Objective: Internet gaming disorder (IGD) has been investigated by many behavioral and neuroimaging studies, for it has became one of the main behavior disorders among adolescents. However, few studies focused on the relationship between alteration of gray matter volume (GMV) and cognitive control feature in IGD adolescents.Methods: Twenty-eight participants with IAD and twenty-eight healthy age and gender matched controls participated in the study. Brain morphology of adolescents with IGD and healthy controls was investigated using an optimized voxel-based morphometry (VBM) technique. Cognitive control performances were measured by Stroop task, and correlation analysis was performed between brain structural change and behavioral performance in IGD group.Results: The results showed that GMV of the bilateral anterior cingulate cortex (ACC), precuneus, supplementary motor area (SMA), superior parietal cortex, left dorsal lateral prefrontal cortex (DLPFC), left insula, and bilateral cerebellum decreased in the IGD participants compared with healthy controls. Moreover, GMV of the ACC was negatively correlated with the incongruent response errors of Stroop task in IGD group.Conclusion: Our results suggest that the alteration of GMV is associated with the performance change of cognitive control in adolescents with IGD, which indicating substantial brain image effects induced by IGD.

Mild traumatic brain injury (mTBI) is the most common neurological insult and leads to long-lasting cognitive impairments. The immune system modulates brain functions and plays a key role in cognitive deficits, however, the relationship between TBI-induced changes in inflammation-related cytokine levels and cognitive consequences is unclear. This was investigated in the present study in two cohorts of individuals within 1 week of mTBI (n = 52, n = 43) and 54 matched healthy control subjects. Patients with mTBI were also followed up at 1 and 3 months post-injury. Measures included cognitive assessments and a 9-plex panel of serum cytokines including interleukin (IL)-1β, IL-4, IL-6, IL-8, IL-10, IL-12, chemokine ligand 2 (CCL2), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). The contribution of cytokine levels to cognitive function was evaluated by multivariate linear regression analysis. The results showed that serum levels of IL-1β, IL-6, and CCL2 were acutely elevated in mTBI patients relative to controls; CCL2 level was remained high over 3 months whereas IL-1β and IL-6 levels were declined by 3 months post-injury. A high level of CCL2 was associated with greater severity of post-concussion symptoms (which survived in the multiple testing correction); elevated IL-1β was associated with worse working memory in acute phase (which failed in correction); and acute high CCL2 level predicted higher information processing speed at 3 months post-injury (which failed in correction). Thus, acute serum cytokine levels are useful for evaluating post-concussion symptoms and predicting cognitive outcome in participants with mTBI.

ObjectivePost-traumatic headache (PTH) is one of the most frequent and persistent physical symptoms following mild traumatic brain injury (mTBI) and develop in more than 50% of this population. This study aimed to investigate the periaqueductal grey (PAG)-seeded functional connectivity (FC) in patients with mTBI with acute post-traumatic headache (APTH) and further examine whether the FC can be used as a neural biomarker to identify patients developing chronic pain 3 months postinjury.Methods70 patients with mTBI underwent neuropsychological measurements and MRI scans within 7 days postinjury and 56 (80%) of patients were followed up at 3 months. 46 healthy controls completed the same protocol on recruitment to the study. PAG-seeded resting-state FC analysis was measured in 54 patients with mTBI with APTH, in comparison with 46 healthy volunteers.ResultsThe mTBI+APTH group presented significantly reduced PAG-seeded FC within the default mode network (DMN), compared with healthy volunteers group. The connectivity strength can also predict patients’ complaints on the impact of headache on their lives. Crucially, the initial FC strength between the PAG-right precuneus as well as the PAG-right inferior parietal lobule became the important predictor to identify patients with mTBI developing persistent PTH 3 months postinjury.ConclusionsPatients with mTBI+APTH exhibited significant PAG-related FC differences mainly within the DMN. These regions extended beyond traditional pain processing areas and may reflect the diminished top-down attention regulation of pain perception through antinociceptive descending modulation network. The disrupted PAG-DMN FC may be used as an early imaging biomarker to identify patients at risk of developing persistent PTH.

Objective: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. Methods: Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. Results: Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. Conclusions: DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.

Being biomarkers that reflect host nutritional and immune status, prognostic nutritional index (PNI) and neutrophil/lymphocyte ratio (NLR) have been identified to be independent prognostic factors in various malignancies. The aim of the present study was to determine the predictive value of these parameters for the prognosis of patients with glioma. Hematological and clinicopathological data were retrospectively analyzed from 128 patients with glioma who underwent brain tumor resection between January 2008 and December 2012. Receiver operating characteristic (ROC) analysis was used to determine the optimal cutoffs for PNI and NLR. Kaplan-Meier survival analysis, and univariate and multivariate analyses based on Cox proportional hazards regression model were used to determine whether NLR and PNI were associated with the prognosis of patients with glioma. R software was used to develop nomograms with all the independent prognostic factors included. Kaplan-Meier analysis followed by log-rank tests indicated that NLR ≥2.8 and PNI <45 were significantly associated with decreased overall survival time. The subsequent multivariate analysis indicated that age ≥50 years [hazard ratio (HR), 2.328; 95% confidence interval (CI), 1.386-3.908; P<0.001], high-grade glioma (HR, 3.088; 95% CI, 1.893-5.037; P<0.001), gross total resection (HR, 0.606; 95% CI, 0.380-0.965; P= 0.035) and NLR ≥2.8 (HR, 2.037; 95% CI, 1.264-3.281; P= 0.003) were independent prognostic factors. The results of the present study indicated that high NLR was an independent risk factor for overall survival rates in patients with glioma, which indicated its value in improving the current prognostic model.

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