Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

Abstract

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Keywords: alpha power; approach-avoidance conflict; cardiac-sympathetics; drift-diffusion model; reward sensitivity; sympathetic nervous system.

Figure 1.

Approach-avoidance and DDM frameworks. A, In the approach-avoidance paradigm participants integrate a reward and a cost in a “take-both-or-leave-both” choice regarding a compound offer. Varying the levels of reward and cost over multiple offers affords a two-dimensional logistic framework that can identify subjective value [p(approach); red–green gradient] and “conflict” (aqua–fuchsia gradient) across the decision space. Conflict is maximal near a “threshold” (dashed line), i.e., as p(approach) nears 0.50. Four example offers are shown (a–d) that vary in subjective value and conflict. B, High conflict (fuchsia) typically makes choices less consistent with lengthier RT. C, The slope of the “threshold” characterizes a sensitivity for reward or cost. Fitting the logistic model separately for each participant accounts for such sensitivities prior to enumerating where in decision space they subjectively experience conflict. D, The DDM assumes choice and RT data can be modeled as a sequential sampling process; following an initial nondecision time (t), the decision process begins at starting point (z) and accumulates evidence at rate (v) toward one of the two boundaries that determines the choice [in our case, approach (+) or avoid (−)]; boundaries are separated by a distance (a). Parameters provide a fine-grained assay of behavior, such as any bias toward one choice (z), how rapidly evidence is integrated during decision formation (v), or the amount of evidence required before a choice is executed (a wider boundary denoting a more conservative criterion). States of high conflict might impact any or all of these parameters. We depict simulated schematics (n = 1,000 trials) of singularly changing the drift rate or the boundary separation. In each, we fixed a set of baseline parameters (t = 0.30; v = 1; a = 2; z = 0.60) and then increased or decreased v or a by 40%. Note that in each panel, there is a bias toward approach (z > 0.50) and identifiably different features in the RT distributions of approach and avoid resulting from the parametric changes. For more in-depth examples, see Ratcliff and McKoon (2008).

Figure 2.

Graded approach-avoidance paradigm reveals fine-grained behavioral responses to conflict. A, Participants approached (accept) or avoided (reject) offers pairing varying levels of monetary reward with varying levels of painful electric shock (communicated via the size of relevant bar) with a single response during the gradual onset of stimuli; see Materials and Methods for success, payout, and error trials. B, Participants integrated reward [rew (b₁, Eq. 1)] and cost [shk (b₂, Eq. 1)] into choices, with a greater weighting of reward (|rew| − |shk| > 0), and a bias toward approach [int (b0, Eq. 1)] indicating reward sensitivity. Error bars are standard error of the mean across parameter estimates for each subject. ***p < 0.001; **p < 0.01. C, Choice consistency (V_choice) was lower, and median RT (med. RT) was longer for states identified (using logistic choice models) as high in conflict. ***p < 0.001. D, In states of high conflict, participants had a wider boundary (a), had a lower rate of evidence accumulation (v), had less of a bias toward approach [starting point (z)], and had a slightly shorter nondecision time (t). Boundary units are arbitrary “evidence,” and the drift rate is in units of “evidence” per second; starting point (z) is on a logit scale where positive values (i.e., >0.50) are closer to approach boundary (see caption for Fig. 1D). Nondecision time (t) is measured in seconds. Digitized violin plots contain 400 samples from parameter posterior. Summary data of posteriors and key comparisons are in Extended Data Table 2-1. Vertical white lines span posterior HDI. *credible Bayesian difference between two parameters (θ1,θ2), i.e., 0∉HDI[D(θ1,θ2)], where D = [p(θ1∣X1) − p(θ2∣X2)].

Figure 3.

Interactive cross-modal collaboration associated with the decision boundary of the DDM. A, Separate flicker rates applied to reward and cost stimuli afforded capture of SS timeseries for reward (SS_rew) and cost (SS_cst). In the “symmetry” timeseries (SS_sym), higher values reflect greater symmetry (more equal power) between the two SS timeseries [−1*ln|(SS_rew − SS_shk|)]. Timeseries were averaged in early [0–1 s] and late [1–2 s] time windows relative to the offer onset. B, Lateralized stimuli afforded capture of alpha power timeseries relevant to reward (alpha_rew) and cost (alpha_cst). In the “symmetry” timeseries (alpha_sym), higher values reflect greater symmetry (more equal power) between the two alpha timeseries [−1*ln|(alpha_rew − alpha_shk|)]. Timeseries were averaged in early [0–1 s] and late [1–2 s] time windows relative to the offer onset. C, Posterior parietal delta and frontal-midline theta power. Timeseries were averaged in early [0–1 s] and late [1–2 s] time windows relative to the offer onset. D, The PEP is recorded with combined ICG and EKG; shorter PEP indicates increased sympathetic beta-adrenergic myocardial contractility. Our contractility estimates, where higher values reflect greater cardiac-sympathetic drive [contractility = −1*ln(PEP)], were averaged across each heartbeat in a [0–2 s] time window relative to the offer onset. E, Singular models for DDM parameters {a,v,z,t} modeled by a single regressor (x₁; i.e., either a neural variable or contractility), separately for states of low and high conflict (Eq. 6). Six models improved fits beyond the baseline model in Figure 2D. Fits assessed relative to the baseline with improvements in DIC (−ΔDIC), positive values reflecting better fit. Double-headed (↔) arrow denotes an association that could be negative or positive. F, Cross-modal models for DDM parameters {a,v,z,t} modeled by either additive or interactive models winnowed from the fits in Figure 3E. Additive models (empty circles) modeled DDM parameters by a neural variable (x₁) in addition to contractility (cont.), separately for states of low and high conflict; 16 regressors in total. Interactive models (circles with crosses) also included a third regressor for the product of the neural signal and contractility (Eqs. 7, 8). Six models improved fits beyond the best-fitting model in Figure 3E. G, Complement models (Eq. 9) asked if the fit of the best-fitting cross–modal model (which included alpha_shk; marked “m1” in Fig. 3F) could be improved by adding the complement (i.e., set difference) of cross-modal models using neural variables that passed the singular model stage, using their best-performing forms (with or without interactions), marked with “m2” in Figure 3F. Each model improved fits beyond the best-fitting model in Figure 3F. In the best overall fitting complement model (marked with *), DDM parameters were modeled by four regressors: alpha_shk, alpha_sym, contractility, and alpha_sym*contractility. H, Proxy of variance explained (R²) by best-fitting baseline, singular, cross-modal, and complement models across varying RT bin sizes. Each trial's RT was simulated using a Wiener-like process with relevant model parameters and regressors, and R² values were derived from Pearson’s correlations between RT bin medians (observed vs simulated).

Figure 4.

Dynamics of the best-fitting complement model. A–D, Parameter posteriors from best-fitting (complement) model of DDM parameters. Most neural and cardiac-sympathetic relations involved the decision boundary (a). In both low- and high-conflict states, wider boundaries were related to greater desynchronization of alpha_shk, greater symmetry in alpha (alpha_sym), and increased contractility. Unique to states of high conflict, the boundary showed additional positive association with the alignment of cross-modal signals [alpha_sym * contractility(cont.)]. Digitized violin plots contain 400 samples from parameter posterior. Summary data of posteriors are in Extended Data Table 4-1. Vertical lines span HDI of coefficient posterior and are white if HDI does not contain 0 (also marked with *), black otherwise. E, Parameter posteriors from a model to discretize the neural and cardiac interactions associated with the decision boundary. Boundary is widest (relative to the baseline, low conflict, low late alpha_sym, and low contractility—Δ boundary) in high conflict when alpha_sym and contractility are both high. The + and – symbols, respectively, reflect high and low (for physiology signals, relative to participant medians). Digitized violin plots contain 400 samples from parameter posterior. Summary data of posteriors and key comparisons are in Extended Data Table 4-2. Vertical lines span the HDI of coefficient posterior and are white if HDI does not contain 0. *** denotes this posterior was credibly larger than all others depicted, i.e., 0∉HDI[D(θ1,θ2)], where D = [p(θ1∣X1) − p(θ2∣X2)] for all possible values of θ2. F, Control models substituted proxy measures for local and global brain activity for all regressors featuring contractility in the best-fitting complement model. The model substituting contractility with GFP was a slightly better fit. However, inspection of the parameters shows opposing associations with the boundary (marked by black arrows). That is, GFP's interaction with alpha_sym was associated with a contraction of the decision boundary. Summary data of posteriors for the GFP control model are in Extended Data Table 4-2. G, Phase-angle timeseries of alpha contralateral to reward (alpha_rewθ, top) and cost (alpha_shkθ, bottom) in high conflict, averaged across subjects separately for trials that were higher (dark red) or lower (light red) than their median contractility. H, Summarizing phase coherence (absolute phase-angle value |θ|) across early and late time windows, we see a three-way interaction, whereby late coherence diminishes significantly in high contractility and only in the alpha timeseries contralateral to reward.