White Matter Deficits Underlying the Impaired Consciousness Level in Patients with Disorders of Consciousness / 神经科学通报·英文版

In this study, we aimed to (1) identify white matter (WM) deficits underlying the consciousness level in patients with disorders of consciousness (DOCs) using diffusion tensor imaging (DTI), and (2) evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest (ROIs), and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.

Individual identification research of amnestic mild cognitive impairment based on support vector machine / 医学研究生学报

Objective In recent years , multivariate pattern analysis ( MVPA) method was proposed and considered to be a promising tool for automated identification of various neuropsychiatric populations .Support vector machine ( SVM) is one of the most widely used methods of MVPA .Using SVM classifier for MVPA of amnestic mild cognitive impairment (aMCI) and normal control (NC) group, the present study aims to build an individual diagnostic model with significant discriminative power and investigate the gray matter abnor-malities of aMCI patients . Methods Fifty-one aMCI patients and 68 normal controls were scanned on the 3-Tesla magnetic resonance imaging (MRI) for high-resolution T1-weighted images.Gray matter volume map was calculated for each subject and used as features for subsequent discriminative analysis .We first applied feature selection to remove redundant information and reduce feature dimension , and then trained an SVM classifier . Leave-one-out cross validation ( LOOCV) was used to estimate the performance of the classifier , and finally the most discriminative features were identified . Results The proposed classifier achieved a classification accuracy of 83.19%with a sensitivity of 76.47%and a specificity of 88.24%.In ad-dition, the area under the receiver operating characteristic (ROC) curve was 0.8368.Further analysis revealed that the most discrimi-native features for classification included bilateral parahippocampal gyri , bilateral hippocampi , bilateral amygdala , bilateral thalamus , right cingulate , right precuneus , left caudate , left superior temporal gyrus , left middle temporal gyrus , left insula and left orbitofrontal cortex. Conclusion The proposed classification model has achieved significant accuracy for aMCI prediction , and it also displayed the whole brain gray matter atrophy pattern in aMCI patients .It suggests that the proposed method may have important implications for early clinical diagnosis of aMCI patients .