Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Fibromyalgia is characterized by widespread pain and hypersensitivity, often linked to altered brain activity.
• Resting-state electroencephalography (EEG) shows abnormalities in chronic pain, potentially worsened by anxiety and symptom duration.
• Identifying objective biomarkers for fibromyalgia is crucial for diagnosis and treatment.

Purpose of the Study:

• To differentiate fibromyalgia patients from healthy controls using resting-state EEG signals via multivariate pattern analysis.
• To investigate the influence of anxiety and symptom duration on EEG patterns in fibromyalgia.
• To identify key scalp regions and frequency bands indicative of fibromyalgia.

Main Methods:

• Fifty-one female participants (25 fibromyalgia, 26 controls) underwent resting-state EEG.
• Multivariate pattern analysis was applied to intertrial EEG signals across pain-processing frequency bands.
• Machine learning classifiers were trained using normalized power spectral density, incorporating anxiety scores and symptom duration.

Main Results:

• Models distinguished fibromyalgia patients from controls with AUC > 0.75 across all bands, reaching 0.99 (beta/gamma) with anxiety.
• Symptom duration was a significant factor, with models differentiating short-term from long-term patients up to 0.96 AUC (beta/gamma).
• Theta power alterations in frontal/parietal regions and frequency-specific patterns indicated disrupted pain processing.

Conclusions:

• Resting-state EEG combined with multivariate pattern analysis shows promise for identifying fibromyalgia biomarkers.
• Anxiety and prolonged symptom duration significantly impact EEG alterations in fibromyalgia.
• These findings support the development of objective diagnostic and therapeutic tools for fibromyalgia.