Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

WORKING WITH AND LEARNING FROM BRIT

I had been closely working with Dr.Britton Chance from 2005-2010 on imaging mitochondrial redox state in cancer and stem cells.I had also learned much from him in daily work and personal life.Here I would like to share some of my memories and experiences including(1)Great science is not necessarily done with top-grade expensive instruments;(2)Brit was genuinely interested in and dedicated to science;(3)Brit's attention to detail and timely action;(4)Brit's open-mindedness;and(5)Brit being modest,appreciative,and caring for others.

INTRODUCTION SPECIAL ISSUE IN MEMORY OF PROFESSOR BRITTON CHANCE

Britton Chance:One of the Most Outstanding Scientists in the World On June 3rd and 4th,2011,over 300 scientists and professionals from all over the world gathered at the Translational Research Center of the University of Pennsylvania to attend a memorial symposium on\Britton Chance:His Life,Times and Legacy"and a\Molecular Spectroscopy/Imaging Workshop:from bench top to bedside"dedicated to Britton Chance as well.

INTRODUCTION TO THE SPECIAL SECTION IN MEMORY OF DR. BRITTON CHANCE

Hanged on the wall outside the office of Dr.Chance at the University of Pennsylvania is a thick plaque(
4-1:5 feet)dedicated to Dr.Chance by his students at his 90'th birthday,saying桃李天下(Tao Li Tian Xia).This is a traditional Chinese idiom praising great teachers.桃李天下literally means"peaches and plums all over the world".

BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional regulation.This paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation schemes.This paper is composed of two parts.In the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection paradigm.Using these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction degree.In the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention recognition.Eight VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene models.Ten subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the system.In future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

Different types of drug abusers prefrontal cortex activation patterns and based on machine-learning classification

Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation.

Predictive value of the proportion of hibernating myocardium in total perfusion defect on reverse remodeling in patients with HFrEF undergoing coronary artery bypass graft

Objective To evaluate the predictive value of the proportion of hibernating myocardium(HM)in total perfusion defect(TPD)on reverse left ventricle remodeling(RR)after coronary artery bypass graft(CABG)in patients with heart failure with reduced ejection fraction(HFrEF)by ^(99m)Tc-methoxyisobutylisonitrile(MIBI)single photon emission computed tomography(SPECT)myocardial perfusion imaging(MPI)combined with ^(18)F-flurodeoxyglucose(FDG)gated myocardial imaging positron emission computed tomography(PET).