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The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

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Advanced qEEG analyses discriminate between dementia subtypes.

Masha Burelo¹, Jack Bray¹, Olga Gulka¹

¹School of Medical Sciences, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, Scotland AB25 2ZD, United Kingdom.

Journal of Neuroscience Methods

|June 18, 2024

Summary

Quantitative electroencephalography (qEEG) effectively distinguishes dementia subtypes like Alzheimer's Disease and Dementia with Lewy Bodies. Advanced spectral analyses, including periodic and aperiodic brain activity, show promise for improving diagnostic accuracy.

Keywords:

Alzheimer Aperiodic Dementia Electroencephalography FOOOF Lewy Body Parkinson Spectral analysis

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Last Updated: Jul 15, 2026

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Published on: December 15, 2023

Area of Science:

Neuroscience
Medical Imaging
Biomedical Engineering

Background:

Dementia diagnosis, including subtypes like Alzheimer's Disease (AD), Dementia with Lewy Bodies (DLB), and Parkinson's Disease with dementia (PDD), presents diagnostic challenges.
Neurodegenerative conditions causing dementia are characterized by cognitive decline.

Purpose of the Study:

To evaluate the effectiveness of quantitative electroencephalography (qEEG) analyses in differentiating dementia subtypes from healthy controls.
To assess qEEG performance under both eyes closed (EC) and eyes open (EO) conditions.

Main Methods:

Employed various quantitative electroencephalography (qEEG) analysis methods, including Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses.
Utilized Fitting-Oscillations-&-One-Over-F (FOOOF) modeling to dissociate periodic and aperiodic spectral components.
Examined brain activity across different frequency ranges (e.g., theta range 4-8 Hz, broader range 4-15 Hz) under EC and EO conditions.

Main Results:

Classic FFT and AR power analyses differentiated between dementia subtypes and controls in the 4-8 Hz theta range.
FFT analysis in the 4-15 Hz range showed significant discriminators, distinguishing AD and DLB, and identified slowing in synucleinopathies.
FOOOF modeling revealed subtype-specific slowing of brain oscillatory peaks and robust differences in aperiodic parameters, indicating diagnostic potential.

Conclusions:

qEEG methods demonstrate reliability in detecting and differentiating dementia subtypes.
Integrated spectral analyses, particularly those separating periodic and aperiodic brain activity, can enhance future dementia diagnostic accuracy.