MVME-RCMFDE framework for discerning hyper-responsivity in Autism Spectrum Disorders

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Autism Spectrum Disorder (ASD) presents significant clinical heterogeneity, complicating therapeutic interventions.
• Impaired sensory processing is a key characteristic of ASD, necessitating subgroup identification for personalized treatment.
• Delineating clinically meaningful subgroups is crucial for advancing individualized medical care in ASD.

Purpose of the Study:

• To develop a novel framework for differentiating the hyper-responsive subgroup within ASD.
• To analyze the complexity patterns of Visual Evoked Potentials (VEPs) in individuals with ASD.
• To identify potential biomarkers for personalized ASD treatment strategies.

Main Methods:

• A new signal decomposition method, Modified Variational Mode Extraction (MVME), was employed.
• MVME segments signals into five modes with reduced spectral overlap in lower frequencies.
• A multiscale entropy approach, Refined Composite Multiscale Fluctuation-based Dispersion entropy (RCMFDE), was applied to extracted modes.

Main Results:

• MVME demonstrated superior performance in simulated and real VEPs compared to existing techniques.
• Relative Complexity analysis using RCMFDE showed an increasing trend in 43%-50% of ASD participants across modes 1-4.
• The analysis identified specific complexity patterns in VEPs associated with a hyper-responsive subgroup.

Conclusions:

• The MVME-RCMFDE approach effectively discriminates the hyper-responsive subgroup in ASD.
• This method identifies distinct patterns in delta, theta, alpha, and beta frequency bands.
• The findings support the development of targeted interventions for specific ASD subgroups.