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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Jun 21, 2025

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

Modern neurophysiological techniques indexing normal or abnormal brain aging.

Angelo Pascarella¹, Lucia Manzo², Edoardo Ferlazzo¹

¹Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy.

|July 7, 2024

Summary

This summary is machine-generated.

Brain aging impacts cognition and function, but new neurophysiological tools like electroencephalography (EEG) and transcranial magnetic stimulation (TMS) offer insights. Artificial intelligence (AI) aids in diagnosing age-related cognitive decline.

Keywords:

Cognitive decline Dementia Electroencephalogram Event-related potentials Machine learning Transcranial magnetic stimulation

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Published on: May 7, 2014

Area of Science:

Neuroscience
Gerontology
Biomedical Engineering

Background:

Brain aging causes cognitive, motor, and sensory decline, with mechanisms like altered neurogenesis and synaptic plasticity implicated.
Understanding physiological and pathological brain aging is crucial for developing effective interventions.

Purpose of the Study:

To review recent advances in electrophysiological techniques for studying brain aging.
To highlight the application of EEG, ERP, TMS, and AI in diagnosing and predicting age-related cognitive decline.

Main Methods:

Review of electroencephalography (EEG), event-related potentials (ERP), and transcranial magnetic stimulation (TMS) methodologies.
Integration of Artificial Intelligence (AI) and machine learning approaches for data analysis.
Analysis of neurophysiological markers in physiological and pathological brain aging.

Main Results:

Physiological aging shows altered EEG spectral power, connectivity, ERP, and TMS parameters.
Pathological aging (e.g., Alzheimer's disease) exhibits more pronounced disruptions in these neurophysiological measures.
AI and machine learning demonstrate potential in classifying cognitive impairment and predicting disease progression.

Conclusions:

Electrophysiological techniques provide valuable insights into neural activity and network function during brain aging.
AI integration enhances diagnostic accuracy and prognostic capabilities for age-related cognitive disorders.
Standardization and multimodal integration are key for advancing brain aging research.