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Abstract

Background

Past studies do not account for avoidance behaviour in migraine as a potential confounder of phonophobia.

Objective

To analyse whether phonophobia is partially driven by avoidance behaviour when using the classic methodology (method of limits).

Methods

This is a case-control study where we tested phonophobia in a cohort of high-frequency/chronic migraine patients (15.5 ± 0.74 headache days/month) and non-headache controls. Auditory stimuli, delivered in both ears, were presented using three different paradigms: the method of limits, the method of constant stimuli, and the adaptive method. Participants were asked to report how bothersome each tone was until a sound aversion threshold was estimated for each method.

Results

In this study, we successfully replicate previously reported reduction in sound aversion threshold using three different methods in a group of 35 patients and 25 controls (p < 0.0001). Avoidance behaviour in migraine reduced sound aversion threshold in the method of limits (p = 0.0002) and the adaptive method (p < 0.0001) when compared to the method of constant stimuli. While thresholds in controls remained the same across methods (method of limits, p = 0.9877 and adaptive method, p = 1).

Conclusion

Avoidance behaviour can exacerbate phonophobia. The current methodology to measure phonophobia needs to be revised.

Keywords

Phonophobia migraine auditory avoidance behaviour chronic migraine high-frequency migraine sound aversion threshold

Introduction

Migraine is a complex brain disorder characterised by the presence of episodic, recurrent and unpredictable attacks of headache and accompanying sensory symptoms such as decreased tolerance to light (photophobia) (1 –5), sound (phonophobia) (2,5 –7), and odours (osmophobia) (8). Phonophobia is present in 70–80% of patients (6,9). It can occur interictally (2,5 –7), but it is exacerbated both ictally (1,6,7) and in patients with allodynia (decreased tolerance to light touch) (10). See Table 1.

Table 1.

Summary of published data on sound aversion threshold in studies quantifying phonophobia in migraine.

Article/Journal/Year	CONTROL			MIGRAINE
Article/Journal/Year	N	Headache frequency	Threshold in dB (frequency)	N	Headache frequency (days/month)	Migraine subgroup classifications		Threshold in dB (frequency)
Woodhouse et al., Cephalalgia 1993¹	16	<12 headaches a year	108 (8000 Hz)	16	NA	Interictal (NA)		98 (8000 Hz)
Woodhouse et al., Cephalalgia 1993¹	16	<12 headaches a year	108 (8000 Hz)	16	NA	Ictal		68 (8000 Hz)⁺
Main et al., Headache 1997²	48	≤1 headache per month	81 (1000 Hz)*	52	NA	Interictal (72h attack free)		69 (1000 Hz)
Main et al., Headache 1997²	48	≤1 headache per month	81 (1000 Hz)*	NA	NA	Ictal		NA
Vanagaite et al., Cephalalgia 1998⁶	80	NA	104,1 (200 Hz)*	46	6.6	No head pain		90 (200 Hz)
Vanagaite et al., Cephalalgia 1998⁶	80	NA	104,1 (200 Hz)*	19	6.6	Head pain		81 (200 Hz)⁺
Sand et al., Cephalalgia 2000⁵	22	NA	104,1 (200 Hz)*	8	4.4	Pre-attack (attack 24h after)		96 dB SPL-A (200 Hz)
Sand et al., Cephalalgia 2000⁵	22	NA	104,1 (200 Hz)*	13	2.8	Interictal (72h attack free)		97 dB SPL-A (200 Hz)
Ashkenazi et al., Cephalalgia 2009⁷	52	<1 tension-type headache per month	∼108 (1000 Hz)∼102 (4000 Hz)∼109 (8000 Hz)*	60	3.5	Interictal (72h attack free)		∼92 (1000 Hz) ∼88 (4000 Hz) ∼92 (8000 Hz)
Ashkenazi et al., Cephalalgia 2009⁷	52	<1 tension-type headache per month	∼108 (1000 Hz)∼102 (4000 Hz)∼109 (8000 Hz)*	60	3.5	Ictal		∼77 (1000 Hz)⁺ ∼74 (4000 Hz)⁺ ∼79 (8000 Hz)⁺
Ashkenazi et al.,J Neurol Neurosurg Psychiatry 2010¹⁰	NA	NA	NA	30	3.3	Allodynic	Interictal (72h attack free)	88 (1000 Hz) 83 (4000 Hz) 87 (8000 Hz)
				30	3.3	Allodynic	Ictal	72 (1000 Hz)^{^}70 (4000 Hz)^{^}87 (8000 Hz)^{^}
				8	4	Non allodynic	Interictal (72h attack free)	93 (1000 Hz)89 (4000 Hz) 72 (8000 Hz)
				8	4	Non allodynic	Ictal	86 (1000 Hz) 85 (4000 Hz) 88 (8000 Hz)

N: Number of participants. *p-values <0.05 for comparisons between controls and migraine patients; ⁺p-values <0.05 for comparisons between migraine phase cycles or headache pain levels; ^{^}p-values <0.05 for comparisons between patients with and without cutaneous allodynia during attacks.

The pathophysiology of auditory hypersensitivity in migraine, remains elusive (11). There is no clear evidence of peripheral receptors being different in migraine (1,6,11). Phonophobia varies interictally and ictally, and may be mediated by noradrenergic or serotonergic pathways in the auditory brainstem (1,5,6). Electrophysiological studies have shown abnormal cortical processing of auditory stimuli in migraine, such as impaired habituation which is suggested to be caused by a reduced cortical pre-activation level associated to a thalamocortical dysrhythmia (12 –14); these have not always been possible to replicate (5). However, a direct relationship between migraine hypersensitivity and the amplitude of auditory evoked potentials has yet to be found (5). Others have suggested that phonophobia can arise from a central integrative mechanism dysfunction as decreased tolerance occurs in several sensory modalities (1,10,11). Recently, Peng and May (11) proposed that a functional change of the hypothalamo-thalamo-brainstem networks might be the most plausible origin of these sensory symptoms in migraine, possibly driving the alterations in cortical processing.

One way to quantify phonophobia is using psychophysics, which allows for the measurement of individual sound aversion thresholds (SAT: sound intensity perceived as being bothersome half of the time and not bothersome the other half of the time). The majority of past literature reporting lower SAT in migraine as compared to non-headache participants has relied on the method of limits (MLI) (Table 1) (1,2,5 –7,10). This method begins with the presentation of low intensity sounds and progressively increases intensity until the participant’s aversion to sound has been reached. An important disadvantage of this method is the predictability of event intensities (15,16), which in turn, might be conducive to migraine patients essentially avoiding higher sound intensities to prevent bothersome or painful stimuli. In fact, phonophobia is defined as a specific phobia of certain sounds involving anticipatory responses and avoidance of sound sources (17). Nevertheless, it has been proposed that phonophobia in migraine might be better defined by the term loudness hyperacusis, in which the threshold for loudness discomfort is reduced and sounds of moderate intensity are judged to be very loud (17). The intricate process underlying phonophobia in migraine should be better described and contextualised in the literature of hyperacusis (prevalence of 2 to 15%) (18), where sound aversion thresholds have been commonly used to quantify it.

The impact of protective behaviours (such as avoidance) on phonophobia in migraine has not been investigated. This fear-avoidance behaviour is probably greater in patients impacted by high-frequency episodic migraine (HFEM ≥ 8 headache days/month) and chronic migraine (CM ≥ 15 headache days/month) (19), who live with anticipatory anxiety and fear of the next attack (20,21).

In fact, the only study that removed predictability of sound intensities during phonophobia testing and hence, their measurements were not influenced by avoidance behaviour, failed to report phonophobia in migraine (16). Kröner-Herwig et al. (16) relied on the method of constant stimuli (MCS) which consists of the pseudorandom presentation of stimuli at several, different intensities. If predictability of the method is critical to detect phonophobia, then, lower thresholds previously reported in migraine patients (2,5 –7,10) may possibly be related to avoidance behaviours and not to hypersensitivity per se. Finally, past literature has not tested the adaptive methods (AM) in migraine, where stimulus intensity presentation is determined by the previous trial stimulus and response. In this method predictability of sound intensity presentation is not as straightforward as in the MLI, and was included to explore whether AM could be the best option to test thresholds in future studies of migraine (15).

To assess whether avoidance behaviour might play a role in the previously reported SAT in migraine, we determined each participant’s threshold using three different methods: MLI, MCS, and AM (15). We hypothesised that migraine patients would report a lower SAT in response to the MLI as compared to the MCS, but that this difference would not be significant in non-headache controls. This procedure would allow us to replicate previous studies with the MLI and confirm phonophobia in migraine using the MCS and the AM for SAT estimation.

Materials and method

This was an observational, case-control study.

Participants

Participants included were diagnosed by a headache specialist and fulfilled the criteria for migraine with or without aura, with or without medication overuse, and had HFEM or CM according to the International Classification of Headache Disorders (ICHD-3) (22). Non-headache controls were age and gender-matched with no personal or family history of a primary headache. Patients were recruited in our outpatient headache clinic at a single facility and neurologists from our Headache Unit were visiting them on a routine basis, whilst controls were recruited using an advertisement through the hospital’s social media account. We excluded patients with more than one headache diagnosis or any other neurological, psychiatric, or comorbid conditions as well as those who were under preventive medication.

All participants were right-handed adults, with no hearing problems (hearing loss ≥25 dBA or tinnitus) or pregnancy. They were recruited between March 2020 and July 2021. Migraine and headache frequency were confirmed using a digital headache diary, which all participants completed during 29.32 ± 3.57 days (see Instruments in the Supplemental material). During the initial interview, participants were encouraged to come to the experimental session without taking acute medication on the day of the session.

Participants provided their informed consent upon inclusion. Patients were offered to voluntarily participate in the study before starting anti-calcitonin gene-related peptide (CGRP) monoclonals antibodies preventive treatment. Controls were remunerated (€35). This research study was approved by the Ethics Committee at Vall d’Hebron Hospital (PR(AG)189/2018).

Phonophobia testing

Apparatus

The experimental paradigms were run on a PC and custom programmed using Psychtoolbox toolbox v3.0.12 extensions (23) on Matlab R2017. The participant sat 70 cm away from a calibrated Display+++LCD monitor (Cambridge Research Systems, Ltd., Rochester, UK, 120 Hz, 1920 × 1080 pixels), in a dark room (Faraday cage, approximately 50 dB external sound attenuation). The auditory stimuli were presented through headphones (Beyerdynamic DT 770 PRO 32 Ohm) using a sound amplifier (Hifi Headphone Amplifier). Accurate timing and synchronisation of auditory stimuli and the central fixation cross was achieved using the Black Box Toolkit (accuracy of <0.005 s (seconds); Black Box Toolkit, Ltd., Sheffield, UK). Responses were made on a 4-button response pad (Black Box Toolkit Ltd., Sheffield, UK).

Individual auditory calibration, used to ensure that participants were exposed to the same sound intensities (accuracy of <1.7 dBA), was performed using two ear-microphones (Hottinger Brüel & Kjaer, 4101B) and a sonometer (Hottinger Brüel & Kjaer, 2270-W).

Stimuli

Auditory stimuli consisted of 1 kHz pure tones, lasting 1.5 s, and presented binaurally. Participants were instructed to fix their eyes on a white central fixation cross (0.5°) throughout the duration of the experimental session. The screen background colour was light grey (19.70 cd/m²).

Procedures

Participants were tested on an auditory detection task as well as a sound aversion task comprised of three different paradigms: the MLI, the MCS, and the AM (Figure 1).

Figure 1.

Schematic representation of a fragment of the sound aversion task for each method. On all the tasks, trials started with the presentation of a white fixation cross for 1.5 s, followed by the onset of a 1 KHz tone lasting 1.5 s. (a) Method of limits. The first tone started at 65 dBA and tone intensity increased by an intensity of 2 dBA until the track reached a sound intensity of 110 dBA or the participant responded that the sound was bothersome. (b) Method of constant stimuli. The first tone was displayed at one of 22 possible sound intensities, in a constant stimulus procedure: ranging from 68 dBA to 110 dBA in two dBA steps. The range of tested sound intensities was adjusted prior to the test for each participant. The fixation cross changed from white to black and a response screen was shown where participants responded whether the tone was bothersome or not bothersome. The fixation turned back to white and after 1.5 s, the next possible intensity was displayed and (c) Adaptive method. The sound aversion task was the same as the one described in the method of constant stimuli, but here, the first tone was presented at 65 dBA and the range of sound intensities could vary between 0–110 dBA.

Auditory detection task

To control for normal hearing (auditory detection <25 dBA) and to test differences in sound detection thresholds between groups, at the beginning of the session each participant carried out a detection task following an adaptive procedure (see Supplemental material) from Kaernbach (24). The duration of the task was five minutes.

B. Method of limits

The MLI paradigm was adapted from Vanagaite et al. (6) Here, participants were instructed to press the “right” button on the response pad when the stimulus was perceived as being bothersome. This method was repeated five times (five tracks) and lasted five minutes.

C. Method of constant stimuli

Participants were asked to judge the bothersomeness of each tone. The response screen displayed the corresponding labels for “left’’ and ‘’right’’ buttons that were randomly assigned to either ‘“bothersome’’ or ‘’not “bothersome’’ responses. Response buttons were counterbalanced throughout the experiment. Participants were instructed to wait until the response screen was presented and were informed that accuracy was preferred over speed. Participants ran a pre-test (two repetitions per tested intensity) and test session (two blocks of eight repetitions per tested intensity). For further details regarding the pre-test and test session, see Supplemental material. The duration of the task was 17 minutes with a short two-minute break between blocks.

D. Adaptive method

The paradigm in the AM was the same as the one used in the MCS. The only difference was the method of tone presentation, whereby previous trial response and sound intensity determined the next trial (see Supplemental material) (24). The duration of the task was five minutes.

Participants completed tasks A-D on the same day. Presentation order for the MLI and the MCS was counterbalanced between participants. The AM was always included at the end of the experimental session. We did not have a precise, a priori hypothesis about the level of predictability of this third method, but it was included to explore whether AM can be optimal (in terms of reliability) to test SAT in migraine.

Participants were familiarised with the detection and sound aversion tasks for <3 minutes prior to the experimental session. All participants filled Patient Reported Outcome as well as Anxiety and Depression scales (see Self-completed questionnaires in the Supplemental material).

Sound maxima were below allowable (criteria for a recommended standard) (25).

Data analyses

Auditory detection and sound aversion thresholds

The individual auditory detection threshold (including dprime in the Supplemental material) was estimated from the average of the last four reversals (change in participant’s response relative to detection) (24).

The SAT for each participant, calculated using the MLI, was obtained by averaging individual participant bothersome responses (five trials) as seen in Vanagaite et al. (6) Participant responses on the sound aversion task using the constant stimuli method were assessed as the proportion of bothersome responses at each tested sound intensity. Then, for each participant, the data were fitted, using the maximum likelihood estimation method, into a cumulative Gaussian function (Matlab code downloaded from http://www.hexicon.co.u/Kielan/#methods) (26). This method has two parameters; the mean which corresponds to the SAT and the standard deviation (discomfort SD) of the cumulative Gaussian function. The bias-corrected and accelerated (BCA) confidence interval was calculated using a bootstrap analysis (27). We were interested in comparing the SAT obtained using the MLI and constant stimuli in patients and controls. To adequately compare both methods, only the first block (eight trials per intensity) was used to calculate the SAT for the MCS. Note that for the MLI the threshold was calculated using five trials per intensity and we were concerned that our SAT differences might be explained due to a greater exposition to the respective stimuli in the MCS. Finally, we estimated the SAT for the second block of trials and examined the potential effect of stimulus exposition (see Supplemental material). The SAT calculated using the AM was estimated from the average of the last four reversals (change in participant’s response relative to bothersomeness) (24).

Demographic, clinical, and psychiatric measurements

Additionally, the mean ± standard error for the demographic data, the clinical and psychiatric questionnaires, and the visual analogue scale for anticipatory anxiety were calculated for each group (patients and controls). Migraine frequency, ASC-12, headache pain level, MIDAS, and HIT-6 were only analysed for migraine patients because we considered them part of the illness. For further information about these measurements see Supplemental material.

Statistical test

To test our SAT, linear mixed-effects models were performed. Fixed-effects were included for various experimental factors (Group, Method, Phobic, and State anxiety), whereas subject-related effects were treated as random. To avoid over-parameterisation of the fixed and random effects, models were compared with and without each term using the Akaike information criterion (AIC) and a chi-square test on the model log-likelihoods (Chisq) (28). Random slopes were not considered in the model. Estimates were obtained by the restricted maximum likelihood. Significance was calculated using the Kenward-Roger degrees of freedom approximation to the F distribution (29). This distribution is more appropriate in small and unbalanced samples as it leads to less type I Error (lmerTest Package: test in linear mixed-effects models). The confidence level was 0.95. The p-values (p) and F-statistics (F) based on this approximation were reported. Post-hoc analyses were evaluated with the Tukey method and adjusted p-values were reported for each comparison (p_adj).

The significance of auditory detection thresholds, discomfort SD, demographic, clinical, and psychiatric measures was statistically assessed applying either a two-sample t-test or a Wilcoxon rank sum test to determine whether the mean differed between participant groups (level of significance set at 0.05). An evaluation of homoscedasticity (Levene’s test) and data normality (Shapiro-Francia normality test) was done for the comparisons. Fisher’s Exact test was applied for categorical data. To examine the relationship between continuous variables we used the Pearson correlation and adjusted p-values were evaluated with the method of Holm. P-values (p), p-adjusted values (p_adj), t-values (t), W-values (W) and correlation values (r) are reported for each of condition’s comparisons.

The statistical analyses were conducted using R software (R-Core Team, 2012), the lme4 package (30), and Matlab (MathWorks, Natick, MA). Effect sizes for parametric statistical tests were calculated using the spreadsheet downloaded from http://openscienceframework.org/project/ixGcd/ (31) and the function anova_stats in R, while the function R wilcox_effsize was used for non-parametric tests.

Results

Participant demographics and migraine characteristics

We included 35 patients and 25 gender (p = 0.7277) and age-matched (t(58) = −0.35, p = 0.7252, Hedges’s gs = 0.09) controls (see demographic and experimental data in Table 2). Participants who were discarded because they did not meet hearing requirements or those who did not complete all the experimental tasks are reported in Figure 2. Auditory detection threshold was not statistically significantly different between groups (W = 482.5, p = 0.5044, rCohen = 0.087).

Table 2.

Demographic and experimental data.

	GROUP
VARIABLE	CONTROL (N = 25)	MIGRAINE (N = 35)	p-values
Age (years old)	37.92 ± 1.67	37.09 ± 1.60	0.7252
Female (%)	80.0	85.71	0.7277
Monthly headache days (days/month)	NA	15.46 ± 0.74	NA
Headache-related disability (MIDAS)	NA	54.91 ± 5.20^*	NA
Headache-related impact (HIT-6)	NA	64.26 ± 0.85	NA
Headache pain intensity (0–10 score)	NA	1.89 ± 0.27	NA
Allodynia assessment (ASC)	NA	4.29 ± 0.59	NA
Accompanying symptoms of headache (% of participants)
Aura	NA	37.14	NA
Subjective presence of photophobia	NA	77.14	NA
Subjective presence of phonophobia	NA	85.71	NA
Nausea/vomiting	NA	54.29	NA
State anxiety (STAI), score	8.20 ± 1.03	16.40 ± 1.42	<0.0001
Trait anxiety (STAI), score	15.60 ± 1.21	21.91 ± 1.47	0.0004
Depression (BDI-II), score	3.04 ± 0.71	8.54 ± 1.31	0.0005
Phobic Anxiety (BSI), score	0.09 ± 0.03	0.27 ± 0.06	0.0348
Auditory detection (dBA)	8.88 ± 0.58	9.42 ± 0.50	0.5044
Dprime	2.20 ± 0.17	2.26 ± 0.14	0.6855
Sound Aversion Threshold (dBA)
Method of limits	90.99 ± 1.88	79.21 ± 1.32	<0.0001
Method of constant stimuli	91.61 ± 1.13	82.97 ± 1.00	0.0009
Adaptive method	91.19 ± 1.60	76.9 ± 1.62	<0.0001
Discomfort SD	1.61 ± 0.17	2.65 ± 0.28	0.0052
VAS (0–10 score)
Method of limits	1.84 ± 0.36	2.86 ± 0.37	0.0791
Method of constant stimuli	1.88 ± 0.35	2.80 ± 0.37	0.0973

Continuous data is represented in mean ± standard error. MIDAS: migraine disability assessment; HIT-6: headache impact test; ASC: Allodynia Symptom Checklist; STAI: State-trait Anxiety, Inventory; BDI-II: Beck depression inventory-second edition; BSI: Brief Symptom Inventory. Bold font indicates statistically significant variables. *One patient did not complete the MIDAS.

Figure 2.

Flowchart of participant selection steps. Of the 28 controls and 46 patients with migraine that entered the experiment, 25 controls and 35 migraine patients successfully met the auditory requirements. The data of 11 patients were discarded because they reported having tinnitus (10 patients) and could not detect sounds below 25 dBA (one patient). The data of three controls werediscarded because they did not experience bothersomeness at intensities greater than 110 dBA (two controls) and due to hearing problems (one control). In both groups, all participants who met the auditory requirements entered the final sample for both the method of limits and the method of constant stimuli. Only one control and three patients did not complete the experimental session and did not enter the final sample for the adaptive method (24 control subjects and 32 migraine subjects). Sound aversion thresholds for each of the methods were estimated in these participants. Data from the method of constant stimuli was selected for subsequent analyses. Participants who successfully ran two blocks in the method of constant stimuli were selected for the Block analysis (24 controls and 33 patients with migraine). Participants who could successfully estimate two sound aversion thresholds as a function of prior trial intensity (18 controls and 30 patients with migraine) and response (19 controls and 30 patients with migraine) were selected for previous trial analyses.

Seventeen patients had HFEM and 18 had CM. Twenty-three percent of patients had medication overuse. Thirteen had migraine with aura and 22 had migraine without aura. Twenty-one patients reported cutaneous allodynia (ASC score > 2) and 14 did not. Nine patients were in the interictal phase (96 h attack free), nine were in the ictal phase (attack on the day of the session), 11 were in the preictal phase (attack 48 h after), and six were in the postictal phase (attack 24 h before). Four patients took medication before starting the experiment (day of the session). The effect of medication intake was evaluated, and no differences were found for SAT (MLI: t(33) = 0.19, p = 0.8515, Hedges’s gs = 0.1; MCS: t(33) = −0.15, p = 0.8839, Hedges’s gs = 0.08; AM: t(30) = 0.14, p =0.8906, Hedges’s gs = 0.08) or detection thresholds (W = 58.5, p = 0.3479). Migraine attack was considered when the pain intensity reported by the patient was between moderate to severe.

We found statistically significant differences in Depression (W = 206, p = 0.0005, rCohen = 0.45), and Phobic (W = 309, p = 0.0348, rCohen = 0.27), State (W = 175, p < 0.0001, rCohen = 0.51), and Trait (W = 202, p = 0.0004, rCohen = 0.46) anxiety scores between patients and controls (Table 2).

Sound aversion thresholds

At a group level, SAT were reported for each method in Figure 3 and Table 2. Model characteristics were described in the Supplemental material. A significant main effect of Group showed phonophobia (lower SAT) in patients relative to controls (F_1,58 = 36.57, p < 0.0001, ɳ_p² = 0.24). We also found a main effect of Method (F_2,112 = 9.66, p = 0.0001, ɳ_p² = 0.14) revealing higher SAT when the MCS is employed. A significant interaction between Group and Method (F_2,112 = 9.45, p = 0.0002, ɳ_p² = 0.14), together with the post-hoc analyses indicated that patients showed higher SAT when the MCS was used as opposed to the MLI (t(112) = 4.59, p_adj = 0.0002, Hedges’s gav = 0.53). In contrast the SAT estimated for both methods in controls were similar (t(112) = 0.64, p_adj = 0.9877, Hedges’s gav = 0.08). In fact, to perceive discomfort in response to the MLI, the auditory intensity had to be presented 3.77 ± 0.74 dBA lower than the MCS (anticipation index). This anticipation index in controls (0.62 ± 1.03 dBA) significantly differed from patients (t(58) = 2.55, p = 0.0135, Hedges’s gs = 0.66). The SAT in patients obtained in the AM was similar to the one estimated with the MLI (t(113) = 1.99, p_adj = 0.3532, Hedges’s gav = 0.26) but it significantly differed from the threshold estimated with the MCS (t(113) = 6.43, p_adj < 0.0001, Hedges’s gav = 0.77). In controls, unlike patients, the SAT obtained with the AM did not differ from the threshold estimated by the other two methods (MLI: t(112) = −0.81, p_adj = 0.9660, Hedges’s gav = 0.02 and MCS: t(112) =−0.18, p_adj = 1, Hedges’s gav = 0.06).

Figure 3.

Individual data of sound aversion threshold in each experimental method for migraine patients and controls. Diamonds (◊) represent the data from the method of limits; Circles (○) the data from the method of constant stimuli; Triangles (Δ) the data from the adaptive method. Large, filled diamond, circles or triangles depict the mean ± standard error of the mean for each condition.

Notably, no significant correlations were found between the detection threshold and any of the estimated SAT (r_MLI = −0.18, p_adj = 0.4852; r_MCS = −0.16, p_adj = 0.6634; r_MA = −0.05, p_adj = 1) ensuring that in our study, differences in sound detection could not explain the reported phonophobia in migraine. For further correlations in the study see Supplemental material.

Discussion

In this study we wanted to understand if an avoidance behaviour to bothersome sounds might partially or totally drive the responses of migraine patients to phonophobia; mainly because the most used methodology (MLI) might induce this due to the experimental paradigm allowing anticipation of aversive sounds (15,16). The SAT estimated during our three experimental methods (MLI, MCS and AM) was in line with previous results using MLI in normal individuals (100–105 dB HL) (32), in individuals with hyperacusis (75–80 dB HL) (33), and in the migraine literature (2,5 –7,10). Indeed, the threshold was lower in migraine patients when comparing it to controls. This implies the reliability of the additional paradigms (MCS and AM) which have not been previously used to measure phonophobia in migraine. Higher thresholds in the MCS relative to the MLI and AM were found in patients, that may be explained by a failure to anticipate to aversive sounds.

Interestingly, the results of our study indicate that this avoidance behaviour in migraine patients might not fully describe the modulation of SAT found in previous studies (1,2,6,7,10). Moreover, in the MCS which is unpredictable, lower SAT were still found in patients when compared to controls. Avoidance behaviour helps reduce the SAT in migraine, but it appears to be evident that other neural processes underlie the reported phonophobia. Past studies addressing the source of these reduced SAT in mainly low-frequency episodic migraine (LFEM < 8 headache days/month) usually correlate them with headache (1,2,6,7), but discrepant results have been found with regard to correlations with age, disease duration, attack frequency (6,7), or the corresponding amplitude of visual/auditory evoked potentials in habituation procedures (5). In our study, none of our variables correlated with the SAT in the three methods.

Furthermore, in line with most of the studies quantifying auditory detection thresholds, we did not find differences between patients and controls or a correlation with phonophobia (1,2,5). Only one recent study did show clear differences in auditory detection thresholds between groups that did not correlate with the severity of phonophobia (34). We tentatively argue that it could have been affected by response biases using the MLI as especially lower thresholds were found in CM patients.

Our results appear to provide evidence for phonophobia arising from a central integrative mechanism, where protective behaviours, such as avoidance, partly contribute to exacerbating this process. Here, to further examine protective behaviours, we explored the influence of the immediate previous trial on the SAT (see Supplemental material for previous trial analyses). The results of the multiple comparison tests suggested that phonophobia might depend on the subjective experience (whether patients reported bothersome or not bothersome) of the preceding trial. To the best of our knowledge, this is the first time that a serial analysis on SAT has been examined in migraine. Other protective learned behaviours such as hypervigilance and monitoring might influence phonophobia in migraine (35). Based on the impact of protective behaviours on phonophobia, we propose the amygdala being a part of this integrative mechanism. Interestingly, the amygdala plays a pivotal role encoding the safety signal necessary to adapt to the environment (habituation) (17,36), it is involved in a number of fear and anxiety related neural circuits (37) and is highly connected to auditory areas of the thalamus and cortex, encoding perceived aversiveness of sound among other features (38).

Our study has some limitations in relation to the characteristics of our sample, which consisted of adults with HFEM or CM. Indeed, the purpose of this study was to quantify the effect of avoidance behaviour on phonophobia testing, thus, we needed patients with greater anticipation of aversive sounds (21). It is possible that our results cannot be generalised to LFEM or paediatric migraine patients because avoidance behaviours might have a lower impact in their perceptual experience. Moreover, the estimation of thresholds as a function of the migraine attack phase was not possible in our HFEM and CM group because of the frequency and distribution of headache days. An interesting question which awaits experimental verification is whether this avoidance behaviour can endorse the interictal sensitivity to sound, which might not arise from the same mechanism than the ictal response. Investigators of the study were not blinded to the diagnosis of participants. However, the sound intensity presentation during phonophobia testing was entirely automatic and probably had little effect. Finally, several considerations about the validity of these new methods to test phonophobia are discussed in Table 3.

Table 3.

Characteristics of the different methods used for phonophobia testing.

	Pilot work needed	Redundant stimulus presentation	Anticipation of sound intensity	Presentation of unpleasant sounds	Phonophobia evaluation	Avoidance evaluation	Experimental length
Method of limits		x	x		x	x	5 min
Method of constant stimuli	x	x		x	x		10 min
Adaptive method			x*	x	x	x*	5 min

X: Presence. *As a further alternative, one should also consider the possibility that these reduced thresholds may partially rely on sensitisation processes and not to avoidance behavior or anticipation of stimulus intensity, given that we always ran this adaptive method at the end of the experimental session.

To conclude, our study showed that avoidance behaviour can reduce migraine sound aversion threshold in the method of limits and should be accounted for in future experiments. We have demonstrated that with the method of constant stimuli we were able to minimise the avoidance effect, but most importantly, have confirmed the presence of phonophobia in migraine using three different methods (method of limits, method of constant stimuli and adaptive method) which correlates this symptom as intrinsic to the disease.

Article highlights

Sound aversion thresholds in high-frequency/chronic migraine patients are evaluated using three different quantification methods, which allowed us to evaluate the effect of avoidance behaviour on phonophobia.

Avoidance behaviour to bothersome sounds can reduce migraine sound aversion threshold when using the classic methodology (method of limits).

Avoidance behaviour should be disentangled from phonophobia in future studies quantifying sound aversion thresholds.

Supplemental Material

sj-pdf-1-cep-10.1177_03331024221111772 - Supplemental material for Avoidance behaviour modulates but does not condition phonophobia in migraine

Supplemental material, sj-pdf-1-cep-10.1177_03331024221111772 for Avoidance behaviour modulates but does not condition phonophobia in migraine by Nara Ikumi, Xim Cerda-Company, Angela Marti-Marca, Adrià Vilà-Balló, Edoardo Caronna, Victor José Gallardo and Patricia Pozo-Rosich in Cephalalgia

Footnotes

Acknowledgements

We would like to thank Marta Torres-Ferrús, and Alicia Alpuente for their help in recruiting patients; Daniel Linares for his advice in the study; and all the participants who patiently collaborated in our study.

Author contributions

The substantial contributions were as follows: Concept or design of the work: NI, XCC, AMM, AVB and PPR. Participant recruitment: EC, NI and AMM. Data acquisition: NI and XCC. Data analyses: NI. Data interpretation: NI, XCC, AMM, VJG, AVB, PPR. Drafted the article: NI. Critically revised the article: all authors. Supervision, project administration and funding acquisition: PPR.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: NI, XCC, AMM, AVB, VJG and EC have no conflicts of interest in relation to this study.

PPR has received in the last 3 years, honoraria as a consultant and speaker from Allergan/Abbvie, Almirall, Biohaven, Chiesi, Eli Lilly, Medscape, Neurodiem, Novartis, and Teva Pharmaceuticals. Her research group has received research grants from La Caixa Foundation, Novartis, Teva Pharmaceuticals, AGAUR, FEDER RIS3CAT, and has received funding for clinical trials from Allergan/Abbvie, Amgen, Eli Lilly, Novartis, and Teva Pharmaceuticals.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: XCC salary has been co-funded by the European Regional Development Fund (001-P-001682) under the framework of the FEDER Operative Programme for Catalunya 2014-2020, with €1,527,637.88.

AVB salary has been partially financed by a Juan de la Cierva-Formación grant (FJC2018-036804-I) from the Spanish Ministry of Science and Innovation.

AMM salary has been partially financed by a predoctoral grant from the “Fundació Institut de Recerca Hospital Universitari Vall d'Hebron” (VHIR/BEQUESPREDOC/2020/MARTI).

EC salary has been funded by Río Hortega grant Acción Estratégica en Salud 2017–2020, instituto de Salud Carlos III (CM20/00217).

ORCID iDs

Nara Ikumi

Adrià Vilà-Balló

Edoardo Caronna

Victor José Gallardo

Patricia Pozo-Rosich

Supplemental material

Supplemental material for this article is available online.

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