Gait disturbances are a prevalent issue in individuals with neurological disorders, affecting millions worldwide. These disorders, including Parkinson's disease, multiple sclerosis, and stroke, compromise the central nervous system's ability to control motor functions, leading to instability and increased fall risk. Falls are a major health concern due to the potential for severe injury and subsequent decreased quality of life. Recent advancements in neuroscience and technology have opened new avenues for understanding the relationship between brain activity and gait impairments. Neuroimaging techniques, such as functional MRI and EEG, offer insights into how brain networks are disrupted in people with gait abnormalities. Meanwhile, wearable sensors provide real-time analysis of gait patterns outside laboratory settings. The integration of these technologies with machine learning has the potential to revolutionize predictive modeling in clinical practice, allowing for precise fall risk prediction and timely interventions. This research seeks to harness these innovations to improve patient outcomes by bridging the gap between complex brain dynamics and observable motor dysfunctions.

The primary problem in this research topic is the high incidence of falls among individuals with neurological disorders, exacerbated by our limited understanding of the precise neurological mechanisms underlying gait disturbances. Despite the prevalence of falls and their critical impact on health and quality of life, current preventive measures and interventions are often inadequate due to a lack of targeted, predictive insights. Recent advances in neuroimaging and wearable technology provide an opportunity to address this gap. By utilizing functional MRI and EEG, researchers can explore disrupted brain dynamics that affect motor control. In tandem, wearable sensors allow continuous monitoring of gait patterns in real-world settings, offering crucial data on individual variability and early signs of instability.

To achieve a comprehensive understanding, this research will integrate these technological advancements with machine learning algorithms. By analyzing large datasets of neuroimaging findings and gait parameters, predictive models can be developed to foresee fall risks with greater accuracy. This approach not only enhances our understanding of the neural bases of gait disorders but also facilitates the design of personalized interventions, ultimately aiming to reduce fall risk and improve clinical outcomes for patients suffering from neurological disorders.

The scope of this research topic encompasses the interdisciplinary investigation of brain dynamics and gait disturbances in neurological disorders, with a focus on predictive modeling and fall prevention. Authors are encouraged to contribute to several core themes, including:

1. Neurodynamic Mechanisms: Studies that explore the neural substrates of gait abnormalities through neuroimaging techniques.

2. Predictive Modeling: Research utilizing machine learning to identify biomarkers and predict fall risk in patients with neurological disorders.

3. Wearable Technology: Innovations in sensor technology for real-time monitoring of gait patterns and mobility in everyday environments.

Are you interested in contributing a manuscript to this Research Topic?

Exploring Brain Dynamics and Gait in Neurological Disorders: Implications for Predictive Modeling and Fall Prevention