Exploring the cortical habituation in migraine patients based on contingent negative variation

Abstract

Introduction: Cognitive dysfunction has frequently been found in patients with migraine. The so-called contingent negative variation (CNV) and EEG power spectral densities may be the best choices to explore the underlining pathophysiology, such as cortical inhibition and habituation.

Methods: Thirty migraine patients without aura and healthy controls matched for sex, age, and education were recruited separately for CNV recording. The amplitudes, latencies, and squares of different CNV components, such as oCNV, iCNV, tCNV, and PINV, were selected and analyzed. Behavioral data, such as manual reaction time (RT), were analyzed. We used the Person correlation coefficient R to analyze different ERP components in relation to clinical characteristics. A multiple regression analysis was conducted for the migraine group. Spectral analysis of EEG data from all channels using the fast Fourier transform (FFT).

Results: The migraine group had longer A-latency, C-latency, and iCNV-latency than the control group. The migraine group had higher iCNV-amplitude, oCNV-amplitude, and tCNV-amplitude than the control group, especially those located in the occipital area. The iCNV-square, oCNV-square, tCNV-square, or PINV-square in the migraine group was significantly larger than the control group. Different correlations were found between clinical characteristics and ERP components. The delta or theta activity in the migraine group was statistically lower than in the control group.

Discussion: Our study has revealed that migraine attacks may influence responsivity, pre-activation, habituation, and cortical inhibition not only on the behavioral level but also on the electrophysiological level. Abnormal changes in cortical habituation and inhibition can be interpreted as CNV components. Additionally, analyses have revealed correlations between CNV components and various factors, including age, the clinical course of the condition, attack frequency, pain intensity, and duration. Thus, repetitive migraine attacks can lead to a reduction in cortical inhibition and subsequent impairment in executive function.

Keywords: FFT (fast Fourier transform); contingent negative variation (CNV); cortical habituation; event-related potential (ERP); migraine.

Figure 1

A-latency between the migraine and control groups located in different regions and a diagrammatic sketch of CNV. A significant difference in A-latency was found between the migraine and control groups (*P < 0.05, **P < 0.01).

Figure 2

C-latency between the migraine group and the control group is located in different regions. A significant difference in C-latency was found between the migraine and control groups (*P < 0.05, **P < 0.01).

Figure 3

iCNV-latency between migraine and control groups located in different regions. A significant difference in iCNV-latency was found between the migraine and control groups (*P < 0.05, **P < 0.01).

Figure 4

Different amplitude and square components between the migraine and control groups. A significant difference in CNV amplitude was found between the migraine and control groups (*P < 0.05, **P < 0.01). A significant difference in CNV-square was found between the migraine and control groups (*P < 0.05, **P < 0.01).

Figure 5

PINV-square between the migraine and control groups located in different regions. A significant difference in PINV-square was found between the migraine and control groups (*P < 0.05, **P < 0.01).

Figure 6

Correlation analysis between A-latency and clinical course, duration, and pain severity. A significant correlation between clinical course and A-latency located in CPZ, CP2, P4, or T8 was found (P = 0.031 r = −0.393, P = 0.010 r = −0.464, P = 0.042 r = −0.373, P = 0.040 r = −0.377). Significant correlation between duration and A-latency located in T8 was found (P = 0.036 r = −0.384). Significant correlation between pain severity and A-latency located in CP1, CPZ, PZ, P4, or O2 was found (P = 0.014 r = −0.443, P = 0.038, r = −0.381, P = 0.038 r = −0.381, P = 0.006 r = −0.489, P = 0.008 r = −0.476).

Figure 7

Correlation analysis between C-latency and duration in education. A significant correlation between duration and C-latency located in FP2, F3, C4, PZ, or O1 was found (P = 0.029 r = 0.399, P = 0.040 r = 0.376, P = 0.010 r = 0.465, P = 0.025 r = 0.408, P = 0.016 r = 0.437). A significant correlation between education and C-latency located in T8 or TP7 was found (P = 0.035 r = −0.386, P = 0.023 r = −0.415).

Figure 8

Correlation analysis between iCNV indicators and clinical course frequency. And correlation analysis between oCNV-amplitude and age, education, and pain intensity. A significant correlation between clinical course and iCNV-latency located in CP1 or CPZ was found (P = 0.043 r = −0.372, P = 0.031 r = −0.394). A significant correlation between clinical course and iCNV-amplitude located in O2 was found (P = 0.048 r = 0.364). A significant correlation between frequency and iCNV-amplitude located in CP1 was found (P = 0.026 r = −0.406). A significant correlation between age and oCNV-amplitude located in FPZ was found (P = 0.016 r = −0.436). A significant correlation between education and oCNV-amplitude located in OZ was found (P = 0.039 r = −0.378). A significant correlation between pain intensity and oCNV-amplitude located in the FPZ was found (P = 0.027 r = −0.402).

Figure 9

Linear regression analysis between clinical course, duration, age, pain severity, and A-latency.

Figure 10

δ activity distributions in different cortex regions. δ activity distributions in the unilateral or bilateral hemisphere (*P < 0.05, **P < 0.01).

Figure 11

θ activity distributions in different cortex regions. θ activity distributions in the unilateral or bilateral hemisphere (*P < 0.05, **P < 0.01).