Machine Learning Shows 90% Accuracy in Identifying Brain Health Improvements

In its continual pursuit of advancing brain health and function, a new study conducted by the Center for BrainHealth delves into neural biomarkers linked to enhancements for the brain health index. 

A cohort of 48 participants performed a basic task during an fMRI scan, and after 6 months, they repeated the task following cognitive conditioning exercises. Scientists used the individual’s blood flow activity to identify neural biomarkers associated with the task. 

Participants underwent a series of online tests assessing memory, critical thinking, and creativity. These evaluations generated an overall score known as the BrainHealth Index, reflecting the fitness of the brain. The refined machine learning system demonstrated remarkable success by predicting significant improvements in the BrainHealth Index with a 90% accuracy rate. This outcome affirms a link between the BrainHealth Index and the neural biomarkers in healthy individuals. 

Sandra Bond Chapman, Ph.D., head executive at the Centre for Brainhealth, commented, “To our knowledge, this is one of the first studies to demonstrate predictive markers of improved brain health with combined neural changes and behavioral gains following cognitive training in young to older age healthy adults.” 

Chapman went on to add, “This study takes us one step closer to precision brain health, which will ultimately allow interventions to be tailored to individuals.” 

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