Multimodal Signatures of Brain Aging: From Descriptive Analyses to Machine Learning-Based Integration
Chiara Caligiuri1, Chiara Feroleto1, Marta Morotti2
1Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
International Journal of Molecular Sciences
|July 28, 2026
Summary
Brain aging involves distinct motor and cognitive declines, with structural changes in neurons but preserved functional connectivity. Machine learning shows potential for identifying age-related brain alterations.
Area of Science:
- Neuroscience
- Aging Research
- Systems Neuroscience
Background:
- Age-related neurophysiological changes impact motor and cognitive functions.
- Identifying brain aging biomarkers is crucial for intervention strategies.
Purpose of the Study:
- To investigate age-related patterns in motor/cognitive performance, functional connectivity, and synaptic organization.
- To analyze brain aging across multiple domains in mice models.
Main Methods:
- Assessed motor and cognitive performance in Young, Adult, and Old mice.
- Utilized Golgi-Cox staining and immunofluorescence for synaptic analysis.
- Performed local field potential recordings and machine learning integration.
Main Results:
- Adult mice showed reduced locomotor activity and forelimb force; Old mice exhibited memory decline.
- Region-specific changes in neuronal spine density and neurotransmitter transporter expression were observed.
- Functional connectivity remained stable, but machine learning achieved high discrimination between Adult and Old mice.
Conclusions:
- Brain aging presents with specific behavioral and structural alterations, not uniform functional decline.
- Synaptic organization changes are region-specific during aging.
- Multimodal analysis, including machine learning, aids in understanding complex age-related brain changes.