Investigating The Efficacy of Neuro-Acoustic Loop Methodology (NALM) As A Therapeutic Avenue for Dementia and Alzheimer's Disease: A Computational Approach.

Abstract

Dementia and Alzheimer's disease (AD) are among the most challenging neurodegenerative conditions facing healthcare systems worldwide. These conditions significantly impair memory, cognitive function, and overall quality of life for affected individuals. With over 25 million individuals currently affected globally and projections suggesting a doubling of cases every 20 years, the urgency to develop innovative therapeutic strategies cannot be overstated. While existing approaches primarily focus on symptomatic relief, regeneration, or disease modification, there is a pressing need for novel therapeutic approaches that can address the underlying mechanisms of these diseases and offer effective treatment options.

In response to this need, this study proposes a novel therapeutic avenue: Neuro-Acoustic loop methodology. This non-invasive methodology aims to not only enable early diagnosis and prognosis but also provide effective treatment strategies for managing the pathology and symptoms associated with Dementia and AD. By targeting specific brain wave frequencies—alpha, beta, theta, delta, and gamma—Neuro-Acoustic loop methodology seeks to modulate neural activity, inducing desired changes in brain wave patterns.

The rationale behind Neuro-Acoustic loop methodology (NALM) lies in the understanding that brain wave patterns are closely linked to cognitive function and neurological disorders. By utilizing sound waves at specific frequencies, this approach aims to influence these brain wave patterns in a targeted manner, potentially mitigating the cognitive decline observed in Dementia and AD. Importantly, NALM is non-invasive and has the potential to be delivered in various settings, making it a promising candidate for widespread adoption in clinical practice.

To evaluate the efficacy of NALM, this study employs advanced machine learning (ML) and deep learning (DL) algorithms. These algorithms are utilized to analyse the effects of NALM, on brain activity and correlate these effects with various biochemical parameters. By leveraging these computational techniques, aims to gain insights into the underlying mechanisms of action of NALM, and its potential impact on cognitive function