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.

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.

Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke.

BACKGROUND: Diffusion-weighted imaging radiomics could be used as prognostic biomarkers in acute ischemic stroke. We aimed to identify a clinical and diffusion-weighted imaging radiomics model for individual unfavorable outcomes risk assessment in acute ischemic stroke. METHODS: A total of 1716 patients with acute ischemic stroke from 2 centers were divided into a training cohort and a validation cohort. Patient outcomes were measured with the modified Rankin Scale score. An unfavorable outcome was defined as a modified Rankin Scale score greater than 2. The primary end point was all-cause mortality or outcomes 1 year after stroke. The MRI-DRAGON score was calculated based on previous publications. We extracted and selected the infarct features on diffusion-weighted imaging to construct a radiomic signature. The clinic-radiomics signature was built by measuring the Cox proportional risk regression score (CrrScore) and compared with the MRI-DRAGON score and the ClinicScore. CrrScore model performance was estimated by 1-year unfavorable outcomes prediction. RESULTS: A high radiomic signature predicted a higher probability of unfavorable outcomes than a low radiomic signature in the training (hazard ratio, 3.19 [95% CI, 2.51-4.05]; P<0.0001) and validation (hazard ratio, 3.25 [95% CI, 2.20-4.80]; P<0.0001) cohorts. The diffusion-weighted imaging Alberta Stroke Program Early CT Score, age, glucose level before therapy, National Institutes of Health Stroke Scale score on admission, glycated hemoglobin' radiomic signature, hemorrhagic infarction, and malignant cerebral edema were associated with an unfavorable outcomes risk after multivariable adjustment. A CrrScore nomogram was developed to predict outcomes and had the best performance in the training (area under the curve, 0.862) and validation cohorts (area under the curve, 0.858). The CrrScore model time-dependent areas under the curve of the probability of unfavorable outcomes at 1 year in the training and validation cohorts were 0.811 and 0.801, respectively. CONCLUSIONS: The CrrScore model allows the accurate prediction of patients with acute ischemic stroke outcomes and can potentially guide rehabilitation therapies for patients with different risks of unfavorable outcomes.

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.

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.

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.

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.

Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.

OBJECTIVES: To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT. METHODS: A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment. RESULTS: Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group. CONCLUSIONS: The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. KEY POINTS: • An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.

OBJECTIVES: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. METHODS: DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. RESULTS: svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. CONCLUSIONS: The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset. KEY POINTS: • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

BACKGROUND In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to distinguish between malignant and benign solid lung nodules. MATERIAL AND METHODS CT images from 202 patients with 210 lesions (malignant: 127, benign: 83) manifesting as solid lung nodules from January 2016 to December 2020 from 3 institutions were retrospectively collected, and each nodule was histopathologically confirmed. Two experienced thoracic radiologists reviewed all images and determined the regions of interest (ROIs) in the three-dimensional (3D) images layer-by-layer. We divided the lesions and images into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training dataset. Five-fold cross-validation was used to choose the optimal model. Receiver operator characteristic curves (ROC curves) for methods to evaluate the performance of the models were drafted. RESULTS In the validation set, the AUC, accuracy, sensitivity, and specificity of Inception V3 model (lesion-level) were 0.999, 0.989, 0.983, and 1.0, respectively, which is higher than the image-level (0.997, 0.933, 0.922, and 0.948, respectively). The Inception V3 model (lesion-level) performed better than the image-level but there was no significant difference between the models (P>0.05). The ResNet50 model based on image level achieved AUC, accuracy, sensitivity, and specificity of 0.963, 0.926, 0.916, and 0.944, respectively, which is lower than that of Inception V3. CONCLUSIONS Our study developed a novel deep learning model based on noncontrast and thin-layer CT scans to classify benign vs malignant lung nodules, and the Inception V3 model greatly improved the differentiation accuracy and specificity.

Disrupted Dynamic Functional Connectivity of the Visual Network in Episodic Patients with Migraine without Aura.

Background: Visual symptoms are common in patients with migraine, even in interictal periods. The purpose was to assess the association between dynamic functional connectivity (dFC) of the visual cortex and clinical characteristics in migraine without aura (MwoA) patients. Methods: We enrolled fifty-five MwoA patients as well as fifty gender- and age-matched healthy controls. Regional visual cortex alterations were investigated using regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF). Then, significant regions were selected as seeds for conducting dFC between the visual cortex and the whole brain. Results: Relative to healthy controls, MwoA patients exhibited decreased ReHo and ALFF values in the right lingual gyrus (LG) and increased ALFF values in the prefrontal cortex. The right LG showed abnormal dFC within the visual cortex and with other core brain networks. Additionally, ReHo values for the right LG were correlated with duration of disease and ALFF values of the right inferior frontal gyrus and middle frontal gyrus were correlated with headache frequency and anxiety scores, respectively. Moreover, the abnormal dFC of the right LG with bilateral cuneus was positively correlated with anxiety scores. Conclusions: The dFC abnormalities of the visual cortex may be involved in pain integration with multinetworks and associated with anxiety disorder in episodic MwoA patients.

Cerebral blood flow alterations in migraine patients with and without aura: An arterial spin labeling study.

BACKGROUND: Migraine aura is a transient, fully reversible visual, sensory, or other central nervous system symptom that classically precedes migraine headache. This study aimed to investigate cerebral blood flow (CBF) alterations of migraine with aura patients (MwA) and without aura patients (MwoA) during inter-ictal periods, using arterial spin labeling (ASL). METHODS: We evaluated 88 migraine patients (32 MwA) and 44 healthy control subjects (HC) who underwent a three-dimensional pseudo-continuous ASL MRI scanning. Voxel-based comparison of normalized CBF was conducted between MwA and MwoA. The relationship between CBF variation and clinical scale assessment was further analyzed. The mean CBF values in brain regions showed significant differences were calculated and considered as imaging features. Based on these features, different machine learning-based models were established to differentiate MwA and MwoA under five-fold cross validation. The predictive ability of the optimal model was further tested in an independent sample of 30 migraine patients (10 MwA). RESULTS: In comparison to MwoA and HC, MwA exhibited higher CBF levels in the bilateral superior frontal gyrus, bilateral postcentral gyrus and cerebellum, and lower CBF levels in the bilateral middle frontal gyrus, thalamus and medioventral occipital cortex (all p values < 0.05). These variations were also significantly correlated with multiple clinical rating scales about headache severity, quality of life and emotion. On basis of these CBF features, the accuracies and areas under curve of the final model in the training and testing samples were 84.3% and 0.872, 83.3% and 0.860 in discriminating patients with and without aura, respectively. CONCLUSION: In this study, CBF abnormalities of MwA were identified in multiple brain regions, which might help better understand migraine-stroke connection mechanisms and may guide patient-specific decision-making.

Motor asymmetry related cerebral perfusion patterns in Parkinson's disease: An arterial spin labeling study.

Persisting asymmetry of motor symptoms are characteristic of Parkinson's disease (PD). We investigated the possible lateralized effects on regional cerebral blood flow (CBF), CBF-connectivity, and laterality index (LI) among PD subtypes using arterial spin labeling (ASL). Forty-four left-sided symptom dominance patients (PDL), forty-eight right-sided symptom dominance patients (PDR), and forty-five matched HCs were included. Group comparisons were performed for the regional normalized CBF, CBF-connectivity and LI of basal ganglia (BA) subregions. The PDL patients had lower CBF in right calcarine sulcus and right supramarginal gyrus compared to the PDR and the HC subjects. Regional perfusion alterations seemed more extensive in the PDL than in the PDR group. In the PDL, correlations were identified between right thalamus and motor severity, between right fusiform gyrus and global cognitive performance. None of correlations survived after multiple comparisons correction. The significantly altered CBF-connectivity among the three groups included: unilateral putamen, unilateral globus pallidus, and right thalamus. LI score in the putamen was significantly different among groups. Motor-symptom laterality in PD may exhibit asymmetric regional and interregional abnormalities of CBF properties, particularly in PDL patients. This preliminary study underlines the necessity of classifying PD subgroups based on asymmetric motor symptoms and the potential application of CBF properties underlying neuropathology in PD.

Hemodynamics is associated with vessel wall remodeling in patients with middle cerebral artery stenosis.

OBJECTIVES: To evaluate the relationship between hemodynamics and vessel wall remodeling patterns in middle cerebral artery (MCA) stenosis based on high-resolution magnetic resonance imaging and computational fluid dynamics (CFD). METHODS: Forty consecutive patients with recent ischemic stroke or transient ischemic attack attributed to unilateral atherosclerotic MCA stenosis (50-99%) were prospectively recruited. All patients underwent a cross-sectional scan of the stenotic MCA vessel wall. The parameters of the vessel wall, the number of patients with acute infarction, translesional wall shear stress ratio (WSSR), wall shear stress in stenosis (WSSs), and translesional pressure ratio were obtained. The patients were divided into positive remodeling (PR) and negative remodeling (NR) groups. The differences in vessel wall parameters and hemodynamics were compared. Correlations between the parameters of the vessel wall and hemodynamics were calculated. RESULTS: Of the 40 patients, 16 had PR, 19 had NR, and the other 5 displayed non-remodeling. The PR group had a smaller lumen area (p = 0.004), larger plaque area (p < 0.001), normal wall index (p = 0.004), and higher WSSR (p = 0.004) and WSSs (p = 0.023) at the most narrowed site. The PR group had more enhanced plaques (12 vs 6, p = 0.03). The number of patients with acute stroke in the PR group was more than that in the NR group (11 vs 4, p = 0.01). The remodeling index (r = 0.376, p = 0.026) and plaque area (r = 0.407, p = 0.015) showed a positive correlation with WSSR, respectively. CONCLUSIONS: Hemodynamics plays a role in atherosclerotic plaques and vessel wall remodeling. Individuals with greater hemodynamic values might be more prone to stroke. KEY POINTS: • Stenotic plaques in middle cerebral artery with positive remodeling have smaller lumen area and larger resp. higher plaque area, normal wall index, translesional wall shear stress ratio, and wall shear stress than negative remodeling. • The remodeling index and plaque area are positively correlated with translesional wall shear stress ratio. • Hemodynamic may help to understand the role of positive remodeling in the development of acute stroke.

Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network.

OBJECTIVE: To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). MATERIALS: In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set (n = 760; age: 55.8 ± 13.4 years; men: 500), a single-centre testing set (n = 134; age: 53.1 ± 14.3 years; men: 90), and two independent multicentre testing sets from two different hospitals (n = 62, age: 57.97 ± 11.88, men: 41; n = 64, age: 57.40 ± 13.36, men: 35). A Faster Region-based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. RESULTS: The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15 s). CONCLUSIONS: A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. KEY POINTS: • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.

Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke.

BACKGROUND AND PURPOSE: This study was conducted to investigate whether capsular stroke (CS) and pontine stroke (PS) have different topological alterations of structural connectivity (SC) and functional connectivity (FC), as well as correlations of SC-FC coupling with movement assessment scores. METHODS: Resting-state functional magnetic resonance imaging and diffusion tensor imaging were prospectively acquired in 46 patients with CS, 36 with PS, and 29 healthy controls (HCs). Graph theoretical network analyses of SC and FC were performed. Patients with left and right lesions were analyzed separately. RESULTS: With regard to FC, the PS and CS groups both showed higher local efficiency than the HCs, and the CS group also had a higher clustering coefficient (Cp) than the HCs in the right lesion analysis. With regard to SC, the PS and CS groups both showed different normalized clustering coefficient (γ), small-worldness (σ), and characteristic path length (Lp) compared with the HC group. Additionally, the CS group showed higher normalized characteristic path length (λ) and a lower Cp than the HCs and the PS group showed higher λ and lower global efficiency than the HCs in the right-lesion analysis. However, γ, σ, Cp and Lp were only significantly different in the PS and CS groups compared with the HC group in the right-lesion analysis. Importantly, the CS group was found to have a weaker SC-FC coupling than the PS group and the HC group in the right-lesion analysis. In addition, both patient groups had weaker structural-functional connectome correlation than the HCs. CONCLUSIONS: The CS and PS groups both showed FC and SC disruption and the CS group had a weaker SC-FC coupling than the PS group in the right lesion analysis. This may provide useful information for individualized rehabilitative strategies.

Assessment of normalized cerebral blood flow and its connectivity with migraines without aura during interictal periods by arterial spin labeling.

BACKGROUND: Migraine constitutes a global health burden, and its pathophysiology is not well-understood; research evaluating cerebral perfusion and altered blood flow between brain areas using non-invasive imaging techniques, such as arterial spin labeling, have been scarce. This study aimed to assess cerebral blood flow (CBF) and its connectivity of migraine. METHODS: This study enrolled 40 patients with episodic migraine without aura (MwoA), as well as 42 healthy patients as control (HC). Two groups of normalized CBF and CBF connectivity were compared, and the relationship between CBF variation and clinical scale assessment was further evaluated. RESULTS: In comparison to HC subjects, MwoA patients exhibited higher CBF in the right middle frontal orbital gyrus (ORBmid.R) and the right middle frontal gyrus, while that in Vermis_6 declined. The increased CBF of ORBmid.R was positively correlated with both the Visual Light Sensitivity Questionnaire-8 (VLSQ-8) and the monthly attack frequency score. In MwoA, significantly decreased CBF connectivity was detected between ORBmid.R and the left superior frontal gyrus, the right putamen, the right caudate, as well as the right angular gyrus. In addition, increased CBF connectivity was observed between the left calcarine cortex and ORBmid.R. CONCLUSIONS: Our results indicate that migraine patients exhibit abnormalities in regional CBF and feature CBF connection defects at the resting state. The affected areas involve information perception, information integration, and emotional, pain and visual processing. Our findings might provide important clues for the pathophysiology of migraine.

Altered static and dynamic functional network connectivity in post-traumatic headache.

BACKGROUND: Post-traumatic headache (PTH) is a very common symptom following mild traumatic brain injury (mTBI), yet much remains unknown about the underlying pathophysiological mechanisms of PTH. Neuroimaging studies suggest that aberrant functional network connectivity (FNC) may be an important factor in pain disorders. The present study aimed to investigate the functional characteristics of static FNC (sFNC) and dynamic FNC (dFNC) in mTBI patients with PTH. METHODS: With Institutional Review Board (IRB) approval, we prospectively recruited 50 mTBI patients with PTH, who were diagnosed with ICHD-3 beta diagnostic criteria and 39 mTBI without PTH who were well matched for age, gender and education. Resting-state functional magnetic resonance imaging (fMRI) scanning (3.0 T, Philips Medical Systems, Netherlands), Montreal Cognitive Assessment (MoCA) and headache symptom measurement (headache frequency and headache intensity) were performed. The resting-state fMRI sequence took 8 min and 10 s. Independent component analysis and sliding window method were applied to examine the FNC on the basis of nine resting-state networks, namely, default mode network (DMN), sensorimotor network (SMN), executive control network (ECN), auditory network (AuN), attention network (AN), salience network (SN), visual network (VN), and cerebellum network (CN). The differences in sFNC and dFNC were determined and correlated with clinical variables using Pearson rank correlation. RESULTS: For sFNC, compared with mTBI patients without PTH, mTB with PTH group showed four altered interactions, including decreased interactions in SN-SMN and VN-DMN pairs, increased sFNC in SN-ECN and SMN-DMN pairs. For dFNC, significant group differences were found in State 2, including increased connectivity alteration in the DMN with CN, DMN with SMN, and AuN with CN. Significant reduced connectivity changes in the DMN with VN was found in State 4. Furthermore, the number of transitions (r=0.394, p=0.005) between states was positively associated with headache frequency. Additionally, dwell time (r=-0.320, p=0.025) in State 1 was negatively correlated with MoCA score. CONCLUSIONS: MTBI patients with PTH are characterized with altered sFNC and dFNC, which could provide new perspective to understand the neuropathological mechanism underlying the PTH to determine more appropriate management, and may be a useful imaging biomarker for identifying and predicting mTBI with PTH.

Altered amygdala effective connectivity in migraine without aura: evidence from resting-state fMRI with Granger causality analysis.

BACKGROUND: Granger causality analysis (GCA) has been used to investigate the pathophysiology of migraine. Amygdala plays a key role in pain modulation of migraine attack. However, the detailed neuromechanism remained to be elucidated. We applied GCA to explore the amygdala-based directional effective connectivity in migraine without aura (MwoA) and to determine the relation with clinical characteristics. METHODS: Forty-five MwoA patients and forty age-, sex-, and years of education-matched healthy controls(HCs) underwent resting-state functional magnetic resonance imaging (fMRI). Bilateral amygdala were used as seed regions in GCA to investigate directional effective connectivity and relation with migraine duration or attack frequency. RESULTS: MwoA patients showed significantly decreased effective connectivity from right amygdala to right superior temporal gyrus, left superior temporal gyrus and right precentral gyrus compared with HCs. Furthermore, MwoA patients demonstrated significantly decreased effective connectivity from the left amygdala to the ipsilateral superior temporal gyrus. Also, MwoA patients showed enhanced effective connectivity from left inferior frontal gyrus to left amygdala. Effective connectivity outflow from right amygdala to right precentral gyrus was negatively correlated to disease duration. CONCLUSIONS: Altered directional effective connectivity of amygdala demonstrated that neurolimbic pain networks contribute to multisensory integration abnormalities and deficits in pain modulation of MwoA patients.

Functional connectivity of the visual cortex differentiates anxiety comorbidity from episodic migraineurs without aura.

BACKGROUND: Migraine is a common neurological disease that is often accompanied by psychiatric comorbidities. However, the relationship between abnormal brain function and psychiatric comorbidities in migraine patients remains largely unclear. Therefore, the present study sought to explore the correlations between the resting-state functional deficits and psychiatric comorbidities in migraine without aura (MwoA) patients. METHODS: Resting-state functional magnetic resonance images were obtained. In addition, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) values were obtained. Thereafter regional abnormalities in MwoA patients with and without anxiety (MwoA-A and MwoA-OA) were chosen as seeds to conduct functional connectivity (FC) analysis. RESULTS: Compared to the healthy controls (HCs), the MwoA-A and MwoA-OA patients had abnormal ALFF and ReHo values in the right lingual gyrus (LG). They also had abnormal FC of the right LG with the ipsilateral superior frontal gyrus (SFG) and middle cingulate cortex (MCC). Additionally, the MwoA-A patients showed higher ReHo values in the left posterior intraparietal sulcus (pIPS) and abnormal FC of the right LG with ipsilateral pIPS and primary visual cortex, compared to the MwoA-OA patients. Moreover, the MwoA-OA patients showed an increase in the FC with the right posterior cingulate cortex/precuneus (PCC/PCUN), left middle frontal gyrus (MFG) and left inferior temporal gyrus (ITG) relative to the HCs. Furthermore, the ALFF values of the right LG positively were correlated with anxiety scores in MwoA-A patients. The abnormal LG-related FCs with the PCC/PCUN, MFG and ITG were negatively associated with the frequency of headaches in MwoA-OA patients. CONCLUSIONS: This study identified abnormal visual FC along with other core networks differentiating anxiety comorbidity from MwoA. This may therefore enhance the understanding of the neuropsychological basis of psychiatric comorbidities and provide novel insights that may help in the discovery of new marks or even treatment targets.