Correlations between hippocampal functional connectivity,structural changes,and clinical data in patients with relapsing-remitting multiple sclerosis:a case-control study using multimodal magnetic resonance imaging

Multiple sclerosis is associated with structural and functional brain alterations leading to cognitive impairments across multiple domains including attention,memory,and the speed of information processing.The hippocampus,which is a brain important structure involved in memory,undergoes microstructural changes in the early stage of multiple sclerosis.In this study,we analyzed hippocampal function and structure in patients with relapsing-remitting multiple sclerosis and explored correlations between the functional connectivity of the hippocampus to the whole brain,changes in local brain function and microstructure,and cognitive function at rest.We retrospectively analyzed data from 20 relapsing-remitting multiple sclerosis patients admitted to the Department of Neurology at the China-Japan Union Hospital of Jilin University,China,from April 2015 to November 2019.Sixteen healthy volunteers were recruited as the healthy control group.All participants were evaluated using a scale of extended disability status and the Montreal cognitive assessment within 1 week before and after head diffusion tensor imaging and functional magnetic resonance imaging.Compared with the healthy control group,the patients with relapsing-remitting multiple sclerosis had lower Montreal cognitive assessment scores and regions of simultaneously enhanced and attenuated whole-brain functional connectivity and local functional connectivity in the bilateral hippocampus.Hippocampal diffusion tensor imaging data showed that,compared with the healthy control group,patients with relapsing-remitting multiple sclerosis had lower hippocampal fractional anisotropy values and higher mean diffusivity values,suggesting abnormal hippocampal structure.The left hippocampus whole-brain functional connectivity was negatively correlated with the Montreal cognitive assessment score(r=-0.698,P=0.025),and whole-brain functional connectivity of the right hippocampus was negatively correlated with extended disability status scale score(r=-0.649,P=0.042).The mean diffusivity value of the left hippocampus was negatively correlated with the Montreal cognitive assessment score(r=-0.729,P=0.017)and positively correlated with the extended disability status scale score(r=0.653,P=0.041).The right hippocampal mean diffusivity value was positively correlated with the extended disability status scale score(r=0.684,P=0.029).These data suggest that the functional connectivity and presence of structural abnormalities in the hippocampus in patients with relapse-remission multiple sclerosis are correlated with the degree of cognitive function and extent of disability.This study was approved by the Ethics Committee of China-Japan Union Hospital of Jilin University,China(approval No.201702202)on February 22,2017.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.