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Review
. 2025 May:128:74-82.
doi: 10.1016/j.seizure.2024.07.001. Epub 2024 Jul 2.

Modern neurophysiological techniques indexing normal or abnormal brain aging

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Free article
Review

Modern neurophysiological techniques indexing normal or abnormal brain aging

Angelo Pascarella et al. Seizure. 2025 May.
Free article

Abstract

Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.

Keywords: Cognitive decline; Dementia; Electroencephalogram; Event-related potentials; Machine learning; Transcranial magnetic stimulation.

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Conflict of interest statement

Declaration of competing interest Dr. Angelo Pascarella reports no disclosures. Dr. Lucia Manzo reports no disclosures. Prof. Edoardo Ferlazzo reports no disclosures.

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