Brain aging is a multidimensional process marked by progressive changes in functional performance, neural structure, and molecular physiology. Among functional indicators, episodic memory and processing speed show the most consistent age-related decline, whereas fine motor coordination is also sensitive to aging. In contrast, executive function, attention, language, sensory abilities, and mood are strongly influenced by medical, psychiatric, and environmental factors, making them less specific markers of neural aging. Advances in digital biomarkers using smartphones and wearables now enable real-world, high-frequency assessment of gait, speech, cognition, gaze, and affect, complementing conventional clinic-based testing. Neuroimaging markers include structural MRI indices of cortical and subcortical atrophy that follow a “last-in, first-out” pattern, white-matter hyperintensities and microbleeds reflecting cerebrovascular aging, and large-scale network alterations such as reduced default-mode and frontoparietal connectivity with age-related reorganization. 18F-fluorodeoxyglucose positron emission tomography provides metabolic signatures that help differentiate normal aging from early Alzheimer’s disease, while MRI-based brain age modeling generates a brain age gap that captures individual differences in vulnerability to accelerated aging. Fluid biomarkers from blood and cerebrospinal fluid index pathways of neuronal injury, glial activation, vascular dysfunction, and systemic aging. These measures are increasingly complemented by proteomic and epigenetic aging clocks that quantify biological aging trajectories across the lifespan. Because no single marker captures the full complexity of brain aging, integrative machine-learning models that combine functional, neuroimaging, and fluid-based data offer a promising framework for estimating biological brain age, identifying high-risk individuals, and informing early detection and prevention of age-related neurodegenerative diseases.
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