Characterizing functional connectivity alterations in functional/ dissociative seizures using resting-state and...

Area of Science:

• Neuroscience
• Medical Imaging

Background:

• Functional/dissociative seizures (FDS) are characterized by complex neurological symptoms.
• Identifying reliable biomarkers for FDS is crucial for accurate diagnosis and treatment.

Purpose of the Study:

• To investigate alterations in brain functional networks in FDS patients using advanced functional connectivity analysis.
• To identify potential network-level biomarkers for differentiating FDS from healthy controls (HC).

Main Methods:

• A 7-Tesla fMRI study was conducted on 11 FDS patients and 11 HC during resting-state and naturalistic movie paradigms.
• Parcel-wise Pearson correlations and eigenvector centrality were used to compute functional connectivity and network influence.
• Group differences were analyzed using motion-controlled general linear models, with logistic regression for classification.

Main Results:

• FDS patients demonstrated consistent eigenvector centrality alterations in limbic, somatomotor, and ventral attention networks across both fMRI conditions.
• High classification accuracies (96% resting-state, 93% naturalistic stimulation) were achieved using key regions of interest (ROIs).
• Logistic regression models utilizing network features successfully distinguished FDS patients from controls.

Conclusions:

• Abnormal brain network hubs across multiple systems were identified in FDS patients using high-field fMRI.
• These findings support a predictive processing model of FDS and suggest potential biomarkers for improved differentiation.
• Further validation in larger cohorts is needed to confirm these preliminary findings.