Improved Alzheimer's Disease versus Frontotemporal Lobar Degeneration Differential Diagnosis Combining EEG and Neurochemical Biomarkers: A Pilot Study

Background: Distinguishing between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) results in poor diagnostic accuracy. Objective: To investigate the utility of electroencephalography (EEG)-based biomarkers in comparison and in addition to established cerebrospinal fluid (CSF) biomarkers in the AD versus FTLD differential diagnosis. Methods: The study cohort comprised 37 AD and 30 FTLD patients, of which 17 AD and 9 FTLD patients had definite diagnoses. All participants had CSF neurochemical (NCM) biomarker analyses (A beta(1-42), T-tau, P-tau181, and Nf-L) and underwent 19-channel resting-state EEG. From the EEG spectra, dominant frequency peaks were extracted in four regions resulting in four dominant frequencies. This produced eight features (4 NCM + 4 EEG). Results: When NCM and EEG markers were combined, the diagnostic accuracy increased significantly. In the whole group, the accuracy went up from 79% (NCM) to almost 82%, while in the definite group only, it went up from around 85% to almost 95%. Two differences in the occurrence of the dominant EEG frequency were discovered: people lacking a clear dominant peak almost all had definite AD, while people with two peaks more often had FTLD. Conclusion: Combining EEG with NCM biomarkers resulted in differential diagnostic accuracies of 82% in clinically diagnosed AD and FTD patients and of 95% in patients having a definite diagnosis, which was significantly better than with EEG or NCM biomarkers alone. This suggests that NCM and EEG markers are complementary, revealing different aspects of the disease and therefore confirms again their relevance in developing additional diagnosis tools.

Automated summary

This study investigates the utility of EEG-based biomarkers in comparison to established CSF biomarkers in differentiating between AD and FTLD. Results show that combining EEG with CSF biomarkers significantly improves diagnostic accuracy, and also identifies differences in dominant EEG frequencies.