# Delta Power in SLC6A1-Related Neurodevelopmental Disorder: Operationalizing Quantitative EEG Metrics for Biomarker Development

## TL;DR

This study identifies elevated delta power in EEGs of patients with SLC6A1-related neurodevelopmental disorder, suggesting it could serve as a biomarker.

## Contribution

The study introduces delta power as a potential biomarker for SLC6A1-NDD using quantitative EEG metrics and automated analysis tools.

## Key findings

- Patients with SLC6A1-NDD had significantly higher delta power compared to controls.
- Delta power increased with age in patients but decreased in controls.
- Persyst's frequency threshold of delta activity best differentiated patients from controls.

## Abstract

Introduction: SLC6A1-related neurodevelopmental disorder (SLC6A1-NDD) is an epileptic encephalopathy linked to mutations in the SLC6A1 gene and is characterized by early-onset seizures and developmental delays. Despite the growing recognition of SLC6A1 as a major cause of early-onset epilepsy, the electrophysiological changes associated with the disorder remain inadequately characterized. This study aims to identify electrophysiological biomarkers of SLC6A1-NDD by characterizing EEG delta power using automated tools, EEGLAB (v2023.1) and Persyst 13, exploring age- and state-related effects. Methods: We analyzed EEG recordings from 20 patients with SLC6A1-NDD and 20 neurotypical age- and sex-matched controls using EEGLAB and Persyst, quantifying delta power and related metrics. The Wilcoxon signed-rank method tested for differences between patients and controls, area under the curve (AUC) values evaluated patient classifier models, and Pearson’s correlation assessed concordance between EEGLAB and Persyst. Results: Patients with SLC6A1-NDD exhibited significantly elevated delta power (19.4 ± 4.1) compared to controls (14.2 ± 3.0; p < 0.001). The mean delta power showed an age-dependent increasing trend in patients (b = 0.5), contrasting with a decline in controls (b = −1.0; p < 0.001). In Persyst, the frequency of delta activity above an optimized threshold best differentiated patients from controls in wake epochs (AUC = 0.93). Concordance between EEGLAB and Persyst was one-to-one but with moderate variability (R2 = 0.644; p < 0.001). Conclusions: Elevated delta power is a notable feature of SLC6A1-NDD. Cross-platform comparison demonstrates the feasibility of quantitative EEG analysis, while imperfect concordance highlights the need for pipeline standardization. Future work should validate these findings in larger cohorts and, as suitable reference data emerge, benchmark delta power metrics against age-matched children with other developmental and epileptic encephalopathies.