Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies

BackgroundBrain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression of neurodegeneration. The BAG captures brain atrophy as measured with structural Magnetic Resonance Imaging (MRI). Electroencephalography (EEG) has also been explored as a functional means for estimating brain age. However, EEG studies showed mixed results for BAG including a seemingly paradoxical negative BAG, i.e. younger predicted age than chronological age, in neurological populations. ObjectivesThis study critically examined brain age estimation from spectral EEG power as common measure brain activity in two of the largest public EEG datasets containing neurological cases alongside controls. MethodsEEG recordings were analyzed from individuals with neurological conditions (n=900, TUAB data; n=417 MCI & n=311 dementia, CAU data) and controls (n=1254, TUAB data; n=459, CAU data). ResultsWe found that age-prediction models trained on the reference population systematically under-predicted age in people with neurological conditions replicating a negative BAG for diseased brain activity. Inspection of age-related trends along the EEG power spectra revealed complex frequency-dependent alterations in neurological groups underlying the seemingly paradoxical negative BAG. ConclusionsThe utility of brain age as an interpretable biomarker relies on the observation from structural MRI that progressive neurodegeneration often broadly resembles accelerated aging. This assumption can be violated for functional assessments such as EEG spectral power and, potentially, different neurological and psychiatric conditions or therapeutic effects. The sign of the BAG may not meaningfully be interpreted as a deviation from normal aging.