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

How EXACTLY will this help survivors recover?  No answer, it was wasted research.

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

Bin Ren^1,2†, Kun Yang^1,2†, Li Zhu^1,2, Lang Hu^1,2, Tao Qiu³, Wanzeng Kong^1,2 and Jianhai Zhang^1,2*

• ¹College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
• ²Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
• ³Department of Neurology, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, China

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.

1. Introduction

Stroke is a kind of cerebrovascular disease affecting the whole world. In most countries, stroke is the leading cause of the disability of adults and hinders the daily routine of patients and their families (Donkor, 2018). In recent years, neuroimaging techniques, such as CT, positron emission computed tomography (PET), functional MRI (fMRI), are often used in clinical treatment and disease research to monitor the neurological function of patients with stroke and explore the plastic reorganization mechanism of the brain (Rossini et al., 2003). However, these techniques are usually not portable and very expensive. Electroencephalogram (EEG) is a convenient and non-invasive technology with a high temporal resolution, which is suitable for monitoring, prognosis, and evaluating stroke disease (Monge-Pereira et al., 2017).

Previous research has proposed several Quantitative EEG (QEEG) features to evaluate brain activity changes after stroke, such as delta/alpha power ratio (Schleiger et al., 2014; Finnigan et al., 2016), brain symmetry index (Sheorajpanday et al., 2009; Sebastian-Romagosa et al., 2020), and laterality coefficients (Park et al., 2016). Besides, nonlinear parameters are also used in stroke research, such as Lempel Ziv complexity, sample entropy (Liu et al., 2016), and nonlinear separate degree (Zeng et al., 2017). These features are mainly analyzed based on single channels and cannot reflect the functional interactions between different brain regions. Therefore, some researchers explored the characteristics of the brain network after stroke. For instance, Philips et al. (2017) constructed brain networks in the beta band based on EEG data of the intensive therapeutic intervention period and found graph theoretical metrics are significant biomarkers to evaluate stroke rehabilitation. Although current studies reveal that oscillations in intra-frequency bands are reliable tools for exploring brain abnormality after stroke, the oscillatory mechanisms between different frequency bands have not been clearly understood.

More and more studies have shown complex brain information interaction between different frequency bands, known as cross-frequency coupling (CFC). Several brain regions of human and non-human primates found CFC phenomena, such as the hippocampus, prefrontal cortex, and sensory cortex (Mormann et al., 2005; Canolty et al., 2006; Jensen and Colgin, 2007; Khamechian and Daliri, 2020). Besides, increasing researchers use CFC to analyze cognitive and perceptual processes. For example, Dimitriadis et al. (2015) explored the coupling between the theta band and alpha band in the frontal lobe, parietal lobe, and occipital lobe during mental arithmetic tasks. Davoudi et al. (2021b) found an important parieto-occipital alpha-gamma coupling mechanism to rapidly select features from visual working memory storage. In the meantime, CFC shows advances in understanding the impact of many neurological diseases, including Alzheimer's disease (Cai et al., 2018), epilepsy (Jacobs et al., 2018; Yu et al., 2020), social anxiety disease (Poppelaars et al., 2018), and multiple sclerosis (Ahmadi et al., 2019). For instance, Jacobs et al. (2018) extracted cross-frequency phase-amplitude coupling features to predict seizures, and Yu et al. (2020) constructed the cross-frequency phase-phase coupling from seizure interval to seizure period. In human stroke-related studies of EEG signals, some studies focused on the CFC between EEG and other physiological signals, such as EMG (Xie et al., 2021) and cerebral blood flow velocity (Liu et al., 2019). Other studies investigated the CFC of EEG signals. For instance, based on the EEG data of the upper limb movement experiment, the effective network of 5 predefined motor cortex areas was constructed by dynamic causal modeling (DCM) to identify the biomarkers for classifying the patients' recovery state (Larsen et al., 2018). In addition, the DCM was utilized to investigate intra-cortex and inter-cortex effective connectivity of the 3 motor cortex areas in the intra-frequency and cross-frequency bands during the precision grip task in the stroke acute and sub-acute phase (Chen et al., 2017). In summary, the existing related studies on the cross-frequency analysis of EEG signals in patients with stroke mainly focus on the motor cortex during motor executive tasks. However, it may result in the obtained information being limited since it may lose potential information interactions between different brain regions. Moreover, the network information interaction of patients with stroke during the motor imagery process, which reveals the motor perception function after stroke, needs to be explored.

Mental rotation, a kind of motor imagery task, is conducive to restoring specific limbs' motor ability. Yan et al. (2013) constructed brain networks in beta bands based on the EEG data of patients with stroke and healthy controls during the mental rotation task and found significant alterations of the stroke brain in several temporal and spatial granularity. But this study has not explored the information interactions in cross-frequency bands. Previous analysis of healthy subjects in the mental rotation task found cross-frequency coupling between posterior parietal and frontal regions (Bertrand and Jerbi, 2009), which reveals the importance of CFC analysis in understanding the brain's mental rotation cognitive process. However, extracting effective features from CFC is more difficult than traditional intra-frequency coupling since the corresponding data is complicated and contains more hidden information. In addition, the cross-frequency features revealing the physiological mechanism of the brain requires to be deeply analyzed.

In this article, EEG data of patients with stroke and healthy controls during the mental rotation task is analyzed. A multi-granularity analysis framework is proposed, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase coupling to evaluate the stroke impact in temporal and spatial granularity. In detail, spatial granularity includes analyzing the average phase synchronization index (PSI) at the whole brain area scale, hemisphere scale, and single-channel pairs scale. Besides, we also explore the brain networks in the temporal granularity, including graph theory analysis in the entire task stage and three sub-stages. The multi-granularity analysis shows that the brain information interaction of patients with stroke is highly affected in cross-frequency bands which demonstrates that the coupling of different frequency bands is an effective tool for exploring the impact of stroke.

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