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

Area of Science:

• Neuroscience
• Computational Biology
• Genetics

Background:

• SYNGAP1-Related Disorder (SYNGAP1-RD) is a genetic condition impacting neurodevelopment.
• Understanding EEG signal alterations in SYNGAP1-RD is crucial for identifying biomarkers.
• Computational markers of network dynamics may offer insights into disease mechanisms and treatment efficacy.

Purpose of the Study:

• To analyze EEG signals in pediatric SYNGAP1-RD patients and neurotypical controls.
• To identify distinguishing computational markers of network dynamics using Detrended Fluctuation Analysis (DFA).
• To explore the potential of these markers as biomarkers for SYNGAP1-RD and treatment response.

Main Methods:

• EEG data from nine pediatric SYNGAP1-RD patients and eight controls were analyzed.
• Detrended Fluctuation Analysis (DFA) was employed to quantify long-range temporal correlations.
• Topographic maps visualized DFA exponent values and their variability across frequency bands (delta, theta, alpha, beta, gamma).

Main Results:

• SYNGAP1-RD patients exhibited significantly higher DFA values in beta and low gamma bands, indicating stronger long-range temporal correlations.
• Increased variability in DFA exponents across channels was observed in SYNGAP1-RD patients in beta and gamma bands.
• Variability in DFA exponents was higher in controls than SYNGAP1-RD in the delta frequency band.

Conclusions:

• Topographical patterns of long-range temporal correlations reveal localized abnormalities specific to SYNGAP1-RD.
• Abnormal DFA exponents (higher or lower than normal) can serve as potential EEG-based biomarkers for SYNGAP1-RD.
• These findings may aid in developing biomarkers for effective treatments in SYNGAP1-RD and related genetic disorders.