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Machine Learning May Support Personalized Mental Health Therapies

Using machine learning, researchers detected variability among children’s neural anatomy that could inform personalized treatments for mental health disorders.

Machine learning may support personalized mental health therapies

Source: Getty Images

By Jessica Kent

- Researchers from Penn Medicine leveraged machine learning techniques to identify the size and shape of brain networks in children, which could lead to improved understanding and more personalized treatment of mental health conditions.

Published in the journal Neuron, the study used machine learning tools to analyze the functional magnetic reasoning imaging (fMRI) scans of nearly 700 children, adolescents, and young adults. The analysis is the first to show that neuroanatomy can vary significantly among children, which is refined during development.

The human brain has a pattern of folds and ridges on its surface that provide physical landmarks for finding brain areas. To study the functional networks that govern cognition, researchers will typically line up activation patterns with these physical landmarks. However, this process assumes that the functions of the brain are located on the same landmarks in each person.