Participants
In the present study, 22 patients with MwoA (age = 30.86 ± 5.69 years; 19 females) and 25 healthy controls (age = 30.24 ± 3.52 years; 18 females) were recruited. All participants had normal or corrected-to-normal vision and were right-handed. Both groups were matched on the basis of sex, age and years of education. For healthy controls, they reported no history of neurological or psychiatric disorders and no migraine in first-degree relatives. For patients with MwoA, they underwent neurologic and physical evaluations by trained neurologists (Z.D. and S.Y.) as well as standard neuropsychological assessment by neuropsychologists (G.C.). The inclusion criteria for patients were used: 1) fulfilling the diagnosed criteria for migraine without aura according to the International Classification of Headache Disorders, 3rd edition (ICHD-3); 2) at least 2 year’s history of migraine and at least one migraine episode per month; 3) no migraine attacks 72 h before and after the experiment and 4) outside migraine attacks during the experiment (the interictal period). Moreover, the following exclusion criteria were used: 1) neurological diseases (i.e., epilepsy, neuromuscular disorders); 2) psychiatric symptoms (i.e., anxiety and depression); 3) mental retardation; 4) a current or past history of substance dependence, 5) receiving prophylactic anti-migraine therapy and 6) having suicide ideation and/or previous suicide attempts; 7) the presence of periodic limb movement disorder (i.e., nocturnal hyperkinesias) and recurrent parasomnias (> 3 episodes per week). All female participants took no oral contraceptives for at least 1 week. Demographic and clinical characteristics are described in Table 1.
Table 1
Demographic and clinical characteristics of the study sample
| MwoA (n = 22) | Controls (n = 25) | Group comparison |
(M ± SEM) | (M ± SEM) | |
Age, years | 30.86 ± 1.21 | 30.24 ± 0.70 | t(45) = -0.46, p > 0.05 |
Gender (F/M) | (19/3) | (18/7) | χ2 = 2.40, p > 0.05 |
Education, years | 15.55 ± 0.59 | 15.32 ± 0.45 | t(45) = -0.31, p > 0.05 |
BMI [kg/m2] | 21.09 ± 0.77 | 21.04 ± 0.52 | t(45) = -0.06, p > 0.05 |
SAS | 44.43 ± 2.27 | 39.45 ± 1.49 | t(45) = -1.88, p > 0.05 |
SDS | 44.67 ± 2.97 | 42.85 ± 2.13 | t(45) = -0.51, p > 0.05 |
Duration of migraine, hours | 30.61 ± 5.47 | | |
History of migraine, years | 12.41 ± 1.34 |
Migraine frequency, times per month | 5.00 ± 0.92 |
Severity of headache (VAS scale) | 8.23 ± 0.25 |
VAS, visual analog scale, with 0 indicating no pain and 10 worst possible pain; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; BMI, body mass index |
All participants volunteered to participate in the present study. They all signed consent forms and the Ethics Committee of the Chinese PLA General Hospital approved the study protocol.
EEG data recording and analysis
EEG data recording procedure is similar to that described in our previous studies (39, 40). Specifically, EEG was recorded (SynAmps amplifier, NeuroScan) with a quick cap carrying 64 Ag/AgCl electrodes placed at standard locations covering the whole scalp (the extended international 10–20 system). 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 0.05–100 Hz and sampled at a rate of 1000 Hz.
Offline time-domain EEG data analysis was conducted using EEGLAB (41) and ERPLAB (42). Data were first re-referenced to linked mastoid (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 artifact were thus removed according to published guidelines (43). The resultant EEG data were then epoched from 200 ms pre-stimulus to 1000 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 ± 75 µV at any channels except the EOG channel. The ERPs were averaged separately for successful Stop trials and correct Go trials in each group.
Our time-frequency analysis was performed using the Matlab FieldTrip toolbox (44) and the procedures were similar to that described in a recent study (45). The filtered EEG data between 0.5–30 Hz was segmented 500 ms pre-stimulus onset to 1000 ms post-stimulus onset separately for Go and Stop trials for each group. Total event-related spectral power was obtained by transforming each epoch into the frequency domain using a sequential and overlapping unique Hanning window of 250 ms in steps of 25 ms with the multitaper time-frequency transformation (MTMCONVOL from ft_freqanalysis Fieldtrip software) method. In addition, the convolution function includes a ‘Zero’ type padding in order to cope with edging effects. After the transformation, we obtained a time-frequency spectrum with 1 Hz and 250 ms resolution. At each frequency, the results employed a dB transform [dB power = 10*log10 (power/baseline)] and were baseline corrected by subtracting the average baseline period (from − 200 to − 0 ms) from each data point. The obtained power values were then averaged over EEG epochs for trial types (i.e., Go trials and successful Stop trials) in each participant. Then, data were grand-averaged across MwoA patients and across healthy controls for each trial type.
Statistical analysis
For statistical analysis on the demographic data, a chi-square test was used to assess a between-group difference in sex ratio and independent sample t-tests were used to examine between-group differences in age, years of education, the rate of anxiety and depression as measured by the self-rating anxiety scale (SAS) and self-rating depresson scale (SDS), and body mass index (BMI). For statistical analysis on the behavioral data, accuracy on Go trials (Go ACC) and reaction times to Go stimuli (Go RT) and the Stop signal (stop signal reaction time: SSRT) were extracted for analyses. Independent sample t-tests were used to examine between-group differences in these behavioral data.
Regarding statistical analysis on electrophysiological data, our data were analyzed according to the topographical distribution of grand averaged ERP activity as well as the methods of previous ERP studies (3, 32, 34, 46). The ERP statistical analysis involved two ERP indices of response inhibition: the stop-N2 and stop-P3. Mean amplitudes for the stop-N2 (time interval = 200–250 ms, at the C3, Cz, C4, CP3, CPz, CP4, P3, Pz, P4 electrodes) and the stop-P3 (time interval = 350–500 ms, at the FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, P3, Pz, P4 electrodes) were calculated. In order to examine effects of migraine on these ERP components, we conducted a mixed analysis of variance (ANOVA), with group as a between-participants factor (patients with MwoA versus healthy controls), and trial type (Go versus Stop trials), laterality (left [C3, CP3, P3], midline [Cz, CPz, Pz], right [C4, CP4, P4] for the N2; left [FC3, C3, CP3, P3], midline [FCz, Cz, CPz, Pz], right [FC4, C4, CP4, P4] for the P3) and area (central [C3, Cz, C4], centroparietal [CP3, CPz, CP4], parietal [P3, Pz, P4] for the N2;frontocentral [FC3, FCz, FC4], central [C3, Cz, C4], centroparietal [CP3, CPz, CP4], parietal [P3, Pz, P4] for the P3) as within-participants factors. Consistent with previous findings showing that delta and theta power account for activity underlying the stop-N2 and stop-P3 components in a stop signal task (32), the same statistical analyses were conducted on event-related delta and theta power. Based on visual inspection of time-frequency plots and the methods of previous studies (45–47), the same area and laterality factors were included in such time-frequency analyses and mean power values in delta (1–4 Hz) and theta (4–8 Hz) frequency bands were extracted in the time windows used to extract mean amplitudes of the stop-N2 and stop-P3 components in order to disentangle the multiple processes underlying the stop N2–P3 complex in response inhibition.
All data were analyzed using IBM SPSS 19.0 (IBM Corp., Armonk, NY, USA). Statistical comparisons were made at p-values of p < 0.05, with the Greenhouse–Geisser correction when violations of sphericity occurred.