Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy.

OBJECTIVE: Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. METHODS: This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. RESULTS: Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. SIGNIFICANCE: Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.

Bilateral glymphatic dysfunction and its association with disease duration in unilateral temporal lobe epilepsy patients with hippocampal sclerosis.

OBJECTIVE: In this study, the diffusion tensor imaging along perivascular space analysis (DTI-ALPS) technique was utilized to evaluate the functional changes in the glymphatic system of the bilateral hemispheres in patients with unilateral temporal lobe epilepsy (TLE) accompanied by hippocampal sclerosis (HS). The aim was to gain insights into the alterations in the glymphatic system function in TLE patients. METHODS: A total of 61 unilateral TLE patients with HS and 53 healthy controls (HCs) from the Department of Neurosurgery at Xiangya Hospital were included in the study. All subjects underwent DTI using the same 3 T MR Scanner, and the DTI-ALPS index was calculated. Differences in the DTI-ALPS index between TLE patients and HCs were evaluated, along with the correlation between the DTI-ALPS index of TLE and clinical features of epilepsy. These features included age, age of onset, seizure duration, and neuropsychological scores. RESULTS: Compared to the bilateral means of the HCs, both the ipsilateral and contralateral DTI-ALPS index of the TLE patients were significantly decreased (TLE ipsilateral 1.41 ± 0.172 vs. HC bilateral mean: 1.49 ± 0.116, p = 0.006; TLE contralateral: 1.42 ± 0.158 vs. HC bilateral mean: 1.49 ± 0.116, p = 0.015). The ipsilateral DTI-ALPS index in TLE patients showed a significant negative correlation with disease duration (r = -0.352, p = 0.005). CONCLUSIONS: The present study suggests the presence of bilateral dysfunctions in the glymphatic system and also highlight a laterality feature in these dysfunctions. Additionally, the study found a significant negative correlation between the ipsilateral DTI-ALPS index and disease duration, underscoring the significance of early effective interventions and indicating potential for the development of innovative treatments targeting the glymphatic system.

Causes of missed MRI diagnosis of radiotherapy-induced temporal lobe injury in nasopharyngeal carcinoma. / MRIæ£€æŸ¥é¼»å’½ç™Œæ”¾å°„æ€§é¢žå¶æŸä¼¤çš„æ¼è¯ŠåŽŸå› .

OBJECTIVES: Radiotherapy is the primary treatment for nasopharyngeal carcinoma, but it frequently leads to radiotherapy-induced temporal lobe injury (RTLI). Magnetic resonance imaging (MRI) is the main diagnostic method for RTLI after radiotherapy for nasopharyngeal carcinoma, but it is prone to missed diagnoses. This study aims to investigate the causes of missed diagnoses of RTLI in nasopharyngeal carcinoma patients undergoing MRI after radiotherapy. METHODS: Clinical and MRI data from nasopharyngeal carcinoma patients diagnosed and treated with radiotherapy at Xiangya Hospital of Central South University, from January 2010 to April 2021, were collected. Two radiologists reviewed all head and neck MRIs (including nasopharyngeal and brain MRIs) before and after radiotherapy of identify cases of late delayed response-type RTLI for the first time. If the original diagnosis of the initial RTLI in nasopharyngeal carcinoma patients did not report temporal lobe lesions, it was defined as a missed diagnosis. The first diagnosis of RTLI cases was divided into a missed diagnosis group and a non-missed diagnosis group. Clinical and imaging data were compared between the 2 groups, and multivariate logistic regression analysis was used to identify independent risk factors for MRI missed diagnoses of initial RTLI. RESULTS: A total of 187 nasopharyngeal carcinoma with post-radiotherapy RTLI were included. The original diagnostic reports missed 120 cases and accurately diagnosed 67 cases, with an initial RTLI diagnosis accuracy rate of 35.8% and a missed diagnosis rate of 64.2%. There were statistically significant differences between the missed diagnosis group and the non-missed diagnosis group in terms of lesion size, location, presence of contralateral temporal lobe lesions, white matter high signal, cystic degeneration, hemorrhage, fluid attenuated inversion recovery (FLAIR), and examination site (all P<0.05). Multivariate logistic regression analysis showed that lesions ≤25 mm, non-enhancing lesions, lesions without cystic degeneration or hemorrhage, lesions located only in the medial temporal lobe, and MRI examination only of the nasopharynx were independent risk factors for missed MRI diagnosis of initial RTLI (all P<0.05). CONCLUSIONS: The missed diagnosis of initial RTLI on MRI is mainly related to lesion size and location, imaging characteristics, and MRI examination site.

Executive function deficit in betel quid-dependence: Evidence from functional and effective connectivity of executive control network.

Betel quid (BQ) ranks fourth in global self-administered psychoactive agents, after caffeine, alcohol and nicotine, with 600 million consumers. Patients with BQ dependence (BQD) disorder demonstrate deficits in executive function. However, the neural correlates of the resting-state executive control network (ECN) and BQD-related pathopsychological characteristics still remain unclear. The present study aimed to assess the functional and effective connectivity of the ECN using resting-state functional magnetic resonance imaging (rs-fMRI). Fifty-five BQD individuals and 54 healthy controls (HCs) were recruited in this study. The executive function of all participants was tested by three tasks. Independent component and Granger causal analysis were employed to investigate the functional connectivity within ECN and ECN-related directional effective connectivity, separately. Behavioural results suggested a marked deficit of executive function in BQD individuals. Compared with HCs, BQD individuals showed overall weaker functional connectivity in the ECN, mainly including dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (IPL) and middle frontal gyrus (MFG). We observed decreased outflow of information from the right DLPFC and IPL to the precentral/pre-supplement motor area (SMA) and increased outflow of information from the MFG to the middle occipital gyrus in BQD individuals. Correlation analysis revealed that the effective connectivity from IPL to precentral/pre-SMA was negatively correlated to the BQD scales in BQD individuals. Our findings revealed impaired executive function, functional connectivity of the ECN and causal interaction between networks in patients with BQD. These results could potentially direct future targets for the prevention and intervention of BQD.

Decreased functional connectivity in salience network is associated with symptom severity in betel quid-dependent chewers.

BACKGROUND: Betel quid (BQ) is the fourth most popular psychoactive agent worldwide. Neuroimaging studies have showed that substance-addicted individuals including alcohol, heroin, nicotine and other addictive substance exhibit altered activity patterns of the salience network (SN). However, no study has yet investigated the neural correlates of the resting-state SN and BQ dependence (BQD)-related physiopathological characteristics. METHODS: Thirty-two BQ-dependent (BQD) chewers and 32 healthy controls were recruited to participate in this study. Resting-state functional magnetic resonance imaging (fMRI) data were analysed by independent component analysis (ICA). RESULTS: BQD chewers exhibited decreased functional connectivity in bilateral insula, anterior cingulate cortex (ACC), medial superior frontal gyrus (MSFG) and inferior orbital frontal gyrus (IOFG) [false discovery rate (FDR) correction, p < 0.05]. In the BQD group, the decreased functional connectivity in left ACC correlated negatively with BQDS (BQD Scale) and the duration of BQ. CONCLUSIONS: We reported decreased functional connectivity in resting-state SN of BQD individuals. The decreased functional connectivity in left ACC correlated negatively with BQDS and the duration of BQ. Our findings provided evidence for the importance of the SN in the pathophysiology of BQD and indicated that the SN dysfunction might provide a potential mechanism in BQD development.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

A logistic regression model for prediction of glioma grading based on radiomics. / 基于影像组学的logistic回归模型预测胶质瘤分级.

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T1-weighted imaging (T1WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy. RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (P=0.808), indicating high predictive accuracy of the model. CONCLUSIONS: The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Clinical and imaging findings of discharged patients with SARS-CoV-2 positive anal swab samples: a descriptive study.

BACKGROUND: To explore the clinical features and CT findings of clinically cured coronavirus disease 2019 (COVID-19) patients with viral RNA positive anal swab results after discharge. METHODS: Forty-two patients with COVID-19 who were admitted to Yongzhou Central Hospital, Hunan, China, between January 20, 2020, and March 2, 2020, were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using anal swab viral RT-PCR. In this report, we present the clinical characteristics and chest CT features of six patients with positive anal swab results and compare the clinical, laboratory, and CT findings between the positive and negative groups. RESULTS: The anal swab positivity rate for SARS-CoV-2 RNA in discharged patients was 14.3% (6/42). All six patients were male. In the positive group, 40% of the patients (2/5) had a positive stool occult blood test (OBT), but none had diarrhea. The median duration of fever and major symptoms (except fever) in the positive patients was shorter than that of the negative patients (1 day vs. 6 days, 4.5 days vs. 10.5 days, respectively). The incidence of asymptomatic cases in the positive group (33.3%) was also higher than that of the negative group (5.6%). There were no significant differences in the CT manifestation or evolution of the pulmonary lesions between the two groups. CONCLUSION: In our case series, patients with viral RNA positive anal swabs did not exhibit gastrointestinal symptoms, and their main symptoms disappeared early. They had similar CT features to the negative patients, which may be easier to be ignored. A positive OBT may indicate gastrointestinal damage caused by SARS-CoV-2 infection.

Prognostic value of magnetic resonance imaging performed during the subacute phase in adult patients with hypoxic-ischemic encephalopathy for long-term neurological outcomes.

OBJECTIVE: To investigate the value of a model based on brain magnetic resonance imaging (MRI) performed in the subacute phase (between the 1st and 30th day) in predicting long-term neurological outcomes of adult hypoxic-ischemic encephalopathy (HIE) patients. METHODS: Ninety-six adult HIE patients who underwent conventional MRI and diffusion-weighted imaging (DWI) during the subacute phase were retrospectively analyzed. Favorable (Cerebral Performance Categories (CPC) 1-2) and unfavorable outcome (CPC 3-5) groups were created based on patient neurological status approximately three months after the onset of hypoxic-ischemic events. A multivariate stepwise regression model was applied after univariate analysis of MRI findings, and then the overall MRI score, Alberta Stroke Program Early Computed Tomography Score (ASPECTS), Bilateral ASPECTS (Bi-ASPECTS), modified ASPECTS (mASPECTS) and Bi-ASPECTS combined with posterior circulation ASPECTS (PC-ASPECTS) were calculated based on MRI findings. Receiver operating characteristic (ROC) curves were used to assess prognostic accuracy. RESULTS: Both univariate and multivariate analyses showed the cerebral cortex and cerebellum, neostriatum, hippocampus, brainstem and postanoxic leukoencephalopathy were independent prognostic factors for unfavorable outcomes. The multivariate regression analysis resulted in an overall classification accuracy of 84.4%, a sensitivity of 84.2% (95% CI, 71.6-92.1%), and a specificity of 92.3% (95% CI, 78.0-98.0%) for unfavorable outcomes. The model had an areas under the ROC curves (AUC) of 0.944 (95% CI, 0.901-0.987); the MRI overall scores were 0.918 (95% CI, 0.866, 0.971), ASPECTS 0.839 (95% CI, 0.755, 0.923), Bi-ASPECTS 0.837 (95% CI, 0.753, 0.922), mASPECTS 0.851(95% CI, 0.771, 0.931) and Bi-ASPECTS+PC-ASPECTS 0.876 (95% CI, 0.806, 0.946). CONCLUSIONS: The multivariate model based on conventional MRI combined with DWI performed in the subacute phase could help predict the prognosis of adult HIE with high performance.

[An artificial neural network model for glioma grading using image information].

OBJECTIVE: To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged.
 Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486.
 Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.

Gray matter structural alterations of cortico-striato-thalamo-cortical loop in familial Paroxysmal Kinesigenic Dyskinesia.

Background: Previous studies have indicated that patients with Paroxysmal Kinesigenic Dyskinesia (PKD) exhibit reduced gray matter volume in certain brain regions within the cortico-striato-thalamo-cortical (CSTC) loop. However, a comprehensive investigation specifically targeting the CSTC loop in PKD has never been conducted. Objectives: To provide evidence for the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, this study carried out a surface-based morphometry (SBM), voxel-based morphometry (VBM), and structural covariance networks (SCN) combined analysis in familial PKD patients. Methods: A total of 8 familial PKD patients and 10 healthy family members were included in the study and underwent Brain MRI examinations. Based on 3D T1 MPRAGE data, neuroimaging metrics of cortical thickness from SBM, subcortical nuclei volume from VBM, and covariance coefficient from SCN were used to systematically investigate the brain structural alterations along the CSTC loop of PKD patients. Results: A significant decrease in the average cortical thickness of the left S1 region in the PKD group was observed. The volumes of subcortical nuclei, including the thalamus, putamen, and globus pallidus were reduced, with a pronounced effect observed in the bilateral putamen. And the structural covariance connection between the left putamen and the left globus pallidus was significantly strengthened. Conclusions: The study confirms the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, and the findings may provide potential targets for objective diagnosis and therapeutic monitoring of PKD.

Association between segmental alterations of white matter bundles and cognitive performance in first-episode, treatment-naïve young adults with major depressive disorder.

BACKGROUND: Cumulative evidence has consistently shown that white matter (WM) disruption is associated with cognitive decline in geriatric depression. However, limited research has been conducted on the correlation between these lesions and cognitive performance in untreated young adults with major depressive disorder (MDD), particularly with the specific segmental alterations of the fibers. METHOD: Diffusion tensor images were performed on 60 first-episode, treatment-naïve young adult patients with MDD and 54 matched healthy controls (HCs). Automated fiber quantification was applied to calculate the tract profiles of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) to evaluate the WM microstructural organization. Correlation analysis was performed to find the associations between the diffusion properties and cognitive performance. RESULTS: Compared with HCs, patients with MDD exhibited predominantly different diffusion properties in bilateral uncinate fasciculus (UF), corticospinal tracts (CSTs), left superior longitudinal fasciculus and anterior thalamic radiation. The FA of the temporal cortex portion of right UF was positively correlated with working memory. The MD of the temporal component of left UF was negatively correlated with working memory and positively correlated with symptom severity. Additionally, a positive correlation between the MD of left CST and the psychomotor speed, negative correlation between the MD of left CST and the executive functions and complex attentional processes were observed. CONCLUSIONS: Our study validated the alterations in spatial localization of WM microstructure and its correlations with cognitive performance in first-episode, treatment-naïve young adults with MDD. This study added to the knowledge of the neuropathological basis of MDD.

The combination of radiomics features and VASARI standard to predict glioma grade.

Background and Purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

Evaluation of iron deposition in the motor CSTC loop of a Chinese family with paroxysmal kinesigenic dyskinesia using quantitative susceptibility mapping.

Introduction: Previous studies have revealed structural, functional, and metabolic changes in brain regions inside the cortico-striatal-thalamo-cortical (CSTC) loop in patients with paroxysmal kinesigenic dyskinesia (PKD), whereas no quantitative susceptibility mapping (QSM)-related studies have explored brain iron deposition in these areas. Methods: A total of eight familial PKD patients and 10 of their healthy family members (normal controls) were recruited and underwent QSM on a 3T magnetic resonance imaging system. Magnetic susceptibility maps were reconstructed using a multi-scale dipole inversion algorithm. Thereafter, we specifically analyzed changes in local mean susceptibility values in cortical regions and subcortical nuclei inside the motor CSTC loop. Results: Compared with normal controls, PKD patients had altered brain iron levels. In the cortical gray matter area involved with the motor CSTC loop, susceptibility values were generally elevated, especially in the bilateral M1 and PMv regions. In the subcortical nuclei regions involved with the motor CSTC loop, susceptibility values were generally lower, especially in the bilateral substantia nigra regions. Conclusion: Our results provide new evidence for the neuropathogenesis of PKD and suggest that an imbalance in brain iron levels may play a role in PKD.

A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension.

Intracranial hypertension (ICH) is a serious threat to the health of neonates. However, early and accurate diagnosis of neonatal intracranial hypertension remains a major challenge in clinical practice. In this study, a predictive model based on quantitative magnetic resonance imaging (MRI) data and clinical parameters was developed to identify neonates with a high risk of ICH. Newborns who were suspected of having intracranial lesions were included in our study. We utilized quantitative MRI to obtain the volumetric data of gray matter, white matter, and cerebrospinal fluid. After the MRI examination, a lumbar puncture was performed. The nomogram was constructed by incorporating the volumetric data and clinical features by multivariable logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Clinical parameters and volumetric quantitative MRI data, including postmenstrual age (p = 0.06), weight (p = 0.02), mode of delivery (p = 0.01), and gray matter volume (p = 0.003), were included in and significantly associated with neonatal intracranial hypertension risk. The nomogram showed satisfactory discrimination, with an area under the curve of 0.761. Our results demonstrated that decision curve analysis had promising clinical utility of the nomogram. The nomogram, incorporating clinical and quantitative MRI features, provided an individualized prediction of neonatal intracranial hypertension risk and facilitated decision making guidance for the early diagnosis and treatment for neonatal ICH. External validation from studies using a larger sample size before implementation in the clinical decision-making process is needed.

Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease.

Background: The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients. Methods: In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve. Results: Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%]. Conclusion: Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.

Neuroimaging uncovers distinct relationships of glymphatic dysfunction and motor symptoms in Parkinson's disease.

BACKGROUND: Studies of glymphatic dysfunction in Parkinson's disease (PD) patients have attracted much attention in recent years. However, the relationships between glymphatic dysfunction and clinical symptoms remains unclear. OBJECTIVES: To determine whether the diffusion tensor image analysis along the perivascular space (DTI-ALPS) affect the severity and types of motor and non-motor symptoms in PD patients. METHODS: De novo PD patients and controls who performed both DTI and 123I-DaTscan single photon emission computed tomography (SPECT) scanning were retrieved from the international multicenter Parkinson's Progression Marker Initiative (PPMI) cohort. Glymphatic system was evaluated by the DTI-ALPS. Motor symptoms were assessed by Movement Disorders Society Unified Parkinson's Disease Rating Scale III (MDS-UPDRS-III). The influence of glymphatic activity on motor and non-motor symptoms was explored by multivariate linear regression models. RESULTS: A total of 153 PD patients (mean age 60.97 ± 9.47 years; 99 male) and 67 normal controls (mean age 60.10 ± 10.562 years; 43 male) were included. The DTI-ALPS index of PD patients was significantly lower than normal controls (Z = - 2.160, p = 0.031). MDS-UPDRS III score (r = - 0.213, p = 0.008) and subscore for rigidity (r = - 0.177, p = 0.029) were negatively correlated with DTI-ALPS index. The DTI-ALPS index was significantly associated with MDS-UPDRS-III score (ß = - 0.160, p = 0.048) and subscore for rigidity (ß = - 0.170, p = 0.041) after adjusting for putamen dopamine transporter availability and clinical factors. CONCLUSIONS: Our results showed distinct relationships between glymphatic dysfunction and the severity and types of PD motor symptoms, suggesting the potential of DTI-ALPS index as a biomarker for PD motor symptoms.