EEG biomarkers for the diagnosis and treatment of infantile spasms

Abstract

Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.

A comprehensive wireless neurological and cardiopulmonary monitoring platform for pediatrics

“…Fig 7 compares the distribution of EEG power (absolute broadband power and relative delta frequency) in sleep without epileptiform spikes versus sleep with epileptiform spikes. These findings are concordant with prior studies [ 26 , 27 ] that showed an increase in both these metrics for individuals with epilepsy.…”

EEG‐based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

“…Overall, we have demonstrated that coherence‐based networks can be constructed using EEG signals in the IESS population. In the future, understanding networks through connectivity and hub localization can be applied clinically to assess treatment options, 41 such as localizing the epileptic zone in pre‐surgical planning, 42 identifying the epileptic network for neuromodulation therapy, 43 or as a marker for treatment response. 44 …”

High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery

“… 28 , 30 , 62 Many studies have shown that different centrality measures can quantify epileptic networks within standard EEG to be potential biomarkers for epileptic tissue. 40 , 45 , 46 , 63 , 64 Similar analyses in the context of HFOs are also promising, as analysing the connectivity of EEG in the HFO frequencies or HFOs themselves shows correlation with patient outcome or can improve specificity to epilepsy. 31-36 However, network analysis with HFOs is challenging because some channels have few events, and most of the detected events on one channel are not detected on neighbouring channels even when there is a clear network discharge—which is precisely the data that are most important to analyse the network (e.g.…”