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Predicting age of human subjects based on structural connectivity from diffusion tensor imaging

Lookup NU author(s): Dr Cheol Han, Dr Luis Peraza RodriguezORCiD, Professor John-Paul TaylorORCiD, Professor Marcus Kaiser


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Predicting brain maturity using noninvasive magnetic resonance images (MRI) can distinguish different age groups and help to assess neurodevelopmental disorders. However, group-wise differences are often less informative for assessing features of individuals. Here, we propose a simple method to predict the age of an individual subject solely based on structural connectivity data from diffusion tensor imaging (DTI). Our simple predictor computed a weighted sum of the strength of all connections of an individual. The weight consists of the fiber strength, given by the number of streamlines following tract tracing, multiplied by the importance of that connection for an observed feature--age in this case. We tested this approach using DTI data from 121 healthy subjects aged 4 to 85 years. After determining importance in a training dataset, our predicted ages in the test dataset showed a strong correlation (rho = 0.77) with real age deviating by, on average, only 10 years.

Publication metadata

Author(s): Han CE, Peraza LR, Taylor J-P, Kaiser M

Publication type: Report

Publication status: Published

Series Title:

Year: 2014

Pages: 1-7

Print publication date: 20/05/2014

Source Publication Date: 20-05-2014

Report Number: 1

Institution: Cornell University Library