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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Regional metabolic heterogeneity in anterior cingulate cortex in major depressive disorder: A multi-voxel 1H magnetic resonance spectroscopy study.

BACKGROUND: Previous studies have shown major depressive disorder (MDD) is associated with altered neuro-metabolites in the anterior cingulate cortex (ACC). However, the regional metabolic heterogeneity in the ACC in individuals with MDD remains unclear. METHODS: We recruited 59 first-episode, treatment-naive young adults with MDD and 50 healthy controls who underwent multi-voxel 1H-MRS scanning at 3 T (Tesla) with voxels placed in the ACC, which was divided into two subregions, pregenual ACC (pACC) and anterior midcingulate cortex (aMCC). Between and within-subjects metabolite concentration variations were analyzed with SPSS. RESULTS: Compared with control subjects, patients with MDD exhibited higher glutamate (Glu) and glutamine (Gln) levels in the pACC and higher myo-inositol (MI) level in the aMCC. We observed higher Glu and Gln levels and lower N-acetyl-aspartate (NAA) level in the pACC than those in the aMCC in both MDD and healthy control (HC) groups. More importantly, the metabolite concentration gradients of Glu, Gln and NAA were more pronounced in MDD patients relative to HCs. In the MDD group, the MI level in the aMCC positively correlated with the age of onset. LIMITATIONS: The use of the relative concentration of metabolites constitutes a key study limitation. CONCLUSIONS: We observed inconsistent alterations and distribution of neuro-metabolites concentration in the pACC and aMCC, revealing regional metabolic heterogeneity of ACC in first-episode, treatment-naive young individuals with MDD. These results provided new evidence for abnormal neuro-metabolites of ACC in the pathophysiology of MDD and suggested that pACC and aMCC might play different roles in MDD.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

Myosteatosis predicting risk of transition to severe COVID-19 infection.

BACKGROUND: About 10-20% of patients with Coronavirus disease 2019 (COVID-19) infection progressed to severe illness within a week or so after initially diagnosed as mild infection. Identification of this subgroup of patients was crucial for early aggressive intervention to improve survival. The purpose of this study was to evaluate whether computer tomography (CT) - derived measurements of body composition such as myosteatosis indicating fat deposition inside the muscles could be used to predict the risk of transition to severe illness in patients with initial diagnosis of mild COVID-19 infection. METHODS: Patients with laboratory-confirmed COVID-19 infection presenting initially as having the mild common-subtype illness were retrospectively recruited between January 21, 2020 and February 19, 2020. CT-derived body composition measurements were obtained from the initial chest CT images at the level of the twelfth thoracic vertebra (T12) and were used to build models to predict the risk of transition. A myosteatosis nomogram was constructed using multivariate logistic regression incorporating both clinical variables and myosteatosis measurements. The performance of the prediction models was assessed by receiver operating characteristic (ROC) curve including the area under the curve (AUC). The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. RESULTS: A total of 234 patients were included in this study. Thirty-one of the enrolled patients transitioned to severe illness. Myosteatosis measurements including SM-RA (skeletal muscle radiation attenuation) and SMFI (skeletal muscle fat index) score fitted with SMFI, age and gender, were significantly associated with risk of transition for both the training and validation cohorts (P < 0.01). The nomogram combining the SM-RA, SMFI score and clinical model improved prediction for the transition risk with an AUC of 0.85 [95% CI, 0.75 to 0.95] for the training cohort and 0.84 [95% CI, 0.71 to 0.97] for the validation cohort, as compared to the nomogram of the clinical model with AUC of 0.75 and 0.74 for the training and validation cohorts respectively. Favorable clinical utility was observed using decision curve analysis. CONCLUSION: We found CT-derived measurements of thoracic myosteatosis to be associated with higher risk of transition to severe illness in patients affected by COVID-19 who presented initially as having the mild common-subtype infection. Our study showed the relevance of skeletal muscle examination in the overall assessment of disease progression and prognosis of patients with COVID-19 infection.

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.

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.

OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND METHODS: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. RESULTS: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. CONCLUSION: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity.

Previous studies have suggested that resting-state functional connectivity plays a central role in the physiopathology of major depressive disorder (MDD). However, the individualized diagnosis of MDD based on resting-state functional connectivity is still unclear, especially in first episode drug-naive patients with MDD. Resting state functional magnetic resonance imaging was enrolled from 30 first episode drug-naive patients with MDD and age- and gender-matched 31 healthy controls. Whole brain functional connectivity was computed and viewed as classification features. Multivariate pattern analysis (MVPA) was performed to discriminate patients with MDD from controls. The experimental results exhibited a correct classification rate of 82.25% (p < 0.001) with sensitivity of 83.87% and specificity of 80.64%. Almost all of the consensus connections (125/128) were cross-network interaction among default mode network (DMN), salience network (SN), central executive network (CEN), visual cortex network (VN), Cerebellum and Other. Moreover, the supramarginal gyrus exhibited high discriminative power in classification. Our findings suggested cross-network interaction can be used as an effective biomarker for MDD clinical diagnosis, which may reveal the potential pathological mechanism for major depression. The current study further confirmed reliable application of MVPA in discriminating MDD patients from healthy controls.

Higher Trait Impulsivity and Altered Frontostriatal Connectivity in Betel-Quid Dependent Individuals.

Objective: Betel quid dependency (BQD) is characterized by functional and structural brain alterations. Trait impulsivity may influence substance dependence by impacting its neurobiological underpinnings in the frontostriatal circuit. However, little is known about the trait impulsivity and its neural correlates in individuals with BQD. Methods: Forty-eight participants with BQD and 22 normal controls (NCs) were recruited and scanned on a 3T MRI scanner. Barratt impulsiveness scale (BIS) was used to measure trait impulsivity: motor, attention, and no plan impulsivity. We used voxel-based morphometry (VBM) to assess the relationship between trait impulsivity and gray matter volumes. The relevant clusters identified were served as regions of interest (ROI) seeds. The whole-volume psycho-physiological interactions (PPI) analysis was used to investigate the changes of functional connectivity related to ROI seeds in the cue-reactivity task condition (BQ and control images). Results: Behaviorally, the BQD group showed significantly higher trait impulsivity including motor and no plan impulsivity than the NCs group. VBM analyses showed that motor impulsivity was negatively associated with gray matter volume of right caudate in the whole sample. No difference in gray matter volume between the two groups was observed. PPI analyses showed that there was a significantly decreased functional connectivity between the right caudate and right dorsolateral prefrontal cortex (DLPFC) when watching BQ related images than control images in individuals with BQD. Furthermore, functional connectivity between the right caudate and right DLPFC was negatively correlated with BQ dependency scores. Conclusions: Our study demonstrated the structural basis of trait impulsivity in the caudate and provided evidence for abnormal interactions within frontostriatal circuitsin individuals with BQD, which may provide insight into the selection of potential novel therapeutic targets for the treatment of BQ dependency.