Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Clinical Breast MRI-based Radiomics for Distinguishing Benign and Malignant Lesions: An Analysis of Sequences and Enhanced Phases.

BACKGROUND: Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear. PURPOSE: To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)-guided biopsy. STUDY TYPE: Retrospective. POPULATION: 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137). FIELD STRENGTH/SEQUENCE: 3.0-T, fast spoiled gradient-echo and fast spin-echo T1-weighted imaging (T1WI), fast spin-echo T2-weighted imaging (T2WI), echo-planar diffusion-weighted imaging (DWI), and fast spoiled gradient-echo contrast-enhanced MRI (CE-MRI). ASSESSMENT: Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE-MRI, CE-MRI + DWI, CE-MRI + DWI + T2WI, CE-MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad-score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad-score) at sensitivity >98%. STATISTICAL TESTS: Wilcoxon paired-samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7). RESULTS: For radiomics models, CE-MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833-0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%. DATA CONCLUSION: The CE-MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%-specificity) from 68.4% to 44.7% while retaining sensitivity >98%. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Gadolinium Retention in the Brain of Mother and Pup Mouse: Effect of Pregnancy and Repeated Administration of Gadolinium-Based Contrast Agents.

BACKGROUND: The association of repeated administration of gadolinium-based contrast agents (GBCAs) with the gadolinium (Gd) retention in the brains of mother and fetus remains unclear. PURPOSE: To investigate the effects of pregnancy and repeated administration of GBCAs on Gd retention in the brains of mother and pup mice. STUDY TYPE: Cross-sectional cohort toxicity study. ANIMAL MODEL: From gestational days 16-19, pregnant (n = 48) BALB/c mice. FIELD STRENGTH: A 9.4 T and fast spin echo sequence. ASSESSMENT: Half of the mother mice (n = 24) were killed at postnatal day 1 (P1) for inductively coupled plasma mass spectrometry (ICP-MS) and transmission electron microscopy (TEM). Besides the ICP-MS and TEM, four pups were randomly selected from each mother and killed at P1 for ultraperformance liquid chromatography mass spectrometry (UPLC-MS) and Nissl staining. STATISTICAL TESTS: One-way analysis of variance and unpaired t-test. RESULTS: In the group of gadodiamide, retention of Gd in the brains of pregnant mice was significantly lower than that of nonpregnant mice in the area of the deep cerebellar nuclei (DCN) (10.35 ± 2.16 nmol/g vs. 18.74 ± 3.65 nmol/g). Retention of Gd in the DCN of pups whose mothers were administered gadoterate meglumine was significantly lower than that of pups whose mothers were administered gadodiamide (0.21 ± 0.09 nmol/g vs. 6.15 ± 3.21 nmol/g) at P1. In mice treated with gadodiamide, most of the retained Gd in the brain tissue was insoluble (19.5% ± 9.5% of the recovered amount corresponded to the intact complex in the DCN). DATA CONCLUSION: In different brain areas of the mother and pup mice, the retention of Gd after gadoterate meglumine administration was lower than that of gadodiamide and gadopentetate dimeglumine administration, and almost all the detected Gd in pups' brains was intact soluble GBCAs. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Capability of intravoxel incoherent motion and diffusion tensor imaging to detect early kidney injury in type 2 diabetes.

OBJECTIVES: To prospectively investigate the capability of intravoxel incoherent motion (IVIM) and conventional diffusion tensor imaging (DTI) to identify early kidney function injury in type 2 diabetes. METHODS: Forty-one diabetes patients (normoalbuminuria: n = 27; microalbuminuria: n = 14) and 28 volunteers were recruited. All participants were examined using DTI and IVIM with 3.0-T MRI. DTI parameters (mean diffusivity [MD], fractional anisotropy [FA]), and IVIM parameters (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion component fraction [f]) were measured in the renal parenchyma (cortex and medulla) by two experienced radiologists independently. Image features were compared among the groups using separate one-way analyses of variance. Diagnostic performances of various diffusion parameters for predicting diabetic renal damage were compared. RESULTS: The medullary D and FA values were significantly different among the microalbuminuria subgroup, normoalbuminuria subgroup, and control group (all p < 0.001). In medulla, area under the curve (AUC) values for combined FA and D were significantly higher than single FA (AUC = 0.938, 0.769, respectively; p = 0.003), and the combined AUC of FA and D was numerically higher than that of single D (0.938 vs 0.878, p > 0.05). AUC of combined FA and D was 0.985, not significantly different from individual AUC for FA and D (AUC = 0.909 and 0.952, respectively; all p > 0.05) in differentiating the microalbuminuria subgroup from the control group. CONCLUSION: IVIM-derived D and DTI-derived FA values were better than other parameters for evaluating early kidney impairment of diabetes. The single indicator FA and D performed as well as the combined diagnostic indicator in the medulla for differentiating the microalbuminuria subgroup from the control group. KEY POINTS: • We speculated that early renal progression in type 2 diabetes result from restricted tubular flow and kidney tubule dysregulation may precede or at least accompany abnormal glomerular changes. • In medulla, the AUC values of FA and D and the combination of FA and D obtained by comparing the microalbuminuria subgroup with the control group were 0.909, 0.952, and 0.985, respectively. • IVIM-derived D and DTI-derived FA are effective MR biomarkers to evaluate early alterations of the renal function in patients with diabetes.

Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.

OBJECTIVE: To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading. METHODS: A total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed. The optimum diagnostic thresholds of the parameters, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were achieved using a receiver operating characteristic (ROC). RESULT: Significant differences were observed not only in 12 metrics of histogram DKI parameters (P<0.05), but also in mean diffusivity (MD) and mean kurtosis (MK) values, including age as a covariate (F=19.127, P<0.001 and F=20.894, P<0.001, respectively), between low- and high-grade gliomas. Mean MK was the best independent predictor of differentiating glioma grades (B=18.934, 22.237 adjusted for age, P<0.05). The partial correlation coefficient between fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) was 0.675 (P<0.001). The AUC of the mean MK, sensitivity, and specificity were 0.925, 88.5% and 84.6%, respectively. CONCLUSIONS: DKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades. KEY POINTS: • DKI is a new and important method. • DKI can provide additional information on microstructural architecture. • Histogram analysis of DKI may be more effective in glioma grading.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Magnetic Resonance Imaging of Contrast-Induced Acute Renal Injury and Related Pathological Alterations In Vivo.

Background: The definitive mechanisms of CI-AKI include contrast medium (CM) nephrotoxicity and CM disturbances in renal blood flow, but how the immune system responds to CM has rarely been mentioned in previous studies, and different cell death pathways have not been clearly distinguished. Aim: To confirm whether MRI detect early CI-AKI and to investigate whether immunity-related responses, pyroptosis, and mitophagy participate in contrast-induced acute renal injury (CI-AKI). Methods: C57BL/6 mice with CI-AKI were established by tail vein injection of iodixanol 320. Magnetic resonance imaging of 9.4 T scanner and microscopic appearance of renal H&E staining were tools to test the occurrence of CI-AKI at different times. Immunohistochemistry and NGAL were used to examine the immune responses in the kidneys with CI-AKI. Transmission electron microscopy and western blot methods were used to distinguish various cell death pathways in CI-AKI. Key Results. The densitometry of T2WI, DTI, and BOLD presents CI-AKI in a regular way. The microscopic appearance presents the strongest renal damage in CI-AKI mice that existed between 12 h (P < 0.0001) and 24 h (P < 0.05) after contrast medium (CM) injection. Strong correlation may exist between MRI densitometry (T2WI, DTI, and BOLD) and pathology. Neutrophil and macrophage chemotaxis occurred in CI-AKI, and we observed that Ly6G was the strongest at 48 h (P < 0.0001). Pyroptosis (Nlrp3/caspase-1, P < 0.05), mitophagy (BNIP/Nix, P < 0.05), and apoptosis (Bax, P < 0.05) occurred in CI-AKI. Conclusions: fMRI can detect early CI-AKI immediately after CM injection. NLRP3 inflammasomes are involved in CI-AKI, and mitophagy may play a role in mitigating kidney injury. The mitochondrion is one of the key organelles in the tubular epithelium implicated in CI-AKI.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach.

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.

Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.

Objectives: This study aims to build radiomics model of Breast Imaging Reporting and Data System (BI-RADS) category 4 and 5 mammographic masses extracted from digital mammography (DM) for mammographic masses characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods: This retrospective study included 288 female patients (age, 52.41 ± 10.31) who had BI-RADS category 4 or 5 mammographic masses with an indication for biopsy. The patients were divided into two temporal set (training set, 82 malignancies and 110 benign lesions; independent test set, 48 malignancies and 48 benign lesions). A total of 188 radiomics features were extracted from mammographic masses on the combination of craniocaudal (CC) position images and mediolateral oblique (MLO) position images. For the training set, Pearson's correlation and the least absolute shrinkage and selection operator (LASSO) were used to select non-redundant radiomics features and useful radiomics features, respectively, and support vector machine (SVM) was applied to construct a radiomics model. The receiver operating characteristic curve (ROC) analysis was used to evaluate the classification performance of the radiomics model and to determine a threshold value with a sensitivity higher than 98% to predict the mammographic masses malignancy. For independent test set, identical threshold value was used to validate the classification performance of the radiomics model. The stability of the radiomics model was evaluated by using a fivefold cross-validation method, and two breast radiologists assessed the diagnostic agreement of the radiomics model. Results: In the training set, the radiomics model obtained an area under the receiver operating characteristic curve (AUC) of 0.934 [95% confidence intervals (95% CI), 0.898-0.971], a sensitivity of 98.8% (81/82), a threshold of 0.22, and a specificity of 60% (66/110). In the test set, the radiomics model obtained an AUC of 0.901 (95% CI, 0.835-0.961), a sensitivity of 95.8% (46/48), and a specificity of 66.7% (32/48). The radiomics model had relatively stable sensitivities in fivefold cross-validation (training set, 97.39% ± 3.9%; test set, 98.7% ± 4%). Conclusion: The radiomics method based on DM may help reduce the temporarily unnecessary invasive biopsies for benign mammographic masses over-classified in BI-RADS category 4 and 5 while providing similar diagnostic performance for malignant mammographic masses as biopsies.

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.

Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis.

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

The Application of Functional Magnetic Resonance Imaging in Type 2 Diabetes Rats With Contrast-Induced Acute Kidney Injury and the Associated Innate Immune Response.

AIMS: Contrast-induced acute kidney injury (CI-AKI) is the third most common in-hospital acquired AKI, and its mechanism is not fully clear. Its morbidity increases among populations with chronic kidney disease (CKD), older age, diabetes mellitus (DM), and so on. Immediate and effective noninvasive diagnostic methods are lacking, so CI-AKI often prolongs hospital stays and increases extra medical costs. This study aims to explore the possibility of diagnosing CI-AKI with functional magnetic resonance imaging (fMRI) based on type 2 DM rats. Moreover, we attempt to reveal the immune response in CI-AKI and to clarify why DM is a predisposing factor for CI-AKI. METHODS: A type 2 DM rat model was established by feeding a high-fat and high-sugar diet combined with streptozotocin (STZ) injection. Iodixanol-320 was the contrast medium (CM) administered to rats. Images were obtained with a SIEMENS Skyra 3.0-T magnetic resonance imager. Renal histopathology was evaluated using H&E staining and immunohistochemistry (IHC). The innate immune response was revealed through western blotting and flow cytometry. RESULTS: Blood oxygenation level-dependent (BOLD) imaging and intravoxel incoherent motion (IVIM) imaging can be used to predict and diagnose CI-AKI effectively. The R 2 ∗ value (r > 0.6, P < 0.0001) and D value (| r| > 0.5, P < 0.0001) are strongly correlated with histopathological scores. The NOD-like receptor pyrin 3 (NLRP3) inflammasome participates in CI-AKI and exacerbates CI-AKI in DM rats. Moreover, the percentages of neutrophils and M1 macrophages increase dramatically in rat kidneys after CM injection (neutrophils range from 56.3 to 56.6% and M1 macrophages from 48 to 54.1% in normal rats, whereas neutrophils range from 85.5 to 92.4% and M1 macrophages from 82.1 to 89.8% in DM rats). CONCLUSIONS/INTERPRETATION: BOLD and IVIM-D can be effective noninvasive tools in predicting CI-AKI. The innate immune response is activated during the progression of CI-AKI and DM will exacerbate this progression.

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging.

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

Neuroprotective and Angiogenesis Effects of Levetiracetam Following Ischemic Stroke in Rats.

Objective: The present study explored whether levetiracetam (LEV) could protect against experimental brain ischemia and enhance angiogenesis in rats, and investigated the potential mechanisms in vivo and in vitro. Methods: The middle cerebral artery was occluded for 60 min to induce middle cerebral artery occlusion (MCAO). The Morris water maze was used to measure cognitive ability. The rotation test was used to assess locomotor function. T2-weighted MRI was used to assess infarct volume. The neuronal cells in the cortex area were stained with cresyl purple. The anti-inflammatory effects of LEV on microglia were observed by immunohistochemistry. Enzyme-linked immunosorbent assays (ELISA) were used to measure the production of pro-inflammatory cytokines. Western blotting was used to detect the levels of heat shock protein 70 (HSP70), vascular endothelial growth factor (VEGF), and hypoxia-inducible factor-1α (HIF-1α) in extracts from the ischemic cortex. Flow cytometry was used to observe the effect of LEV on neuronal cell apoptosis. Results: LEV treatment significantly increased the density of the surviving neurons in the cerebral cortex and reduced the infarct size (17.8 ± 3.3% vs. 12.9 ± 1.4%, p < 0.01) after MCAO. Concurrently, the time required to reach the platform for LEV-treated rats was shorter than that in the saline group on day 11 after MCAO (p < 0.01). LEV treatment prolonged the rotarod retention time on day 14 after MCAO (84.5 ± 6.7 s vs. 59.1 ± 6.2 s on day 14 compared with the saline-treated groups, p < 0.01). It also suppressed the activation of microglia and inhibited TNF-α and Il-1ß in the ischemic brain (135.6 ± 5.2 pg/ml vs. 255.3 ± 12.5 pg/ml, 18.5 ± 1.3 pg/ml vs. 38.9 ± 2.3 pg/ml on day 14 compared with the saline-treated groups, p < 0.01). LEV treatment resulted in a significant increase in HIF-1α, VEGF, and HSP70 levels in extracts from the ischemic cerebral cortex. At the same time, LEV reduced neuronal cell cytotoxicity and apoptosis induced by an ischemic stroke (p < 0.01). Conclusion: LEV treatment promoted angiogenesis and functional recovery after cerebral ischemia in rats. These effects seem to be mediated through anti-inflammatory and antiapoptotic activities, as well as inducing the expression of HSP70, VEGF, and HIF-1α.