Author(s)

Guenadie Nibbs¹, Peter Bajorski²

¹Dirección General de Impuestos Internos, Santo Domingo, Dominican Republic.
²Rochester Institute of Technology, Rochester, USA.

In this study, we analyze brain activity data describing functional magnetic resonance imaging (MRI) imaging of 820 subjects with each subject being scanned at 4 different times. This multiple scanning gives us an opportunity to observe the consistency of imaging characteristics within the subjects as compared to the variability across the subjects. The most consistent characteristics are then used for the purpose of predicting subjects’ traits. We concentrate on four predictive methods (Regression, Logistic Regression, Linear Discriminant Analysis and Random Forest) in order to predict subjects’ traits such as gender and age based on the brain activities observed between brain regions. Those predictions are done based on the adjusted communication activity among the brain regions, as assessed from 4 scans of each subject. Due to a large number of such communications among the 116 brain regions, we performed a preliminary selection of the most promising pairs of brain regions. Logistic Regression performed best in classifying the subject gender based on communication activity among the brain regions. The accuracy rate was 85.6 percent for an AIC step-wise selected Logistic Regression model. On the other hand, the Logistic Regression model maintaining the entire set of ranked predictor was capable of getting an 87.7 percent accuracy rate. It is interesting to point out that the model with the AIC selected features was better classifying males, whereas the complete ranked model was better classifying females. The Random Forest technique performed best for prediction of age (grouped within five categories as provided by the original data) with 48.8 percent accuracy rate. Any set of predictors between 200 and 1600 was presenting similar rates of accuracy.

Functional Magnetic Resonance Imaging, Regression, Logistic Regression, Linear Discriminant Analysis, Random Forest

Nibbs, G. and Bajorski, P. (2020) Predictive Models for Functional MRI Data. Open Journal of Statistics, 10, 1-9. doi: 10.4236/ojs.2020.101001.