Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

PURPOSE: The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. METHODS: Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. RESULTS: We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. CONCLUSION: Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

Correlation of diffusion tensor tractography with obstructive sleep apnea severity.

INTRODUCTION: Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity. METHODS: We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections. RESULTS: Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI. CONCLUSION: Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Structural connectivity as a predictive factor for perampanel response in patients with epilepsy.

OBJECTIVES: This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. METHODS: We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. RESULTS: 106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). CONCLUSION: This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.

Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies.

PURPOSE: With the slice thickness routinely used in elbow MRI, small or subtle lesions may be overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm MRI) with deep learning reconstruction (DLR) to 3 mm slice thickness MRI (3 mm MRI) without/with DLR, and 1 mm MRI without DLR regarding image quality and diagnostic performance for elbow tendons and ligaments. METHODS: This retrospective study included 53 patients between February 2021 and January 2022, who underwent 3 T elbow MRI, including T2-weighted fat-saturated coronal 3 mm and 1 mm MRI without/with DLR. Two radiologists independently assessed four MRI scans for image quality and artefacts, and identified the pathologies of the five elbow tendons and ligaments. In 19 patients underwent elbow surgery after elbow MRI, diagnostic performance was evaluated using surgical records as a reference standard. RESULTS: For both readers, 3 mm MRI with DLR had significant higher image quality scores than 3 mm MRI without DLR and 1 mm MRI with DLR (all P < 0.01). For common extensor tendon and elbow ligament pathologies, 1 mm MRI with DLR showed the highest number of pathologies for both readers. The 1 mm MRI with DLR had the highest kappa values for all tendons and ligaments. For reader 1, 1 mm MRI with DLR showed superior diagnostic performance than 3 mm MRI without/with DLR. For reader 2, 1 mm MRI with DLR showed the highest diagnostic performance; however, there was no significant difference. CONCLUSIONS: One mm MRI with DLR showed the highest diagnostic performance for evaluating elbow tendon and ligament pathologies, with similar subjective image qualities and artefacts.

Brain connectivity in status epilepticus as a predictor of outcome: A diffusion tensor imaging study.

BACKGROUND AND PURPOSE: We aimed to explore structural connectivity in status epilepticus. METHODS: We enrolled participants who underwent diffusion tensor imaging. We applied graph theory to investigate structural connectivity. We compared the structural connectivity measures between patients and healthy controls and between patients with poor (modified Rankin Scale [mRS] >3) and good (mRS ≤3) admission outcomes. RESULTS: We enrolled 28 patients and 31 healthy controls (age 65.5 vs.62.0 years, p = .438). Of these patients, 16 and 12 showed poor and good admission outcome (age 65.5 vs.62.0 years, p = .438). The assortative coefficient (-0.113 vs. -0.121, p = .021), mean clustering coefficient (0.007 vs.0.006, p = .009), global efficiency (0.023 vs.0.020, p = .009), transitivity (0.007 vs.0.006, p = .009), and small-worldness index (0.006 vs.0.005, p = .021) were higher in patients with status epilepticus than in healthy controls. The assortative coefficient (-0.108 vs. -0.119, p = .042), mean clustering coefficient (0.007 vs.0.006, p = .042), and transitivity (0.008 vs.0.007, p = .042) were higher in patients with poor admission outcome than in those with good admission outcome. MRS score was positively correlated with structural connectivity measures, including the assortative coefficient (r = 0.615, p = .003), mean clustering coefficient (r = 0.544, p = .005), global efficiency (r = 0.515, p = .007), transitivity (r = 0.547, p = .007), and small-worldness index (r = 0.435, p = .024). CONCLUSION: We revealed alterations in structural connectivity, showing increased integration and segregation in status epilepticus, which might be related with neuronal synchronization. This effect was more pronounced in patients with a poor admission outcome, potentially reshaping our understanding for comprehension of status epilepticus mechanisms and the development of more targeted treatments.

Sarcopenia and anti-seizure medication response in juvenile myoclonic epilepsy.

INTRODUCTION: This study aimed to investigate the presence of sarcopenia in patients with juvenile myoclonic epilepsy (JME) and the association between sarcopenia and response to anti-seizure medication (ASM) in patients with JME. METHODS: We enrolled 42 patients with JME and 42 healthy controls who underwent brain magnetic resonance imaging with three-dimensional T1-weighted imaging. We measured the temporal muscle thickness (TMT), a radiographic marker for sarcopenia, using T1-weighted imaging. We compared the TMT between patients with JME and healthy controls and analyzed it according to the ASM response in patients with JME. We also performed a receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated the groups. RESULTS: The TMT in patients with JME did not differ from that in healthy controls (9.630 vs. 9.956 mm, p = .306); however, ASM poor responders had a lower TMT than ASM good responders (9.109 vs. 10.104 mm, p = .023). ROC curve analysis revealed that the TMT exhibited a poor performance in differentiating patients with JME from healthy controls, with an area under the ROC curve of .570 (p = .270), but good performance in differentiating between ASM good and poor responders, with an area under the ROC curve of .700 (p = .015). CONCLUSION: The TMT did not differ between patients with JME and healthy controls; however, it was reduced in ASM poor responders compared to ASM good responders, suggesting a link between ASM response and sarcopenia in patients with JME. TMT can be used to investigate sarcopenia in various neurological disorders.

Alterations in the multilayer network in patients with rapid eye movement sleep behaviour disorder.

This study aimed to reveal the pathophysiology of isolated rapid eye movement sleep behaviour disorder (RBD) in patients using multilayer network analysis. Participants eligible for isolated RBD were included and verified via polysomnography. Both iRBD patients and healthy controls underwent brain MRI, including T1-weighted imaging and diffusion tensor imaging. Grey matter matrix was derived from T1-weighted images using a morphometric similarity network. White matter matrix was formed from diffusion tensor imaging-based structural connectivity. Multilayer network analysis of grey and white matter was performed using graph theory. We studied 29 isolated RBD patients and 30 healthy controls. Patients exhibited a higher average overlap degree (27.921 vs. 23.734, p = 0.002) and average multilayer clustering coefficient (0.474 vs. 0.413, p = 0.002) compared with controls. Additionally, several regions showed significant differences in the degree of overlap and multilayer clustering coefficient between patients with isolated RBD and healthy controls at the nodal level. The degree of overlap in the left medial orbitofrontal, left posterior cingulate, and right paracentral nodes and the multilayer clustering coefficients in the left lateral occipital, left rostral middle frontal, right fusiform, right inferior posterior parietal, and right parahippocampal nodes were higher in patients with isolated RBD than in healthy controls. We found alterations in the multilayer network at the global and nodal levels in patients with isolated RBD, and these changes may be associated with the pathophysiology of isolated RBD. Multilayer network analysis can be used widely to explore the mechanisms underlying various neurological disorders.

Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network.

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach.

OBJECTIVE: The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments. METHODS: Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups. RESULTS: The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%. CONCLUSION: Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.

Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction.

OBJECTIVE: Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradient echo (GRE) sequences, and heavily T2-weighted FLAIR (hvT2F) with and without deep learning-based reconstruction (DLR) in detecting perilymphatic enhancement. METHODS: Fifty-four patients with unilateral SSNHL who underwent ear MRI with three sequences were included. We compared asymmetry scores, confidence scores, and detection rates of perilymphatic enhancement among the three sequences and obtained 3D hvT2F with DLR from 35 patients. The above parameters and subjective image quality between 3D hvT2F with and without DLR were compared. RESULTS: Asymmetry scores and detection rate of 3D hvT2F were significantly higher than 3D GRE T1 and SE T1 (respectively, 1.37, 0.11, 0.19; p < 0.001). Asymmetry scores significantly increased with DLR compared to 3D hvT2F for experienced and inexperienced readers (respectively, 1.77 vs. 1.40, p = 0.036; 1.49 vs. 1.03, p = 0.012). The detection rate significantly increased only for the latter (57.1% vs. 31.4%, p = 0.022). Patients with perilymphatic enhancement had significantly higher air conduction thresholds on initial (77.96 vs. 57.79, p = 0.002) and 5 days after presentation (63.38 vs. 41.85, p = 0.019). CONCLUSION: 3D hvT2F significantly increased the detectability of perilymphatic enhancement compared to 3D GRE T1 and SE T1. DLR further improved the conspicuity of perilymphatic enhancement in 3D hvT2F. 3D hvT2F and DLR are useful for evaluating blood-labyrinthine barrier leakage; furthermore, they might provide prognostic value in the early post-treatment period. CLINICAL RELEVANCE STATEMENT: Ten-minute post-contrast 3D heavily T2-weighed FLAIR imaging is a potentially efficacious sequence in demonstrating perilymphatic enhancement in patients with sudden sensorineural hearing loss and may be further improved by deep learning-based reconstruction. KEY POINTS: • 3D heavily T2-weighted FLAIR (3D hvT2F) is a sequence sensitive in detecting low concentrations of contrast in the perilymphatic space. • 3D hvT2F sequences properly demonstrated perilymphatic enhancement in sudden sensorineural hearing loss compared to T1 sequences and were further improved by deep learning-based reconstruction (DLR). • 3D hvT2F and DLR are efficacious sequences in detecting blood-labyrinthine barrier leakage and with potential prognostic information.

Impact of deep learning-based reconstruction and anti-peristaltic agent on the image quality and diagnostic performance of magnetic resonance enterography comparing single breath-hold single-shot fast spin echo with and without anti-peristaltic agent.

Background: While anti-peristaltic agents are beneficial for high quality magnetic resonance enterography (MRE), their use is constrained by potential side effects and increased examination complexity. We explored the potential of deep learning-based reconstruction (DLR) to compensate for the absence of anti-peristaltic agent, improve image quality and reduce artifact. This study aimed to evaluate the need for an anti-peristaltic agent in single breath-hold single-shot fast spin-echo (SSFSE) MRE and compare the image quality and artifacts between conventional reconstruction (CR) and DLR. Methods: We included 45 patients who underwent MRE for Crohn's disease between October 2021 and September 2022. Coronal SSFSE images without fat saturation were acquired before and after anti-peristaltic agent administration. Four sets of data were generated: SSFSE CR with and without an anti-peristaltic agent (CR-A and CR-NA, respectively) and SSFSE DLR with and without an anti-peristaltic agent (DLR-A and DLR-NA, respectively). Two radiologists independently reviewed the images for overall quality and artifacts, and compared the three images with DLR-A. The degree of distension and inflammatory parameters were scored on a 5-point scale in the jejunum and ileum, respectively. Signal-to-noise ratio (SNR) levels were calculated in superior mesenteric artery (SMA) and iliac bifurcation level. Results: In terms of overall quality, DLR-NA demonstrated no significant difference compared to DLR-A, whereas CR-NA and CR-A demonstrated significant differences (P<0.05, both readers). Regarding overall artifacts, reader 1 rated DLR-A slightly better than DLR-NA in four cases and rated them as identical in 41 cases (P=0.046), whereas reader 2 demonstrated no difference. Bowel distension was significantly different in the jejunum (Reader 1: P=0.046; Reader 2: P=0.008) but not in the ileum. Agreements between the images (Reader 1: ĸ=0.73-1.00; Reader 2: ĸ=1.00) and readers (ĸ=0.66 for all comparisons) on inflammation were considered good to excellent. The sensitivity, specificity, and accuracy in diagnosing inflammation in the terminal ileum were the same among DLR-NA, DLR-A, CR-NA and CR-A (94.42%, 81.83%, and 89.69 %; and 83.33%, 90.91%, and 86.21% for Readers 1 and 2, respectively). In both SMA and iliac bifurcation levels, SNR of DLR images exhibited no significant differences. CR images showed significantly lower SNR compared with DLR images (P<0.001). Conclusions: SSFSE without anti-peristaltic agents demonstrated nearly equivalent quality to that with anti-peristaltic agents. Omitting anti-peristaltic agents before SSFSE and adding DLR could improve the scanning outcomes and reduce time.

Sarcopenia in patients with isolated rapid eye movement sleep behavior disorder.

OBJECTIVES: Evaluating of sarcopenia is important for promoting healthy aging, preventing functional decline, reducing the risk of falls and fractures, and improving overall quality of life. This study aimed to investigate sarcopenia in patients with isolated rapid eye movement sleep behavior disorder (RBD) using temporal muscle thickness (TMT) measurement. METHODS: This investigation was retrospectively conducted at a single tertiary hospital. We recruited patients diagnosed with isolated RBD confirmed by polysomnography and clinical history and healthy participants as controls. Patients with isolated RBD and healthy controls underwent brain MRI scans, including three-dimensional T1-weighted imaging. We measured TMT, a radiographic marker of sarcopenia, based on the T1-weighted imaging. We compared the TMT between the groups and performed receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated patients with isolated RBD from healthy controls. We also conducted a correlation analysis between the TMT and clinical factors. RESULTS: Our study included 28 patients with isolated RBD and 30 healthy controls. There was a significant difference in the TMT of both groups. The TMT was reduced in patients with isolated RBD than in healthy controls (11.843 vs. 10.420 mm, p = 0.002). In the ROC curve analysis, the TMT exhibited good performance in differentiating patients with isolated RBD from healthy controls, with an area under the curve of 0.708. Furthermore, age was negatively correlated with TMT in patients with isolated RBD (r = -0.453, p = 0.015). CONCLUSION: We demonstrate that TMT is reduced in patients with isolated RBD compared with healthy controls, confirming sarcopenia in patients with isolated RBD. The result suggests an association between neurodegeneration and sarcopenia. TMT can be used to evaluate sarcopenia in sleep disorders.

Cerebellar volumes and the intrinsic cerebellar network in patients with obstructive sleep apnea.

PURPOSE: This research aimed to explore changes in both cerebellar volume and the intrinsic cerebellar network in patients with obstructive sleep apnea (OSA). METHODS: Newly diagnosed OSA patients and healthy controls were included in the study. All participants underwent three-dimensional T1-weighted imaging using a 3-T MRI scanner. Cerebellar volumes, both overall and subdivided, were quantified using the ACAPULCO program. The intrinsic cerebellar network was assessed using the BRAPH program, which applied graph theory to the cerebellar volume subdivision. Comparisons were drawn between the patients with OSA and healthy controls. RESULTS: The study revealed that the 26 patients with OSA exhibited a notably lower total cerebellar volume compared to the 28 healthy controls (8.330 vs. 9.068%, p < 0.001). The volume of the left lobule VIIB was reduced in patients with OSA compared to healthy controls (0.339 vs. 0.407%, p = 0.001). Among patients with OSA, there was a negative correlation between the volume of the left lobule X and apnea-hypopnea index during non-rapid eye movement sleep (r = - 0.536, p = 0.005). However, no significant differences were observed in the intrinsic cerebellar network between patients and healthy controls. CONCLUSION: This study established that patients with OSA exhibited decreased total cerebellar volumes and particularly reduced volumes in subdivisions such as the left lobule VIIB compared to healthy controls. These findings suggest potential involvement of the cerebellum in the underlying mechanisms of OSA.

Structural connectivity as a predictive factor for responsiveness to levetiracetam treatment in epilepsy.

PURPOSE: To investigate whether structural connectivity or glymphatic system function is a potential predictive factor for levetiracetam (LEV) response in patients with newly diagnosed epilepsy. METHODS: We enrolled patients with newly diagnosed epilepsy who were administered LEV as initial monotherapy and underwent diffusion tensor imaging (DTI) at diagnosis. We categorized the patients into drug response. We used graph theory to calculate the network measures for structural connectivity based on the DTI scans in patients with epilepsy. Additionally, we evaluated glymphatic system function by calculating the DTI analysis along the perivascular space (DTI-ALPS) index based on DTI scans. RESULTS: We enrolled 84 patients with epilepsy. The clinical factors and DTI-ALPS index did not differ between the groups. However, some of the structural connectivity measures significantly differ between the groups. The poor responders exhibited a higher mean clustering coefficient, global efficiency, and small-worldness index than the good responders (p = 0.003, p = 0.048, and p = 0.038, respectively). In the receiver operating characteristic curve analysis, the mean clustering coefficient exhibited the highest performance in predicting the responsiveness to LEV (area under the curve of 0.677). In the multiple logistic regression analysis, the mean clustering coefficient of the structural connectivity measures was the only significant predictor of LEV response (p = 0.014). Furthermore, in the survival analysis, the mean clustering coefficient was the only significant predictor of LEV response (p = 0.026). CONCLUSION: We demonstrated that structural connectivity is a potential predictive factor for responsiveness to LEV treatment in patients with newly diagnosed epilepsy.

Anti-seizure medication response and the glymphatic system in patients with focal epilepsy.

BACKGROUND AND PURPOSE: We aimed to evaluate (i) glymphatic system function in patients with focal epilepsy in comparison with healthy controls, and (ii) the association between anti-seizure medication (ASM) response and glymphatic system function by using diffusion tensor image analysis along the perivascular space (DTI-ALPS). METHODS: We retrospectively enrolled 100 patients with focal epilepsy who had normal brain magnetic resonance imaging (MRI) findings, and classified them as "poor" or "good" ASM responders according to their seizure control at the time of brain MRI. We also included 79 age- and sex-matched healthy controls. All patients and healthy controls underwent conventional brain MRI and diffusion tensor imaging. The DTI-ALPS index was calculated using the DSI studio program. RESULTS: Of the 100 patients with focal epilepsy, 38 and 62 were poor and good ASM responders, respectively. The DTI-ALPS index differed significantly between patients with focal epilepsy and healthy controls and was significantly lower in patients with focal epilepsy (1.55 vs. 1.70; p < 0.001). The DTI-ALPS index also differed significantly according to ASM response and was lower in poor ASM responders (1.48 vs. 1.59; p = 0.047). Furthermore, the DTI-ALPS index was negatively correlated with age (r = -0.234, p = 0.019) and duration of epilepsy (r = -0.240, p = 0.016) in patients with focal epilepsy. CONCLUSION: Our study is the first to identify, in focal epilepsy patients, a greater reduction in glymphatic system function among poor ASM responders compared to good responders. To confirm our results, further prospective multicenter studies with large sample sizes are needed.

Altered cerebellar volumes and intrinsic cerebellar networks in patients with transient global amnesia.

This study aimed to investigate the differences in cerebellar volumes and intrinsic cerebellar networks between patients with transient global amnesia (TGA) and healthy controls. We retrospectively enrolled patients with TGA and age- and sex-matched healthy controls. We used three-dimensional T1-weighted imaging at the time of TGA diagnosis to obtain cerebellar volumes, and the intrinsic cerebellar network was calculated by applying graph theory based on cerebellar volumes. The nodes were defined as individual cerebellar volumes, and edges as partial correlations, controlling for the effects of age and sex. The cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. We enrolled 44 patients with TGA and 47 healthy controls. The volume of the left cerebellar white matter in patients with TGA was significantly lower than that in healthy controls (1.0328 vs. 1.0753%, p = 0.0094). In addition, there were significant differences in intrinsic cerebellar networks between the two groups. The small-worldness index in patients with TGA was higher than that in the healthy controls (0.951 vs. 0.880, p = 0.038). In the correlation analysis, the volumes of the right cerebellar cortex and lobules VIIIB were significantly correlated with age in patients with TGA (r = -0.323, p = 0.033; r = -0.313, p = 0.038, respectively). Patients with TGA exhibit alterations in cerebellar volumes and intrinsic cerebellar networks compared with healthy controls. These findings may contribute to a better understanding of the pathophysiology of the TGA.

Early cellular and molecular signatures correlate with severity of West Nile virus infection.

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Multilayer network changes in patients with migraine.

INTRODUCTION: To investigate changes in the multilayer network in patients with migraine compared to healthy controls. METHODS: This study enrolled 82 patients with newly diagnosed migraine without aura and 53 healthy controls. Brain magnetic resonance imaging (MRI) was conducted using a 3-tesla MRI scanner, including three-dimensional T1-weighted and diffusion tensor imaging (DTI). A gray matter layer matrix was created with a morphometric similarity network using T1-weighted imaging and the FreeSurfer program. A white matter layer matrix was also created with structural connectivity using the DTI studio (DSI) program. A multilayer network analysis was then performed by applying graph theory using the BRAPH program. RESULTS: Significant changes were observed in the multilayer network at the global level in patients with migraines compared to the healthy controls. The multilayer modularity (0.177 vs. 0.160, p = .0005) and average multiplex participation (0.934 vs. 0.924, p = .002) were higher in patients with migraines than in the healthy controls. In contrast, the average multilayer clustering coefficient (0.406 vs. 0.461, p = .0005), average overlapping strength (56.061 vs. 61.676, p = .0005), and average weighted multiplex participation (0.847 vs. 0.878, p = .0005) were lower in patients with migraine than in the healthy controls. In addition, several regions showed significant changes in the multilayer network at the nodal level, including multiplex participation, multilayer clustering coefficients, overlapping strengths, and weighted multiplex participation. CONCLUSION: This study demonstrated significant changes in the multilayer network in patients with migraines compared to healthy controls. This could aid an understanding of the complex brain network in patients with migraine and may be associated with the pathophysiology of migraines. Patients with migraine show multilayer network changes in widespreading brain regions compared to healthy controls, and specific brain areas seem to play a hub role for pathophysiology of the migraine.

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records.

Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.