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Objective:To investigate the effect of insular involvement on the outcomes of patients with acute anterior circulation ischemic stroke.Methods:Patients with acute anterior circulation ischemic stroke admitted to the Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2015 to December 2020 were retrospectively included. Demographic data, vascular risk factors, clinical and laboratory data, as well as treatment and outcomes were collected. Firstly, the correlation between the insular involvement and the outcomes was investigated, and then the bootstrap method was used to clarify the mediating role of infarct volume between the insular involvement and the poor outcomes.Results:A total of 450 patients with acute anterior circulation ischemic stroke were enrolled, among whom 79 cases (17.6%) had insular involvement and 41 (9.1%) had left insular involvement. There were 111 (24.7%) with poor outcomes, including 5 (1.1%) died. Compared to the non-insular involvement group, the insular involvement group had a higher proportion of patients with atrial fibrillation, shorter onset to door time, higher neutrophil-to-lymphocyte ratio (NLR), higher National Institutes of Health Stroke Scale (NIHSS) score at admission, larger infarct volume, and higher proportion of patients with poor outcomes (all P<0.05). In addition, patients with left insular involvement were younger than those with right insular involvement, had a higher baseline NIHSS score, a lower proportion of patients with minor stroke (NIHSS score ≤8), and had a longer onset to door time (all P<0.05). Compared to the good outcome group, the poor outcome group was older, with a higher proportion of female patients, higher systolic blood pressure, blood glucose, NLR, and NIHSS scores at admission, larger infarct volume, and a higher proportion of patients with insular involvement (all P<0.05). Mediation analysis suggested that the mediating effect of infarct volume between the insular involvement and the poor outcomes was significant (95% confidence interval 0.033-0.230; P=0.008). Conclusions:insular involvement in patients with acute anterior circulation ischemic stroke is associated with the poor outcomes, and this association may be mediated by infarct volume. Patients with left insular involvement may have more severe symptoms than those with right insular involvement, but there is no significant difference in the outcomes.
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Objective:To comprehensively evaluate the ability of common resting state functional magnetic resonance imaging (rs-fMRI) indices to detect abnormal brain activity in childhood absence epilepsy (CAE).Methods:Simultaneous electroencephalography-functional magnetic resonance imaging (fMRI) data of 20 patients with CAE who were treated in the Jinling Hospital, Nanjing University School of Medicine from February 2010 to September 2021 were retrospectively collected. After excluding 2 patients with CAE with greater head movement, 44 fMRI data containing discharges from 18 patients were obtained finally. The generalized spike and slow-wave discharges (GSWD) related fMRI activation mappings were obtained by using the generalized linear model. At the same time, 94 age- and sex-matched healthy controls underwent rs-fMRI scanning. Meanwhile, 12 indices of rs-fMRI were calculated respectively [amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), functional connectivity density (FCD), long FCD, local FCD, granger causality density (GCD)-in, GCD-out, GCD-int, resting state functional magnetic resonance imaging lag analysis (RSLA), Hurst index and brain entropy]. Two-sample t-tests were employed to detect significant differences in 12 indices of rs-fMRI. The Dice coefficient was used to evaluate the overlap between different brain maps of 12 indices of rs-fMRI and the GSWD-related blood oxygenation level dependent (BOLD) activation. Results:Positive activation of GSWD-related BOLD in CAE was mainly in the bilateral thalamus, and negative activation was mainly in default mode network (DMN) related brain regions. There was a significant overlap between the abnormal brain regions detected by various resting-state indicators: compared with normal controls, ALFF, fALFF, ReHo, GCD-in, GCD-out and local FCD were elevated in the bilateral thalamus, while FCD, long FCD, GCD-int and RSLA were decreased in CAE; ALFF, fALFF, ReHo, local FCD, GCD-out, RSLA and brain entropy were decreased in the DMN, while FCD, long FCD, GCD-in and GCD-int were increased in CAE. The Dice coefficient of long FCD was the highest (0.365),FCD was 0.362, while the Hurst index showed the lowest (0.142).Conclusions:Rs-fMRI indices variously revealed abnormal brain activity in CAE, in which the FCD is better for detection of epileptic activity. Rs-fMRI could be helpful to understand the pathophysiological mechanism of CAE, and to find reliable imaging markers.
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Objective:To explore the changes of brain activity in drug-resistant or drug-controlled medial temporal lobe epilepsy patients by the method of functional connectivity density (FCD), and to analyze their correlation with the course of the disease.Methods:According to the definition of drug-resistant epilepsy by the International League Against Epilepsy in 2010, 146 patients with medial temporal lobe epilepsy who were clearly diagnosed as unilateral hippocampal sclerosis in Jinling Hospital, Nanjing University School of Medicine from July 2009 to February 2019 were divided into drug control group ( n=73) and drug-resistant group ( n=73). The 3.0 T resting state functional magnetic resonance scan was performed on all subjects to compare the difference in FCD between the two groups, and calculate the correlation between the FCD value of the brain area and the course of the disease between the two groups of patients. Results:There was significant difference between the two groups in FCD. Compared with the drug control group, the drug-resistant group had significantly lower FCD values in the insula, lenticular nucleus, thalamus, hippocampus and precentral gyrus on the side of the epileptogenic focus. The FCD value of the precuneus on the side of the epileptogenic focus in the drug-resistant group was negatively correlated with the duration ( r=-0.30, P=0.01). Conclusions:The FCD of patients with drug-resistant medial temporal lobe epilepsy was lower than that of the drug control group. In addition, there may be progressive damage to the brain. The difference is helpful for exploring the pathophysiological mechanisms related to drug resistance in patients with medial temporal lobe epilepsy, and finding reliable neuroimaging markers related to drug resistance.
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Objective:To construct a multi-label learning MRI model for assisting diagnosis of sports injury in knee.Methods:A total of 1 391 knee MRI cases from 1 343 young adults with sports injury in Affiliated Jinling Hospital Nanjing University School of Medicine were retrospectively enrolled. The image cases were randomly divided into training set ( n=973), validation set ( n=139) and test set ( n=279) with ratio of 7∶1∶2. The knee injuries were divided into six categories: meniscus injury, tendon injury, ligament injury, osteochondral injury, synovial bursa disorder and soft tissue injury. Using PyTorch V1.1.0 algorithm package, the Yolo model of deep learning was used to construct the MRI knee joint sports injury detection model. The model was validated on the test set, and the sensitivity, specificity and mean average precision of lesion detection were evaluated. Results:Among the 279 patients in test set, the mean average precision of meniscus injury, tendon injury, ligament injury, osteochondral injury, synovial bursa disorder and soft tissue injury were 83.1%, 89.0%, 88.0%, 85.8%, 85.5% and 83.2%, respectively, and the overall mean average precision was 85.8%. The model was most effective in detecting tendon injury. The sensitivity and specificity of the model for tendon injury were 91.2% and 87.1% respectively.Conclusions:The multi-label MRI knee joint exercise-related injury detection model based on deep learning can effectively assist in detecting the exercise-related injury of knee joint in each tissue structure, and is expected to improve the efficiency of diagnosis and treatment in orthopedics.
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Objective@#To investigate whether a deep learning-based model using unenhanced computed tomography (CT) at baseline could predict the malignancy of pulmonary nodules.@*Methods@#A deep learning model was trained and applied for the discrimination of pulmonary nodule in Dr. Wise Lung Analyzer. This study retrospectively recruited 130 consecutive participants with pulmonary nodules detected on CT who undergoing biopsy or surgery from May 2009 to June 2017 in Jinling hospital. A total of 136 pulmonary nodules were included in this study, including 86 malignant nodules and 50 benign ones. All patients underwent CT scans 2 times at least, the first scan was defined as baseline and the last scan before the pathological results was defined as final scan. The ROC curve of deep learning model was plotted and the AUCs were calculated. Delong test was used to examine the difference of AUCs baseline and final scan. The nodules were further divided into subsolid nodule group (pure ground-glass nodule and part solid nodule) (n=87) and solid nodule group (n=49). The difference of AUCs at baseline and final scans was evaluated intra two groups.@*Results@#The AUCs of the deep learning model at final and baseline scans were 0.876 and 0.819, respectively. There was no significant difference between them (P=0.075). The result indicated that the model could predict the consequences of pulmonary nodules well at baseline. In small nodules (longest diameter ≤10mm), the AUC at final scan (0.847) was better than it at baseline scan (0.734), but there was no significant difference between them (P=0.058). In solid nodule group, The AUC at final scan (0.932) was better than it at baseline scan (0.835), but there was no significant difference between them (P=0.066). In subsolid nodule group, the deep learning model exhibited consistent performance at final scan (AUC, 0.759) with the baseline scan (AUC, 0.728, P=0.580).@*Conclusions@#The deep learning model could predict the malignancy of pulmonary nodules including small ones at baseline, and the model exhibited consistent performance between baseline and final scans in subsolid nodules.
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Objective At present, there is no study on effect of levetiracetam(LEV) on the gray matter structure remodeling in benign epilepsy children with central temporal spikes(BECTS).The purpose of this study was to study the influence of LEV on the gray matter structure in BECTS and to evaluate the mechanism of LEV on the brain structure of BECTS through using voxel-based MRI morphological(VBM) methods.Methods From January 2014 to September 2016, twenty-four BECTS treated with LEV(LEV group), twenty-four drug-na?ve BECTS(untreated group) and twenty-four normal children(normal group) consulted in department of Neurology, Nanjing Children′s Hospital and the Nanjing Military Region, Nanjing General Hospital were continuously included to receive three-dimensional T1-weighted imaging with 3T MRI and the gray matter volume was calculated by VBM.We compared the difference of grey matter volumes of the three groups and analyzed their correlation with epilepsy duration, age of onset and medication time and other clinical index.Results Compared with the normal group, the grey matter volume of bilateral thalamus were decreased, and the volume of bilateral Rolandic areas, anterior insula/frontal operculum/frontal triangle, left supplementary motor area, paracentral lobule, precentral gyrus, superior frontal gyrus and right middle frontal gyrus were increased in the untreated group, but the grey matter volume of the bilateral Rolandic areas, frontal operculum and left supplementary motor area were decreased in the LEV group.Compared with the untreated group, the grey matter volume of bilateral supplementary motor, left paracentral lobule, precentral gyrus, bilateral anterior insula/frontal operculum/frontal triangle, left superior frontal gyrus and right middle frontal gyrus in the LEV group were decreased.The grey matter volume of left anterior insula/frontal operculum areas was negatively correlated with the medication time in LEV group(r=-0.527, P<0.01).Conclusion T The mainly representations of BECTS are thalamic gray matter damage and epileptic-related cortical area irritation structural abnormalities, but the LEV could reshape the epilepsy-related cortical area and the gray matter in the brain area associated with clinical symptoms.