ObjectiveNeuroimaging evidence suggested that the thalamic nuclei may play different roles in the progress of idiopathic generalized epilepsy (IGE). This study aimed to demonstrate the alterations in morphometry and functional connectivity in the thalamic nuclei in IGE.MethodsFifty-two patients with IGE characterized by generalized tonic-clonic seizures and 67 healthy controls were involved in the study. The three-dimensional high-resolution T1-weighted MRI data were acquired for voxel-based morphometry (VBM) analysis, and resting-state blood-oxygenation level functional MRI data were acquired for functional connectivity analysis. The thalamic nuclei of bilateral medial dorsal nucleus (MDN) and pulvinar, as detected with decreased gray matter volumes in patients with IGE through VBM analysis, were selected as seed regions for functional connectivity analysis.ResultsDifferent alteration patterns were found in functional connectivity of the thalamic nuclei with decreased gray matter volumes in IGE. Seeding at the MDN, decreased connectivity in the bilateral orbital frontal cortex, caudate nucleus, putamen and amygdala were found in the patients (P<0.05 with correction). However, seeding at the pulvinar, no significant alteration of functional connectivity was found in the patients (P<0.05 with correction).ConclusionsSome specific impairment of thalamic nuclei in IGE was identified using morphological and functional connectivity MRI approaches. These findings may strongly support the different involvement of the thalamocortical networks in IGE.

BackgroundCarotid atherosclerotic plaque rupture is an important source of ischemic stroke. However, the prevalence of high‐risk plaque (HRP) defined as plaques with luminal surface disruption, a lipid‐rich necrotic core occupying >40% of the wall, or intraplaque hemorrhage in Chinese population remains unclear. This study uses carotid magnetic resonance imaging (CMRI) to investigate HRP prevalence in carotid arteries of Chinese patients with cerebrovascular symptoms.Methods and ResultsPatients with cerebral ischemic symptoms in the anterior circulation within 2 weeks and carotid plaque determined by ultrasound were recruited and underwent CMRI. The HRP features were identified and compared between symptomatic and asymptomatic arteries. Receiver‐operating‐characteristic analysis was used to calculate area‐under‐the‐curve (AUC) of stenosis and maximum wall thickness for discriminating presence of HRP. In 1047 recruited subjects, HRP detected by CMRI was nearly 1.5 times more prevalent than severe stenosis (≥50%) in this cohort (28% versus 19%, P<0.0001). Approximately two thirds of HRPs were found in arteries with <50% stenosis. The prevalence of HRP in symptomatic carotid arteries was significantly higher than that of the contralateral asymptomatic carotid arteries (23.0% versus 16.4%, P=0.001). Maximum wall thickness was found to be a stronger discriminator than stenosis for HRP (AUC: 0.93 versus 0.81, P<0.0001).ConclusionsThere are significantly more high‐risk carotid plaques than carotid arteries with ≥50% stenosis in symptomatic Chinese patients. A substantial number of HRPs were found in arteries with lower grade stenosis and maximum wall thickness was a stronger indicator for HRP than luminal stenosis.Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT02017756.

Mounting evidence has shown that periodontitis is associated with diabetes. However, a causal relationship remains to be determined. Recent studies reported that periodontitis may be associated with gut microbiota, which plays an important role in the development of diabetes. Therefore, we hypothesized that gut microbiota might mediate the link between periodontitis and diabetes. Periodontitis was induced by ligatures. Glycemic homeostasis was evaluated through fasting blood glucose (FBG), serum glycosylated hemoglobin (HbA1c), and intraperitoneal glucose tolerance test. Micro–computed tomography and hematoxylin and eosin staining were used to evaluate periodontal destruction. The gut microbiota was analyzed using 16S ribosomal RNA gene sequencing and bioinformatics. Serum endotoxin, interleukin (IL) 6, tumor necrosis factor α (TNF-α), and IL-1β were measured to evaluate the systemic inflammation burden. We found that the levels of FBG, HbA1c, and glucose intolerance were higher in the periodontitis (PD) group than in the control (Con) group ( P < 0.05). When periodontitis was eliminated, the FBG significantly decreased ( P < 0.05). Several butyrate-producing bacteria were decreased in the gut microbiota of the PD group, including Lachnospiraceae_NK4A136_group, Eubacterium_fissicatena_group, Eubacterium_coprostanoligenes_group, and Ruminococcaceae_UCG-014 ( P < 0.05), which were negatively correlated with serum HbA1c ( P < 0.05). Subsequently, the gut microbiota was depleted using antibiotics or transplanted through cohousing. Compared with the PD group, the levels of HbA1c and glucose intolerance were decreased in the gut microbiota-depleted mice with periodontitis (PD + Abx) ( P < 0.05), as well as the serum levels of endotoxin and IL-6 ( P < 0.05). The serum levels of IL-6, TNF-α, and IL-1β in the PD + Abx group were higher than those of the Con group ( P < 0.05). Antibiotics exerted a limited impact on the periodontal microbiota. When the PD mice were cohoused with healthy ones, the elevated FBG and HbA1c significantly recovered ( P < 0.05), as well as the aforementioned butyrate producers ( P < 0.05). Thus, within the limitations of this study, our data indicated that the gut microbiota may mediate the influence of periodontitis on prediabetes.

Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.