some parts of the writing, the\nrelationship between avoidance and anxiety appears somewhat reductionist.\nThe MFG’s theta dynamics (Figure 2C-D) appear temporally nuanced, with an initial rise followed by a drop. A cluster-based\npermutation test might better capture this temporal pattern than the current 1.5 s window averaging. Clarify the rationale for\nthe chosen time windows and reference periods in the analysis.\nThe contrast labels in Figure 2 (e.g., \"Approach\" vs. \"Avoidance\") are confusing. Ensure the axes and legends explicitly\ndefine the compared conditions, especially for control trials where \"approach\" and \"avoidance\" windows need clearer\noperational definitions.\nSeveral critical task parameters (e.g., the distinction between pacman and ghost, ghost attack, and trial types) are presented\nin a fragmented manner across the manuscript. For instance, the exclusion criterion for attack trials (line 378) is introduced\nafter the corresponding analysis in Figure 2, which may hinder interpretation.\nI recommend:\nConsolidating all task specifications (including attack trial mechanics and exclusion criteria) in a dedicated subsection\nbefore the results\nMaintaining consistent terminology between behavioral and neural analyses\nAdding a schematic timeline of trial events to Figures\nExplicitly stating in each analysis section which trial types were included/excluded\nThis reorganization would prevent readers from needing to cross-reference multiple sections to reconstruct the experimental\ndesign, particularly for time-sensitive neural analyses where trial selection criteria directly impact interpretation. The current\nstructure risks important methodological details being overlooked until the Discussion.\nConnectivity Analysis:\nThe connectivity section reads as exploratory but aligns with hypotheses. Restructure to emphasize hypothesis-driven\ncomparisons (e.g., limbic-limbic vs. limbic-MFG connectivity).\nWhile the rationale for using Bayesian mixed modeling in connectivity analyses is understood [\"Bayesian mixed-effects\nmodel within each region to predict theta coherence, using a four-level factor representing the remaining regions as the\npredictor.\" ], its evaluation is impossible without clear specification in the Methods section (especially since code isn't\navailable at this stage). I recommend explicitly detailing the applied model.\nFor Bayesian \"purists\", this statement is correct: \"This analysis can be thought of as a frequentist; however, because the\nentire model is estimated at once, pairwise differences in coherence strength can be assessed directly without the need for\npost-hoc tests\". However, to strengthen result robustness and better assess significance levels, I suggest additional metric:\nPosterior probability thresholds (e.g., P+ > 0.95. as indicated in some analysis)\nor Markov Chain Monte Carlo-derived p-values (p_MCMC)\nBayes Factors for key comparisons\nEffective sample sizes\nDirectionality of Correlations:\nHowever, caution is warranted when claiming that theta-gamma power correlations predict approach times, as correlation\ncannot guarantee directionality. In fact, the authors' interpretations appear to suggest the opposite: increased power\ncorrelations may instead result from escalating risk/conflict as approach time lengthens.\nWhile the positive association between theta-band synchrony and approach duration (Fig. 4) aligns with theta's putative role\nin risk assessment, there are caveats that merit discussion:\nDirectionality Ambiguity: The correlational nature of the analysis precludes causal claims. The observed effects could reflect\neither:\nTop-down control: Theta synchrony prolongs the approach to optimize reward-threat tradeoffs\nBottom-up drive: Accumulating risk/conflict (as proximity increases) enhances theta coupling\nThe authors explain that there is a theta drop similar to avoidance. This result is interesting and strange—the hypothesis\nwould be that limbic activity relates to the probability of being attacked, but it seems to also have something to do with the\nsubjects' \"spatial\" behavior. [Transition from decision-making (approach/avoidance conflict) to action execution (escape\nnavigation) or Shift from cognitive control (OFC/ACC theta) to motor planning (parietal beta/gamma), as seen in spatial\ntasks]\nThe authors frame it in terms of proximal vs. distal threat, linking it to anxiety literature. But it's still odd in this context that\nhippocampal, amygdala, and OFC activity would decrease when facing a greater threat, even if it's distal. I would like to see\nmore discussion on this.\nIn this context, literature on uncertainty and decision timing could help reconcile these results. For example, delta/theta band\nactivity and neuronal spikes in the ACC are implicated in estimating and re-estimating ambiguity as a type of uncertainty\n[10.1371/journal.pbio.3002452, 10.1038/s41467-017-00072-y].\nIn such a context, a computational cognitive model could dissect variables like threat probability or distance, strengthening\nmechanistic claims. Address this as a future direction.\nCausal Claims:\nThe conclusion’s phrasing (\"We provide evidence for a dynamic cortico-limbic network supporting approach and\navoidance\") overstates causal inferences. Qualify as \"correlational evidence\" unless causal methods (e.g., stimulation) are\nused.\nElectrode Reporting:\nExplicitly state the proportion of electrodes showing significant effects per region in all analisis (e.g., \"39/127 MFG contacts\nexhibited HFA increases\"). This avoids selection bias and clarifies regional heterogeneity.\nFigure 5F:\nInclude amygdala data in Figure 5F for completeness, given its role in threat processing.\nMFG Theta and Conflict Anticipation:\nIn the theta discussion, it would be interesting to consider MFG activity as reflecting conflict/danger expectation. For\nexample, this paper [10.1038/s41467-024-54244-8] shows how MFG theta activity causally encodes conflict probability. This\nfits well with what was found here, especially with the high gamma activity that was the only cortical signal showing a\nrelationship with conflict/damage probability, considering both the approach/avoidance analyses and the\nghost attack analyses.\nMinor Edits:\nClarify the \"avoidance window\" definition in control trials (line 183).\nLabel chase/strike trials in Figure 5A for consistency.\nCheck axis labels in time-frequency plots to avoid confusion (e.g., negative times = approach).", "reviewer_2": "In this manuscript, the authors define and investigate the role of a distributed prefrontal-limbic brain network in approach\navoidance conflict. They use stereo EEG (sEEG) recorded from 20 epilepsy patients undergoing invasive monitoring for\nseizure localization while they performed a continuous-choice, approach-avoidance conflict decision-making task inspired\nby the arcade game Pac-Man. In this task, patients trade off rewards against potential losses from attack by a ghost. They\nshow 1) Increased theta power in the hippocampus, amygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex\n(ACC), as patients approached increasing rewards and threats, and a subsequent drop during avoidance; 2) An increase in\ntheta band connectivity within the limbic circuit and with the medial frontal gyrus (MFG) during approach and decrease\nduring avoidance; 3) Directional connectivity of theta from the amygdala and MFG to OFC and ACC, 4) degree of network\nconnectivity predicted how long patients approach, with enhanced network synchronicity extending approach times and 5)\nwhen threat is imminent while being attacked by the ghost, the system dynamically switches to a sustained increase in high-\nfrequency activity (70-150Hz) in the MFG, tracking the degree of threat. The results from this work provide evidence for a\ndynamic limbic-prefrontal cortical network underlying approach avoidance conflict processing in human subjects. Overall,\nthis is a very well written article with clear description of methods and hypotheses being tested for each result. The findings\nin my opinion are significant in mapping a frontal-limbic brain circuit that is involved in approach avoidance conflict which\ncan further be used to design interventions in patient population who have difficulty with resolving such conflict. Examples of\nsuch population as provided by the authors would be ones with anxiety disorders. However, information on such symptoms\nin their studied cohort of 20 patients with epilepsy is missing. A considerable population of patients with intractable epilepsy\nhave comorbid anxiety disorders, but its not clear if any of the participants also had anxiety disorder and how that was\nfactored in the analysis. As such, there is no information on neuropsychological characterization of the studied cohort.\nAnother moderate concern is that it wasn’t clear to me why the authors chose pairwise GC instead of assessing directional\nconnectivity on the whole network such as using a partial directed coherence or directed transfer function that uses a\nmultivariate model.\nSome other minor concerns/comments are the following:\n1) The paper title can be a bit more specific\n2) On line 286, I believe its Fig 3e and not 3g\n3) In the methods section (line 610), it wasn’t explained why the high frequency power was calculated as averages of\noverlapping sub bands instead of the full band.\n4) The zenodo link where code will be available was missing.", "reviewer_3": "The manuscript „Circuit dynamics of approach-avoidance conflict in humans” by Staveland and colleagues used intracranial\nEEG recordings from N=20 patients to define a prefrontal-limbic network, comprising of Amygdala, Hippocampus, OFC\nACC, and MFG, that is engaged during approach-avoidance conflicts. The authors report distinct modulations of theta and\nHFA power during approach and avoidance, increased theta coherence during approach and decreased theta power during\navoidance. The degree of network connectivity positively predicted approach time. Furthermore, high frequency activity in\nthe MFG was observed during imminent threat. These results help to further our understanding about the neuronal basics\nunderlying approach-avoidance in humans.\nThe manuscript is well written and the methods used are sound given some few clarifications. Accordingly, I have only minor\ncomments that the authors can hopefully answer.\n- Please clarify whether the movement of Pac-Man and the ghost was continuous (i.e. as long as the subject pressed a key)\nor stepwise (i.e. move a certain distance every time a key was pressed? This is not clear from the current manuscript.\n- The fact that the ghost was moving back and forth at the opposite end of the corridor is not mentioned in text, only in caption\nof figure 1. Could participants synchronize their movements of Pac-Man with those of the ghost? For instance, collect dots\nwhen the ghost moves away from the center and retreating when it returned towards the center – or would such a retreat\nhave ended a trial?\n- In the 20% conflict-free trials, was there no ghost or did the ghost simply not attack? If the latter, how was it obvious for the\nsubjects that these trials were conflict-free?\n- Figure 1c: I see the variability within participants but where do I see the variability across participants?\n- Why was no statistical test for the different turnaround distances for large and small rewards performed?\n- Significant theta-band coherence was defined as showing at least 100ms of coherence above the shuffled distribution. Can\nthe authors explain why precisely 100ms? Does it matter whether the 100ms were before or after the decision to avoid?\n- Regarding the Bayesian model on theta coherence (line 224): I understand that for each region one coherence value was\nextracted per subject and predicted by the coherence from the remaining four regions. Please provide information whether\nthis was the average coherence over the whole-time window (-1500ms to +1500ms) or something else.\n- Line 279: I assume this sentence refers to the theta power in the MFG? This is, however, not explicitly mentioned and not\nimmediately clear.\n- For the net Granger score, 200 permutations of the trial labels were used whereas for the theta coherence 1000\npermutations were used. Could the authors clarify the rationale for this discrepancy in the number of permutations?\nFurthermore, was the permutation procedure applied separately for each electrode pair, or was a common null distribution\ncomputed against which all net Granger values were compared?\n- Pairwise synchrony during approach: I understand that the correlation between envelopes of the theta signal of two\nelectrodes was used as connectivity marker. However, the rest of the paragraph lacks sufficient detail. It is not mentioned\nthat values were z-scored, which is only evident when looking at figure 4c. Furthermore, the authors mention Bayesian\nmixed model (I assume with time as predictor) but show no test statistics, only that “… coordinated theta activity within\nnumerous region pairs in this circuit correlated with approach times.\" Can the authors please provide a more detailed\ndescription of the analysis and results?\n- Line 348: Figure 4a-b shows the result from the Granger causality analysis, not the correlation with approach times.\n- Colors for Amygdala and MFG in figures makes it hard to differentiate between the two.\n- Given the different sampling rates between the two hospitals: Were the iEEG data down sampled to a common sampling\nrate?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "R1C1: The introduction and discussion could better contextualize anxiety. Anxiety is a very\ncomplex symptom that involves several areas related to the anticipation of adverse or uncertain\nevents. While the avoidance of these situations can be damaging or excessively persistent—\nmaking it a key component of anxiety—there are also other critical components, such as: The\nperception or management of uncertainty in situations where avoidance is not possible.\nAlterations in learning and the acquisition of statistical contingencies in the environment, where,\nagain, uncertainty reflects a key aspect. In this context, the introduction and discussion should\nbetter frame these aspects. In some parts of the writing, the relationship between avoidance and\nanxiety appears somewhat reductionist.\nResponse: We thank the reviewer for highlighting this issue. Anxiety is indeed a complex and\nmultifaceted construct, and we do not intend to imply that either clinical or ‘healthy’ anxiety\ncould be reduced to avoidance decisions. We have made edits to both the introduction and the\ndiscussion to better highlight the complexity of anxiety, and to highlight the value of using\napproach-avoidance paradigms as a tool for eliciting anxiety-related behavior, rather than being a\nsubcomponent or reductionist model of anxiety behavior. We have especially highlighted that\noptimal approach-avoidance decisions require healthy functioning of systems that assess and\nlearn about uncertainty and future threats.\nSpecifically, we have altered the first paragraph of the introduction as follows with changes in\nyellow:\n“Anxiety is a complex response to ambiguous and potentially negative future states.\nAnxiety comprises threat appraisal and uncertainty management, among other\nconstituents, and dysfunction in any of these domains could result in persistent or\npathological anxiety. A key behavioral paradigm for investigating both clinical and\nnormative anxiety is approach-avoidance conflict. Approach-avoidance conflict\ndescribes the daily situations where a single choice entails potential gains and losses\nsimultaneously, which reliably induces anxiety in everyday life (Dymond and Roche,\n2009; Kirlic et al., 2017). For example, consider the decision to approach someone new\nwhile attending a party. The same action could result in the positive outcome of a nice\nconversation or a new friend, or a negative outcome such as social rejection or\nembarrassment. While anxiety in this context is normative, when avoidance becomes\nexcessive (never speaking to anyone new, or avoiding social gatherings altogether) it\nbecomes maladaptive (American Psychiatric Association, 2022; Dymond, 2019;\nDymond and Roche, 2009). Approach-avoidance decisions are dysregulated in\ngeneralized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), and\nagoraphobia. Specifically, in clinical disorders there is maladaptive avoidance of\npotentially aversive cues at the cost of large rewards (Ball and Gunaydin, 2022; Kirlic\net al., 2017; Salters-Pedneault et al., 2004) and this can be disambiguated from\nadaptive avoidance, such as avoiding cues that are likely to result in large costs, with\nlow probability of reward (e.g., avoiding speaking to a known rude or unkind person at\na party). Understanding the cortical and limbic neural circuits that regulate healthy and\nadaptive approach-avoidance conflict is key to understanding the neural underpinnings\nof both normal and excessive clinical anxiety.”\nWe also introduce a discussion of possible roles that theta oscillations may be playing under\napproach-avoidance conflict on lines 540-545 as follows:\n‘However, while there is converging evidence that theta oscillations provide cross-\nregion mechanism for neural communication and that theta oscillations are prominent\nduring approach-avoidance decision-making, there are open hypotheses about what\nkind of information is being coordinated across regions via theta oscillations.\nApproach-avoidance behavior is dependent on a multitude of functions, such as threat\ndetection, uncertainty estimation, and conflict resolution between competing goals\n(Bryant and Barker, 2020; Ito and Lee, 2016).’\nR1C2:The MFG’s theta dynamics (Figure 2C-D) appear temporally nuanced, with an initial rise\nfollowed by a drop. A cluster-based permutation test might better capture this temporal pattern\nthan the current 1.5 s window averaging. Clarify the rationale for the chosen time windows and\nreference periods in the analysis.\nResponse: We thank the reviewer for noting the nuanced temporal profile of MFG theta in\nFigure 2. In the TFRs, there appears to be an early rise in power (−1500 to −1000ms relative to\navoidance decision; see the MFG TFR in the top panel in the figure below), but supplemental\nanalyses (now included as Supplemental Fig. 3) show that this transient broadband increase (0–\noscillations. The ERP terminates ~1 s after trial onset, before movement begins, as can be seen in\nthe figure on the below.\nTo avoid contamination of approach/avoidance\ncomparisons by this non-oscillatory activity, our\noriginal analysis excluded the initial stationary\nperiod after trial onset, and we have now clarified\nthis in multiple areas of the manuscript. Across\nparticipants, movement began on average 1.02 ±\n0.46 s after trial onset, so we averaged theta power\nover the subsequent 1.5 s of approach and first 1.5 s\nof avoidance. This window captures sustained\nmovement-related activity, consistent with our\nhypotheses, and omits the transient ERP-driven\nchange.\nLegend: Time-frequency plots of neural activity in each region in conflict trials time-locked to three different trial\nevents: (top) the final choice to stop approaching and begin avoiding, (middle) trial onset, and (bottom)\nmovement onset. Red (blue) indicates increases (decreases) in power. Power was log-transformed and z-scored\nto the mean power across the time window. It appears that MFG has increased theta power at the beginning of\nthe approach period while time-locking to the decision to avoid. However, this is non-oscillatory, event-related\nactivity, rather than an increase in theta oscillations.\nWhile non-oscillatory transients can be informative, they fall outside our specific focus on theta-\nband oscillations. For clarity, we have (1) added an Analysis Guide to the start of the Methods,\nsummarizing model specifications and exclusion criteria, (2) included Supplemental Fig. 3 to\nshow the ERP-driven increase, and (3) clarified in the Results and Methods the rationale for the\nchosen time windows and exclusion of the stationary period. These changes are detailed below:\n1. In response to this comment, as well as others below, we have now added an Analysis\nGuide to the beginning of the Methods sections (lines 684-686). This guide includes each\nmajor finding discussed in the manuscript and clearly outlines important information\nsuch as, the model specifications, number of models estimated, and the exclusion criteria.\nThe relevant rows for this comment from this guide are included below:\nNumber of Number of\nDependent Fixed Random Number of Exclusion Figures/\nFinding Estimated Observatio\nVariable Effect Effect Levels Criteria Tables\nModels ns\nExcluded\nconflict-free\ntrials, time\nTheta power Mean theta periods\n2-level\nis higher power before the\nfactor,\nduring during the Pac-Man Figure 2;\nspecifying\napproach 1500ms subject/ 5, one for 13335 - 15-20/73- initiated Extended\navoidance\ncompared to before/after electrode each region 30665 167 movement; Data Table\n(after turrn)\navoidance turning trials where 1\nor approach\nduring towars the 0 dots were\n(before turn)\nconflict trials exit collected;\ntrials where\nGhost\nattacked\nExcluded\nTheta power conflict\nMean theta\nis not higher 2-level trials, time\npower\nduring factor, periods Supplement\nduring the\napproach specifying before the al Figure 2;\n1500ms subject/ 5, one for 4718 - 15-20/73-\ncompared to return (after Pac-Man Extended\nbefore/after electrode each region 11146 167\nreward turrn) or initiated Data Table\nturning\nduring approach movement; 1\ntowards the\nconflict-free (before turn) trials where\nexit\ntrials 0 dots were\ncollected\n2. The figure in this response is included in the supplement as Supplemental Figure 3. We\ndirect the reader to this figure on lines 247-248:\n“see Supplemental Figure 3 for an analysis of the brief increase in low frequency\npower from an event-related potential at the beginning of the approach period in\nthe MFG contacts.”\n3. We have added text to explain the choice to only include movement-related behavior on\nlines 213-218:\n“We next tested if the approach period correlated with greater theta power compared\nto the avoidance period across the different regions. We excluded the initial period of\nthe trial where the Pac-Man was stationary to best compare the approach activity to\navoidance activity. On average, patients began moving 1.02+/- 0.46 seconds after trial\nonset and approached both the threat (Ghost) and reward (dots) for 1.39 +/- 0.22\nseconds. We therefore averaged theta power during the final 1.5 seconds of approach\nand first 1.5 seconds of avoidance in each electrode and patient, on trials without a\nGhost Attack. We estimated a Bayesian mixed effects model of theta power using their\napproach/avoidance decisions…”\nR1C3: The contrast labels in Figure 2 (e.g., \"Approach\" vs. \"Avoidance\") are confusing. Ensure\nthe axes and legends explicitly define the compared conditions, especially for control trials\nwhere \"approach\" and \"avoidance\" windows need clearer operational definitions.\nResponse: We thank the reviewer for identifying this point of confusion and have taken multiple\nsteps to clarify the time windows used in conflict and conflict-free trials.\nFirst, we made changes to the text:\n• On lines 228-232: “In the subset of control trials that were conflict-free, i.e. where\npatients collected reward without threat of the ghost, we ran an equivalent\nmodel, where the approach period (e.g., 1.5 seconds before the decision to turn\naround, made after all rewards were collected) was compared again the 1.5\nseconds after the decision to turn. We did not find a significant difference in\ntheta power between the approach and return windows…”\nSecond, we made changes to Figure 2. We added a small task schematic, which identifies the\napproach and avoidance periods. We use “Return” to identify the period in Conflict-free trials\nafter the final decision to turn back, which corresponds with the “Avoid” window in the Conflict\ntrials, and now highlight the lack of ghost with a small symbol. You can review the changes to\nFigure 2 on the next page.\nFinally, in the Figure 2 Legend we made the following adjustments for clarity:\nFig. 2 a Time-frequency analysis of neural activity in conflict trials time-locked to the\nfinal choice to stop approaching and begin avoiding across all electrodes in the limbic\nregions (hippocampus, amygdala, OFC and ACC). Red (blue) indicates increases\n(decreases) in power. Power was log-transformed and z-scored to the mean power\nacross the 3-second time window. Dotted box indicates the theta band (3-8Hz) See\nSupplemental Figure 2 for time-frequency analysis of the conflict-free trials. b Average\ntheta power in the 1500ms before (called “Approach”) and after the choice to turn\naround on each trial (called “Avoid” or “Return) for each limbic region in conflict-trials\n(ghost present) or conflict-free trials (ghost absent). Dots indicate average theta power\nin electrodes across trials, triangles indicate average theta power across electrodes\nwithin a participant. Significance was assessed using linear-mixed effects models with\nrandom effects of participant and electrode. Stars indicate that the 95% credible\ninterval did not include 0. c Time-frequency analysis of the conflict trials and theta-\npower analysis for the MFG….\nR1C4: Several critical task parameters (e.g., the distinction between pacman and ghost, ghost\nattack, and trial types) are presented in a fragmented manner across the manuscript. For\ninstance, the exclusion criterion for attack trials (line 378) is introduced after the corresponding\nanalysis in Figure 2, which may hinder interpretation.\nI recommend:\n● Consolidating all task specifications (including attack trial mechanics and exclusion\ncriteria) in a dedicated subsection before the results\n● Maintaining consistent terminology between behavioral and neural analyses\n● Adding a schematic timeline of trial events to Figures\n● Explicitly stating in each analysis section which trial types were included/excluded\nThis reorganization would prevent readers from needing to cross-reference multiple sections to\nreconstruct the experimental design, particularly for time-sensitive neural analyses where trial\nselection criteria directly impact interpretation. The current structure risks important\nmethodological details being overlooked until the Discussion.\nResponse: We appreciate the suggestion to consolidate the task parameters and to better link\neach analysis to the trial types and timepoints used. We agree with each point and have made two\nmajor changes to increase the clarity of the manuscript. First, we expanded on the description of\nthe task in a subsection before the results, as was suggested (copied below). Second, we have\nadded an “Analysis Guide” to beginning of the methods. Here we have listed each of the main\nfindings, the specifications of the model, the exclusion criteria, and the corresponding figures\nand tables.\nHere is the new Task Description (lines 130-168):\nTask Design: We designed an approach-avoidance conflict task based on the arcade\ngame Pac-Man (See Figure 1a). On each trial, Pac-Man, the Ghost, and dots are placed\nalong a single corridor that runs horizontally across the screen. The participant controls\nthe Pac-Man and they are instructed to collect as many dots as they can while avoiding\nthe Ghost, which is moving back and forth at a constant speed at the opposite side of\nthe corridor. The participant makes a single button press, using the left or right arrow\nkeys, to initiate movement along the corridor at a constant speed. By moving towards\nthe center of the corridor, they can collect up to 5 dots, resulting in points, which varied\nin either small (10 points) or large (20 point) sizes and were placed in pseudorandom\nconfigurations between Pac-Man and the Ghost. Importantly, at any point in the trial,\nthey could also choose to move away from the center of the corridor (and, thus, away\nfrom both the dots and the Ghost) where they could exit to the next trial at the end of\nthe corridor. If Pac-Man was “caught” by the Ghost, they lost one of their three lives,\nthey also lost all the points acquired on that trial, and the Ghost capture was\naccompanied by a “death animation” used in the arcade game (e.g., a loud sound and\nthe Pac-Man shape rotates and disappears). The Ghost’s starting direction and\nlocation, the distance between the dots and Pac-Man, and Pac-Man’s starting location\nare counterbalanced across trials.\nThe Ghost had three states, which we term “Pace”, “Chase”, and “Strike. The default\nstate was “Pace”, where the Ghost moves in a set path back and forth at the end of the\ncorridor. The end of the path was a jittered distance away from the last (5th) dot. In this\nstate, the Ghost moved at the same speed at the Pac-Man and did not deviate from its\npath to capture the Pac-Man. The remaining two states, which we jointly call a “Ghost\nAttack”, were designed to motivate behavior on a typical trial (See Results for Details).\nOn these Attack trials, the Ghost would begin moving towards the Pac-Man. If the\nGhost was in a “Chase” state, it still moved at its constant speed, meaning that if the\nparticipant responded quickly, they could turn and safely exit the trial. If the Ghost\nentered the “Strike” state, the speed was increased such that even if the participant\nquickly tried to exit the trial, they would be caught by the Ghost. Please see below for\na visual description of the Attack trials.\nCritically, the probability of a Ghost Attack was directly determined by a beta\ndistribution over the distance between Pac-Man and the Ghost, such that Attacks\nwhen the Pac-Man was far away from the Ghost were rare, while Attacks when Pac-\nMan was close were common. Whether the Ghost Attack was a Chase or Strike was\nrandomized. Participants were directly instructed that the distance to the Ghost\ndetermined the probability of an attack. Furthermore, they were instructed that, to get\nthe highest possible score, they would need to “balance the risks of being caught by\nthe ghost and the rewards of getting as many dots as possible.”\nFinally, 20% of trials are conflict-free, where the participant is free to collect the 5\ndots without threat of the Ghost. On these trials, the Ghost was not present on the\ncorridor, though participants were still instructed that they had to return to exit the trial\nat the side of the corridor on which they began the trial.\nAnd here are the first rows of the Analysis Table, for the full Table see Table 1 beginning on line\n684 of the manuscript:\nNumber of Number of\nDependent Fixed Random Number of Exclusion Figures/\nFinding Estimated Observatio\nVariable Effect Effect Levels Criteria Tables\nModels ns\nBehavioral\nResult: If the\ndistance\nlast dot 2-level Excluded\nbetween 211 (191\n(reward) factor, trials where Figure 1d;\nPac-Man online\nwas large, specifying if no turn was Extended\nand the subject 1 34298 participants\nparticipants the last dot made; Data Table\nGhost at the + 20 iEEG\nwere closer was large or conflict-free 1\nturnaround patients)\nto the ghost small trials\npoint\nat\nturnaround\nExcluded\nconflict-free\ntrials, time\nTheta power Mean theta periods\n2-level\nis higher power before the\nfactor,\nduring during the Pac-Man Figure 2;\nspecifying\napproach 1500ms subject/ 5, one for 13335 - 15-20/73- initiated Extended\navoidance\ncompared to before/after electrode each region 30665 167 movement; Data Table\n(after turrn)\navoidance turning trials where 1\nor approach\nduring towars the 0 dots were\n(before turn)\nconflict trials exit collected;\ntrials where\nGhost\nattacked\nWe further address each bullet:\n● “Consolidating all task specifications…”:\n○ Response: We have done this, please see above.\n● “Maintain consistent terminology”:\n○ Response: In reworking the task description and in adding details to each\nanalysis, we have maintained the language that we introduce in the task\ndescription.\n● “Adding a schematic”\n○ Response: In addition to the task schematics in Figure 1 and Figure 5, we have\nnow added a small schematic to Figure 2 to better clarify the approach and\navoidance windows.\n● “Explicitly stating in each analysis”:\n○ Response: We have done this, please see above response.\nR1C5: The connectivity section reads as exploratory but aligns with hypotheses. Restructure to\nemphasize hypothesis-driven comparisons (e.g., limbic-limbic vs. limbic-MFG connectivity).\nResponse: We have revised the connectivity section to better clarify our hypothesis-driven\nanalyses and exploratory analyses in this section. We now break the results of this section into\nthree paragraphs: (1) evidence that supports our hypothesis of elevated limbic-to-limbic\nconnectivity compared to limbic-to-MFG connectivity, (2) evidence against our hypothesis of\nelevated limbic-to-limbic connectivity compared to limbic-to-MFG connectivity, and (3)\nexploratory connectivity within the MFG. Here are those following sections (beginning on line\n292):\n“Evidence for elevated limbic-to-limbic connectivity compared to limbic-to-MFG\nconnectivity: As predicted, we found that the amygdala exhibited higher theta\ncoherence with the hippocampus compared to the MFG, with an estimated connectivity\nstrength ratio of 1.49 (95% credible interval [1.29, 1.75], P+ > 0.99 , ESS = 7582.3;\nbulk\nSee Figure 3c). This indicates that amygdala-hippocampus coherence was ~1.5x\nstronger than amygdala-MFG coherence during the approach. Similarly, we found that\namygdala-OFC coherence and amygdala-ACC coherence were both stronger than\namygdala-MFG coherence, with a connectivity strength ratio of 1.22 (95% CI [1.07,\n1.36] , P+ > 0.99 , ESS =6426.1; see Supplemental Figure 5) and 1.14 (95% CI [1.03,\nbulk\n1.28], P+ > 0.99 , ESS = 11399.5; see Supplemental Figure 5), respectively. We\nbulk\nfound similar results in the hippocampus (See Figure 3c; Supplemental Figure 5;\nSupplemental Table 3); hippocampus coherence with the amygdala was 1.41 (95% CI\n[1.23, 1.65], P+ > 0.99 , ESS = 5789.6) times greater than its theta coherence with\nbulk\nthe MFG, and 1.14 (95% CI [1.03, 1.27], P+ > 0.99 , ESS = 5719.2) times greater\nbulk\nwith the OFC compared to the MFG. Finally, we found that within the OFC, there is\nstronger OFC-ACC connectivity compared to OFC-MFG connectivity, with a\nconnectivity strength ratio of 1.2 (95% CI [1.07-1.36], P+ > 0.99 , ESS = 5742.9).\nbulk\nThese results provide evidence that the amygdala and hippocampus are more strongly\nconnected to other limbic regions via theta compared to the MFG (See Supplemental\nFigure 5 for all regional comparisons using Imaginary Coherence; see Supplemental\nFigure 6 for results using Pairwise Phase Consistency and Phase Lag Index; see\nSupplemental Table 3 for full model results across the three connectivity metrics; see\nSupplemental Table 4 for posterior predictive checks across connectivity models; see\nExtended Data Table 1 for model estimates).\nEvidence against elevated limbic-to-limbic connectivity compared to limbic-to-MFG\nconnectivity: Within the ACC, we see stronger theta coherence with the MFG\ncompared to the amygdala and hippocampus, where ACC-amygdala coherence was\nonly 0.76 (95% CI [0.65, 0.88], P+ < 0.01 , ESS = 4476.4) times the ACC-MFG\nbulk\ncoherence and ACC-hippocampus was only 0.77 (95% CI [0.65, 0.90], P+ < 0.01 ,\nESS = 3670.9) times ACC-MFG coherence (See Figure 3c, Supplemental Figure 5\nbulk\nand Supplementary Table 3 for details). Similarly, the ACC showed preferential\nconnectivity with the OFC compared to the subcortical limbic regions (See Figure 3c,\nSupplemental Figure 5 and Supplementary Table 3 for details), meaning that, while\nthe ACC shared similar theta power modulations as the subcortical limbic regions, the\nACC was preferentially connected to other prefrontal regions compared to the two\nsubcortical limbic regions. Furthermore, while the OFC had preferential connectivity\nwith the ACC compared to the MFG, there was no difference in connectivity strength\nbetween OFC-hippocampus and OFC-MFG connectivity, nor any difference between\nOFC-amygdala and OFC-MFG connectivity (See Figure 3c, Supplementary Figure 5,\nSupplementary Figure 6, Supplementary Table 3-4 for details). Thus, instead of finding\nevidence for increased limbic-to-limbic connectivity, we found that the OFC had a more\ndistributed connectivity pattern, while the ACC was more strongly connected to the\nOFC and MFG. Contrary to our initial hypothesis, the prefrontal regions, including the\nMFG, shared elevated theta coherence, indicating the connectivity was not limited to\nthe limbic regions.\nConnectivity within the MFG: We examined the connectivity patterns within the MFG.\nWe found that the MFG also had preferential connectivity with the other prefrontal\nregions (OFC, ACC) compared to subcortical regions (See Figure 3c, Supplemental\nFigure 5 and Supplementary Table 3 for details), matching the profile we found in the\nACC.”\nR1C6: While the rationale for using Bayesian mixed modeling in connectivity analyses is\nunderstood [\"Bayesian mixed-effects model within each region to predict theta coherence, using\na four-level factor representing the remaining regions as the predictor.\" ], its evaluation is\nimpossible without clear specification in the Methods section (especially since code isn't\navailable at this stage). I recommend explicitly detailing the applied model.\nResponse: We thank the reviewer for the helpful comment. We addressed this comment in three\nways. First, we have made an Analysis Guide (beginning on line 684) which is at the beginning\nof the Methods and can be used as a shorthand guide for matching a given result to its statistical\nmodel. Second, we have included the model output summaries in excel sheets as and Extended\nData Table, so readers can easily view and interpret the output for themselves. Third, we have\nadded details to the analysis description in the Results.\nThe relevant portion of the Analysis Guide is copied below:\nNumber of Number of\nDependent Fixed Random Number of Exclusion Figures/\nFinding Estimated Observatio\nVariable Effect Effect Levels Criteria Tables\nModels ns\nAverage\ntheta\ncoherence Figure 3 c;\nPrefrontal\nduring the Supplement\nand\napproach 4-level conflict-free al Figures 5\nsubcortical\nperiod factor, subject/ first trials; trials & 6;\nregions form 5, one for 15-20/115-\n(1500ms specifying electrode of 1923 - 4097 where 0 Supplemental\nsubnetworks each region 355\npreceding partner pair dots were Tables 3 &4;\nwithin a\nthe choice region collected Extended\nwider theta\nto avoid) for Data Table\ncircuit\na given 1\nregion,\nlogged\nHere is the relevant change of the updated result on lines 281-285:\n“We then tested if the consistent limbic theta power elevations were indicative of\nelevated theta coherence between limbic regions, as compared to the MFG. To test\nthis, we ran a Bayesian mixed effects model within each region to predict the average\ntheta coherence during the approach window (1500ms preceding the avoidance\ndecision), using a four-level factor representing the remaining regions as the predictor.”\nR1C7: For Bayesian \"purists\", this statement is correct: \"This analysis can be thought of as a\nfrequentist; however, because the entire model is estimated at once, pairwise differences in\ncoherence strength can be assessed directly without the need for post-hoc tests\". However, to\nstrengthen result robustness and better assess significance levels, I suggest additional metric:\n● Posterior probability thresholds (e.g., P+ > 0.95. as indicated in some analysis)\n● or Markov Chain Monte Carlo-derived p-values (p_MCMC)\n● Bayes Factors for key comparisons\n● Effective sample sizes\nResponse: We thank the reviewer for this comment, we now include (1) the posterior probability\nthresholds, (2) effective sample sizes, and (3) the number of electrodes that demonstrate a given\neffect (when appropriate), as suggested in a later comment.\n• On lines 181-183: ‘As predicted, when the last reward in the corridor was large,\nparticipants were willing to move closer to the ghost to collect the large reward\n(Estimate : 0.11, 95% CI [0.09-0.13], P+ > 0.99, BF > 100, ESS =\nsmall>large 10 bulk\n14861.4’\n• On lines 220-228: ‘We found that theta power was elevated during approach\ncompared to avoidance in conflict trials within the hippocampus (See Figure 2,\nPanel a; Conflict-Trial Estimate : 0.24, 95% CI [0.10-0.37], P+ >\napproach>avoidance\n0.99, BF > 4, ESS = 3838.9, Ratio of Sig. Electrodes = 72/115),\n10 bulk P+ > 0.95\namygdala (Conflict-Trial Estimate : 0.16, 95% CI [0.07-0.26], P+ >\napproach>avoidance\n0.99, BF > 3, ESS = 5903.8, Ratio of Sig. Electrodes = 59/101), OFC\n10 bulk P+ > 0.95\n(Conflict-Trial Estimate : 0.21, 95% CI [0.13-0.29], P+ > 0.99, BF >\napproach>avoidance 10\n100, ESS = 4243.2, Ratio of Sig. Electrodes = 127/167) and ACC\nbulk P+ > 0.95\n(Conflict-Trial Estimate : 0.26, 95% CI [0.14-0.38], P+ > 0.99, BF >\napproach>avoidance 10\n18, ESS = 7222.9, Ratio of Sig. Electrodes = 50/73).’\nbulk P+ > 0.95\n• On lines 231-237: ‘We did not find a significant difference in theta power\nbetween the approach and return windows (Hippocampus Estimate :\napproach>return\n0.08, 95% CI [-0.08-0.25], P+ = 0.84, BF = 0.07, ESS = 2778.1, Ratio of Sig.\n10 bulk\nElectrodes = 72/115; Amygdala Estimate : 0.11, 95% CI [-0.02-\nP+ > 0.95 approach>return\n0.22], P+ = 0.96, BF = 0.14, ESS = 4697.1, Ratio of Sig. Electrodes =\n10 bulk P+ > 0.95\n38/101; OFC Estimate : 0.13, 95% CI [-.01-0.27], P+ = 0.97, BF >\napproach>return 10\n0.21, ESS = 4350.9, Ratio of Sig. Electrodes = 71/167; ACC\nbulk P+ > 0.95\nEstimate : 0.12, 95% CI [-0.07-0.31], P+ = 0.90, BF > 0.11, ESS =\napproach>return 10 bulk\n7437.9, Ratio of Sig. Electrodes = 24/73)’\nP+ > 0.95\n• On lines 243-247: ‘We did not find evidence for increased theta power in the\napproach period compared to the avoidance period in the MFG (Conflict-Trial\nEstimate : 0.06, 95% CI [-0.08-0.19], P+ = 0.82, BF = 0.05, ESS =\napproach>avoidance 10 bulk\n4642.4, Ratio of Sig. Electrodes = 31/143; Conflict-Free\nP+ > 0.95\nEstimate : -0.06, 95% CI [-0.21-0.11], P+ = 0.24, BF = 0.05, ESS =\napproach>return 10 bulk\n4166.8, Ratio of Sig. Electrodes = 11/143’.\nP+ > 0.95\n• On lines 294-295: ‘… with an estimated connectivity strength ratio of 1.49 (95%\ncredible interval [1.29, 1.75], P+ > 0.99 , ESS = 7582.3’\nbulk\n• On lines 298-299: ‘… with a connectivity strength ratio of 1.22 (95% CI [1.07,\n1.36] , P+ > 0.99 , ESS =6426.1; see Supplemental Figure 5) and 1.14 (95%\nbulk\nCI [1.03, 1.28], P+ > 0.99 , ESS = 11399.5; see Supplemental Figure 5),\nbulk\nrespectively’\n• On lines 301-303: ‘... hippocampus coherence with the amygdala was 1.41 (95%\nCI [1.23, 1.65], P+ > 0.99 , ESS = 5789.6) times greater than its theta\nbulk\ncoherence with the MFG, and 1.14 (95% CI [1.03, 1.27], P+ > 0.99 , ESS =\nbulk\n5719.2) times greater with the OFC compared to the MFG.’\n• On lines 304-305: ‘… with a connectivity strength ratio of 1.2 (95% CI [1.07-\n1.36], P+ > 0.99 , ESS = 5742.9).’\nbulk\n• On lines 313-317: ‘Within the ACC, we see stronger theta coherence with the\nMFG compared to the amygdala and hippocampus, where ACC-amygdala\ncoherence was only 0.76 (95% CI [0.65, 0.88], P+ < 0.01 , ESS = 4476.4)\nbulk\ntimes the ACC-MFG coherence and ACC-hippocampus was only 0.77 (95% CI\n[0.65, 0.90], P+ < 0.01 , ESS = 3670.9) times ACC-MFG coherence.’\nbulk\n• On lines 347-349: ‘We found that average connectivity values increased during\nthe time the patient approaches (Estimate: 0.09, 95% CI [0.05-0.14], P+ > 0.99,\nBF > 9, ESS = 7680.4, Ratio of Sig. Electrodes = 1782/2816…’\n10 bulk P+ > 0.95\n• On lines 363-365: ‘…theta coherence drops after the patients began avoiding and\nmoved to exit the trial (Estimate: -0.11, 95% CI [-0.16 - -0.06], P+ < 0.01, BF >\n10\n14, ESS = 3520.3, Ratio of Sig. Electrodes = 1824/2816…’\nbulk P+ > 0.95\n• On lines 424-426: ‘As hypothesized, the model found an overall significant\ncorrelation between approach times and theta synchrony (Estimate: 0.07, 95%\nCI [0.04-0.09], P+ > 0.99, BF > 100, ESS = 2816.6)’\n10 bulk\n• On lines 439-441: ‘While the model did not find an overall significant correlation\nbetween approach times and HFA synchrony (Estimate: -0.01, 95% CI [-0.04-\n0.01], P+ = 0.1, BF = 0.01, ESS = 2159.4…’\n10 bulk\n• On lines 480-481: ‘… where right MFG contacts resulted in a significant elevation\nof HFA in the second after an attack began (estimate: -0.16 95% CI [-0.29 - -\n0.04], P+ < .01, BF > 1.75, ESS = 6782.9; See Figure 5d).’\n10 bulk\n• On lines 488-492: As predicted, we found a main effect of trial type, where chase\ntrials had decreased HFA compared to strike trials (Estimate: -0.16 95% CI [-\n0.21 - -0.12] ], P+ < .01, BF > 100, ESS = 13160.1) and a significant\n10 bulk\ninteraction with time where chase trials showed dropping HFA as patients\nneared the end of the trial (estimate: -0.12 95% CI [-0.17 - -0.08], P+ < .01, BF\n10\n> 100, ESS = 13324.6)…’\nbulk\nWe did not report Bayes Factors for the factor-level contrasts because in this hierarchical setting\nthey strongly depend on the prior for contrasts between the factors, which is not explicitly\nparameterized in the model. This makes the Bayes Factors for these values numerically unstable\nand difficult to interpret. Instead, we report Probability Positive (P+), 95% confidence intervals,\nand effective sample sizes.\nR1C8: However, caution is warranted when claiming that theta-gamma power correlations\npredict approach times, as correlation cannot guarantee directionality. In fact, the authors'\ninterpretations appear to suggest the opposite: increased power correlations may instead result\nfrom escalating risk/conflict as approach time lengthens.\nWhile the positive association between theta-band synchrony and approach duration (Fig. 4)\naligns with theta's putative role in risk assessment, there are caveats that merit discussion:\n● Directionality Ambiguity: The correlational nature of the analysis precludes causal\nclaims. The observed effects could reflect either:\n● Top-down control: Theta synchrony prolongs the approach to optimize reward-threat\ntradeoffs\n● Bottom-up drive: Accumulating risk/conflict (as proximity increases) enhances theta\ncoupling\nResponse: This valuable comment resulted in an expansion of the Discussion to discuss these\ninterpretations and better clarify our results. We explicitly highlight the directional ambiguity of\nthe theta synchrony result and elaborate on the possible mechanistic role theta oscillations might\nbe playing during approach-avoidance conflict.\nHere is the relevant portion (beginning on line 518) of the reworked Discussion:\n“Low-frequency theta oscillations coordinate neural activity between distal regions\n(Fries, 2015, 2005; Helfrich and Knight, 2016; Phillips et al., 2014) facilitating\ninformation transfer (Canolty and Knight, 2010; Yuste, 2015). Absent between-region\nsynchrony, inputs may arrive at random phases of the excitability cycle hindering\neffective inter-regional communication. Notably, the degree of theta coherence\ncorrelates with behavioral performance in a wide range of attention, learning,\nemotional and working-memory tasks (Backus et al., 2016; Harris and Gordon, 2015;\nJohnson et al., 2017; Watrous et al., 2013; Zheng et al., 2019, 2017). Our results\nsupport a key role for theta synchrony during approach-avoidance. We found many\ntheta-cohering pairs of electrodes across the prefrontal-subcortical circuit with theta\nband synchrony peaking at the final decision to avoid– when the levels of threat,\nconflict and the need for control were the highest. The prefrontal cortex is thought to\nhave a particular role in coordinating the oscillatory activity underlying cognitive\ncontrol and goal-directed behavior (Helfrich and Knight, 2016), and midline theta\noscillations recorded from scalp EEG have been long-associated with uncertainty and\ncognitive control (Cavanagh et al., 2012; Cavanagh and Frank, 2014). Furthermore, in\nthe context of approach-avoidance, Amemori et al 2024 reported that reduced\nconnectivity in a PFC-limbic circuit results in more avoidant behavior in non-human\nprimates (Amemori et al., 2024b). In line with these results, we found that the MFG\nhad Granger causal influence on theta oscillations in both the OFC and ACC, and\nincreased theta synchrony across multiple nodes in the prefrontal-limbic theta network\ncorrelated with longer approach times in the task, meaning that patients took on more\nrisk to receive increased reward in those trials. Importantly, while Amemori focused on\nalpha oscillations in a restricted set of limbic regions, they defined the alpha band as\nbetween 5-13Hz, and reported peaks at 7Hz, which overlaps with our 3-8Hz theta\nband definition for this study.\nHowever, while there is converging evidence that theta oscillations provide a cross-\nregion mechanism for neural communication and that theta oscillations are prominent\nduring approach-avoidance decision-making, there are open hypotheses about what\nkind of information is being coordinated across regions via theta oscillations.\nApproach-avoidance behavior is dependent on a multitude of functions, such as threat\ndetection, uncertainty estimation, and conflict resolution between competing goals\n(Bryant and Barker, 2020; Ito and Lee, 2016). For example, in our task increased theta\noscillations and coherence could reflect increased volitional cognitive control to\nsuccessfully obtain more reward in the face of increasing task demands. Alternatively,\nthe accumulating risk and conflict over the trial could drive increasing theta synchrony\nin a bottom-up manner. The Ghost attack trials are informative as we observe a drop in\ntheta power after the turn to avoid a direct attack by the Ghost, similar to the decrease\nin theta power during an attack-free trial. Alternatively, we could have seen theta\noscillations continue or even increase as the Ghost attacks and gets closer to the Pac-\nMan, which would suggest a greater correspondence between risk or anxiety and theta\noscillations. Instead, theta power falls approximately one second after the onset of the\nGhost Attack, corresponding to after the participant has turned to avoid the attack. This\nis compatible with a role for theta oscillations in navigating uncertainty, conflict, or\nexerting cognitive control—before an attack the ghost needs to be closely monitored,\nbut after an attack and a turn is made the need to control behavior concludes.\nFurthermore, our findings that theta coherence drops during the avoidance period\nsupport the idea that theta oscillations track uncertainty or cognitive control—while\nthere may still be conflicting goals after turning to avoid due to remaining reward in\nthe trial, theta coherence begins dropping during this time, suggesting a decreased\nneed to predict the ghost’s actions (uncertainty) and to adjust one’s behavior in\nresponse to those predictions (cognitive control). Computational models are needed to\nfully test these interpretations, but the current results are in accord with work showing\na role for theta oscillations handling uncertainty in the ACC, especially when that\nuncertainty requires behavioral adaptation (Monosov, 2017; Valdebenito-Oyarzo et al.,\n2024). Given that the OFC and ACC were the main receivers of theta oscillations in our\ntask, they may be well-suited to integrating information from the other regions in order\nto respond to uncertainty.”\nWe also adjusted any lines in the Results or Discussion that implied a specific causal direction\nfor the theta or HFA synchrony findings:\n• On line 415: ‘Pairwise synchrony during approach behavior correlates with\nchoice to avoid:’\n• On lines 441-442: ‘…we did find a more restricted set of region pairs that\ncorrelated with approach time’\n• On lines 452-453: ‘Additionally, we have found that synchrony in both the theta\nband and HFA in these regions correlates with approach times and that the OFC\nconnects the prefrontal regions with the hippocampus and amygdala.’\n• On lines 568-569: ‘While the OFC-MFG and OFC-ACC HFA synchrony correlated\nwith longer approach times, hippocampus-MFG HFA synchrony correlated with\nshorter approach times.’\n• In the Figure 4 legend, sections c-e: ‘Trial-by-trial theta and HFA synchrony\ncorrelates with approach times’ & ‘Lines indicate that the connected regions\ncorrelated with approach times’\nR1C9: The authors explain that there is a theta drop similar to avoidance. This result is\ninteresting and strange—the hypothesis would be that limbic activity relates to the probability of\nbeing attacked, but it seems to also have something to do with the subjects' \"spatial\" behavior.\n[Transition from decision-making (approach/avoidance conflict) to action execution (escape\nnavigation) or Shift from cognitive control (OFC/ACC theta) to motor planning (parietal\nbeta/gamma), as seen in spatial tasks]\n● The authors frame it in terms of proximal vs. distal threat, linking it to anxiety literature.\nBut it's still odd in this context that hippocampal, amygdala, and OFC activity would\ndecrease when facing a greater threat, even if it's distal. I would like to see more\ndiscussion on this.\n● In this context, literature on uncertainty and decision timing could help reconcile these\nresults. For example, delta/theta band activity and neuronal spikes in the ACC are\nimplicated in estimating and re-estimating ambiguity as a type of uncertainty\n[10.1371/journal.pbio.3002452, 10.1038/s41467-017-00072-y].\n● In such a context, a computational cognitive model could dissect variables like threat\nprobability or distance, strengthening mechanistic claims. Address this as a future\ndirection.\nResponse: We thank the reviewer for highlighting a finding that was underdiscussed in the\ninitial submission. We agree that the result is interesting and worthy of further exploration, as it\nprovides insight as to the role of theta oscillations in the task. In response, we have added lines to\nthe Discussion (also included above) explicitly discussing this finding and how it relates to the\nrole of theta oscillations in approach-avoidance conflict.\nHere is the relevant portion of the reworked Discussion, beginning on line 540:\n“However, while there is converging evidence that theta oscillations provide a cross-\nregion mechanism for neural communication and that theta oscillations are prominent\nduring approach-avoidance decision-making, there are open hypotheses about what\nkind of information is being coordinated across regions via theta oscillations.\nApproach-avoidance behavior is dependent on a multitude of functions, such as threat\ndetection, uncertainty estimation, and conflict resolution between competing goals\n(Bryant and Barker, 2020; Ito and Lee, 2016). For example, in our task increased theta\noscillations and coherence could reflect increased volitional cognitive control to\nsuccessfully obtain more reward in the face of increasing task demands. Alternatively,\nthe accumulating risk and conflict over the trial could drive increasing theta synchrony\nin a bottom-up manner. The Ghost attack trials are informative as we observe a drop in\ntheta power after the turn to avoid a direct attack by the Ghost, similar to the decrease\nin theta power during an attack-free trial. Alternatively, we could have seen theta\noscillations continue or even increase as the Ghost attacks and gets closer to the Pac-\nMan, which would suggest a greater correspondence between risk or anxiety and theta\noscillations. Instead, theta power falls approximately one second after the onset of the\nGhost Attack, corresponding to after the participant has turned to avoid the attack. This\nis compatible with a role for theta oscillations in navigating uncertainty, conflict, or\nexerting cognitive control—before an attack the ghost needs to be closely monitored,\nbut after an attack and a turn is made the need to control behavior concludes.\nFurthermore, our findings that theta coherence drops during the avoidance period\nsupport the idea that theta oscillations track uncertainty or cognitive control—while\nthere may still be conflicting goals after turning to avoid due to remaining reward in\nthe trial, theta coherence begins dropping during this time, suggesting a decreased\nneed to predict the ghost’s actions (uncertainty) and to adjust one’s behavior in\nresponse to those predictions (cognitive control). Computational models are needed to\nfully test these interpretations, but the current results are in accord with work showing\na role for theta oscillations handling uncertainty in the ACC, especially when that\nuncertainty requires behavioral adaptation (Monosov, 2017; Valdebenito-Oyarzo et al.,\n2024). Given that the OFC and ACC were the main receivers of theta oscillations in our\ntask, they may be well-suited to integrating information from the other regions in order\nto respond to uncertainty.”\nR1C10: The conclusion’s phrasing (\"We provide evidence for a dynamic cortico-limbic network\nsupporting approach and avoidance\") overstates causal inferences. Qualify as \"correlational\nevidence\" unless causal methods (e.g., stimulation) are used.\nResponse: We have adjusted the phrasing to better reflect the types of evidence used in the\npaper. We have made the following changes to line 500:\n“We provide behavioral and electrophysiological evidence for a dynamic cortico-limbic\nnetwork supporting approach and avoidance… “\nR1C11: Explicitly state the proportion of electrodes showing significant effects per region in all\nanalyses (e.g., \"39/127 MFG contacts exhibited HFA increases\"). This avoids selection bias and\nclarifies regional heterogeneity.\nResponse: We thank the reviewer for this suggestion. We have now added the number of\nsignificant electrodes to each of the main results. Here, we count an electrode as “significant” if\nthat electrode’s posterior distribution had a Probability Positive (P+) value > 0.95 and have made\nthe corresponding changes:\n• On lines 220-228: ‘We found that theta power was elevated during approach\ncompared to avoidance in conflict trials within the hippocampus (See Figure 2,\nPanel a; Conflict-Trial Estimate : 0.24, 95% CI [0.10-0.37], P+ >\napproach>avoidance\n0.99, BF > 4, ESS = 3838.9, Ratio of Sig. Electrodes = 72/115),\n10 bulk P+ > 0.95\namygdala (Conflict-Trial Estimate : 0.16, 95% CI [0.07-0.26], P+ >\napproach>avoidance\n0.99, BF > 3, ESS = 5903.8, Ratio of Sig. Electrodes = 59/101), OFC\n10 bulk P+ > 0.95\n(Conflict-Trial Estimate : 0.21, 95% CI [0.13-0.29], P+ > 0.99, BF >\napproach>avoidance 10\n100, ESS = 4243.2, Ratio of Sig. Electrodes = 127/167) and ACC\nbulk P+ > 0.95\n(Conflict-Trial Estimate : 0.26, 95% CI [0.14-0.38], P+ > 0.99, BF >\napproach>avoidance 10\n18, ESS = 7222.9, Ratio of Sig. Electrodes = 50/73).’\nbulk P+ > 0.95\n• On lines 231-237: ‘We did not find a significant difference in theta power\nbetween the approach and return windows (Hippocampus Estimate :\napproach>return\n0.08, 95% CI [-0.08-0.25], P+ = 0.84, BF = 0.07, ESS = 2778.1, Ratio of Sig.\n10 bulk\nElectrodes = 72/115; Amygdala Estimate : 0.11, 95% CI [-0.02-\nP+ > 0.95 approach>return\n0.22], P+ = 0.96, BF = 0.14, ESS = 4697.1, Ratio of Sig. Electrodes =\n10 bulk P+ > 0.95\n38/101; OFC Estimate : 0.13, 95% CI [-.01-0.27], P+ = 0.97, BF >\napproach>return 10\n0.21, ESS = 4350.9, Ratio of Sig. Electrodes = 71/167; ACC\nbulk P+ > 0.95\nEstimate : 0.12, 95% CI [-0.07-0.31], P+ = 0.90, BF > 0.11, ESS =\napproach>return 10 bulk\n7437.9, Ratio of Sig. Electrodes = 24/73)’\nP+ > 0.95\n• On lines 243-247: ‘We did not find evidence for increased theta power in the\napproach period compared to the avoidance period in the MFG (Conflict-Trial\nEstimate : 0.06, 95% CI [-0.08-0.19], P+ = 0.82, BF = 0.05, ESS =\napproach>avoidance 10 bulk\n4642.4, Ratio of Sig. Electrodes = 31/143; Conflict-Free\nP+ > 0.95\nEstimate : -0.06, 95% CI [-0.21-0.11], P+ = 0.24, BF = 0.05, ESS =\napproach>return 10 bulk\n4166.8, Ratio of Sig. Electrodes = 11/143’.\nP+ > 0.95\n• On lines 347-349: ‘We found that average connectivity values increased during\nthe time the patient approaches (Estimate: 0.09, 95% CI [0.05-0.14], P+ > 0.99,\nBF > 9, ESS = 7680.4, Ratio of Sig. Electrodes = 1782/2816…’\n10 bulk P+ > 0.95\n• On lines 363-365: ‘…theta coherence drops after the patients began avoiding and\nmoved to exit the trial (Estimate: -0.11, 95% CI [-0.16 - -0.06], P+ < 0.01, BF >\n10\n14, ESS = 3520.3, Ratio of Sig. Electrodes = 1824/2816…’\nbulk P+ > 0.95\nWe additionally include the number of significant electrodes driving the directionality results in\nSupplemental Table 7, which we have copied here:\nTotal Electrode Total Sig. First Region First Region\nRegion Pair P+\nPairs Electrode Pairs Leads (Count) Leads (Percent)\nNet Granger Analysis\nMFG - > ACC 0.99 397 260 157 0.6\nMFG - > OFC 0.99 552 313 218 0.7\nAmyg - > OFC 0.98 321 168 104 0.62\nAmyg - > ACC 0.97 65 29 27 0.93\nHC - > OFC 0.94 385 170 88 0.52\nHC - > ACC 0.71 90 40 25 0.62\nAmyg - > MFG 0.63 95 55 25 0.45\nHC - > MFG 0.61 179 63 29 0.46\nOFC - > ACC 0.41 396 265 132 0.5\nHC - > Amyg 0.08 336 209 107 0.51\nCross Correlation Analysis\nMFG - > ACC 0.97 397 334 189 0.57\nMFG - > OFC 0.96 552 464 281 0.61\nAmyg - > OFC 0.96 321 259 148 0.57\nAmyg - > ACC 0.96 65 50 33 0.66\nACC - > HC 0.94 90 58 36 0.62\nAmyg - > MFG 0.82 95 70 41 0.59\nAmyg - > HC 0.7 336 305 128 0.42\nHC - > OFC 0.46 385 273 124 0.45\nACC - > OFC 0.2 396 324 159 0.49\nHC - > MFG 0.17 179 105 35 0.33\nSupp. Table 7. Results for both the net Granger and cross-correlation analysis for each region pair. P+ represents the\nproportion of posterior samples where the effect is greater than zero, and we consider P+ > 0.95 as strong evidence for\ndirectionality. For each region pair, we also include the total number of region pairs in the dataset, the total number of\npairs that showed significant directionality, the number of pairs where the first region led the second region, and the\npercentage of pairs where the first region led the second region out of the total number of pairs with significant\ndirectionality.\nR1C12: Figure 5F: Include amygdala data in Figure 5F for completeness, given its role in\nthreat processing.\nResponse: We thank the reviewer for this suggestion, and we have added the amygdala time-\nfrequency plot to Figure 5f.\nR1C13: In the theta discussion, it would be interesting to consider MFG activity as reflecting\nconflict/danger expectation. For example, this paper [10.1038/s41467-024-54244-8] shows how\nMFG theta activity causally encodes conflict probability. This fits well with what was found here,\nespecially with the high gamma activity that was the only cortical signal showing a relationship\nwith conflict/damage probability, considering both the approach/avoidance analyses and the\nghost attack analyses.\nResponse: We appreciate this point and agree with the reviewer that the MFG activity could\nrepresent conflict/danger expectation and we have adapted the Discussion (beginning on line\n500) to better highlight this possibility.\n“Fitting with a model of approach-avoidance behavior where distal and uncertain\nthreat results in anxiety-like behavior while proximal and certain threat induces fear\nbehaviors (Mobbs et al., 2020; Perusini and Fanselow, 2015), we see rapid changes in\nthe cortical-subcortical circuit during ghost attack. Specifically, the time-frequency\nplots were dominated by a rapid increase in high-frequency activity in the right MFG\nduring ghost attack. Previous work has reported evidence of left/right asymmetry in\nthe lateral PFC, including the MFG, especially in the context of emotional regulation\n(Brunoni et al., 2017; White et al., 2023). It has been shown that the right dorsolateral\nPFC is activated during periods of unpredictable threat, and this increased dlPFC\nactivation is associated with reduced anxiety (Drevets and Raichle, 1992). These\nauthors interpret their results as the right dlPFC enacting emotional regulation by\nproviding top-down control on the limbic regions. In our study, the high-frequency\nactivity in the right MFG during the acute threat period may provide a control signal as\npatients try to regulate their escape in the face of a near-term threat. Once patients\nrealize they will make a successful escape the need for this control drops and is\naccompanied by a rapid decrease in high-frequency activity. Alternatively, or perhaps\nin addition to a role for emotional regulation, the activity in the MFG in this task may\nreflect conflict or danger expectation, as has been shown in other studies investigating\napproach-avoidance and other types of conflict (Abivardi et al., 2020b; Martínez-\nMolina et al., 2024; Zorowitz et al., 2019). MFG-limbic theta coherence increased\nduring the approach period, and dropped during avoidance, particularly with the\namygdala, and the MFG Granger causal theta oscillations in the OFC and ACC during\nthe approach period. It is possible that threat probability is calculated or encoded in\nMFG neurons and then transmitted to the OFC and ACC via theta oscillations to the\nOFC and ACC, which integrate that information with other signals to guide behavior. “\nR1C14: Minor Edits:\n● Clarify the \"avoidance window\" definition in control trials (line 183)\n○ Response: We have done this, please see changes on line 230.\n● Label chase/strike trials in Figure 5A for consistency.\n○ Response: Chase and strike trials are labeled in Figure 5a.\n● Check axis labels in time-frequency plots to avoid confusion (e.g., negative times =\napproach).\n○ Response: We have worked to make this more clear, please see our response to\ncomment R1C3 for a detailed explanation of the changes.\nReviewer #2 (Remarks to the Author):\nSummary of the Article: In this manuscript, the authors define and investigate the role of a\ndistributed prefrontal-limbic brain network in approach avoidance conflict. They use stereo EEG\n(sEEG) recorded from 20 epilepsy patients undergoing invasive monitoring for seizure\nlocalization while they performed a continuous-choice, approach-avoidance conflict decision-\nmaking task inspired by the arcade game Pac-Man. In this task, patients trade off rewards against\npotential losses from attack by a ghost. They show 1) Increased theta power in the hippocampus,\namygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), as patients\napproached increasing rewards and threats, and a subsequent drop during avoidance; 2) An\nincrease in theta band connectivity within the limbic circuit and with the medial frontal gyrus\n(MFG) during approach and decrease during avoidance; 3) Directional connectivity of theta from\nthe amygdala and MFG to OFC and ACC, 4) degree of network connectivity predicted how long\npatients approach, with enhanced network synchronicity extending approach times and 5) when\nthreat is imminent while being attacked by the ghost, the system dynamically switches to a\nsustained increase in high-frequency activity (70-150Hz) in the MFG, tracking the degree of\nthreat. The results from this work provide evidence for a dynamic limbic-prefrontal cortical\nnetwork underlying approach avoidance conflict processing in human subjects.\nComments\nR2C1: Overall, this is a very well written article with clear description of methods and\nhypotheses being tested for each result. The findings in my opinion are significant in mapping a\nfrontal-limbic brain circuit that is involved in approach avoidance conflict which can further be\nused to design interventions in patient population who have difficulty with resolving such\nconflict. Examples of such population as provided by the authors would be ones with anxiety\ndisorders. However, information on such symptoms in their studied cohort of 20 patients with\nepilepsy is missing. A considerable population of patients with intractable epilepsy have\ncomorbid anxiety disorders, but its not clear if any of the participants also had anxiety disorder\nand how that was factored in the analysis. As such, there is no information on\nneuropsychological characterization of the studied cohort.\nResponse: We thank the reviewer for their encouraging comments and for highlighting and\nimportant gap in the description of our patient sample. We have clinical information on our\npatient sample, which we now include in the description of the patient sample. Specifically,\npatients completed the 26-item Mood and Anxiety Symptom Questionnaire (MASQ-Mini) after\ncompleting the behavioral experiment. The online behavioral sample (n=122) also completed the\nMASQ-Mini and we break down the comparisons within this sample between online participants\nwho reported at least one mental health disorder (n = 59) and those without (n= 63; note that we\nexcluded our pilot sample (n=49) from these analyses, as we did not have information about any\nmental health diagnosis in this sample, but these data are included in the other behavioral\nanalyses). There were three iEEG participants with elevated clinical symptoms, meaning they\nwere above the average clinical score in the online sample that reported a mental health disorder,\nbut otherwise symptom load was similar to the healthy sample.\nLegend: Clinical symptom histograms for each subscale of the MASQ across three\nparticipant groups. The Y-axis is the number of participants and X-axis is the subscale score.\nThe online participants are broken into two groups, those who reported having at least one\nmood disorder (n=59), and those who did not (n= 63; marked ‘Healthy’ in the plot). The\nhorizontal line denotes the average subscale score for the online participants who reported at\nleast one mood disorder. The majority (17/20) of the iEEG participants reported less\nsymptoms than then the average symptoms in the cohort with at least one mood disorder, and\nthe distribution of symptoms in the iEEG sample is qualitatively similar to the healthy\ncohort.\nWe examined these three patients’ neural activity in terms of their random effects from the\nfollowing analyses: regional theta power elevation during the approach compared to avoidance\nperiod, increase in theta coherence during the approach period, and the correlation between\napproach times and theta synchrony. The three iEEG participants did not fall outside the normal\ndistribution of the remaining intracranial cohort in any of these analyses.\nWe now include the above figure as Supplemental Figure 9.\nR2C2: Another moderate concern is that it wasn’t clear to me why the authors chose pairwise\nGC instead of assessing directional connectivity on the whole network such as using a partial\ndirected coherence or directed transfer function that uses a multivariate model.\nResponse: We thank their attentive read of this, as it is more traditional to calculate GC on the\nentire network. We chose to do the GC analysis in a pairwise manner to limit the contacts to\nthose that already demonstrated elevated theta coherence in our previous analyses. If we had\nassessed directional connectivity to the whole network, we would have to include edges in the\nnetwork that did not have evidence of theta coherence. However, to ensure that these results\nwere not just specific to this cohort of electrodes, we reran the Granger analysis across three\nadditional groups with different thresholds for theta coherence. In looking at the results across\nthe four thresholds, we see similar results with equivalent directionality. These results are also\nincluded below as a response to subsequent reviewer comment (R3C6).\nProbability Positive Values by Threshold Group\n100ms\nRegion Pair (Original) 100ms (App. Only) 500ms 1000ms\nMFG -> ACC 0.994 0.993 0.991 0.988\nMFG -> OFC 0.993 0.995 0.994 0.994\nAmyg -> OFC 0.975 0.968 0.974 0.974\nAmyg -> ACC 0.972 0.974 0.947 0.943\nHC -> OFC 0.937 0.955 0.927 0.896\nHC -> ACC 0.706 0.722 0.55 0.548\nAmyg -> MFG 0.628 0.646 0.688 0.776\nHC -> MFG 0.609 0.538 0.666 0.689\nOFC -> ACC 0.412 0.321 0.341 0.323\nHC -> Amyg 0.079 0.072 0.076 0.076\nLegend: Probability Positive values, which describe the posterior distribution, for models\nestimating the directionality between region pairs, based on the Granger Causality analysis. The\nthreshold group is defined in the following manner: ‘100ms’ includes electrode pairs with 100ms\nof elevated theta coherence (corresponding to the original result), ‘100ms (App.)’ includes\nelectrode pairs with 100ms of elevated coherence in the approach window only, and ‘500ms’ and\n‘1000ms’ include pairs with 500ms and1000ms of elevated theta coherence, respectively. Grey\nshading indicates the Probability Positive values were greater than 0.95 in both the original\nthresholding and in the cross-correlation analysis.\nWe have worked to clarify this rationale when introducing this analysis, as reported on Lines\n375-377:\n“We next assessed the directionality of the prefrontal-limbic circuit using two\nindependent methods, State-Space spectral Granger causality and cross-correlation\nanalysis (Adhikari et al., 2010a; Barnett and Seth, 2015). We calculated the net\nspectral Granger causality between all electrode pairs with significant levels of theta-\nband coherence. We implemented the Granger analysis in a pairwise manner to limit\nthe electrodes to those with elevated theta coherence, as well as to better match our\ncontrol analysis, which used cross-correlation (see Supplemental Tables 7-8 for\nestimates of results across different thresholds). As there may be bidirectional\ninformation flow, we identified which signals dominated the information flow by\ncalculating the difference between the Granger values calculated in both directions.”\nR2C3: Some other minor concerns/comments are the following:\n● 1) The paper title can be a bit more specific\n○ Response: We have updated the title to: “Cortical-limbic circuit dynamics of\napproach-avoidance conflict in humans”\n● 2) On line 286, I believe its Fig 3e and not 3g\n○ Response: While it was Figure 3g, we have clarified this on lines 355-358: ‘We\nnext included an interaction term between time and region pairs to\nidentify which region pairs were driving this effect. We found that the\nfollowing pairs demonstrated strong time-coherence modulations:\namygdala-MFG, amygdala-hippocampus, hippocampus-MFG,\nhippocampus-OFC, ACC-OFC, and ACC-MFG (See Figure 3g; Extended\nData Table 1).’\n● 3) In the methods section (line 610), it wasn’t explained why the high frequency power\nwas calculated as averages of overlapping sub bands instead of the full band.\n○ Response: We thank the reviewer for highlighting this missing detail. We have\nchanged the text on lines 718-722 as follows:\n○ “For the high-frequency activity estimate, we followed a similar approach\nbut averaged the individual frequencies in 20Hz overlapping sub-bands\n(e.g., 70-90Hz, 80-100Hz, … 130-150Hz) before averaging the power in\neach sub-band for a final estimate of high-frequency activity. We used a\nsub-band approach to minimize the effect of 1/f power scaling, which\nbiases the power estimates to the lower frequencies in a large band, such\nas estimated from 70-150Hz.”\n● 4) The zenodo link where code will be available was missing.\n○ Response: We have added the necessary GitHub links (which will alsobe\nuploaded to Zenodo).\n■ The link to repo for processing the neural data is here:\nhttps://github.com/bstavel/Staveland_et_al_Pacman_Neural_Analyses\n■ The link to repo for our statistical procedures are here:\nhttps://github.com/bstavel/Staveland_et_al_Pacman_Statistics_and_Behav\nior\nReviewer #3 (Remarks to the Author):\nSummary of the Article: The manuscript “Circuit dynamics of approach-avoidance conflict in\nhumans” by Staveland and colleagues used intracranial EEG recordings from N=20 patients to\ndefine a prefrontal-limbic network, comprising of Amygdala, Hippocampus, OFC ACC, and\nMFG, that is engaged during approach-avoidance conflicts. The authors report distinct\nmodulations of theta and HFA power during approach and avoidance, increased theta coherence\nduring approach and decreased theta power during avoidance. The degree of network\nconnectivity positively predicted approach time. Furthermore, high frequency activity in the\nMFG was observed during imminent threat. These results help to further our understanding about\nthe neuronal basics underlying approach-avoidance in humans.\nThe manuscript is well written and the methods used are sound given some few clarifications.\nAccordingly, I have only minor comments that the authors can hopefully answer.\nComments\nR3C1: Please clarify whether the movement of Pac-Man and the ghost was continuous (i.e. as\nlong as the subject pressed a key) or stepwise (i.e. move a certain distance every time a key was\npressed? This is not clear from the current manuscript\nResponse: We thank the reviewers for identifying several aspects of the task description that\nneeded improvement. Both the Pac-Man and the Ghost moved continuously, with a single button\npress initiating movement in one direction, which could reverse upon pressing the arrow key in\nthe other direction. Based on this comment and others, we have rewritten the description of the\nbehavioral experiment in greater detail, and the relevant changes on lines 134-135 are copied\nbelow from response to Reviewer 1.\n“… The participant controls the Pac-Man and they are instructed to collect as many\ndots as they can while avoiding the Ghost, which is moving back and forth at a\nconstant speed at the opposite side of the corridor. The participant makes a single\nbutton press, using the left or right arrow keys, to initiate movement along the corridor\nat a constant speed. By moving towards the center of the corridor, they can collect up\nto 5 dots, resulting in points, which varied in either small (10 points) or large (20 point)\nsizes and were placed in pseudorandom configurations between Pac-Man and the\nGhost…”\nR3C2: The fact that the ghost was moving back and forth at the opposite end of the corridor is\nnot mentioned in text, only in caption of figure 1. Could participants synchronize their\nmovements of Pac-Man with those of the ghost? For instance, collect dots when the ghost moves\naway from the center and retreating when it returned towards the center – or would such a\nretreat have ended a trial?\nResponse: We thank the reviewer for highlighting this important aspect of the task. We have\nnow highlighted the Ghost’s continual movement in a section preceding the results.\nParticipants could synchronize their movements with the Ghost. In some participants, on some\ntrials, participants would synchronize their movements with the Ghost, such that they would\nretreat from the Ghost when the Ghost was moving towards them and then turn back when the\nGhost retreated. However, there was a single approach and avoidance event per trial on most\ntrials. The figure below shows a representative sample of online participants turning behavior\n(n=69). Most participants (including within the iEEG sample) only turned one time. Importantly,\nin cases where there were multiple turns, we always chose the last timepoint the Pac-Man turned\naway from the Ghost as the final avoidance decision.\nLegend: Distribution of the number of turns for a subsample of online participants. The Y-\naxis is the trial count, and the X-axis is a subsample of participants. The stacked bar chart\nshows the number of trials in which there were 0 turns (participant ran into the Ghost), a\nsingle turn, 2 turns, etc. Darker colors correspond to more turns within a single trial. While\nmost participants synchronized their movements with the Ghost on some trials, the majority\nof participants approached and then turned to avoid a single time.\nHere is the first paragraph (beginning on line 130) of the updated task description. We have\nhighlighted the sentences concerning the Ghost’s movement:\n“Task Design: … The participant controls the Pac-Man and they are instructed to\ncollect as many dots as they can while avoiding the Ghost, which is moving back and\nforth at a constant speed at the opposite side of the corridor. The participant makes a\nsingle button press, using the left or right arrow keys, to initiate movement along the\ncorridor at a constant speed. …”\nHere is the relevant portion of the updated results on lines 206-209:\n“In each region, we calculated time-frequency plots time-locked to the patients’ choice\nto stop approaching and turn back to exit the trial. If the participant turned multiple\ntimes, we selected the final timepoint as the decision to avoid. …”\nR3C3: In the 20% conflict-free trials, was there no ghost or did the ghost simply not attack? If\nthe latter, how was it obvious for the subjects that these trials were conflict-free?\nResponse: We appreciate the thoughtful questions about the task and have expanded details of\nthe task design throughout the paper, and especially in a section preceding the Results.\nOn the conflict-free trials, there was no ghost present on the corridor. During the instructions, we\nexplained that the PacMan always had to exit the corridor at the side it began the trial on, in to\nmotivate similar approach and return behavior on the conflict-free trials.\nHere is the relevant portion of the updated task description on lines 166-168:\n‘Finally, 20% of trials are conflict-free, where the participant is free to collect the 5\ndots without threat of the Ghost. On these trials, the Ghost was not present on the\ncorridor, though participants were still instructed that they had to return to exit the trial\nat the side of the corridor on which they began the trial.’\nR3C4: Figure 1c: I see the variability within participants but where do I see the variability\nacross participants?\nResponse: We thank the reviewer query. Each boxplot in Figure 1c is from a unique participant.\nThe grey dots show that participant’s final turning distance on each trial, and the boxplot\nsummarizes the mean and variation within each person. By looking across the plot, it is possible\nto both compare which participants stayed farther from the ghost (larger means on the Y-axis),\nand which participants were more variable in their choices (taller boxes, more widespread dots).\nHowever, we agree our text in the manuscript for this section was confusing. We have altered in\nthe following way:\n‘Participants on average turned away from the ghost when they were 35.3+/ 3.4 units\naway from the ghost (corresponding to ~17% of the length of the corridor), though\nthere was across-subject variability in how close participants were willing to get to the\nghost to receive more reward (See Figure 1c).’\nR3C5: Why was no statistical test for the different turnaround distances for large and small\nrewards performed?\nResponse: A statistical test was run to test the association between turnaround distances and\nlarge and small rewards, but the details were not explained in the manuscript and only noted in\nthe Figure. We now have details of the model (which we updated to a Bayesian model to match\nthe neural analyses) in three places. First, it is now described the in behavioral results section on\nlines 181-184, which is copied below. Second, it is included the Analysis Guide (beginning on\nline 684). Finally, the model output summary is included in Extended Data Table 1.\n‘We also hypothesized that participants would make risk/reward trade-offs tolerating\nmore risk to get large rewards. We tested this by running a linear mixed effects model,\nwhere a two-level factor specifying if the last reward was large or small was used to\npredict the distance to the ghost at the time of the decision to avoid. As predicted,\nwhen the last reward in the corridor was large, participants were willing to move\ncloser to the ghost to collect the large reward (Estimatesmall>large: 0.11, 95% CI\n[0.09-0.13], P+ > 0.99, BF10 > 100, ESSbulk = 14861.4; see Figure 1d, see Table 1 for\nmodel details; see Extended Data Table 1).’\nR3C6: Significant theta-band coherence was defined as showing at least 100ms of coherence\nabove the shuffled distribution. Can the authors explain why precisely 100ms? Does it matter\nwhether the 100ms were before or after the decision to avoid?\nResponse: This is an important issue, as this threshold determines the group of electrode pairs\ncarried forward to the remaining analyses. We tested how our results changed across different\nthresholds, including 1000ms, 500ms, 100ms within the approach window only, and without\nthresholding. We found that increasing the severity of the threshold generally increased the effect\nsizes or did not change the results. We include figures or tables demonstrating this for each\nanalysis below, and we include each of these as a supplemental figure or table.\nCoherence rises over the approach period and falls over the avoidance period: In our original\nanalysis we found that time positively correlates with theta coherence in the approach period and\nnegatively correlates with theta coherence during avoidance. We tested this across multiple time\nperiods, including 1.5, 1.6, 1.7, 1.8, and 2 seconds before and after the final choice to avoid. This\nanalysis only included electrode pairs that initially showed 100ms of elevated theta coherence\nacross either the approach or avoidance period. Here, using the 2 seconds before and after the\nfinal choice to avoid, we show how effect strength changes when using different thresholds for\ntheta coherence. Overall, the strength of the effect increases as we restrict the number of pairs of\nelectrodes to those with the highest levels of theta coherence.\nLegend: More stringent thresholds result in stronger estimated effects of time on theta\ncoherence in both the approach (top) and avoidance (bottom) periods. The Y-axis is the beta\nestimate of the effect of time on theta coherence. The X-axis is the threshold group: ‘All\nPairs’ shows the estimated effect without thresholding, ‘100ms’ shows the estimated effect\nwith a 100ms threshold (corresponding to the original result), ‘100ms (App.)’ shows the\nestimated effect when thresholding by 100ms in the approach window only, and ‘500ms’ and\n‘1000ms’ show the estimated effect when thresholding by 500ms and1000ms, respectively.\nError bars represent the 95% confidence intervals around the effect.\nTheta oscillations in the OFC and ACC are driven by theta in the MFG and Amygdala: In our\noriginal analysis, we found that theta oscillations in the OFC and ACC were more likely to be\ndriven by oscillations in the MFG and Amygdala than to drive them. We assessed this using two\ncomplementary approaches: pairwise Granger causality and pairwise cross-correlation analyses.\nBoth analyses only included electrode pairs that initially showed at least 100ms of elevated theta\ncoherence during either the approach or avoidance period.\nHere, we show how P+ values change when applying different thresholds for the minimum\nduration of elevated theta coherence. P+ is the posterior probability that a model parameter is\ngreater than zero. For example, in our original analysis, the MFG→ACC P+ value was 0.994,\nmeaning that—given our data—there is a 99.4% probability that the MFG leads the ACC, and a\n0.1% probability of the reverse. We interpreted results only when P+ exceeded 0.95 in both the\nGranger and cross-correlation analyses. Regions meeting this criterion are highlighted in grey in\nthe table below.\nOverall, the direction and statistical strength of P+ remained stable across threshold choices. The\none exception was the Amygdala→ACC effect, which no longer exceeded the P+ > 0.95\ncriterion when requiring ≥ 500 ms or ≥ 1000 ms of elevated theta coherence, though the effect\nstill trended in the expected direction.\nProbability Positive Values by Threshold Group\n100ms\nRegion Pair (Original) 100ms (App. Only) 500ms 1000ms\nMFG -> ACC 0.994 0.993 0.991 0.988\nMFG -> OFC 0.993 0.995 0.994 0.994\nAmyg -> OFC 0.975 0.968 0.974 0.974\nAmyg -> ACC 0.972 0.974 0.947 0.943\nHC -> OFC 0.937 0.955 0.927 0.896\nHC -> ACC 0.706 0.722 0.55 0.548\nAmyg -> MFG 0.628 0.646 0.688 0.776\nHC -> MFG 0.609 0.538 0.666 0.689\nOFC -> ACC 0.412 0.321 0.341 0.323\nHC -> Amyg 0.079 0.072 0.076 0.076\nLegend: Probability Positive values, which describe the posterior distribution, for models\nestimating the directionality between region pairs, based on the Granger Causality analysis. The\nthreshold group is defined in the following manner: ‘100ms’ includes electrode pairs with 100ms\nof elevated theta coherence (corresponding to the original result), ‘100ms (App.)’ includes\nelectrode pairs with 100ms of elevated coherence in the approach window only, and ‘500ms’ and\n‘1000ms’ include pairs with 500ms and1000ms of elevated theta coherence, respectively. Grey\nshading indicates the Probability Positive values were greater than 0.95 in both the original\nthresholding and in the cross-correlation analysis.\nTheta/HFA Synchrony correlate with approach times: In our original analysis we found that\nsynchrony between many region pairs correlated with approach times. This analysis only\nincluded electrode pairs that initially showed 100ms of elevated theta coherence across either the\napproach or avoidance period. Here, we show how the strength of the effect of approach times on\nsynchrony changes when using different thresholds for theta coherence. Overall, for the theta\nsynchrony results, the strength of the effect increases as we restrict the number of pairs of\nelectrodes to those with the highest levels of theta coherence and are similar across thresholds for\nthe HFA synchrony results.\nLegend: Effect of different thresholds on estimated correlation between approach times and\ntheta synchrony (top) and HFA synchrony (bottom). The Y-axis is the beta estimate of the\ncorrelation between of approach time and synchrony. The X-axis is the threshold group: ‘All\nPairs’ shows the estimated effect without thresholding, ‘100ms’ shows the estimated effect\nwith a 100ms threshold (corresponding to the original result), ‘100ms (App.)’ shows the\nestimated effect when thresholding by 100ms in the approach window only, and ‘500ms’ and\n‘1000ms’ show the estimated effect when thresholding by 500ms and1000ms, respectively.\nError bars represent the 95% confidence intervals around the effect. Stars indicate if the\nregion pair was significant in the original analysis.\nR3C7: Regarding the Bayesian model on theta coherence (line 224): I understand that for each\nregion one coherence value was extracted per subject and predicted by the coherence from the\nremaining four regions. Please provide information whether this was the average coherence\nover the whole-time window (-1500ms to +1500ms) or something else.\nResponse: We thank the reviewer for asking for more analysis details. First, we have clarified in\nthe Results on lines 281-285 that the Bayesian model was predicting the average coherence value\nacross the approach window, specifically including the timepoints 1500ms before the avoidance\ndecision. Second, as discussed above, we have included an Analysis Guide (Table 1) in the\nMethods to help clarify details like this for each of the models in the manuscript.\nHere is the relevant change of the updated results:\n“We then tested if the consistent limbic theta power elevations were indicative of\nelevated theta coherence between limbic regions, as compared to the MFG. To test\nthis, we ran a Bayesian mixed effects model within each region to predict the average\ntheta coherence during the approach window (1500ms preceding the avoidance\ndecision), using a four-level factor representing the remaining regions as the predictor.”\nHere is the relevant section of the Analysis Guide (Table 1):\nNumber of Number of\nDependent Fixed Random Number of Exclusion Figures/\nFinding Estimated Observatio\nVariable Effect Effect Levels Criteria Tables\nModels ns\nAverage\ntheta\ncoherence Figure 3 c;\nPrefrontal\nduring the Supplement\nand\napproach 4-level conflict-free al Figures 5\nsubcortical\nperiod factor, subject/ first trials; trials & 6;\nregions form 5, one for 15-20/115-\n(1500ms specifying electrode of 1923 - 4097 where 0 Supplemental\nsubnetworks each region 355\npreceding partner pair dots were Tables 3 &4;\nwithin a\nthe choice region collected Extended\nwider theta\nto avoid) for Data Table\ncircuit\na given 1\nregion,\nlogged\nR3C8: Line 279: I assume this sentence refers to the theta power in the MFG? This is, however,\nnot explicitly mentioned and not immediately clear.\nResponse: We thank the reviewer for identifying this potential point of confusion. On line 279\nof the initial submission, we explain that the increase in theta coherence is unlikely to be driven\nby increases in theta power during the approach window because theta power in not increasing\nin the limbic regions during the approach window. However, we are not referring to theta power\nin the MFG, but theta power in the limbic regions, which we have clarified below.\nWhile theta power is elevated in\nthe approach window compared\nto the avoidance window in the\nlimbic regions, theta power in\nthese regions begins as either\nstable or beginning to drop\nbefore patients make the\ndecision to avoid. This can be\nseen in Figure 2d, which we\nhave copied here.\nChanges in power, especially\nincreases in power, can\nsometimes artificially inflate\nLegend: Time-course of theta power (left subplot) and high-frequency coherence estimates. By using\nactivity (HFA, right-subplot) across approach and avoidance windows.\nmultiple metrics, such as\nPower data was extracted from the TFR and z-scored using the\nimaginary coherence, and\naverage power in the ITI. Color indicates region, shading is the\ncreating a permuted distribution\nstandard error of the mean power across patients.\nto test for elevated coherence,\nwe reduce the likelihood of finding spurious increases in coherence that are driven by changes in\npower. Our additional point is that the increases in coherence are unlikely to be driven by\nincrease in power, because theta power, while elevated, does not continue to increase over the\napproach window.\nHere is the relevant change one lines 347-355 we have made to the results to clarify this:\n“We found that average connectivity values increased during the time the patient\napproaches (Estimate: 0.09, 95% CI [0.05-0.14], P+ > 0.99, BF > 9, ESS = 7680.4,\n10 bulk\nRatio of Sig. Electrodes = 1782/2816; See Figure 3d-f, Extended Data Table 1).\nP+ > 0.95\nWhile local theta power increases might potentially inflate connectivity estimates, this\nis unlikely the case because theta power – even though initially elevated – drops or\nremains constant at the end of the approach period, corresponding to when coherence\nacross the network is highest (See Figure 2d). We confirmed this result using two\nother connectivity metrics (Pairwise Phase Consistency, Phase Lag Index) and across\nfour different time windows ranging from 1.5-2 seconds (Supplementary Table 5).”\nR3C9: For the net Granger score, 200 permutations of the trial labels were used whereas for the\ntheta coherence 1000 permutations were used. Could the authors clarify the rationale for this\ndiscrepancy in the number of permutations? Furthermore, was the permutation procedure\napplied separately for each electrode pair, or was a common null distribution computed against\nwhich all net Granger values were compared?\nResponse: We thank the reviewer for identifying this concern. While some papers do report\nusing 200 permutations for similar analyses, we have increased the number of permutations to\n1000 and it has not changed the results.\nThe permutation procedure was applied individually for every electrode pair as we only wanted\nto include pairs with elevated theta coherence. This resulted in lengthy computation times,\ncontributing to the original decision to have 200 permutations.\nTotal Electrode Total Sig. Electrode First Region First Region Leads\nRegion Pair Prob Pos.\nPairs Pairs Leads (Count) (Percent)\nMFG - > ACC 0.99 397 260 157 0.6\nMFG - > OFC 0.99 552 313 218 0.7\nAmyg - > OFC 0.98 321 168 104 0.62\nAmyg - > ACC 0.97 65 29 27 0.93\nWe have changed the description of the analysis accordingly on lines 381-383:\n‘This final net Granger score was compared to a permuted distribution where trial\nlabels from one electrode were shuffled 1000 times.’\nR3C10: Pairwise synchrony during approach: I understand that the correlation between\nenvelopes of the theta signal of two electrodes was used as connectivity marker. However, the\nrest of the paragraph lacks sufficient detail. It is not mentioned that values were z-scored, which\nis only evident when looking at figure 4c. Furthermore, the authors mention Bayesian\nmixed model (I assume with time as predictor) but show no test statistics, only that “… coordinated\ntheta activity within numerous region pairs in this circuit correlated with approach times.\" Can\nthe authors please provide a more detailed\ndescription of the analysis and results?\nResponse: We have taken three steps to make both the analysis and results more clear. First, as\nis mentioned above, we added an Analysis Guide to the beginning of the Methods with the\nimportant details for each of the main analyses. Second, we include a summary of the model\noutput in the Extended Data Table. Third, we have modified the text as follows:\n• On lines 421-424: “We tested this using Bayesian mixed effects models with\nrandom effects of patient and electrode in the set of electrode pairs that showed\nsignificantly high theta coherence. Times were log-transformed and scaled and\nsynchrony scaled before estimating the model (see Methods and Table 1 for\ndetails).”\n• On lines 436-439: “We used the same high theta coherence electrodes and\ncalculated the Pearson correlation of the high-frequency activity during\napproach between the electrodes. Times were also log-transformed and scaled\nand synchrony scaled before estimating the model (see Methods and Table 1 for\ndetails).”\nR3C11: Line 348: Figure 4a-b shows the result from the Granger causality analysis, not the\ncorrelation with approach times.\nResponse: We thank the reviewer for noting this and we have fixed this.\nR3C12: Colors for Amygdala and MFG in figures makes it hard to differentiate between the two.\nResponse: We agree that the similar colors between the Amygdala and MFG were confusing,\nand we have now updated every figure so that the MFG is easier to disambiguate from the other\nregions.\nR3C13: Given the different sampling rates between the two hospitals: Were the iEEG data down\nsampled to a common sampling rate?\nResponse: Thank you for identifying a missing component from our methods. Yes, the iEEG\nwere downsampled to a common sampling rate, though this was done after we calculated power\nwithin each frequency band. We have edited the text one lines 722-744 as follows:\n“Power estimates: Theta-band and high-frequency power estimates were extracted\nfrom the TFR for all statistical analyses. Power was log-transformed and each specific\nfrequency was z-scored using an ITI baseline. Each frequency between 3-8Hz was\nthen averaged, resulting in a theta-band power estimate. For the high-frequency\nactivity estimate, we followed a similar approach but averaged the individual\nfrequencies in 20Hz overlapping sub-bands (e.g., 70-90Hz, 80-100Hz, … 130-150Hz)\nbefore averaging the power in each sub-band for a final estimate of high-frequency\nactivity. We used a sub-band approach to minimize the effect of 1/f power scaling,\nwhich biases the power estimates to the lower frequencies in a large band, such as\nestimated from 70-150Hz. As the two hospitals had different sampling rates (2000Hz\nand 512Hz) the data were downsampled to common rate (20Hz) after power was\nestimated.\nChristian Wienke & Marcus Grüschow\nEditor Comment: Please note that we require that the following recommendations from the\nguidelines are followed:\n1. If the research findings apply to only one sex or gender, that must be indicated in the\ntitle and/or abstract.\nResponse: The findings do not only apply to one sex or gender.\n2.\na. For studies involving vertebrates animal and cell lines- The Reporting Summary\nshould include whether sex was considered in the study design.\nResponse: Not applicable.\nb. For studies involving human research participants- The Reporting Summary should\ninclude whether sex and/or gender was considered in the study design and whether\nsex and/or gender of participants was determined based on self-report or assigned\n(and methodology used).\nResponse: The Reporting Summary includes the following explanation of how sex\nand/or gender was considered in the study design:\n“Reporting on sex and gender: Sex (n=20, 10 female for intracranial patients;\nn=191, 93 female, 3 did not report for Online behavioral participants) was self-\nreported and not considered in the study design due to opportunistic sampling\nstrategy. Sex- and gender-based analyses were not performed due to the lack of\nstatistical power in the intracranial sample. A table with patient demographics,\nincluding sex, is included in the supplement.\nRecruitment: Participants in the iEEG study were recruited from all patients\nundergoing intracranial monitoring for clinical treatment of epilepsy during the\nduration of the study that met the following inclusion criteria of being over 18 years\nold and spoke fluent English, meaning these results may not generalize beyond these\npopulations. Only participants with electrode coverage in at least two of the\nfollowing regions were included due to prior anatomical hypotheses: amygdala,\nhippocampus, orbitofrontal cortex, anterior cingulate cortex, and middle frontal\ngyrus. Participants were informed about the possibility to participate in basic\nresearch before undergoing implantation of stereotactic EEG electrodes for the\nlocalization of seizure foci. Informed consent was obtained from those still interested\nin participating after implantation of the electrodes. We additionally collected 191\nparticipants, balanced for from the online recruitment platform, Prolific, using the\n‘Representative sample’ option, which balances the sample with regard to\ndemographics like sex, based on census data from the US.”\n3. Data should be reported disaggregated for sex and gender where this information has\nbeen collected and consent has been obtained for reporting and sharing individual-level\ndata; disaggregated numbers for individual experiments must be provided in the source\ndata as appropriate whereas overall numbers may be provided in the Nature Portfolio\nReporting Summary.\nResponse: We now include a supplemental table with demographic information, including\nsex and age for each intracranial participant. This table will also uploaded along with the\nsource data to Zenodo. Overall numbers are reported in the manuscript and in the Nature\nPortfolio Reporting Summary.\n4. In addition, please note that if sex- and gender-based analyses have been\nperformed a priori, results should be reported regardless of positive or negative\noutcome. We discourage conducting post hoc sex- and gender-based analysis\nif the study design is insufficient (for example, low sample size) to enable\nmeaningful conclusions.\nResponse: We do report one null report in the online behavioral sample that did not find\nevidence that risk/reward tradeoffs differed based on the self-reported sex of the online\nparticipants. These results are described on lines 184-187 of the manuscript:\n“We did not find evidence that the self-reported sex of the participant altered\nhow close participants were willing to get to the ghost (Estimate : -0.04,\nmale>female\n95% CI [-0.16-0.08], ESS = 1052), nor had any interaction with reward size\nbulk\n(Estimate : 0.03, 95% CI [-0.02-0.18], ESS = 15356).”\nSmallReward:Male bulk\nSex- and gender-based analyses were not conducted in the intracranial sample, as the there\nwas not a sufficient sample size to enable meaningful conclusions.\n5. If no sex- and gender-based analyses have been performed, please indicate\nthe reasons for the lack of these analyses in the Reporting Summary.\nResponse: Not applicable."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The changes address all reviewer comments and have improved the overall clarity and flow of the paper. The expanded\ndiscussion of anxiety, the clearer methodological structure, and the updated figures make the study easier to follow and\nmore compelling. We also appreciate the addition of the analysis guide and the clearer explanation of the connectivity\nresults. It has been a pleasure to see the manuscript evolve through the review process, and we believe the final version will\nmake a valuable contribution to the field and encourage further research on the neural mechanisms of approach–avoidance\nbehavior in humans.\nWith best regards,\nPablo Billeke", "reviewer_2": "Authors have satisfactorily addressed my and other reviewers' concerns raised with detailed response to each concern. I do\nnot have any additional concerns", "reviewer_3": "All our concerns have been fully addressed. We commend the authors for their efforts and an impressive paper.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}]

Decision letter