Connectome-based prediction of the severity of autism spectrum disorder

Background:Autism spectrum disorder(ASD)is characterized by social and behavioural deficits.Current diagnosis relies on be-havioural criteria,but machine learning,particularly connectome-based predictive modelling(CPM),offers the potential to uncover neural biomarkers for ASD.Objective:This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes,seeking to enhance diagnosis and understanding of ASD.Methods:Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model.CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule(ADOS)scores.After the model was constructed,it was applied to independent samples to test its replicability(172 ASD patients)and specificity(36 healthy control participants).Furthermore,we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results.Results:The CPM successfully identified negative networks that significantly predicted ADOS total scores[r(df=150)=0.19,P=0.008 in all patients;r(df=104)=0.20,P=0.040 in classic autism]and communication scores[r(df=150)=0.22,P=0.010 in all patients;r(df=104)=0.21,P=0.020 in classic autism].These results were reproducible across independent databases.The networks were characterized by enhanced inter-and intranetwork connectivity associated with the occipital network(OCC),and the sensorimotor network(SMN)also played important roles.Conclusions:A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD.Large-scale net-works,including the OCC and SMN,played important roles in the predictive model.These findings may provide new directions for the diagnosis and intervention of ASD,and maybe could be the targets in novel interventions.

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.