Pattern Classification of Large-Scale Functional Brain Networks: Identification of Informative Neuroimaging Markers for Epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders

Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics

This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas

HIF-α/MIF and NF-κB/IL-6 Axes Contribute to the Recruitment of CD11b+Gr-1+ Myeloid Cells in Hypoxic Microenvironment of HNSCC

CD11b+Gr-1+ myeloid cells have gained much attention due to their roles in tumor immunity suppression as well as promotion of angiogenesis, invasion, and metastases. However, the mechanisms by which CD11b+Gr-1+ myeloid cells recruit to the tumor site have not been well clarified. In the present study, we showed that hypoxia could stimulate the migration of CD11b+Gr-1+ myeloid cells through increased production of macrophage migration inhibitory factor (MIF) and interleukin-6 (IL-6) by head and neck squamous cell carcinoma (HNSCC) cells. Hypoxia-inducible factor-1α (HIF-1α)- and HIF-2α-dependent MIF regulated chemotaxis, differentiation, and pro-angiogenic function of CD11b+Gr-1+ myeloid cells through binding to CD74/CXCR2, and CD74/CXCR4 complexes, and then activating p38/mitogen-activated protein kinase (MAPK) and phosphatidylinositide 3-kinases (PI3K)/AKT signaling pathways. Knockdown (KD) of HIF-1α and HIF-2α in HNSCC cells decreased MIF level but failed to inhibit the CD11b+Gr-1+ myeloid cell migration, because HIF-1α/2α KD enhanced nuclear factor κB (NF-κB) activity that increased IL-6 secretion. Simultaneously blocking NF-κB and HIF-1α/HIF-2α had better inhibitory effect on CD11b+Gr-1+ myeloid cell recruitment in the hypoxic zone than individually silencing HIF-1α/2α or NF-κB. In conclusion, the interaction between HIF-α/MIF and NF-κB/IL-6 axes plays an important role in the hypoxia-induced accumulation of CD11b+Gr-1+ myeloid cells and tumor growth in HNSCC

SocioDemographic

Socio-demographic and clinical information of included participant

Cortex_PC

PC values for the cortical parcels for the entire sampl