Functional Connectivity Analysis

Beamformer-based imaging of functional connectivity

Abstract

Spatial resolution problem of MEG and EEG may cause underestimated connectivity. Estimation of source activity for specific brain regions may include the contribution of multiple neural populations. In other words, each neural region contains multiple sources with various orientations and temporal profiles.

Therefore, activity measured in individual brain regions may actually comprise multiple components and unrelated components may result in underestimated source connectivity. To decrease the influence of unrelated components in connectivity estimation, computing connectivity by using source components related to reference signal can increase sensitivity.

A. Imaging of inter-regional correlation

Considering connectivity measures, coherence is suitable for the detection of rhythmic synchronization, whereas temporal correlation is appropriate for transient synchronization.

We proposed a beamformer-based imaging method, called spatiotemporal imaging of linearly-related source component (SILSC), which is capable of estimating connectivity at the cortical level by extracting the source component with the maximum temporal correlation between the activity of each targeted region and a reference signal.

The spatiotemporal correlation dynamics can be obtained by applying SILSC at every brain region and with various time latencies.

The results of six simulation studies demonstrated that SILSC is sensitive to detect the source activity correlated to the specified reference signal and is accurate and robust to noise in terms of source localization.

In a facial expression imitation experiment, the correlation dynamics estimated by SILSC revealed the regions with mirror properties and the regions involved in motor control network when performing the imitation and execution tasks, respectively, with the left inferior frontal gyrus specified as the reference region.

 

Figure 1. Contrast-weighted correlation dynamics obtained in the facial expression imitation experiment. Temporal dynamics of the regional contrast-weighted correlation in the six regions: the left IPL, left AG, right insula, right amygdala, left SM1, and left occipital lobe.

 

B. Imaging of inter-regional phase-amplitude coupling

Phase-amplitude coupling (PAC) between neural oscillations of different frequencies plays a crucial role in cognitive processing. Assessing the PAC at both sensor and source levels may encounter the problem of spurious coupling because of the volume conduction, field spread, and source leakage.

We proposed a novel method, beamformer-based imaging of PAC (BIPAC), to estimate PAC between sources from electromagnetic signals. For each targeted brain region, this method can extract the source component with the maximum PAC to the reference signal. The results from simulated MEG data sets demonstrated that the proposed method can achieve high localization accuracy and low spurious coupling.

 

Figure 2. Analysis results of multiple-source simulation data set by using BIPAC. Left panel shows the locations, orientations, and waveforms of the ground-truth sources. One 50 Hz waveform is placed at r1 and four source components with various envelope-to-signal coupling (ESC) to the 50-Hz waveform are placed at r2. Location r1 is set to be the reference point for BIPAC analysis. The right panel displays the estimated source located at r2 by using the conventional source imaging method, MCB, and, the proposed method, BIPAC. The source estimated by using BIPAC has higher ESC than that using MCB.

 

Reference

H.-L. Chan, L.-F. Chen, I-T. Chen, and Y.-S. Chen,” Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals ”, NeuroImage 114: 1-17 [SCI].

H.-L. Chan, Y.-S. Chen, L.-F. Chen, and S. Baillet, ” Beamformer-based imaging of phase-amplitude coupled sources from electromagnetic brain signals ”, 37th IEEE EMBC, 2015. (Oral, Student paper award)