Fatigue is a debilitating and prevalent symptom of multiple sclerosis (MS). The thalamus is atrophied at an earlier stage of MS and although the role of the thalamus in the pathophysiology of MS-related fatigue has been reported, there have been few studies on intra-thalamic changes. We investigated the alterations of thalamic nuclei volumes and the intrinsic thalamic network in people with MS presenting fatigue (F-MS). The network metrics comprised the clustering coefficient (Cp), characteristic path length (Lp), small-world index (σ), local efficiency (Eloc), global efficiency (Eglob), and nodal metrics. Volumetric analysis revealed that the right anteroventral, right central lateral, right lateral geniculate, right pulvinar anterior, left pulvinar medial, and left pulvinar inferior nuclei were atrophied only in the F-MS group. Furthermore, the F-MS group had significantly increased Lp compared to people with MS not presenting fatigue (NF-MS) (2.9674 vs. 2.4411, PAUC = 0.038). The F-MS group had significantly decreased nodal efficiency and betweenness centrality of the right mediodorsal medial magnocellular nucleus than the NF-MS group (false discovery rate corrected p < 0.05). The F-MS patients exhibited more atrophied thalamic nuclei, poorer network global functional integration, and disrupted right mediodorsal medial magnocellular nuclei interconnectivity with other nuclei. These findings might aid the elucidation of the underlying pathogenesis of MS-related fatigue.

Background: Glioma is the most common brain tumor disease. Magnetic resonance can help the clinical diagnosis according to the location of the glioma and the degree of malignancy, in which the segmentation of glioma site plays an important role for clinicians. The work of manual segmentation is very time-consuming and cumbersome, therefore automatic and efficient segmentation methods are very necessary. Methods: This paper proposed an AIUNet to give a more efficient segmentation of glioma, where a new block---Attention based Inception Block (AI Block), combining convolution and Self-Attention, is introduced. This module combines the smaller receptive field of convolution with the larger receptive field of Self-Attention, so as to extract more diverse feature maps to meet the needs of refined segmentation, and some deformations of AI block will be introduced and applied to the segmentation of glioma lesion. The AIUNet image segmentation network is constructed by merging AI block with the U-shaped network, this network has excellent segmentation performance, as well as low amount of parameters and calculation. Moreover, a loss function combined with GHM loss and Dice loss is plugged into the network, thereby improving the robustness of the network. Results: Experiments show that the proposed network can improve segmentation effects comparing with state-of-the-art methods.

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

The objective of this study was to elucidate the no-motor component of fatigue mechanism in multiple sclerosis (MS) patients by studying the resting state functional connectivity (RS-FC) of default mode network (DMN). 12 with fatigue (F) and 10 without fatigue (nF) MS underwent resting sate magnetic resonance imaging (MRI). We selected 11 priori regions within the DMN as the regions of interests (ROIs) and ROI to ROI and ROI to global FC were calculated. Compared to nF-MS patients, F-MS patients showed abnormal increased functional connectivity in brain regions associated with cognitive function within the DMN.

This study aims to analyze the metabolic information in both tumor solid and peritumoral area of gliomas to predict its genotype by using Amide Proton Transfer weighted (APTw) imaging. As a complementary method of pathological evaluation, APTw based MRI technique could provide a prediction of the gliomas genotype. Unlike other studies focus on tumor core region, our research investigated the APTmean value of tumor solid and the peritumoral area. Interestingly, the results showed that the AUC value of peritumoral area was higher than the tumor solid. The APTw images may serve as a potential marker for the genotyping of gliomas.