A Convolutional Neural Network Approach to Personalized Neuropil Density Prediction

Motivation: Bottom-up energy budgets provide a way to quantify electrical activity in the brain using metabolic imaging. However, existing models are not patient-specific, instead using generalized neural cell counts, preventing direct measures of cognitive activity in the brain. Goal(s): Our goal was to use a convolutional neural network (CNN) to demonstrate the possibility of predicting individualized neural cell counts. Approach: Multi-modal MRI from nine patients was used to model neural and synaptic density predictions, which were compared to silver standard counts using correlation coefficient in a cross-validation study. Results: The model demonstrates an ability to predict patient-specific energy budgets. Impact: The success of machine learning methods in predicting neural cell and synaptic density paves the way for the use of CNNs to generate patient-specific energy budgets, improving understanding of brain energetics at a microscopic level in health and disease.