A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea.

. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.