Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus.

INTRODUCTION: Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. METHODS: In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. RESULTS: Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). CONCLUSION: These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. METHODS: ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. RESULTS: A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). CONCLUSIONS: The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. CLINICAL RELEVANCE STATEMENT: Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. KEY POINTS: • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

Modular Edge Analysis Reveals Chemotherapy-induced Brain Network Changes in Lung Cancer Patients.

BACKGROUND: Lung cancer patients with post-chemotherapy may have disconnected or weakened function connections within brain networks. OBJECTIVE: This study aimed to explore the abnormality of brain functional networks in lung cancer patients with post-chemotherapy by modular edge analysis. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 40 patients after chemotherapy, 40 patients before chemotherapy and 40 normal controls. Patients in all three groups were age and sex well-matched. Then, modular edge analysis was applied to assess brain functional network alterations. RESULTS: Post-chemotherapy patients had the worst MoCA scores among the three groups (p < 0.001). In intra-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strengths in the occipital lobe module (p < 0.05). Compared with the nonchemotherapy group, the patients after chemotherapy had decreased connection strengths in the subcortical module (p < 0.05). In inter-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strength in the frontal-temporal lobe modules (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strength in the subcortical-temporal lobe modules (p < 0.05). CONCLUSION: The results reveal that chemotherapy can disrupt connections in brain functional networks. As far as we know, the use of modular edge analysis to report changes in brain functional brain networks associated with chemotherapy was rarely reported. Modular edge analysis may play a crucial part in predicting the clinical outcome for the patients after chemotherapy.

Disrupted Effective Connectivity within the Fronto-Thalamic Circuit in Pontine Infarction: A Spectral Dynamic Causal Modeling Study.

This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker.

Brain connectome-based imaging markers for identifiable signature of migraine with and without aura.

Background: Cortical spreading depression (CSD) has been considered the prominent theory for migraine with aura (MwA). However, it is also argued that CSD can exist in patients in a silent state, and not manifest as aura. Thus, the MwA classification based on aura may be questionable. This study aimed to capture whole-brain connectome-based imaging markers with identifiable signatures for MwA and migraine without aura (MwoA). Methods: A total of 88 migraine patients (32 MwA) and 49 healthy controls (HC) underwent a diffusion tensor imaging and resting-state functional magnetic resonance imaging scan. The whole-brain structural connectivity (SC) and functional connectivity (FC) analysis was employed to extract imaging features. The extracted features were subjected to an all-relevant feature selection process within cross-validation loops to pinpoint attributes demonstrating substantial efficacy for patient categorization. Based on the identified features, the predictive ability of the random forest classifiers constructed with the 88 migraine patients' sample was tested using an independent sample of 32 migraine patients (eight MwA). Results: Compared to MwoA and HC, MwA showed two reduced SC and six FC (five increased and one reduced) features [all P<0.01, after false discovery rate (FDR) correction], involving frontal areas, temporal areas, visual areas, amygdala, and thalamus. A total of four imaging features were significantly correlated with clinical rating scales in all patients (r=-0.38 to 0.47, P<0.01, after FDR correction). The predictive ability of the random forest classifiers achieved an accuracy of 78.1% in the external sample to identify MwA. Conclusions: The whole-brain connectivity features in our results may serve as connectome-based imaging markers for MwA identification. The alterations of SC and FC strength provide possible evidence in further understanding the heterogeneity and mechanism of MwA which may help for patient-specific decision-making.

Aberrant brain functional hubs and causal effective connectivity in menstrually-related and non-menstrually-related migraine without aura.

Background: Menstrual migraine without aura (MRM) is common in female migraineurs and is closely related to cerebral functional abnormalities. However, whether the whole brain networks and directional functional connectivity of MRM patients are altered remains unclear. The purpose of this study was to detect the alterations of resting-state functional networks and directional functional connectivity between MRM and non-menstrual migraine without aura (NMM) patients using functional magnetic resonance imaging (fMRI) with degree centrality (DC) and Granger causality analysis (GCA) methods. Methods: In this retrospective and cross-sectional study, 45 MRM and 40 NMM patients (matched in age, gender, and years of education) were recruited in the study between May 2018 and June 2022. All participants had undergone resting-state fMRI scanning at the Neurology and Pain Outpatient Department of Nanjing First Hospital. Their brain functions were analyzed in terms of DC and GCA, with the significant threshold at voxel level P<0.01 and cluster level P<0.05, Gaussian random field corrected. Correlation analysis was adopted to assess the relationships between the fMRI results and clinical features (P<0.05, Bonferroni corrected). Results: Compared with those in the NMM group, MRM patients showed decreased DC in the right insula (T=-4.253). Using the right insula as the seed region, patients with MRM demonstrated enhanced effective connectivity from the right insula to the ipsilateral middle temporal gyrus (T=4.138) and contralateral superior temporal gyrus (T=3.523). Furthermore, the MRM group also showed decreased effective connectivity from several brain regions to the right insula, which included the right inferior occipital gyrus (T=-4.498), left middle frontal gyrus (T=-4.879), right precuneus (T=-4.644), and left inferior parietal gyrus (T=-4.113). The average Self-rating Anxiety Scale score of the MRM group was significantly higher than that of the NMM group [P=0.032, 95% confidence interval (CI): 0.363-7.761]. In the MRM group, disease duration was negatively correlated with the mean value of DC in right insula (r=-0.428, P=0.01). Conclusions: The present research demonstrated that patients with MRM have disruption in insula resting-state functional networks. Disrupted networks contained regions associated with cognitive processes, emotional perception, and migraine attack in MRM patients. These results may improve our comprehension of the neuromechanism of menstrually-related migraine.

Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space in presbycusis.

PURPOSE: Previous studies have suggested that presbycusis (age-related hearing loss) is accompanied with cognitive decline and dementia. However, the neural mechanism underlying the cognitive decline in presbycusis remains unclear. This study aimed to evaluate the glymphatic system function in presbycusis patients compared to healthy controls using diffusion tensor imaging (DTI) with the perivascular space (DTI-ALPS) method. METHODS: DTI scans were obtained from 30 presbycusis patients with cognitive decline (PCD), 30 presbycusis patients with no cognitive decline (PNCD) and 40 age-, gender-, and education-matched healthy controls (HCs). The DTI-ALPS index was calculated for each group. We evaluated the differences in the DTI-ALPS index among PCD, PNCD and HCs. In addition, we conducted a correlation analysis between the DTI-ALPS index and cognitive performance. RESULTS: There were significant differences of the DTI-ALPS index among three groups. Post-hoc analysis suggested that the DTI-ALPS index in PCD was significantly lower patients in relative to PNCD and HCs (1.49147 vs. 1.57441 vs. 1.62020, p < 0.001). After correcting for age, gender, and education, the DTI-ALPS index is positively correlated with the MoCA scores (rho = 0.426, p = 0.026). CONCLUSION: Presbycusis patients with cognitive impairment exhibited decreased glymphatic activity than those without cognitive impairment and HCs. The DTI-ALPS index may provide useful disease progression or treatment biomarkers for patients with presbycusis as an indicator of modulation of glymphatic activity.

Abnormal causal connectivity of anterior cingulate cortex-visual cortex circuit related to nonsteroidal anti-inflammatory drug efficacy in migraine.

The anterior cingulate cortex (ACC) and visual cortex are integral components of the neurophysiological mechanisms underlying migraine, yet the impact of altered connectivity patterns between these regions on migraine treatment remains unknown. To elucidate this issue, we investigated the abnormal causal connectivity between the ACC and visual cortex in patients with migraine without aura (MwoA), based on the resting-state functional magnetic resonance imaging data, and its predictive ability for the efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs). The results revealed increased causal connectivity from the bilateral ACC to the lingual gyrus (LG) and decreased connectivity in the opposite direction in nonresponders compared with the responders. Moreover, compared with the healthy controls, nonresponders exhibited heightened causal connectivity from the ACC to the LG, right inferior occipital gyrus (IOG) and left superior occipital gyrus, while connectivity patterns from the LG and right IOG to the ACC were diminished. Based on the observed abnormal connectivity patterns, the support vector machine (SVM) models showed that the area under the receiver operator characteristic curves for the ACC to LG, LG to ACC and bidirectional models were 0.857, 0.898, and 0.939, respectively. These findings indicate that neuroimaging markers of abnormal causal connectivity in the ACC-visual cortex circuit may facilitate clinical decision-making regarding NSAIDs administration for migraine management.

Optimization of structural connectomes and scaled patterns of structural-functional decoupling in Parkinson's disease.

Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.

Reorganization of the cortical connectome functional gradient in age-related hearing loss.

Age-related hearing loss (ARHL), one of the most common sensory deficits in elderly individuals, is a risk factor for dementia; however, it is unclear how ARHL affects the decline in cognitive function. To address this issue, a connectome gradient framework was used to identify critical features of information integration between sensory and cognitive processing centers using resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ARHL and 36 healthy controls (HCs). The first three functional gradient alterations associated with ARHL were investigated at the global, network and regional levels. Using a support vector machine (SVM) model, our analysis distinguished individuals with ARHL with normal cognitive function from those with cognitive decline. Compared to HCs, individuals with ARHL had a contracted principal primary-to-transmodal gradient axis, especially in the visual and default mode networks, with an altered gradient explained ratio and variance. Among individuals with ARHL, cognitive decline was detected in the visual network in the principal gradient as well as in the limbic, salience and default mode networks in the third gradient (salience to frontoparietal/default mode). These results suggest that ARHL is associated with disrupted information processing from the primary sensory networks to higher-order cognitive networks and highlight the key nodes closely associated with cognitive decline during cognitive processing in ARHL, providing new insights into the mechanism of cognitive impairment and suggesting potential treatments related to ARHL.

Disrupted resting-state functional connectivity and network topology in mild traumatic brain injury: an arterial spin labelling study.

Mild traumatic brain injury can cause different degrees of cognitive impairment and abnormal brain structure and functional connectivity, but there is still a lack of research on the functional connectivity and topological organization of cerebral blood flow fluctuations. This study explored the cerebral blood flow, functional connectivity and topological organization of the cerebral blood flow network in acute mild traumatic brain injury patients. In total, 48 mild traumatic brain injury patients and 46 well-matched healthy controls underwent resting-state arterial spin labelling perfusion MRI and neuropsychological assessments. The functional connectivity and topological organization of the cerebral blood flow network were analysed. Then, the correlation between the changes in cerebral blood flow network characteristics and cognitive function was explored. Acute mild traumatic brain injury patients showed decreased cerebral blood flow in the right insula and increased cerebral blood flow in the right inferior temporal gyrus and left superior temporal gyrus. Abnormal cerebral blood flow network connection patterns mainly occur in sensorimotor network, default mode network, cingulo-opercular network and occipital network-related regions. Furthermore, mild traumatic brain injury disrupted the topological organization of the whole brain, which manifested as (i) reduced global efficiency; (ii) abnormal degree centrality, betweenness centrality, nodal clustering coefficient and nodal efficiency; and (iii) decreased intermodular connectivity between the occipital network and sensorimotor network. Finally, the change in network topology was correlated with the cognitive score of the mild traumatic brain injury. This study provided evidence of abnormal functional connectivity and network topology based on cerebral blood flow in acute mild traumatic brain injury patients, revealing their potential use as early markers for mild traumatic brain injury, which may contribute to both disease diagnosis and assessment.

Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images.

Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression.

BACKGROUND: Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP. METHODS: A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RESULTS: RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001). CONCLUSIONS: The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis.

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.

Detection of intermediate- and high-risk prostate cancer with biparametric magnetic resonance imaging: a systematic review and meta-analysis.

Background: This systematic review and meta-analysis evaluated the diagnostic performance of biparametric magnetic resonance imaging (bpMRI) for the detection of intermediate- and high-risk prostate cancer (IHPC). Methods: Two medical databases (PubMed and Web of Science) were systematically reviewed by 2 independent researchers. Studies published before March 15, 2022, that used bpMRI (i.e., T2-weighted images combined with diffusion-weighted imaging) to detect prostate cancer (PCa) were included. The results of prostatectomy or prostate biopsy were the reference standards for the studies. The Quality Assessment of Diagnosis Accuracy Studies 2 tool was used to assess the quality of the included studies. Data on true- and false-positive and -negative results were extracted to complete 2×2 contingency tables, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each study. Summary receiver operating characteristic (SROC) plots were constructed using these results. Results: In all, 16 studies (6,174 patients) that used Prostate Imaging Reporting and Data System version 2 or other scoring systems, such as Likert, SPL and Questionnaire were included. Sensitivity, specificity, positive and negative likelihood ratios, and the diagnosis odds ratio of bpMRI in the detection of IHPC were 0.91 (95% CI: 0.87-0.93), 0.67 (95% CI: 0.58-0.76), 2.8 (95% CI: 2.2-3.6), 0.14 (95% CI: 0.11-0.18), and 20 (95% CI: 15-27), respectively, with an area under the SROC curve of 0.90 (95% CI: 0.87-0.92). There was considerable heterogeneity between the studies. Conclusions: bpMRI exhibited a high negative predictive value and accuracy in the diagnosis of IHPC, and may be valuable for detecting PCa with poor prognosis. However, the bpMRI protocol needs to be standardized further to improve its wider applicability.

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke.

Objective: We explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis. Methods: A total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0-2) and an unfavourable outcome group (mRS of 3-6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. Results: The FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876-0.968), 0.875 (95% CI: 0.815-0.935), and 0.895 (95% CI: 0.840-0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90-0.98) and test set (AUC = 0.94; 95% CI: 0.87-1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88-1) and validation set 2 (AUC = 0.90; 95% CI: 0.768-1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram. Conclusion: This non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.

Hypoperfusion Intensity Ratio Correlates with Angiographic Collaterals and Infarct Growth in Acute Stroke with Thrombectomy.

BACKGROUND: The assessment of collaterals before endovascular thrombectomy (EVT) therapy play a pivotal role in clinical decision-making for acute stroke patients. OBJECTIVE: To investigate the correlation between hypoperfusion intensity ratio (HIR), collaterals on digital subtraction angiography (DSA), and infarct growth in acute stroke patients who underwent EVT therapy. METHODS: Patients with acute ischemic stroke (AIS) who underwent EVT therapy were enrolled retrospectively. HIR was assessed through magnetic resonance imaging (MRI) and was defined as the Tmax > 10 s lesion volume divided by the Tmax > 6 s lesion volume. Collaterals were assessed on DSA using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) scale. Good collaterals were defined as ASITN/SIR score 3-4 and poor collaterals were defined as ASITN/SIR score 0-2. Spearman's rank correlation analysis was used to evaluate the correlation between HIR, collaterals, infarct growth, and functional outcome. RESULTS: A total of 115 patients were included. Patients with good collateral (n = 59) had smaller HIR (0.29 ± 0.07 vs. 0.52 ± 0.14; t = 10.769, P < 0.001) and infarct growth (8.47 ± 2.40 vs. 14.37 ± 5.28; t = 7.652, P < 0.001) than those with poor collateral (n = 56). DISCUSSION: The ROC analyses showed that the optimal cut-off value of HIR was 0.40, and the sensitivity and specificity for predicting good collateral were 85.70% and 96.61%, respectively. With the optimal cut-off value, patients with HIR < 0.4 (n = 67) had smaller infarct growth (8.86 ± 2.59 vs. 14.81 ± 5.52; t = 6.944, P < 0.001) than those with HIR ≥ 0.4 (n = 48). Spearman's rank correlation analysis showed that HIR had a correlation with ASITN/SIR score (r = -0.761, P < 0.001), infarct growth (r = 0.567, P < 0.001), and mRS at 3 months (r = -0.627, P < 0.001). CONCLUSION: HIR < 0.4 is significantly correlated with good collateral status and small infarct growth. Evaluating HIR before treatment may be useful for guiding EVT and predicting the functional outcome of AIS patients.

Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury.

Background: This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment. Methods: Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses. Results: Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395). The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functional-structural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05). Conclusions: Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.

Flexible human serum albumin nanocapsules to enhance drug delivery and cellular uptake for photodynamic/chemo cancer therapy.

As a non-invasive cancer treatment, photodynamic therapy (PDT) has great applications in superficial tumors because of its high selectivity and low cumulative toxicity. However, the poor tumor-targeting ability and short blood circulation time of conventional photosensitizers (PSs) limit the efficacy of PDT to some extent. In this study, we synthesized flexible hollow human serum albumin (HHSA) and loaded photosensitizer Chlorin e6 (Ce6) and the chemotherapeutic drug Doxorubicin (DOX) for synergistic cancer therapy. HHSA can enhance drug delivery and cellular uptake through targeting gp60 and SPARC receptors and unique flexible hollow structures. The TEM images show that HHSA possesses distinct flexible hollow structures, as well as good monodispersity and deformability. After loading Ce6 and DOX, HHSA@Ce6-DOX displays better therapeutic effects than HHSA@DOX on the growth of 4T1 breast cancers without irradiation. Remarkably, it has a significantly higher therapeutic effect (relative cell activity: 45% vs. 74%) than HHSA@Ce6 under 660 nm irradiation. Furthermore, the excellent biocompatibility of HHSA@Ce6-DOX has been proved both in vitro and in vivo, indicating that it has a promising future in synergistic tumor treatments.

A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events.

Background A noninvasive coronary CT angiography (CCTA)-based radiomics technique may facilitate the identification of vulnerable plaques and patients at risk for future adverse events. Purpose To assess whether a CCTA-based radiomic signature (RS) of vulnerable plaques defined with intravascular US was associated with increased risk for future major adverse cardiac events (MACE). Materials and Methods In a retrospective study, an RS of vulnerable plaques was developed and validated using intravascular US as the reference standard. The RS development data set included patients first undergoing CCTA and then intravascular US within 3 months between June 2013 and December 2020 at one tertiary hospital. The development set was randomly assigned to training and validation sets at a 7:3 ratio. Diagnostic performance was assessed internally and externally from three tertiary hospitals using the area under the curve (AUC). The prognostic value of the RS for predicting MACE was evaluated in a prospective cohort with suspected coronary artery disease between April 2018 and March 2019. Multivariable Cox regression analysis was used to evaluate the RS and conventional anatomic plaque features (eg, segment involvement score) for predicting MACE. Results The RS development data set included 419 lesions from 225 patients (mean age, 64 years ± 10 [SD]; 68 men), while the prognostic cohort included 1020 lesions from 708 patients (mean age, 62 years ± 11; 498 men). Sixteen radiomic features, including two shape features and 14 textural features, were selected to build the RS. The RS yielded a moderate to good AUC in the training, validation, internal, and external test sets (AUC = 0.81, 0.75, 0.80, and 0.77, respectively). A high RS (≥1.07) was independently associated with MACE over a median 3-year follow-up (hazard ratio, 2.01; P = .005). Conclusion A coronary CT angiography-derived radiomic signature of coronary plaque enabled the detection of vulnerable plaques that were associated with increased risk for future adverse cardiac outcomes. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by De Cecco and van Assen in this issue.