Linking Age Changes in Human Cortical Microcircuits to Impaired Brain Function and EEG Biomarkers

Area of Science:

• Neuroscience
• Computational Biology
• Gerontology

Background:

• Human brain aging involves complex cellular and synaptic alterations.
• Understanding these changes' impact on brain function is limited by experimental constraints in humans.

Purpose of the Study:

• To model human brain aging using detailed cortical microcircuit simulations.
• To link specific cellular and synaptic changes to functional impairments and EEG biomarkers.

Main Methods:

• Integrated known human aging-related cellular/synaptic changes (e.g., loss of inhibitory cells, NMDA receptors, spines) into computational models.
• Simulated microcircuit activity in middle-aged and older individuals.
• Generated and analyzed simulated EEG potentials and power spectral changes.
• Employed machine learning to estimate aging mechanisms from simulated EEG biomarkers.

Main Results:

• Simulated aging mechanisms led to reduced neuronal firing rates and impaired signal detection.
• Emergent EEG power spectral changes in simulations mirrored key human aging biomarkers (reduced aperiodic offset, exponent, peak frequency).
• Machine learning accurately estimated cellular/synaptic aging from simulated EEG biomarkers.

Conclusions:

• Cellular and synaptic aging mechanisms directly contribute to impaired cortical function.
• Computational models can link micro-level aging changes to macro-level physiological biomarkers in brain signals.
• Findings provide a mechanistic link between cellular aging and observed EEG changes in humans.