Abstract
Adapting to the ever-changing demands of the environment requires a complex interplay between cognitive-affective, neuronal, and autonomic processes. Vagally-mediated heart rate variability (vmHRV) is positively associated with both cognitive-affective functioning and prefrontal cortex (PFC) activity. Accordingly, the Neurovisceral Integration Model has posited a shared role of the PFC in the regulation of cognitive-affective processes and autonomic nervous system (ANS) activity. While there are numerous correlational findings in this regard, no study so far has investigated whether the manipulation of PFC activity induces changes in vmHRV and cognitive-affective processing in an inter-dependent manner. In this study, we examined the effects of continuous (cTBS) and intermittent theta-burst stimulation (iTBS) over the left dorsolateral PFC (dlPFC) on vmHRV and cognitive-affective processing within an emotional stop-signal task (ESST) in 66 participants. Our results revealed that both resting vmHRV and reactivity, at least partly, predicted cognitive-affective processing. Furthermore, we found a dampening effect of cTBS on resting vmHRV, as well as an enhancing effect of iTBS on ESST performance. Our results show no direct association between vmHRV changes and ESST performance alterations following stimulation. We interpret our results in the light of a hierarchical model of neurovisceral integration, suggesting a dynamical situation-dependent recruitment of higher-order cortical areas like the dlPFC in the regulation of the ANS. In conclusion, our results highlight the complex interplay between PFC activity, autonomic regulation, and cognitive-affective processing, emphasizing the need for further research to understand the causal dynamics of the underlying neural mechanisms.
Introduction
To successfully adapt to the ever-changing demands of the environment, individuals need to process environmental stimuli, both cognitively and affectively. It has been suggested that the same neuronal circuits and mechanisms involved in cognitive-affective processing contribute to the regulation of the autonomic nervous system (ANS), thus allowing an efficient and coordinated adaption on different levels (Smith et al., 2017; Thayer et al., 2009). Consistent with these assumptions, vagally-mediated heart rate variability (vmHRV), a measure of vagal regulation of the heart or so called cardiac vagal activity, has been found to correlate with cognitive and affective functioning (Appelhans & Luecken, 2006; Balzarotti et al., 2017; Magnon et al., 2022). Furthermore, higher vmHRV has been associated with higher activity of the prefrontal cortex (PFC) which displays a key node involved in cognitive, attentional, and affective processes (Thayer et al., 2012). Although these results suggest a shared prefrontal regulation of cognitive, affective, and autonomic functions, they do not allow causal inferences about potential common underlying mechanisms given their correlative nature. In this study, we used repetitive transcranial magnetic stimulation (rTMS) to clarify whether common neuronal processes within the PFC directly contribute to cognitive-affective and autonomic processing alike.
The PFC is well known for its contribution to cognitive and affective control (see e.g., Etkin et al., 2011; Miller & Cohen, 2001; Ochsner & Gross, 2005). Besides that, it has been suspected to be integrated into a neuronal network consisting of various cortical and subcortical structures regulating the ANS, termed the central autonomic network (CAN, Benarroch, 1993). The Neurovisceral Integration Modelsuggests that activity in different areas of the PFC regulates the heartbeat via GABAergic inhibitory projections to subcortical cardioacceleratory circuits of the CAN which are eventually transmitted to the heart via the vagus nerve (Thayer & Lane, 2000). Accordingly, these inhibitory projections are also involved in processes such as cognitive control and emotion regulation, leading to cognitive-affective and autonomic regulation being closely linked in adaptation to environmental demands. Supporting this suggested link, higher levels of vmHRV have been widely demonstrated to be associated with better cognitive functioning, emotion regulation and mental health (Appelhans & Luecken, 2006; Holzman & Bridgett, 2017; Magnon et al., 2022. Further support is provided by results from brain imaging studies in which higher vmHRV levels have been related to greater activity in various prefrontal areas such as the dorsolateral PFC (dlPFC) and ventromedial PFC (vmPFC) (McIntosh et al., 2020; Thayer et al., 2012). While activity in the vmPFC has been found to be positively correlated with vmHRV across situations, activity in the dlPFC appears to be correlated with vmHRV only in certain circumstances, such as when high cognitive or affective control is required (Lane et al., 2009). While these results suggest a close relationship between vmHRV and prefrontal activity, as well as associated cognitive and affective processes, they do not allow any inferences regarding causality or direction of effects given their correlational nature. Thus, it remains to be clarified whether the link between prefrontal activity and vmHRV reflects top-down regulation of both cardiac and cognitive-affective outcomes or bottom-up processing of cardiac signals which in turn facilitates cognitive-affective processes, or even both.
Repetitive TMS has proven to be a powerful non-invasive tool for establishing causal relationships between activity in specific brain regions and behavioral and physiological outcomes by inducing long lasting changes in the stimulated cortical area and areas with functional connectivity. Theta burst stimulation (TBS) is a relatively new rTMS protocol that produces long-lasting aftereffects while having a short duration of implementation. Whereas continuous TBS (cTBS) over the primary motor cortex has been shown to suppress motor evoked potentials, intermittent TBS (iTBS) enhances motor evoked potentials (Huang et al., 2005). Thus, cTBS is assumed to inhibit the targeted cortical area by producing long-term depression-like effects, whereas iTBS is thought to produce long-term potentiation-like effects (Kirkovski et al., 2023). Previous findings suggest that the application of TBS over prefrontal areas, particularly over the left dlPFC, affects both cognitive and affective processing, with iTBS having enhancing effects (Pabst et al., 2022, Dumitru et al., 2020, Moulier et al., 2021) and cTBS having disrupting effects (see Ngetich et al., 2020, e.g., Keuper, Dellert, Junghoefer et al., 2019, Lowe, Staines, Mannochio et al., 2018, Maier, Rosenbaum, Haeussinger et al., 2018, Perach-Barzilay, Tauber et al., 2013, Rosemann, Dellert, Junghoefer et al., 2019). In addition, there is recent meta-analytic evidence that rTMS targeting the left dlPFC exerts modulatory effects on CVC (Makovac et al., 2017; Schmaußer et al., 2022). In specific, inhibition of the left dlPFC by cTBS has been found to decrease vmHRV (Era et al., 2021) whereas iTBS increases levels of vmHRV (Iseger et al., 2020). While these results demonstrate that stimulation of the left dlPFC influences cognitive and affective processing as well as vmHRV, it remains unclear whether the induced changes are directly related to each other, as posited by theNeurovisceral Integration Model .
As a result, this study was designed to investigate whether cTBS and iTBS applied to the left dlPFC will induce alterations in vmHRV during both resting state and cognitive-affective processing. Furthermore, the study aimed to examine TBS-induced changes in cognitive-affective processing and explore whether these changes are directly related to vmHRV alterations. Cognitive-affective processing was operationalized as the performance in an emotional Stop-Signal Task (ESST). Recently, we found that resting vmHRV , vmHRV reactivity, and the interaction between both measures significantly predicted stop-signal reaction times (SSRTs), a measure of response inhibition, in an ESST (Schmaußer & Laborde, 2023). More specifically, our results indicated higher levels of resting vmHRV to be associated with faster SSRTs, whereas on-task decreases in vmHRV predicted slower SSRTs. Interestingly, these effects of vmHRV reactivity were more pronounced in individuals with low levels of resting vmHRV, suggesting that high levels of vmHRV protect from adverse effects of vagal withdrawal on cognitive-affective processing.
In this study, we aim to replicate these findings and extend them by investigating whether TBS induced changes in vmHRV will be related to changes in cognitive-affective processing. Consequently, we hypothesized that individuals with higher resting vmHRV levels will produce faster SSRTs than individuals with low resting vmHRV levels. In addition, we expected that vagal withdrawal during performance of the ESST would predict slower SSRTs, with this effect being more pronounced in participants with low resting vmHRV levels. As we have outlined above, cTBS and iTBS have been found to exert differential effects on both cognition and vagal activity. Accordingly, we further hypothesized that the excitatory effect of iTBS over the left dlPFC would lead to both improved cognitive-affective processing (i.e., faster SSRTs) and increased levels of vmHRV during rest and during the performance of the ESST. Opposite effects were expected for cTBS given its proposed inhibitory effects. Lastly, based on the assumptions of theNeurovisceral Integration Model proposing a shared role of the PFC in the regulation of cognitive-affective as well as autonomic processing, we hypothesized that TMS-induced changes in vmHRV and SSRT would be directly linked to each other. In specific, we expected increases in resting vmHRV to predict fast SSRTs in the ESST and vice versa.
Methods
Participants
A required sample size of 57 was determined using G*Power . To account for the potential loss of ECG data and the possibility of a smaller TBS effect reported in the published research literature, a total of 78 participants was recruited in this study. According to the TMS safety guidelines (Rossi et al., 2021) and HRV study recommendations (Laborde et al., 2017), exclusion criteria included a history or current diagnosis of a psychiatric, neurologic or cardiovascular disorder, family history of epilepsy or hearing loss, use of psychopharmacological or cardioactive medication, pregnancy, inner ear prosthesis, recent neurosurgical procedures, pacemaker or other electronic implants, metal objects or magnetic object in the brain or around the head (only removable earrings and piercing were allowed), and skin disorders at the level of the head. Further, participants were asked to follow a normal sleep routine and to have no intense physical training the day before the experiment, to have no meal or any caffeinated drinks 2 hours before the experiment and to abstain from alcohol 24 hours before the experiment. 12 participants were excluded following the stop signal task quality control (see section 2.5.1). Two more participants were excluded after outlier detection (see section 2.5.1). The exclusion of 12 participants resulted in a final sample of 66 participants.
Experimental design and procedure
Upon arrival, all participants provided written informed consent. The local ethics committee approved the study.
At the beginning of the session, the participants were attached to ECG electrodes and completed sociodemographic questionnaires. Afterwards, participants were asked to sit and rest quietly in the sound-isolated experimental room for 10 minutes to habituate to the situation and to collect their resting baseline HRV. Participants then performed the ESST. Each participant received standardized written instructions. The investigator was available to answer any remaining questions. To become familiar with the ESST, each participant performed a practice run including 5 stop trials. After completion of the first ESST, the respective TMS protocol was applied, including determination of the resting motor threshold (RMT), determination of the left dlPFC by neuronavigation, and the specific stimulation protocol (iTBS vs. cTBS. vs. sham). Participants were assigned to the specific stimulation protocol in a randomized manner. Specifically, participants were assigned to the protocols by using an excel table, where the order of the respective protocols to apply were shuffled by a randomization algorithm. The TMS protocol was followed by a 10-minute resting period and the completion of the second ESST. Finally, the participants were debriefed about the purpose of the study and received 20 euros for their participation. See Figure 1 for an overview of the experimental procedure.
ECG recordings and analyses
VmHRV was indexed using the root mean square of successive differences (RMSSD). VmHRV was measured via electrocardiography (ECG; Faros 180°, Bittium, Kuopio, Finland), at a sampling rate of 500 Hz. Two disposable pregelled ECG electrodes (Ambu L-00-S/25, Ambu GmbH, Bad Nauheim, Germany) were used. The negative electrode was placed on the right infraclavicular fossa (just below the right clavicle). The positive electrode was placed on the left side of the chest, below the pectoral muscle in the left anterior axillary line. To extract RMSSD, we usedEDFbrowser software (van Beelen, 2019) and the RHRVpackage in R, software version 4.0.2 (R Core Team, 2020). To remove artifacts in the ECG signals, the default settings of theFilterNIHR() function of RHRV were used. Subsequently, the ECG signals were visually inspected, and remaining abnormal beats were manually removed. The ECG signal cleared of artifacts was interpolated using the InterpolateNIHR() function from the RHRVpackage. To determine vmHRV during rest periods, RMSSD of the last 5 min of the 10-min habituation period and the 10-min post-TMS rest period was used. For the vmHRV reactivity scores, RMSSD was taken from the course of the respective ESST, and this value was subtracted from the baseline RMSSD. To determine the average RMSSD throughout the ESST, the RMSSD recording of the entire ESST was divided into five 5-minute periods, for each of which an RMSSD value was calculated. The average of these five RMSSD values was then calculated. A positive reactivity score indicates a decrease in vmHRV, whereas a negative reactivity score indicates an increase in vmHRV.
Theta Burst Stimulation
Both continuous and intermittent theta burst stimulation (TBS) were administered using a D70 Alpha figure-of-8 coil connected to a Magstim2 stimulator (Magstim Company Limited, Minneapolis, USA). For sham stimulation, a D70 Alpha figure-of-8 sham coil was used, which mimics the acoustic and somatosensory effects of active TBS. The Visor 2.0 Neuronavigation system (ANT Neuro, Enschede, The Netherlands) was employed to identify the left dorsolateral prefrontal cortex (dlPFC) and the corresponding scalp site where the coil was positioned during MRI-guided rTMS. Instead of utilizing individual MRI scans, a template MNI-152 scan was transformed to match the participant’s head for neuronavigated rTMS. Previous research has demonstrated that this approach accurately determines the location of the left dlPFC with minimal deviation compared to the use of individual scans (Caulfield et al., 2022). The coordinates reported in Rusjan et al. (Rusjan et al., 2010) were employed to determine the position of the left dlPFC.
In the cTBS protocol, a theta burst stimulation pattern consisting of 3 pulses at 50 Hz every 200ms was administered continuously within a 40-second train, resulting in a total of 600 pulses. In the intermittent iTBS protocol, the same stimulation pattern was delivered for 2 seconds every 10 seconds for a total of 600 pulses (Huang et al., 2005). Both cTBS and iTBS were administered at an intensity of 80% of each participant’s resting motor threshold (RMT). RMT was determined prior to the respective stimulation protocol and defined as the minimum intensity of TMS output required to elicit a visible motor response in the right abductor brevis muscle in 5 out of 10 consecutive attempts.
Emotional Stop Signal Task
The ESST is a modified version of the traditional stop-signal task, initially developed by Logan and Cowan (1984) to examine response inhibition by estimating so-called stop-signal reaction times (SSRT). In the ESST, participants are asked to categorize via keypress the valence of serially presented stimuli from the International Affective Picture System (IAPS, Lang et al., 2008), randomized by valence within-block, as either ‘pleasant/positive’ or ‘unpleasant/negative’ as quickly and accurately as possible (i.e., go response). Given that in our experimental design, the ESST had to be performed before and after stimulation, two equivalent versions of the ESST were designed, each containing 64 images (32 per IAPS category, i.e., negative, positive) from the IAPS. In both versions, negative and positive pictures were matched regarding arousal and intensity. Furthermore, to reach equivalence between both ESST versions, we aimed to use thematic similar images with comparable arousal and intensity levels in both versions (see supplementary files). Both versions consisted of 4 blocks with 110 trials each leading to a total of 440 trials. On a minority of the trials, a stop signal (i.e., auditory beep tone) was presented after a variable delay, instructing the participants to suppress the ongoing go response. As recommended , the task was designed so that approximately 25% of the trials (120 out of 440 trials) were stop trials, i.e., contained an auditory stop-signal. In our version of the ESST, the temporal delay of the stop signal was continuously adjusted in 50ms steps based on individual performance using a single staircase tracking algorithm. This adaptive component is essential to estimate SSRTs using the integration method (see Verbruggen et al., 2019), which requires the accuracy or the total commission errors (false alarms) to remain around 50%. The order in which the two versions of the ESST were presented to the single participants was randomized.
Data analysis
Data cleaning
As mentioned above, a total of 12 participants were excluded prior to the final data analysis. Nine participants were excluded due to poor or non-compliant performance on the ESST, following current recommendations (Verbruggen et al., 2019). Of these: (a) One participant was excluded due to a false alarm rate deviating strongly from 50% (i.e., 40%); (b) Seven participants with more than 10% go omissions in either the first or the second ESST or in both, which may lead to biased SSRT estimates given the number of trials used in this study; (c) One participant reported after the experimental procedure to have employed a strategy wherein they awaited the occurrence of a stop signal before executing the respective key presses.
Three additional participants were excluded from data analysis due to extreme values in pre-TMS SSRTs (9.39 ms; z = 4.04), pre-TMS on-task RMSSD (282.14 ms; z = 6.33), and in the change of RMSSD reactivity (74.82 ms; z = 4.11), respectively.
Statistical analysis