Pathophysiological changes in incentive processing in episodic migraine

doi:10.21203/rs.3.rs-2832002/v1

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Research Article

Pathophysiological changes in incentive processing in episodic migraine

https://doi.org/10.21203/rs.3.rs-2832002/v1

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Background

Multiple lines of research suggests that dysregulation in the dopaminergic system may contribute to migraine pain. However, it is only in recent years that researchers have begun to investigate this by exploring how the system is dysregulated during incentive processing in migraineurs. Still little is known about the pathophysiological changes in incentive processing along the temporal scale in migraineurs. Therefore, the present study examined migraine-related changes in neural processing implicated in incentive anticipation and its delivery.

Methods

A total of 19 episodic migraine (EM) patients (mean age = 31.95 ± 1.42, 17 females) and 19 healthy controls (HCs) (mean age = 30.16 ± 0.98, 16 females) underwent a monetary incentive delay (MID) task, while event-related potentials (ERPs) were recorded in their brains.

Results

Electrophysiologically, during the incentive anticipation phase, both Cue-N2 and Cue-P3 amplitudes were of higher magnitude for the reward-anticipation and punishment-anticipation cues compared to the control cue across both groups. This indicates no significant differences in neural activity supporting incentive/no incentive cue evaluation between groups. During the outcome phase, the amplitude of the FRN, an ERP component related to performance evaluation, was significantly larger for punishing feedback than rewarding feedback across both groups. However, the Feedback-P3 amplitude, an ERP component related to attentional processing of motivational value of outcome feedback, was significantly larger for rewarding feedback than punishing feedback in HCs, but not in EM patients. Moreover, a negative correlation was observed between the Feedback-P3 amplitude difference for rewarding minus punishing feedback and subjective pain intensity measured by the VAS in EM patients. Finally, the amplitude of the Feedback-LPP, an ERP component related to attentional processing of the affective value of outcome feedback, was significantly larger for punishing feedback than rewarding feedback only in HCs, but not in EM patients.

Conclusions

Our findings suggest that pathophysiological changes in incentive processing may act as a core mechanism underlying migraine pathophysiology. This study may also provide sensitive and reliable biomarkers for evaluating the efficacy of migraine therapeutics.

Migraine

Reward

Punishment

Incentive

ERPs

FRN

FB-P3

FB-LPP

MID

Migraine is a common neurological disease that typically causes moderate to severe headaches, and has been recognized to be an important medical issue affecting the quality of life of patients (1, 2). A better understanding of the exact cause of migraine might aid in the establishment of efficient diagnosis and the development of more effective evidence-based treatments (3, 4). Increasing research efforts have been devoted to elucidating the pathogenetic mechanisms behind migraine by investigating the neural characteristics of migraine patients’ brains over the past decades (5–7). In this field, the high temporal resolution of ERP methodology makes it a useful tool for yielding novel insights into migraine pathophysiology by isolating the rapidly unfolding stages of how the migraine brain processes incoming stimuli (8–10). The majority of previous ERP studies have identified hypersensitivities to visual, auditory, somatosensory, and olfactory stimuli in the sensory domain in migraineurs, usually reflected by an increased amplitude of evoked responses (11–14). Furthermore, recent findings extend the scope of our knowledge by showing similar pathophysiological changes underlying migraineurs’ abilities to overcome distraction from irrelevant information and suppress prepotent responses (15–17). This indicates that migraine is associated with abnormal regulation of cortical excitability at the later stage of information processing. Thus, the existing literature strongly suggests that abnormal regulation of cortical excitability can act as a critical element in migraine pathophysiology from the early to the late stage of information processing.

Despite such advances, exploring how the migraine brain processes incentives (e.g. monetary reward and punishment) has been largely ignored in previous research (18). Such research might provide insight by identifying a possible role of dopamine (DA) in migraine pathophysiology (18–21). The relevant literature shows that migraine is associated with the hypoactivation of dopaminergic neurons during the interictal period, resulting in DA receptor hypersensitivity during headache attacks (22–25). Given that DA is an important neuromodulator that exerts widespread effects on both cortical and subcortical regions, where its receptors are widely distributed, a low dopamine tone may have profound effects on dopamine-mediated neurocircuitries in migraineurs. A few resting-state magnetic resonance imaging (MRI) studies provide limited but interesting evidence regarding dysregulation of dopamine-mediated neurocircuitry by showing aberrant functional connectivity within the meso-corticolimbic system in migraineurs relative to HCs in the interictal period (26, 27). Given the central role of this brain circuit in incentive processing (28, 29), the altered functional connectivity within the meso-corticolimbic system raises the possibility that migraine may also be associated with pathological brain processing of incentives. It is only in recent years that researchers have begun conducting empirical investigations to advance our understanding of the above issue. For example, a recent functional magnetic resonance imaging (fMRI) study showed that EM patients, during the interictal period, have blunted neural responses to rewarding (monetary gains) and punishing (monetary losses) feedback during the outcome phase, but not during the anticipation phase (30). However, because fMRI lacks the temporal resolution to distinguish the rapidly unfolding stages within the anticipatory and consummatory phases of incentive processing, no work to date has examined potential migraine-related changes in neural processing during the successive stages of incentive anticipation and delivery.

The present study was designed to address this issue by using a combination of the MID task and ERPs in EM patients in the interictal period. In the MID task, participants were required to react to a target stimulus presented after an incentive cue to obtain a monetary incentive or to avoid losing the indicated incentive, a protocol similar to that described in previous ERP work (31). This task can allow the detailed examination of anticipatory and outcome-related ERP components during incentive processing in EM patients relative to HCs. Based on the current literature (32), two commonly observed ERP components are particularly relevant during the anticipation phase: the Cue-N2 and Cue-P3. The Cue-N2 refers to a negative-going wave that occurs 200–300 ms after cue onset, with its maximum amplitude over the frontocentral scalp region (33, 34). The Cue-P3 is a positive-going wave that peaks from approximately 300 to 600 ms after the Cue-N2, with its maximum amplitude over the centro-parietal scalp region (35, 36). Despite some ongoing debates concerning the functional significance of these two ERP components (32), they are thought to reflect cue evaluation. Meanwhile, outcome-related ERPs can be separated into three different components occurring in different time windows: feedback-related negativity (FRN), Feedback P300 (FB-P3), and feedback late-positive potential (FB-LPP). The FRN is a negative-going wave peaking around 200–300 ms after outcome feedback, over the frontocentral scalp region. Traditionally, the FRN is thought to be sensitive to performance evaluation (e.g., gain vs. loss or good vs. bad) during outcome processing (37). The FB-P3 follows the FRN, a centro-parietal positive-going deflection that occurs 300–600 ms after the outcome feedback. This outcome-related ERP component is argued to involve attention-driven categorization of salient outcome-related information (35, 38). The FB-LPP, a centro-parietal positive-going wave, is the final ERP during outcome processing, and occurs 500–800 ms after the FB-P3. Given its functional difference from the FB-P3 during outcome processing, it has been argued to reflect attentional processing of the affective value of outcome feedback (39, 40). Analysis of these incentive-related ERPs may elucidate pathophysiological changes in incentive anticipation and its delivery in EM patients. Given that recent research has revealed blunted neural responses to rewarding and punishing stimuli, only during the outcome phase, but not during the anticipation phase in EM patients (30), we can expect dysfunctional outcome-related ERP components in EM patients compared to HCs.

Participants

Our participant recruitment procedure was described at length in our recent study (15). The patient group included 19 EM patients (mean age = 31.95 ± 1.42, 17 females). Each patient’s headache diary and structured questionnaires on demographics, headache profile, medical history, and medication use were kept. The headache profile examined migraine history (years), the attack frequency (number per month) and duration (days per month) of headaches, the presence of an aura (yes: no), the severity of migraines, and included the six-item Headache Impact Test (HIT)-6, Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS). The inclusion criteria for patients with EM were: 1) fulfilling the diagnosed criteria for migraine according to the International Classification of Headache Disorders, 3rd edition (ICHD-3) and 2) a history of at least two years of migraine and at least one migraine episode per month. Moreover, EM patients were excluded according to the following criteria: 1) neurological diseases (i.e., epilepsy, cerebral infarction, encephalitis); 2) mental retardation; 3) a current or past history of substance dependence; 4) receiving prophylactic anti-migraine therapy; 5) depressive and anxiety disorders. Five migraineurs reported experiencing aura with their migraine, and the remaining 14 reported no aura. We also employed an age- and sex-matched healthy control group consisting of 19 healthy individuals (mean age = 30.16 ± 0.98, 16 females). None of them reported any personal or family history of psychiatric or neurological disorders, which was confirmed by both a self-reported past history and a psychiatric examination of the present mental state using the DSM-IV criteria of axis I. None of the female participants from either group took any oral contraceptives for at least 1 week prior to involvement in this study. All participants in the two groups were right-handed. Informed consent was obtained from all participants prior to the experiment. The study protocol was approved by the Ethics Committee of the Chinese PLA General Hospital. Demographic and clinical characteristics are presented in Table 1.

Table 1

Demographic and clinical characteristics of the study sample.
	EM (n = 19)	HCs (n = 19)	Group comparison
	(M ± SE)	(M ± SE)	Group comparison
Age, years	31.95 ± 1.42	30.16 ± 0.98	t(36) = 1.04, p > 0.05
Gender (F/M)	(17/2)	(16/3)	p > 0.05
Education, years	15.63 ± 0.71	14.53 ± 0.46	t(36) = 1.31, p > 0.05
BMI [kg/m²]	20.47 ± 0.70	20.44 ± 0.81	t(36) = 0.03, p > 0.05
SAS	44.57 ± 3.06	44.61 ± 2.39	t(36) = 1.75, p > 0.05
SDS	15.63 ± 0.71	14.53 ± 0.46	t(36) = -0.01, p > 0.05
Duration of migraine, days per month	13.37 ± 1.40
History of migraine, years	12.74 ± 1.38
Migraine frequency, times per month	4.39 ± 0.93
Severity of headache (VAS scale)	7.84 ± 0.33
HIT-6	63.84 ± 1.18
VAS, visual analog scale, with 0 indicating no pain and 10 worst possible pain; BMI, body mass index; SAS, self-rating anxiety scale; SDS, self-rating depression scale; HIT-6, the six-item Headache Impact Test; M, mean; SE, the standard error of the mean; EM, episodic migraine; HCs, healthy controls.

Monetary Incentive Delay Task

We used a version of a monetary incentive delay (MID) task, which is similar to that described in previous ERP work (31). The MID task involved both monetary reward and punishment. In each trial, it included an anticipation phase, a target identification task, and an outcome phase (Fig. 1). During the anticipation phase, participants saw one of three cues informing them of the type of upcoming incentive, which was presented for 750 ms. A circle crossed by a horizontal line indicated a potential monetary reward (+¥), while a circle crossed by a vertical line indicated a potential monetary punishment (-¥). In addition, a control cue (a triangle), was also included, signaling that there was no incentive outcome (reward/punishment). After a variable delay period (750–1250 ms), during which a question mark was shown, a white square was presented as the target stimulus for 240–390 ms. Similar to previous ERP work (31), the duration of this target stimulus was adjusted, which results in an approximate hit rate of 55%~65%. Participants were required to respond to the target by pressing a button as quickly as possible, which followed a delay of 800 to 1200 ms. If the participant responded to the target fast enough following a reward-anticipation cue, reward feedback (range: +¥10 to +¥20; mean: +¥15) displayed on a safe was presented. If they were too slow, then the no reward feedback (‘scrambled’ picture) was presented. Similarly, if they did not respond fast enough following a punishment-anticipation cue, punishment feedback (range: -¥10 to -¥20; mean: -¥15) displayed on a safe was presented. However, if they responded fast enough, the no punishment feedback (‘scrambled’ pictures) was presented. In trials with a control cue, participants were presented with ‘scrambled’ pictures (control feedback) except for the extremely fast (“<200 ms”) or slow (“>600 ms”) feedback, which participants were required to avoid. Following the participant's response to the target, after an interval of 800–1200 ms, the feedback was presented for 1000 ms. Each trial was followed by a blank screen for a variable intertrial interval (1000–2500 ms). Participants did the task in three separate blocks. Each block consisted of 100 trials, with a short break between blocks. To familiarize participants with the task, the formal experiment was preceded by a short practice block.

EEG data recording and analysis

The EEG data recording procedure was the same as that described in our recent studies (41, 42). Specifically, we used a quick cap carrying 64 Ag/AgCl electrodes placed at standard locations covering the whole scalp (the extended international 10–20 system) to record EEG (SynAmps amplifier, NeuroScan). The reference electrode was attached to the right mastoid (M2), and the ground electrode was placed on the forehead. The vertical electrooculogram (VEOG) was recorded with electrodes placed above and below the left eye. The horizontal electrooculogram (HEOG) was recorded with electrodes placed beside the two eyes. The impedance was kept below 5 kΩ. The electrophysiological data were continuously recorded with a bandwidth of 0.05–100 Hz and sampled at a rate of 1000 Hz.

Offline EEG data analysis was conducted using EEGLAB (43) and ERPLAB (44). Data were first re-referenced to linked mastoids (M1 and M2). An independent component analysis (ICA)-based artifact correction was achieved by using the ICA function of EEGLAB. Independent components with topographies representing saccades, blinks, and heart rate artifacts were thus removed according to published guidelines (45). The resultant EEG data were then epoched from 200 ms pre-stimulus to 800 ms post-stimulus and digitally low pass filtered by 30 Hz (24 dB/octave). The 200 ms pre-stimulus period was used for baseline correction. In order to remove movement artifacts, epochs were rejected when fluctuations in potential values exceeded ± 75µV on any channels except the EOG channel. In the anticipation phase, cue-locked ERPs were averaged separately for each anticipation cue type (reward vs. punishment vs. control) in each group. In the delivery phase, feedback-locked ERPs were averaged for each outcome feedback type (rewarding feedback vs. punishing feedback vs. outcome feedback) in each group.

Statistical analysis

With regard to statistical analysis of the demographic data, a chi-square test was employed to examine a between-group difference in sex ratio and independent sample t-tests were used to assess between-group differences in age, years of education, SAS scores, SDS scores, and body mass index (BMI). For statistical analysis of the behavioral data, both reactions times (RTs) and accuracy were analyzed using a two-way mixed Analysis of Variance (ANOVA) with group (EM patients vs. HCs) as a between-participant factor and anticipation cue type (reward vs. punishment vs. control) as a within-participant factor.

For statistical analysis of electrophysiological data, our data were analyzed according to the topographical distribution of grand averaged ERP activity as well as previous ERP studies (31–33). In the anticipation phase, our ERP statistical analysis involved two ERP indices of incentive-anticipation cue processing: Cue-N2, and Cue-P3. The mean amplitudes of these two ERP components were calculated at the midline electrodes (Cue-N2: time interval = 250–400 ms, at Fz, FCz, Cz electrodes; Cue-P3: time interval = 400–600 ms, at Cz, CPz, Pz electrodes). To explore the effects of migraine on these incentive cue-related ERP components, we conducted two separate mixed ANOVA, with group as a between-participants factor (EM patients vs. HCs), and anticipation cue type (reward vs. punishment vs. control) and electrodes (Fz, FCz, Cz for Cue-N2; Cz, CPz, Pz for Cue-P3) as within-participant factors. Moreover, in the delivery phase, our ERP statistical analysis involved three ERP indices of outcome feedback processing: feedback-related negativity (FRN), Feedback P300 (FB-P3), and feedback late-positive potential (FB-LPP). Like previous ERP work (31, 32), the mean amplitudes of these three ERP components following outcome feedback were calculated at the midline electrodes (FRN: time interval = 210–310 ms, at Fz, FCz, Cz electrodes; FB-P3: time interval = 280–400 ms, at Cz, CPz, Pz electrodes; FB-LPP: time interval = 550–650 ms, at Cz, CPz, Pz electrodes). To examine the effects of migraine on these outcome-related ERP components, we also conducted three mixed ANOVA, with group as a between-participants factor (EM patients vs. HCs), and outcome feedback type (rewarding feedback vs. punishing feedback vs control feedback) and electrode sites (Fz, FCz, Cz for FRN; Cz, CPz, Pz for FB-P3; Cz, CPz, Pz for FB-LPP) as within-participant factors. Finally, regarding ERP indices of incentive-anticipation cue and incentive feedback processing that show significant between-group differences, we further performed Pearson’s correlations to assess the possible relationships between these ERP indices and clinical measures.

All data were analyzed using R. Statistical comparisons were made at p-values of p < 0.05, with the Greenhouse–Geisser correction when violations of sphericity occurred.

Behavioral results

Regardless of RTs or accuracy, our mixed ANOVA revealed a significant main effect of anticipation cue type (RTs: F(2, 72) = 8.06, p < .001, η2 p = 0.18; accuracy: F(2, 72) = 22.77, p < 0.001, η2 p = 0.39). Post-hoc analysis revealed that RTs and accuracy on both reward and punishment trials were significantly faster and more accurate than those on control trials (all ps < .01) across both groups. However, our analysis did not reveal a significant main effect of group (RTs: F(1, 36) = 0.01, p = .98, η2 p = 0.00; accuracy: F(1, 36) = 0.05, p = .82, η2 p = 0.001) or a significant group × incentive type interaction (RTs: F(2, 72) = 0.03, p = .969; accuracy: F (2, 72) = 0.83, p = .44).

Electrophysiological results

ERP responses to incentive-anticipation cues

Cue-N2

Our mixed ANOVA revealed a significant main effect of anticipation cue type on the Cue-N2 amplitude (F(2, 72) = 8.12, p < .001, η2 p = 0.18). Follow-up post hoc testing revealed that the Cue-N2 amplitude was highly sensitive to anticipation cue type, with increased amplitudes for the reward-anticipation cue (t(36) = 2.93, p < .01, d = 0.44) and punishment-anticipation cue (t(36) = 3.33, p < .01, d = 0.48), compared to the control cue. Moreover, there was also a significant main effect of electrode sites (F(2, 72) = 11.70, p < .001, η2 p = 0.25). The post hoc analysis found a significant difference in Cue-N2 amplitude between Fz and two other electrode sites (FCz and Cz), with increased amplitudes of the Cue-N2 in electrodes FCz (t(36) = 3.68, p < .001, d = 0.35) and Cz (t(36) = 3.64, p < .001, d = 0.64), compared to that in electrode Fz. However, no other significant effects were found (all ps > .05).

Cue-P3

Regarding the Cue-P3, our mixed ANOVA revealed a significant main effect of anticipation cue type on the Cue-P3 amplitude (F(2, 72) = 15.22, p < .001, η2 p = 0.30). The post hoc testing revealed that the Cue-P3 amplitude was also sensitive to anticipation cue type, with its amplitude being larger for the reward-anticipation cue (t(36) = 4.19, p < .001, d = 0.68) and punishment-anticipation cue (t(36) = 4.81, p < .001, d = 0.57) than for the control cue. Moreover, there was also a significant main effect of electrode sites (F(2, 72) = 3.79, p < .05, η2 p = 0.10). Follow-up post hoc testing found a significant difference in Cue-P3 amplitude between CPz and Pz, with its amplitude being smaller in electrode Pz than in electrode CPz (t(36) = -3.37, p < .01, d = -0.33). However, no other significant effects were found (all ps > .05).

ERP responses to incentive feedback

FRN

Concerning the FRN, our mixed ANOVA showed a significant main effect of outcome feedback type on its amplitude (F(2, 72) = 15.64, p < .001, η2 p = 0.30) (Fig. 2). The post hoc testing revealed that both rewarding and punishing feedback elicited a larger amplitude of FRN than the control feedback (all ps < .005). Moreover, punishing feedback evoked a larger amplitude of FRN than rewarding feedback (p < .05). In addition, there was a significant main effect of electrode sites (F(2, 72) = 24.07, p < .001, η2 p = 0.40), with the amplitude of FRN being larger in electrodes FCz (p < .001) and Fz (p < .001) than in electrode Cz. Finally, no other significant effects were found (all ps > .05).

FB-P3

Regarding FB-P3, our mixed ANOVA showed a significant main effect of outcome feedback type on its amplitude (F(2, 72) = 113.79, p < .001, η2 p = 0.76). The post hoc testing revealed that both rewarding and punishing feedback elicited a larger amplitude of the FB-P3 than the control feedback (all ps < .001). Moreover, reward feedback evoked a larger amplitude of the FB-P3 than punishing feedback (p < .05). This effect is further quantified by a significant interaction between group and outcome feedback type (F(2, 72) = 5.63, p < .01, η2 p = 0.14) (Fig. 3). An analysis of simple effects revealed that although both rewarding and punishing feedback elicited a larger amplitude of FB-P3 than the control feedback in both groups (EM patients: all ps < .001; HCs: all ps < .001), rewarding feedback elicited a larger amplitude of FB-P3 than punishing feedback only in HCs (p < .05) but not in EM patients (p = .15) (Fig. 3). In addition, we also found a significant main effect of electrode sites (F(2, 72) = 11.13, p < .001, η2 p = 0.24), with the amplitude of the FB-P3 being larger in electrodes CPz (p < .001) and Pz (p < .005) than in electrode Cz. Finally, no other significant effects were found (all ps > .05).

FB-LPP

With regard to the FB-LPP, there was a significant main effect of outcome feedback type on its amplitude (F(2, 72) = 58.41, p < .001, η2 p = 0.62). Follow-up post hoc testing revealed that both rewarding and punishing feedback elicited a larger amplitude of FB-LPP than the control feedback (all ps < .001). Meanwhile, punishing feedback induced a larger amplitude of FB-LPP than rewarding feedback (p < .05). Moreover, there was a significant interaction between group and outcome feedback type (F(2, 72) = 3.15, p < .05, η2 p = 0.09) (Fig. 3). Our analysis of simple effects revealed that although both rewarding and punishing feedback evoked a larger amplitude of FB-LPP than the control feedback across both groups (EM patients: all ps < .001; HCs: all ps < .001), punishing feedback elicited a larger amplitude of FB-LPP than rewarding feedback only in HCs (p < .01) but not in EM patients (p = .46) (Fig. 3). In addition, there was a significant main effect of electrode sites (F(2, 72) = 67.02, p < .001, η2 p = 0.65), with the amplitude of FB-LPP being larger in electrode Cz than in electrodes CPz (p < .001) and Pz (p < .001), and with the amplitude of FB-LPP being larger in electrode CPz than in electrode Pz (p < .001). Finally, no other significant effects were found (all ps > .05).

Brain-behavior relationships

To examine brain-behavior relationships in EM patients, FB-P3 mean values, FB-LPP mean values, and the difference scores of FB-P3 and FB-LPP amplitudes to rewarding versus punishing feedback were correlated with the SAS score, the SDS score, frequency, duration, days, and Hit-6 and VAS, respectively. Difference scores were computed regarding the FB-P3 and FB-LPP amplitudes because our ERP analyses revealed within-group differences between the rewarding and punishing feedback in EM patients. These correlational analyses revealed only significant negative correlations between the difference scores of FB-P3 amplitudes to rewarding versus punishing feedback and the VAS at Cz (r = -0.54, p < .05) and CPz (r = -0.55, p < .05) electrodes (Fig. 4). No other significant correlational results were found (all ps > .05).

To our knowledge, this study is the first to investigate incentive-related neural activity along the temporal scale in EM patients in the interictal period. Behaviorally, in line with findings in a recent study (30), both groups did not manifest any significant difference in behavioral performance as assessed by accuracy and reaction time on the MID task. At the neural level, in the anticipation phase, both Cue-N2 and Cue-P3 amplitudes were larger for the reward-anticipation and punishment-anticipation cues compared to the control cue. However, these two ERP components implicated in the incentive/no incentive cue evaluation did not differ significantly between EM patients and HCs. This result confirmed previous findings showing that during the interictal period, EM patients did not show blunted neural responses to anticipation of either reward or punishment as was reported by a recent fMRI study (30). In contrast, dysfunctional outcome-related ERP components in EM patients were observed. Although both groups showed a significantly larger FRN to punishing feedback than rewarding feedback, EM patients showed dysfunctional FB-P3 and FB-LPP to incentive feedback. Specifically, despite that both rewarding and punishing feedback elicited significantly larger amplitudes of the FB-P3 than the control feedback across both groups, rewarding feedback elicited a larger FB-P3 amplitude than punishing feedback only in HCs, but not in EM patients. Our correlational analyses further revealed a significant negative correlation between the difference scores of FB-P3 amplitudes to rewarding minus punishing feedback and the VAS in EM patients. Meanwhile, even though both rewarding and punishing feedback evoked larger FB-LPP amplitudes than the control feedback across both groups, punishing feedback elicited a larger FB-LPP amplitude than rewarding feedback only in HCs, but not in EM patients. Given these findings, we will focus on these outcome-related ERP components along the temporal scale of outcome evaluation and discuss possible implications for advancing our understanding of migraine pathophysiology.

The first sub-stage of outcome evaluation can be quantified through the FRN, a negative ERP component at frontocentral sites after outcome presentation (46). Most previous ERP studies have demonstrated that the FRN tends to be larger for positive performance feedback (e.g., gains) than for negative performance feedback (e.g., losses) (32), thus reflecting a binary evaluation of positive versus negative performance feedback (38, 47, 48). By doing so, individuals are able to assess whether their prior behavior was good or bad in meeting their goals of obtaining rewards or avoiding punishment. Although the functional significance of the FRN is still debated, the FRN is often argued to be an indicator of the goodness of outcome feedback during outcome evaluation and, consequently, to be particularly sensitive to performance evaluation (32). Our results concerning the FRN demonstrated that EM patients were still able to differentiate outcome feedback indicating a bad from a good performance, as reflected by the significant main effect of outcome feedback type across the two groups, and the lack of significant interaction between outcome feedback type and group. Meanwhile, according to source localization, the FRN is thought to index activity in the medial prefrontal cortex (mPFC), most probably in the anterior cingulate cortex (ACC) (49). In this case, one may further assume that, compared to HCs, EM patients may not exhibit a functional deficit in negative/positive outcome processing (e.g., losses/gains) in the ACC. Indeed, the major findings of a recent fMRI study support this assumption by showing that EM patients did not display alterations in the ACC activity related to processing gains and losses compared to HCs (30). Here, ERP methodology offers high temporal resolution and thus allows a further step toward studying migraine-related changes over multiple stages during outcome evaluation. Our results concerning the FRN indicate that the neurophysiological responses to the goodness of outcome feedback that occurred in the early stage of outcome evaluation were not disrupted in EM patients.

The ERP component typically used to quantify the second sub-stage of outcome evaluation is the FB-P3, which is a positive ERP component, most pronounced at the centroparietal sites following outcome presentation. Unlike the FRN, the FB-P3 is often argued to be sensitive to the motivationally salient nature of outcome feedback (32, 38). Hence, the FB-P3 should be primarily modulated by reward evaluation such that incentive feedback (reward and punishment) would enhance the FB-P3 relative to the control/neutral feedback (50). Our results of the FB-P3 showed that both rewarding and punishing feedbacks were associated with enhanced FB-P3 positivity relative to the control feedback across both groups. Thus, EM patients were able to discriminate between incentive feedback (reward and punishment) and control feedback in terms of motivational salience. Furthermore, the motivational salience of incentive feedback seems to scale with incentive valence. Despite some controversy (38, 51–53), most ERP studies have consistently reported that positive feedback evokes a more positive FB-P3 than negative feedback (54–57). Given that there is overwhelming evidence supporting a motivational salience account of attentional control (58–60), this effect has been interpreted to reflect attention-driven categorization of motivationally salient outcome-related information (38). According to this interpretation, the enhanced positivity for rewarding feedback compared to punishing feedback reflects increased attention to the motivational salience of rewarding feedback relative to punishing feedback. In the present study, the influence of incentive valence on the amplitude of the FB-P3 in HCs was consistent with previous findings. However, the FB-P3 amplitude for rewarding and punishing feedback did not differ significantly in EM patients. The blunted difference in FB-P3 amplitude indicates a deficit in the discrimination of motivational salience to rewarding and punishing feedback during reward evaluation in EM patients. Moreover, this blunting was apparently associated with the severity of migraine-related symptoms as revealed by the significant negative correlation between FB-P3 amplitude difference for rewarding minus punishing feedback and subjective pain intensity measured by the VAS in these patients. Thus, the larger the blunting of the difference in FB-P3 amplitude between rewarding and punishing feedback, the higher the subjective evaluation of pain intensity by EM patients. Here, the blunting of the difference in FB-P3 amplitude may result from reduced neurophysiological sensitivity to the motivational salience of rewarding feedback, enhanced neurophysiological sensitivity to the motivational salience of punishing feedback, or both. To clarify this issue, we performed further analysis on the significant interaction between outcome feedback type and group. The results showed that the FB-P3 amplitude for rewarding feedback was significantly lower in EM patients than in HCs (t(36) = -2.21, p < .05), while there was no difference for the FB-P3 amplitude in response to punishing feedback between groups (t(36) = -1.92, p > .05) and control feedback (t(36) = 0.14, p > .05) between groups. This clearly reveals that the blunted difference in FB-P3 amplitude between rewarding and punishing feedback was caused by reduced neurophysiological sensitivity to the motivational salience of rewarding feedback. In addition, according to source localization, the FB-P3 is commonly thought to index activity in multiple neural sources principally including the temporoparietal junction (TPJ), anterior insula (AI), and amygdala. Most of these brain areas are key components of the salience network (SN), which plays a crucial role in assigning motivational salience to incoming stimuli (61). Therefore, our results seem to provide additional EEG-based evidence regarding how migraine impacts neural activities in the SN. Within this domain, our recent resting-state EEG study has found a reduction in neural activities in the SN at baseline in migraineurs as captured by the significantly decreased presence of microstate class C (62). This indicates a potential alteration in the processing of motivationally salient information in migraineurs. By taking advantage of task-oriented EEG, our present study has provided direct neural evidence supporting this argument by showing relatively blunted neurophysiological responses to rewarding feedback in EM patients.

Following the FB-P3, the final ERP component during outcome evaluation is the FB-LPP, which is a positive-going centroparietal ERP component that follows outcome presentation (63). Due to the fact that the FB-LPP is not typically studied in the context of incentive processing, there seems to be a very limited understanding of its functional significance and its functional differences from the FB-P3 during outcome evaluation (32). Despite this, given that the FB-LPP displays a similar scalp topography and functionality as the relatively well-studied FB-P3, it is commonly believed that the FB-LPP is an affective counterpart of the FB-P3 (39, 40). Thus, the FB-LPP has been interpreted to reflect extended cognitive and attentional processing of the affective value of the outcome feedback (32). As a consequence, like the FB-P3, the FB-LPP has also been found to be sensitive to reward evaluation (36). Our results concerning the FB-LPP revealed that both rewarding and punishing feedback elicited significantly larger FB-LPP amplitudes than the control feedback across both groups. This suggests that EM patients were still capable of discriminating between incentive feedback (reward and punishment) and control feedback in terms of affective value. Moreover, despite limited evidence, a few ERP studies have further found the influence of incentive valence on the FB-LPP by showing enhanced positivity following negative feedbacks over positive feedbacks (36, 40, 64, 65). In the present study, such a negativity bias was observed in HCs, but not in EM patients. This finding suggests an impairment in the discrimination of the affective value between rewarding and punishing feedback during outcome evaluation in these patients. Similar to the FB-P3, the blunted difference in FB-LPP amplitude may result from reduced neurophysiological sensitivity to the affective value of punishing feedback, enhanced neurophysiological sensitivity to the affective value of rewarding feedback, or both. To address this issue, we performed further analysis on the significant interaction between outcome feedback type and group. The results showed that the FB-P3 amplitude for punishing feedback was marginally significantly lower in EM patients than in HCs (t(36) = -1.72, p = .06), while no difference for the FB-P3 amplitude in response to rewarding feedback (t(36) = -0.47, p > .05) and control feedback (t(36) = 1.22, p > .05) between groups was found. Our results clearly revealed that the blunted difference in FB-P3 amplitude between rewarding and punishing feedback could be attributable to reduced neurophysiological sensitivity to the affective value of punishing feedback in EM patients compared to HCs.

Potential limitation

In spite of our promising findings, there were several potential limitations of this study that need to be indicated. First, the sample size in the present study was relatively small. Future research should extend our examination by using a larger sample size. Second, the present study included patients suffering from migraine with and without aura. Differences between chronic migraine (CM) patients and EM patients and differences between migraine with aura and migraine without aura have been demonstrated previously (66–69). This may affect the “generalizability” of these findings in migraineurs. Future research should explore the similarities and differences in pathophysiological signatures of incentive processing across migraine phenotypes. Third, the majority of EM patients (17/19), in the present study were female. Due to the fact that differences between male and female migraine patients have been identified at both behavioral and brain levels (70–72), it remains unclear whether these findings can be generalized to male migraine patients. Future research should take this into account. Despite these limitations, our findings remain robust and may foster further EEG-based research to identify the pathophysiological characteristics of incentive processing along the temporal scale in migraineurs.

The present study was designed to characterize pathophysiological features of incentive processing using ERPs in the interictal period in EM patients. We did not find migraine-related changes in ERP components implicated in incentive/no incentive cue evaluation (Cue-N2 and Cue-P3). However, migraine-related changes in ERP components implicated in outcome evaluation were observed. Specifically, although rewarding and punishing feedback elicited larger FB-P3 and FB-LPP amplitudes than the control feedback across both groups, the difference in FB-P3 and FB-LPP amplitudes between rewarding and punishing feedback was found to be blunted in EM patients. This blunting in EM patients was caused by a reduced FB-P3 amplitude to rewarding feedback and reduced FB-LPP amplitude to punishing feedback. These findings seem to reflect decreased attention to both the motivational significance of rewarding feedback and the affective value of punishing feedback. However, we did not observe aberrant FRN in EM patients, which indicates that EM patients were still able to differentiate outcome feedback indicating bad performance from that indicating good performance. The present study offers novel insights into the temporal dynamics of migraine-related changes in incentive processing in migraineurs. In addition, our findings may also have important implications for stimulating future research to characterize altered incentive processing in migraineurs to optimize putative clinical interventions.

episodic migraine

HCs

healthy controls

MID

monetary incentive delay

ERPs

Event-related potentials

FB-P3

Feedback-P3

FB-LPP

Feedback late-positive potential

TPJ

temporoparietal junction

anterior insula

salience network

VAS

visual analog scale

fMRI

functional magnetic resonance imaging

mPFC

medial prefrontal cortex (mPFC)

ACC

anterior cingulate cortex

ANOVA

Analysis of Variance

RTs

reactions times. SAS:Self-rating Anxiety Scale

SDS

Self-rating Depression Scale

HIT

Headache Impact Test

ICHD

International Classification of Headache Disorders.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Chinese PLA General Hospital and informed written consent was obtained for all participants.

Consent for publication

Written informed consent for publication was obtained.

Availability of data and materials

The datasets used and analyzed during the present study are available from the corresponding authors on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 31600929 to Y.L.). The work was also supported by the Dalian Medical Science Research Program Project (Grant No. 2012032 to G.C.).

Authors contributions

Y.L. conceived and designed the study; Z.W. programmed the task; G.C. collected the data. G.C. W.D. and C.L. analyzed the data.; G.C. and C.L. made figures. Y.L. drafted the manuscript. E.D. and B.Z. edited the manuscript. All authors approved the final manuscript

Acknowledgements

The authors wish to thank the volunteers involved in this study.

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No competing interests reported.

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Pathophysiological changes in incentive processing in episodic migraine

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Abstract

Figures

Introduction

Methods

Participants

Monetary Incentive Delay Task

EEG data recording and analysis

Statistical analysis

Results

Behavioral results

Electrophysiological results

ERP responses to incentive-anticipation cues

Cue-N2

Cue-P3

ERP responses to incentive feedback

FRN

FB-P3

FB-LPP

Brain-behavior relationships

Discussion

Potential limitation

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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