Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

Background Individual differences have been detected in individuals with opioid use disorders(OUD)in rehabilitation following protracted abstinence.Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders(SUD).Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling(CPM).Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging(fMRI)scans at baseline.The Heroin Craving Questionnaire(HCQ)was used to assess craving levels at baseline and at the 8-month follow-up of abstinence.CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ(HCQtolow V-up-HCQpa baseline).Then,the follow-up aseline predictive ability of identified networks was tested in a separate,heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.Results CPM could predict craving changes induced by long-term abstinence,as shown by a significant correlation between predicted and actual HCQ fllow-up(r=0.417,p<0.001)and changes in HCQ(negative:r=0.334,p=0.002;positive:r=0.233,p=0.038).Identified craving-related prediction networks included the somato-motor network(SMN),salience network(SALN),default mode network(DMN),medial frontal network,visual network and auditory network.In addition,decreased connectivity of frontal-parietal network(FPN)-SMN,FPN-DMN and FPN-SALN and increased connectivity of subcortical network(SCN)-DMN,SCN-SALNandSCN-SMN were positively correlated with craving levels.Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence,as well as the generalisation ability;the identified brain networks might be the focus of innovative therapies in the future.

Attribute-based supervised deep learning model for action recognition

Deep learning has been the most popular feature learning method used for a variety of computer vision ap- plications in the past 3 years. Not surprisingly, this tech- nique, especially the convolutional neural networks （Con- vNets） structure, is exploited to identify the human actions, achieving great success. Most algorithms in existence directly adopt the basic ConvNets structure, which works pretty well in the ideal situation, e.g., under stable lighting conditions. However, its performance degrades significantly when the intra-variation in relation to image appearance occurs within the same category. To solve this problem, we propose a new method, integrating the semantically meaningful attributes into deep learning＇s hierarchical structure. Basically, the idea is to add simple yet effective attributes to the category level of ConvNets such that the attribute information is able to drive the learning procedure. The experimental results based on three popular action recognition databases show that the em- bedding of auxiliary multiple attributes into the deep learning framework improves the classification accuracy significantly.