[Quantitative EEG analysis in Alzheimer's disease: spectral, coherence and complexity parameters]

Area of Science:

• Neuroscience
• Biomedical Engineering
• Clinical Neurology

Context:

• Alzheimer's disease (AD) diagnosis and monitoring remain challenging, particularly in early stages.
• Conventional electroencephalography (EEG) analysis methods have limitations in detecting subtle neurological changes.
• There is a need for sensitive, widely available tools to track disease progression and aid differential diagnosis.

Purpose:

• To investigate the linear and non-linear characteristics of EEG signals in early-stage Alzheimer's disease (AD).
• To evaluate the efficacy of advanced complexity measures (Omega-complexity, synchronised probability) compared to conventional methods (frequency spectrum, coherence).
• To explore the potential of EEG analysis for monitoring AD therapy and progression, and for differential diagnosis.

Summary:

• Frequency spectrum analysis revealed EEG slowing in early AD, a feature previously associated with later stages.
• Coherence analysis showed reduced values in most frequency bands but lacked consistency across leads, limiting its diagnostic utility.
• Complexity measures demonstrated the most robust changes, with reduced synchronised probability and significantly higher Omega-complexity in AD patients compared to controls.

Impact:

• The study identifies novel EEG complexity measures as highly sensitive indicators of early Alzheimer's disease.
• These findings suggest potential for developing accessible methods for early AD detection, monitoring treatment efficacy, and supporting differential diagnosis.
• The observed changes in Omega-complexity and synchronised probability may reflect underlying neurobiological alterations, such as impaired cortical afferentation and synaptic loss.