Detrended Fluctuation Analysis to Assess Long-Range Temporal Correlations in EEG from SYNGAP1-Related Disorder

This study aimed to analyze EEG signals from individuals with SYNGAP1-Related Disorder (SYNGAP1-RD) to identify distinguishing features from neurotypical controls, with a focus on computational markers of network dynamics and potential biomarkers of treatment response. A detailed analysis was performed on the EEG signals using Detrended Fluctuation Analysis (DFA), a method to assess the fractal properties of time series data and quantify long-range temporal correlations in the EEG. We analyzed the EEGs of nine pediatric SYNGAP1 patients and eight neurotypical pediatric control subjects. We calculated the DFA exponent for each channel for each patient and plotted the median DFA value and the standard deviation of the DFA values across subjects on topographic maps. The DFA exponent maps demonstrate distinct frequency-specific alterations in fractal dynamics, with SYNGAP1-RD patients showing higher DFA values in the upper-frequency bands [beta (13-30 Hz) and low gamma (30-55 Hz)] when compared to controls, indicating pathologically strong long-range temporal correlations in these frequency ranges. The standard deviation maps reveal statistically significantly higher DFA exponent variability across channels in SYNGAP1-RD when compared to controls in beta and gamma bands, while no significant differences were observed in theta and alpha bands. Variability in DFA exponents was higher in Controls than SYNGAP1-RD in the delta frequency band. This approach highlights long-range temporal correlations as a potential EEG-based biomarker to better understand and assess treatment in SYNGAP1-RD.Clinical Relevance-This study establishes how topographical patterns of long-range temporal correlations could uncover localized abnormalities specific to SYNGAP1-RD. Our findings could aid in developing biomarkers for effective treatments. We show how abnormal long-range temporal correlations (higher or lower than normal DFA exponents) can be used as potential biomarkers of genetic conditions that present with epilepsy, autism, and developmental disorders.