Adam Woods

Predicts Patient Response to Electrical Brain Stimulation and Enables Subject-Specific Treatment Optimization

This machine learning algorithm predicts treatment outcome improvements in adults undergoing electrical brain stimulation and enables the delivery of individualized dosing. For example, declines in cognitive function typically present themselves in older adults, who start to naturally experience a decline in functions such as working memory, reasoning, and processing speed. Its prevalence is estimated to be over 11% in adults aged 45 years or older and in almost all adults over the age of 65 years. Transcranial direct current stimulation (tDCS) has been widely used for the last two decades as a therapeutic tool to improve cognitive function, mental health (e.g., depression, anxiety, etc.), and chronic pain in older adults. Its number of applications only continues to grow. However, optimal dosing parameters that underlie positive outcomes, such as electric field intensity or electrode placement, remain evasive. In addition, conventional approaches typically utilize fixed parameters across patients without taking into consideration individual anatomical factors, impacting therapeutic effectiveness.

Researchers at the University of Florida have developed an algorithm that employs machine learning and MRI scans to determine an optimized dosing strategy for the delivery of transcranial direct current stimulation (tDCS), with applications to many forms of electrical brain stimulation (transcranial magnetic stimulation, electroconvulsive therapy, deep brain stimulation, etc.). This information predicts working memory response to tDCS in any individual, enabling precision treatment using individual doses and improving tDCS treatment outcomes.

Application

Machine learning algorithm provides individualized and precise tDCS dosing and predicts treatment outcome improvements from electrical stimulation of the brain

Advantages

• Predicts treatment response after electrical brain stimulation, enabling precision dosing tailored to each individual
• Provides information on accurate and subject-specific electrode placement and electric current intensity, increasing tDCS treatment effectiveness
• This algorithm can be applied to treat various clinical conditions, such as depression or chronic pain, on any type of population and with any other method that applies electrical current to the brain via surface electrodes

Technology

This machine learning algorithm provides precise dosing of electrical stimulation of the brain. By combining finite element computational modeling derived from MRI scans of the brain, identifying tissue types with machine learning, it has the ability to design an optimized dosing strategy for the delivery of electrical current through electrodes on the scalp. This optimized strategy involves subject-specific electrode placement and electric field intensity that can be applied to any new subject so long as a basic MRI scan is acquired.