Walking as a Window to the Brain: Redefining Gait in Neurology

Area of Science:

• Neuroscience
• Neurology
• Biomedical Engineering

Background:

• Gait disturbances are a major disabling feature in numerous neurological diseases, impacting independence and survival.
• Traditional bedside gait observation lacks sensitivity and reproducibility for early or subtle abnormalities.
• Advances in digital mobility analysis offer promising, ecologically valid biomarkers for neurological function.

Purpose of the Study:

• To explore the potential of digital mobility outcomes as biomarkers in neurological disorders.
• To highlight the role of advanced gait analysis in diagnosis, prognosis, and therapeutic trials.
• To discuss the integration of gait analysis with other biomarkers for a systems-level understanding of neural integrity.

Main Methods:

• Utilizing wearable sensors, motion capture, pressure-sensitive walkways, and force plates.
• Applying artificial intelligence and machine learning to analyze gait parameters.
• Integrating digital gait data with neuroimaging, electrophysiology, and molecular biomarkers.

Main Results:

• Digital gait analysis can detect subtle, early abnormalities and support differential diagnosis across various neurological conditions.
• These measures serve as sensitive endpoints for therapeutic trials and provide prognostic information on falls and survival.
• Gait analysis is increasingly applied in conditions like Parkinson's disease, ataxia, multiple sclerosis, and post-stroke rehabilitation.

Conclusions:

• Reconceptualizing gait as a "sixth vital sign" reframes mobility as a multidimensional biomarker of neural and systemic health.
• Harmonized protocols, robust validation, and multimodal integration are crucial for translating gait analysis into clinical practice.
• Addressing ethical considerations like equity, accessibility, and algorithmic bias is essential for widespread adoption in precision neurology.