Classification of mild cognitive impairment based on cerebral white matter fiber tracts

The aim of this study is to research the features of white matter in the brain by Diffusion Tensor Image (DTI) from the patients with mild cognitive impairment (MCI) and using the features to identify MCI and Normal Control (NC) to explore new methods for MCI diagnosis. In this study, 38 brain DTI images of MCI patients and NC were extracted respectively, and the parameters of cerebral white matter fiber tracts were analyzed. Using automatic fiber tract quantification (AFQ) technology, the index values with significant differences between MCI patients and NC were calculated. Support Vector Machine (SVM) model was built to classify MCI patients from NC. We found significant differences in right cortical spinal tract (CST_R), right uncinate fasciculus (UNC_R), left internal fronto-occipital tract (IFOF_L), and Callosum Forceps major (FP) in MCI patients and NC. The classification accuracy, sensitivity and specificity of the training set and test set were 94.73%, 92.11% and 97.36%, respectively. This study demonstrates that there are significant differences in certain fiber tracts in MCI patients compared with NC and using these fiber tract groups can effectively classify the MCI patients and NC, which can provide novel information for MCI white matter decline.