Children with multiple sclerosis (MS) frequently have reduced visual processing speed, which can affect learning, social interaction and daily functioning. Identification of cognitive impairment at an early stage is essential for timely intervention. This study explores if electroencephalography (EEG)-based functional connectivity (FC) can be utilized as a non-invasive biomarker for visual processing speed in children with MS.

Resting state EEG data and neuropsychological assessments were analyzed from ten children diagnosed with MS. A custom preprocessing pipeline was developed and validated to ensure data quality for FC analysis. Connectivity metrics, including fronto-occipital and interhemispheric connectivity, network efficiency based on minimum spanning tree analysis, and individual alpha peak frequency, were extracted from source-reconstructed EEG. Correlations were tested between these metrics and visual processing speed, measured with the Processing Speed Index of the Wechsler Intelligence Scale for Children.

As hypothesized, trends toward greater network efficiency and stronger interhemispheric and fronto-occipital connectivity were associated with higher visual processing speed. Although none of these associations reached statistical significance, the results support the potential of EEG-based functional connectivity to evolve into a clinically relevant biomarker, enabling early diagnosis, personalized treatment and improved prognosis for children with MS.

Code repository - https://github.com/Ilse2001/code-repository-msc-thesis-eeg-fc