Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke

Abstract

Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500-800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.

Keywords: brain network; cross-frequency coupling; functional connectivity; mental rotation; stroke.

Figure 1

The framework of the proposed method. (A) The layout of channel location, (B) Visualization of structure between intra-frequency and cross-frequency connectivity matrices. Matrices show intra-frequency (diagonal tiles) and cross-frequency bands (off-diagonal tiles), and (C) Multi-granularity analysis framework.

Figure 2

Average phase synchronization index (PSI) of the whole brain. (A) In intra-frequency bands during left-hand mental rotation task, (B) In intra-frequency bands during the right-hand mental task, (C) In cross-frequency bands during left-hand mental rotation task, and (D) In cross-frequency bands during right-hand mental rotation task (^*p < 0.05).

Figure 3

The distribution of channel pairs with the significant differences in each delta-alpha, delta-low beta, and delta-high beta band. (A,C,E) during left-hand mental rotation task and (B,D,F) during the right-hand mental rotation task.

Figure 4

The characteristic path length of the brain networks based on the manual threshold method. (A) In paired delta-alpha band during left-hand mental rotation task. (B) In paired delta-alpha band during right-hand mental rotation task. (C) In paired delta-low beta band during left-hand mental rotation task. (D) In paired delta-low beta band during right-hand mental rotation task. (E) In paired delta-high beta band during left-hand mental rotation task. (F) In paired delta-high beta band during right-hand mental rotation task (* p < 0.05, ** p < 0.01).

Figure 5

The clustering coefficient metric of the brain networks based on the manual threshold method. (A) In paired delta-alpha band during left-hand mental rotation task. (B) In paired delta-alpha band during right-hand mental rotation task. (C) In paired delta-low beta band during left-hand mental rotation task. (D) In paired delta-low beta band during right-hand mental rotation task. (E) In paired delta-high beta band during left-hand mental rotation task. (F) In paired delta-high beta band during right-hand mental rotation task (* p < 0.05, ** p < 0.01).