The core model can accurately measure brain activity regardless of the specific disorder or disease. The team used 80,000 scans from 40,000 subjects to train the model and create BrainLM. This allows researchers to use BrainLM to customize specific tasks and formulate questions in other studies.
The researchers also tested the model on different samples to see how well it generalized to data collected from different browsers and from different populations, such as older and younger adults. They found that BrainLM performed well across a variety of samples and predicted the severity of depression, anxiety, and post-traumatic stress disorder better than other machine learning models.
The researchers’ plans include using this model to predict brain-related diseases in the future.
Source: Ferra
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