EEG phase-amplitude coupling to stratify encephalopathy severity in the developing brain

https://doi.org/10.1016/j.cmpb.2021.106593Get rights and content

Highlights

  • Developed computational quantification of EEG Mean Phase Amplitude Coupling (PACm).

  • PACm reflects the maturity of brain development.

  • PACm can successfully determine the severity of Hypoxic-Ischemic Encephalopathy.

Abstract

Background

Neonatal hypoxic ischemic encephalopathy (HIE) is difficult to classify within the narrow therapeutic window of hypothermia. Neurophysiological biomarkers are needed for timely differentiation of encephalopathy severity within the short therapeutic window for initiation of hypothermia therapy.

Methods

A novel analysis of mean Phase Amplitude Coupling index, PACm, of amplitudes high frequencies (12–30 Hz) coupled with phases of low (1,2 Hz) frequencies was calculated from the 6 h EEG recorded during the first day of life. PACm values were compared to identify differences between mild versus higher-grade HIE, respectively, for each of the EEG electrodes. A receiver operating characteristic curve was generated to examine the performance of PACm.

Results

38 newborns with different HIE grades were enrolled in the first 6 h of life. Threshold PACm 0.001 at Fz, O1, O2, P3, and P4 had AUC >0.9 to differentiate HIE severity and predict the persistence of moderate to severe encephalopathy that requires treatment with hypothermia.

Conclusion

PAC is a promising biomarker to identify mild from higher severity of HIE after birth.

Introduction

Neonatal asphyxia is a worldwide problem that affects 4 million newborns each year [1]. The underlying disturbances in cerebral blood flow and neuronal activity result in an evolving neonatal encephalopathy (NE), the severity of which can be challenging to classify immediately after birth [2,3]. Persistence of moderate to severe encephalopathy is associated with death and or disability and requires treatment with hypothermia therapy [4]. However the 6 h narrow therapeutic window of hypothermia necessitates early recognition with adjunct biomarkers to stratify the evolving encephalopathy severity [5], [6], [7], [8]. The bottle neck in neonatal care has been a lack of quantifiable neurophysiological measures of injury at the bedside which is critically needed for clinicians to make prompt neuroprotective interventions for those at high risk. In particular, this area of research has a timely impact as new trials are targeting those with mild hypoxic ischemic encephalopathy (HIE), a heterogeneous group where a subset has deficits [7,9].

Continuous brain EEG monitoring is routinely used in newborns with HIE to assess brain function [10], [11], [12]. It is known that neuronal activities in the human brain are interconnected and processed among and/or across cerebral regions at distinct spectrum of frequencies [13], [14], [15].

The neonatal brain functions networks are operational especially at very low frequencies [13,16].

In this study, we took an innovative approach by investigating whether the phase amplitude coupling (PAC) indexes derived from an EEG tracing can serve as a physiological biomarker in HIE as it was demonstrated to be sensitive to subtle brain injuries [15,17,18]. PAC can be estimated from intracortical or scalp EEG and refers to the amplitude modulation of a high-frequency brain oscillations by the phase of a low-frequency brain oscillation. PAC has not been measured in the neonatal brain to this date [13,19] but has been reported as critical in encoding, storage, and memory [20], [21], [22], [23] functions that are all affected by an asphyxia insult at birth [24].

Our hypothesis was that PAC measured with a cross-frequency PAC index from scalp EEG in the first six hours of life is associated with encephalopathy severity in a cohort with HIE.

Section snippets

Study participants

The institutional review board at the University of Texas Southwestern Medical Center approved the study. A written informed consent was then obtained from parents prior to enrollment.

In this study, term newborns with a birth weight of more than 1800 g, admitted to the Parkland Hospital's neonatal intensive care unit (NICU) from November 2017 to December 2019 with metabolic acidosis and signs of encephalopathy within the first six hours of life (HOL) were enrolled. The classification of HIE was

Results

38 infants were enrolled after meeting the inclusion criteria and informed consent was obtained. Five infants were excluded due to high level of noise and short data length after preprocessing, resulting in a total of 33 newborns for further data analysis in the study. EEG data obtained in the first six hours were used to quantify PACm for each neonate. The difference in averaged time (after birth) to initiate the EEG recording between mild vs. higher severity HIE groups was 12 min with t-tests

Discussion

The timely and accurate identification of the HIE severity is of great significance in promptly initiating efficient therapeutic processes in order to optimize outcomes. This study demonstrates the feasibility of using the PAC index quantified from the neonatal EEG signal as a sensitive indicator to identify the severity of HIE within the first 6 h of life.

With growth and maturation the brain electrical signature of brain oscillations undergoes changes that indicates an intact brain

Short summary

PAC analysis reveals the spatiotemporal relationship between fast and slow waves like the delta brushes in the neonatal developing brain. In this prospective study, EEG-based PAC indexes between the amplitudes of high (12–30 Hz) frequencies coupled with phases of low (1, 2 Hz) frequencies differentiated infants with true mild HIE from higher-grade encephalopathy.

Funding source

Dr. Lina Chalak is funded by NIH Grant R01NS102617-03.

CRediT authorship contribution statement

Xinlong Wang: Methodology, Software, Formal analysis, Writing – original draft. Hanli Liu: Writing – review & editing, Supervision. Srinivas Kota: Writing – review & editing. Yudhajit Das: Writing – review & editing. Yulun Liu: Formal analysis. Rong Zhang: Writing – review & editing. Lina Chalak: Data curation, Conceptualization, Writing – review & editing, Supervision, Visualization, Project administration, Funding acquisition.

Declaration of Competing Interest

no conflicts to disclose

Acknowledgment

We thank Pollieanna Sepulveda and Maricel Maxey who helped with data collection and Parkland Hospital support where the study was conducted.

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