Comprehensive Summary
This paper studies the use of quantitative electroencephalography (qEEG) as a biomarker for Alzheimer’s disease. The research was performed by reviewing and synthesizing findings from multiple EEG studies that examined spectral power, connectivity, and nonlinear complexity in Alzheimer’s and mild cognitive impairment (MCI) patients compared with healthy controls. The findings show consistent spectral slowing, with decreased alpha and beta activity and increased theta and delta activity, along with reduced connectivity between brain regions. The metrics revealed widespread network disintegration in fronto-parietal networks and diminished the adaptability of neural circuits. These indicate a loss of neural complexity, synaptic loss, and correlate with cognitive decline. The biomarkers show high diagnostic accuracy, with studies reporting sensitivities and specificities often exceeding 80-90% for distinguishing Alzheimer’s Disease from controls or predicting MCI progression to Alzheimer’s. Machine learning integration further enhances classification, achieving up to 99% accuracy in some EEG-based models.
Outcomes and Implications
The study has significant implications because it demonstrates that qEEG could serve as an accessible, cost-effective diagnostic and monitoring tool for Alzheimer's, offering significant advantages over PET imaging and CSF biomarkers that are invasive, expensive, or not widely available. qEEG has applications in early screening of at-risk individuals, distinguishing Alzheimer’s from other dementias, monitoring disease progression, and evaluating therapeutic response to pharmacological treatments like cholinesterase inhibitors or non-invasive brain stimulation therapies. Its sensitivity and specificity, which is around 90% in multiple studies, support its relevance for real-world use, especially in community or primary care settings. qEEG aligns with Alzheimer’s pathophysiology like neuroinflammation and neurodegeneration and has superiority for early preclinical detection over imaging. The clinical implementation of qEEG requires acquisition protocols, normative databases, and integration with multiple biomarker systems, but with advances in machine learning and portable EEG devices, qEEG can eventually be a defining identifier and biomarker for Alzheimer’s disease.