Implication of Cerebral Small-Vessel Disease on Perihematomal Edema Progress in Patients With Hypertensive Intracerebral Hemorrhage.

BACKGROUND: Perihematomal edema (PHE) is an important determinant of outcome in spontaneous intracerebral hemorrhage (ICH) due to cerebral small vessel disease (CSVD). However, it is not known to date whether the severity of CSVD is associated with the extent of PHE progression in the acute phase. PURPOSE: To investigate the association between the magnetic resonance imaging (MRI) marker of severe chronic-ischemia cerebral small vessel changes (sciSVC) and PHE growth or hematoma absorption among ICH patients with hypertension. STUDY TYPE: Retrospective. POPULATION: Three hundred and sixty-eight consecutive hypertensive ICH patients without surgical treatment. FIELD STRENGTH/SEQUENCE: 3 T; spin-echo echo-planar imaging-diffusion-weighted imaging (DWI); T2-weighted, fluid-attenuated inversion recovery (FLAIR), T2*-weighted gradient-recalled echo and T1-weighted. ASSESSMENT: The hematoma and PHE volumes at 24 hours and 5 days after symptom onset were measured in 121 patients with spontaneous ICH who had been administered standard medical treatment. Patients were grouped into two categories: those with sciSVC and those without. The imaging marker of sciSVC was defined as white matter hyperintensities (WMHs) Fazekas 2-3 combined cavitating lacunes. STATISTICAL TESTS: Univariable analyses, χ2 test, Mann-Whitney U test, and multiple linear regression. RESULTS: The presence of sciSVC (multiple lacunes and confluent WMH) had a significant negative influence on PHE progress (Beta = -5.3 mL, 95% CI = -10.3 mL to -0.3 mL), and hematoma absorption (Beta = -3.2 mL, 95% CI = -5.9 mL to -0.4 mL) compared to that observed in the absence of sciSVC, as determined by multivariate linear regression analysis. DATA CONCLUSIONS: The presence of sciSVC (multiple lacunes and confluent WMH) negatively influenced hematoma absorption and PHE progress in ICH patients. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.

Functional reorganization of contralesional networks varies according to isocitrate dehydrogenase 1 mutation status in patients with left frontal lobe glioma.

PURPOSE: The study aimed to assess how isocitrate dehydrogenase 1 (IDH1) mutation status in patients with glioma may alter functional connectivity (FC) in the default mode network (DMN) and fronto-parietal network (FPN). METHODS: Using resting-state functional magnetic resonance imaging, a seed-based FC analysis was employed to investigate connectivity within and between networks in seventeen patients with IDH1-mutant glioma (IDH1-M), eleven patients with IDH1-wildtype glioma (IDH1-WT), and nineteen healthy controls (HC). RESULTS: For FC within the DMN, compared to HC, both IDH1-M and IDH1-WT exhibited significantly increased FC between the posterior cingulate cortex (PCC) and the right retrosplenial cortex, right precuneus/cuneus, and right middle cingulate cortex and between the right lateral parietal cortex (LP_R) and the right middle temporal gyrus. For FC within the FPN, compared with HC, IDH1-M showed significantly greater FC between the right posterior parietal cortex (PPC_R) and the right inferior, right medial, and right middle frontal gyrus, and IDH1-WT showed significantly increased FC between the PPC_R and the right middle frontal gyrus. For FC between the DMN and FPN, relative to IDH1-WT and HC, IDH1-M exhibited significantly increased FC between the LP_R and the right superior frontal gyrus and between the PPC_R and the right precuneus/cuneus. In contrast, compared to IDH1-M and HC, IDH1-WT showed significantly reduced FC between the PPC_R and the right angular gyrus. CONCLUSION: The preliminary findings revealed that there should be differences in the patterns of network reorganization between IDH1-M and IDH1-WT with different growth kinetics.

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas.

OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (1H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade. METHODS: This study included 112 glioma patients who were divided into the training (n = 74) and validation (n = 38) sets based on the time of hospitalization. Twenty-six metabolic features were extracted from the preoperative 1H-MRS image. The Student's t-test was conducted to screen for differentially expressed features between low- and high-grade gliomas (WHO grades II and III/IV, respectively). Next, the minimum Redundancy Maximum Relevance (mRMR) algorithm was performed to further select features for a support vector machine (SVM) classifier building. Performance of the predictive model was evaluated both in the training and validation sets using ROC curve analysis. RESULTS: Among the extracted 1H-MRS metabolic features, thirteen features were differentially expressed. Four features were further selected as grade-predictive imaging signatures using the mRMR algorithm. The predictive performance of the machine-learning model measured by the AUC was 0.825 and 0.820 in the training and validation sets, respectively. This was better than the predictive performances of individual metabolic features, the best of which was 0.812. CONCLUSIONS: 1H-MRS metabolic features could help in predicting the grade of gliomas. The machine-learning model achieved a better prediction performance in grading gliomas than individual features, indicating that it could complement the traditionally used metabolic features.

Effects of Multi-Session Repetitive Transcranial Magnetic Stimulation on Motor Control and Spontaneous Brain Activity in Multiple System Atrophy: A Pilot Study.

Background: Impaired motor control is one of the most common symptoms of multiple system atrophy (MSA). It arises from dysfunction of the cerebellum and its connected neural networks, including the primary motor cortex (M1), and is associated with altered spontaneous (i.e., resting-state) brain network activity. Non-invasive repetitive transcranial magnetic stimulation (rTMS) selectively facilitates the excitability of supraspinal networks. Repeated rTMS sessions have been shown to induce long-term changes to both resting-state brain dynamics and behavior in several neurodegenerative diseases. Here, we hypothesized that a multi-session rTMS intervention would improve motor control in patients with MSA, and that such improvements would correlate with changes in resting-state brain activity. Methods: Nine participants with MSA received daily sessions of 5 Hz rTMS for 5 days. rTMS targeted both the cerebellum and the bilateral M1. Before and within 3 days after the intervention, motor control was assessed by the motor item of the Unified Multiple System Atrophy Rating Scale (UMSARS). Resting-state brain activity was recorded by blood-oxygen-level dependency (BOLD) functional magnetic resonance imaging. The "complexity" of resting-state brain activity fluctuations was quantified within seven well-known functional cortical networks using multiscale entropy, a technique that estimates the degree of irregularity of the BOLD time-series across multiple scales of time. Results: The rTMS intervention was well-attended and was not associated with any adverse events. Average motor scores were lower (i.e., better performance) following the rTMS intervention as compared to baseline (t8 = 2.3, p = 0.003). Seven of nine participants exhibited such pre-to-post intervention improvements. A trend toward an increase in resting-state complexity was observed within the motor network (t8 = 1.86, p = 0.07). Participants who exhibited greater increases in motor network resting-state complexity demonstrated greater improvement in motor control (r2= 0.72, p = 0.004). Conclusion: This pilot study demonstrated that a five-session rTMS intervention targeting the cerebellum and bilateral M1 is feasible and safe for those with MSA. More definitive, well-controlled trials are warranted to confirm our preliminary results that rTMS may alleviate the severity of motor dysfunction and modulate the multiscale dynamics of motor network brain activity.

Evaluation of cerebral glioma using 3T diffusion kurtosis tensor imaging and the relationship between diffusion kurtosis metrics and tumor cellularity.

Purpose To evaluate the clinical utility of diffusion kurtosis tensor imaging in the characterization of cerebral glioma and investigate correlations between diffusion and kurtosis metrics with tumor cellularity. Materials and Methods A group of 163 patients (age: 40.5 ± 11.5 years) diagnosed with cerebral glioma underwent diffusion kurtosis tensor imaging with a 3 T scanner. Diffusion and kurtosis metrics were measured in the solid part of tumors, and their abilities to distinguish between tumor grades was evaluated. In addition, we analyzed correlations between the metrics and tumor cellularity. Results Mean kurtosis (MK) revealed a significant difference between each pair of tumor grades ( P < 0.05) and produced the best performance in a receiver operating characteristics analysis (area under the curve [AUC] = 0.89, sensitivity/specificity = 83.3/90). In contrast, mean diffusivity (MD) revealed a significant difference only for tumor grade II versus IV ( P < 0.05). No significant differences between grades were detected with fractional anisotropy (FA; P > 0.05). Thus, kurtosis metrics exhibited a positive and strong correlation with tumor cellularity, while MD exhibited a negative or weak correlation with tumor cellularity. Conclusion Diffusion kurtosis metrics, particularly MK, demonstrated superior performance in distinguishing cerebral glioma of different grades compared with conventional diffusion metrics, and were closely associated with tumor cellularity.