Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke.

This study aimed to create machine learning models for predicting early neurological deterioration and risk classification in acute ischemic stroke (AIS) before intravenous thrombolysis (IVT). The study included 704 AIS patients categorized into END and non-END groups. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the best predictors from clinical indicators, leading to the creation of Model 1. Univariate and multivariate logistic regression analyses identified independent predictive factors for END from inflammatory cell ratios. These factors were combined with clinical indicators, forming Model 2. Receiver operating characteristic (ROC) curves assessed the models' predictive performance. Key variables for Model 1 included the NIHSS score, systolic blood pressure, and lymphocyte percentage. Neutrophil-to-Lymphocyte ratio, Platelet-to-Neutrophil ratio, and Platelet-to-Lymphocyte ratio independently predicted END. Model 1 exhibited moderate predictive ability (AUC 0.721 in training, AUC 0.635 in test). Model 2, which integrated clinical indicators and inflammatory cell ratios, demonstrated strong performance in both training (AUC 0.862) and test (AUC 0.816). Machine learning models, combining clinical indicators and inflammatory cell ratios before IVT, accurately predict END and associated risk in AIS.

Clinical Value of a Nomogram Model Based on Apparent Diffusion Coefficient Values Within 1 cm of the Tumor Cavity to Predict Postoperative Progression of Glioma.

OBJECTIVE: To explore the clinical value of constructing a nomogram model based on apparent diffusion coefficient values within 1 cm of the residual tumor cavity to predict the postoperative progression of gliomas. METHODS: Clinical data of patients with glioma who underwent surgery were retrospectively retrieved from the First Hospital of Qinhuangdao. The mean apparent diffusion coefficient (mADC) was measured using a picture archiving and communication system. The Kaplan-Meier survival curve was constructed with the optimal mADC threshold determined by the X-tile. A nomogram was developed based on the independent risk factors determined using the Cox proportional hazards model (Cox regression model) to predict the progression of postoperative glioma. A receiver operating characteristic curve was drawn to evaluate the prediction accuracy of the model, and decision curve analysis was performed to assess the clinical value of the nomogram. RESULTS: There was good agreement between the mADC values of the 2 repeated measurements before and after, with a consistency correlation coefficient of 0.83. Multivariate Cox regression analysis showed that peritumoral mADC values, degree of peritumoral enhancement, age, pathological grading, and degree of tumor resection were independent risk factors for predicting postoperative progression of glioma (all P < 0.05). The receiver operating characteristic curves of the nomogram predicting 1, 2, and 3 years postoperative progression were 0.86, 0.82, and 0.91, respectively. The calibration curve showed good consistency between the observed and predicted values in the model. The curve showed that the nomogram model has a good clinical application value. CONCLUSIONS: The peritumoral mADC values, degree of peritumoral enhancement, age, pathological grade, and degree of tumor resection were independent factors affecting the postoperative progression of glioma. The nomogram model established for the first time based on mADC values within 1 cm of the tumor can predict the postoperative condition of patients with glioma intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualize the evaluation of survival and prognosis, and formulate treatment plans for patients.

Effects of hypertension and aging on brain function in spontaneously hypertensive rats: a longitudinal resting-state functional magnetic resonance imaging study.

To investigate the dynamic evolution of brain function under the comorbidities of hypertension and aging. Resting-state functional magnetic resonance imaging scans were longitudinally acquired at 10, 24, and 52 weeks in spontaneously hypertensive rats (SHRs) and Wistar-Kyoto rats. We computed the mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), and functional connectivity (FC). There was no interaction between hypertension and aging on brain function. The main effect of aging reflects primarily the cumulative increase of brain activity, especially the increase of mALFF in amygdala and mReHo in cingulate cortex, accompanied by the decrease of brain activity. The main effect of hypertension reflects primarily decreased brain activity in default modal network, accompanied by increased brain activity. The main effect of aging shows reduced brain FC as early as 24 weeks, and the main effect of hypertension shows higher brain FC in SHRs. The novel discovery is that 1 brain FC network increased linearly with age in SHRs, in addition to the linearly decreasing FC. Hypertension and aging independently contribute to spatiotemporal alterations in brain function in SHRs following ongoing progression and compensation. This study provides new insight into the dynamic characteristics of brain function.

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI.

Introduction: Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction. Methods: A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier. Results: The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively. Discussion: The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.

The Value of First-Order Features Based on the Apparent Diffusion Coefficient Map in Evaluating the Therapeutic Effect of Low-Intensity Pulsed Ultrasound for Acute Traumatic Brain Injury With a Rat Model.

Purpose: In order to evaluate the neuroprotective effect of low-intensity pulsed ultrasound (LIPUS) for acute traumatic brain injury (TBI), we studied the potential of apparent diffusion coefficient (ADC) values and ADC-derived first-order features regarding this problem. Methods: Forty-five male Sprague Dawley rats (sham group: 15, TBI group: 15, LIPUS treated: 15) were enrolled and underwent magnetic resonance imaging. Scanning layers were acquired using a multi-shot readout segmentation of long variable echo trains (RESOLVE) to decrease distortion. The ultrasound transducer was applied to the designated region in the injured cortical areas using a conical collimator and was filled with an ultrasound coupling gel. Regions of interest were manually delineated in the center of the damaged cortex on the diffusion weighted images (b = 800 s/mm2) layer by layer for the TBI and LIPUS treated groups using the open-source software ITK-SNAP. Before analysis and modeling, the features were normalized using a z-score method, and a logistic regression model with a backward filtering method was employed to perform the modeling. The entire process was completed using the R language. Results: During the observation time, the ADC values ipsilateral to the trauma in the TBI and LIPUS groups increased rapidly up to 24 h. After statistical analysis, the 10th percentile, 90th percentile, mean, skewness, and uniformity demonstrated a significant difference among three groups. The receiver operating characteristic curve (ROC) analysis shows that the combined LR model exhibited the highest area under the curve value (AUC: 0.96). Conclusion: The combined LR model of first-order features based on the ADC map can acquire a higher diagnostic performance than each feature only in evaluating the neuroprotective effect of LIPUS for TBI. Models based on first-order features may have potential value in predicting the therapeutic effect of LIPUS in clinical practice in the future.

Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer.

Background: Relapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse. Purpose: To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC. Methods: For this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (ModelN) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Results: The ModelN demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (ModelC), the discrimination ability of ModelN showed an improvement with P < 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (ModelR), ModelN discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer-Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively). Conclusion: This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.

Three-dimensional reconstruction of magnetic resonance images of carp brain for brain control technology.

BACKGROUND: In the field of animal robot control, brain control technology is currently used to achieve control. It is usually necessary to accurately implant brain electrodes into the animal's brain movement area with the help of a brain stereotaxic apparatus, and apply electrical stimulation to achieve control of the animal. The prerequisite for accurate electrode implantation is to study the internal tissues of the carp skull. NEW METHOD: With the help of 3.0 T magnetic resonance imaging (MRI) instrument and 8_CH MRI scanning coil, carp brain magnetic resonance images was obtained. The visualization tool package VTK and the marching cube algorithm were used for surface rendering, the ray casting algorithm was used for volume rendering and reconstruction. RESULTS: The three-dimensional reconstruction results could show the carp skull surface contour and internal tissue details, and the measured coordinates after three-dimensional reconstruction of magnetic resonance images could be transformed into three-dimensional positioning coordinates suitable for brain stereotaxic apparatus. COMPARISON WITH EXISTING METHODS: The three-dimensional reconstruction images based on magnetic resonance could analyze the relative spatial position relationship between the surface structure of the carp's brain and the internal tissue at any angle, and the three-dimensional positioning coordinates of the brain could be obtained quickly and accurately. CONCLUSIONS: The visualization of carp brain magnetic resonance images based on marching cubes algorithm and ray projection algorithm could obtain ideal reconstruction effects, which could be used in the brain control technology of carp robot.

Ferroptosis-related genes for predicting prognosis of patients with laryngeal squamous cell carcinoma.

BACKGROUND: Previous studies reported that ferroptosis-related genes can regulate the process of tumor cell changes by regulating iron metabolism. However, the prognostic value of ferroptosis-related genes in LC remains to be further elucidated. METHODS: Ferroptosis-related gene expression profiles of coexisting ferroptosis-related genes were extracted from both cohorts (TCGA and GSE27020) for eligible analysis. LASSO Cox regression was utilized to build an optimum ferroptosis-related prognostic model. Kaplan-Meier curve was performed by log-rank test, and time-dependent ROC curve was constructed to evaluate the predictive power of this signature in both cohorts. GO and KEGG enrichment analysis was used to investigate the potential mechanism of differential enrichment signal pathways. RESULTS: 112 LC patients from the TCGA cohort and 108 LC patients with clinical information from the GEO cohorts were eventually included in the study. Three ferroptosis-related genes were identified as an independent risk factor to establish the prognostic risk score. Kaplan-Meier curve represented that patients with high-risk group favors with worse OS than their low-risk group (P = 0.04). The good performance of the gene signature for predicting OS was evaluated by area under the curve (AUC) of time-dependent ROC curves achieved 0.74 at 3 years, and 0.70 at 5 years. Similar performance has been proved in the external validation cohort. GO and KEGG enrichment analysis have been performed to explore the signaling pathways and underlying mechanisms were significantly active in LC patients. CONCLUSION: In summary, our study developed a ferroptosis-related model that could be an effective biomarker to predict the prognosis of laryngeal cancer.

Assessment of Neuroprotective Effects of Low-Intensity Transcranial Ultrasound Stimulation in a Parkinson's Disease Rat Model by Fractional Anisotropy and Relaxation Time T2∗ Value.

Background: Low-intensity transcranial ultrasound (LITUS) may have a therapeutic effect on Parkinson's disease (PD) patients to some extent. Fractional anisotropy (FA) and relaxation time T2∗ that indicate the integrity of fiber tracts and iron concentrations in brain tissue have been used to evaluate the therapeutic effects of LITUS. Purpose: This study aims to use FA and T2∗ values to evaluate the therapeutic effects of LITUS in a PD rat model. Materials and Methods: Twenty Sprague-Dawley rats were randomly divided into a hemi-PD group (n = 10) and a LITUS group (n = 10). Single-shot spin echo echo-planar imaging and fast low-angle shot T2WI sequences at 3.0 T were used. The FA and T2∗ values on the right side of the substantia nigra (SN) pars compacta were measured to evaluate the therapeutic effect of LITUS in the rats. Results: One week after PD-like signs were induced in the rats, the FA value in the LITUS group was significantly larger compared with the PD group (0.214 ± 0.027 vs. 0.340 ± 0.032, t = 2.864, P = 0.011). At the 5th and 6th weeks, the FA values in the LITUS group were significantly smaller compared with the PD group (5th week: 0.290 ± 0.037 vs. 0.405 ± 0.027, t = 2.385, P = 0.030; 6th week: 0.299 ± 0.021 vs. 0.525 ± 0.028, t = 6.620, P < 0.0001). In the 5th and 6th weeks, the T2∗ values in the injected right SN of the LITUS group were significantly higher compared with the PD group (5th week, 12.169 ± 0.826 in the LITUS group vs. 7.550 ± 0.824 in the PD group; 6th week, 11.749 ± 0.615 in the LITUS group vs. 7.550 ± 0.849 in the PD group). Conclusion: LITUS had neuroprotective effects and can reduce the damage of 6-OHDA-induced neurotoxicity in hemi-PD rats. The combination of FA and T2∗ assessments can potentially serve as a new and effective method to evaluate the therapeutic effects of LITUS.

Prognostic nomograms based on immune scores for head-neck squamous cell carcinoma patients.

PURPOSE: We aim to develop an immune-score nomograms for predicting overall survival (OS) of patients with HNSCC and assess the association of immune scores with prognosis. METHODS: The data of 530 patients used in this study were retrieved from The Cancer Genome Atlas database. The optimization cut-point for immune scores was expressed by X-tile 3.6.1 tool. Possible prognostic factors from univariate Cox analysis were further included in a multivariate Cox proportional hazards analysis to obtain significant risk factors. Prognostic nomograms were constructed based on the factors of significant multivariate prognostic using R version 3.5.1. A calibration map was generated by comparing the nomogram prediction probability and the observation for the 3-year and 5-year OS rates. RESULTS: We retrospectively analyzed 462 patients downloaded from TCGA dataset. Prognostic nomograms was integrated following risk factors of significant multivariate prognostic, such as age, angiolymphaic invasion (AI), perineura invasion(Per_invasion),tumor site, immune score, tumor-node-metastasis(TNM) stage. The concordance Index (C-index) for OS predictions was 0.723 (95% CI 0.671-0.785). Moreover, we compared the powerful efficiency of the nomograms with that of the TNM staging system. OS prediction determined on immune score set compared with the TNM staging with C-index = 0.723 vs 0.612. The calibration curves for the probability of OS of 3-year or 5-year showed no deviations between the prediction by nomograms and actual reference line. CONCLUSION: The present study indicate that high and intermediate immune scores are as independent prognostic variables for OS of head-neck squamous cell carcinoma patients. We constructed novel nomograms may has the potential to provide individualized survival risk assessments and guide treatment decisions.

Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis.

BACKGROUND: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. At present, it is generally believed that the prognosis of GIST is closely related to its risk classification. It may add value to correctly diagnose and evaluate the risk of invasion using a noninvasive imaging examination prior to surgery. MRI has the advantages of multiple parameters and high soft tissue resolution, which may be the potential method to preoperatively evaluate the risk of GIST. PURPOSE: To retrospectively evaluate the diagnostic accuracy of multi-parameter MR analysis for preoperative risk classification of GIST. MATERIALS AND METHODS: In this 6-year retrospective study, full MRI examination was performed on all 60 GIST cases confirmed classified by pathology, including 35 cases of very low-to-low-risk GIST and 25 cases of intermediate-to-high-risk GIST. Dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Volume, ADC ratio, three wash-in indexes (WII) were calculated and compared using t-test or Kruskal-Wallis nonparametric test. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy using ROC analyses. Differences were considered significant at p < 0.05. RESULTS: All GISTs were resected. Patient age, sex, tumor location and tumor shape did not differ significantly across the two groups (p = 0.798, 0.767, 0.822 and 0.096, respectively). GIST in the intermediate-to-high-risk group presented significantly greater volume (p = 0.0045), lower ADC ratio (p = 0.0125) and faster enhancement (for WII2, p < 0.0001; for WII3, p = 0.0358) than that of GIST in the very low-to-low-risk group. This combination of the volume, ADC ratio and WII2 provided sensitivity of 88%, specificity of 94.29%, and accuracy of 91.7% for the risk classification of GIST. CONCLUSION: Multi-parameter MR analysis provides a preoperative imaging standard for accurately distinguishing very low-to-low-risk GIST from intermediate-to-high-risk GIST.

MRI Texture-Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors.

BACKGROUND: Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment. PURPOSE: To develop and validate an MRI texture-based model to predict the mitotic index and its risk classification. STUDY TYPE: Retrospective. POPULATION: Ninety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T. ASSESSMENT: GIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk. RESULTS: The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922). DATA CONCLUSION: The texture-based model can be used to predict GIST mitotic index and risk classification preoperatively. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.

Development and validation of a novel metabolic signature for predicting prognosis in patients with laryngeal cancer.

PURPOSE: Despite advances in the development of treatments for laryngeal cancer (LC), including surgical treatments and radio-chemotherapy, the survival rate of LC remains low. Therefore, novel metabolic signatures are urgently needed to evaluate the prognosis of LC patients. METHODS: Differentially expressed metabolic genes were extracted via bioinformatics analysis from the raw data of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and LASSO analyses were performed to identify metabolic genes that were significantly correlated with overall survival (OS). Using the Kaplan-Meier analysis and receiver operating characteristics, the prognostic power of candidate signatures was evaluated in the two databases. Gene Set Enrichment Analysis (GSEA) was performed to explore significant signaling pathways and underlying mechanisms in the high- and low-risk groups. RESULTS: Thirteen metabolism genes showed superior ability to predict OS for LC when compared to clinical variables, and patients in the high-risk group showed significantly poorer OS than those in the low-risk group. The area under the curve of receiver operating curves for 5- and 3-year OS was 0.929 and 0.899, respectively, which were better than the OS obtained with clinicopathological variables. Similar results obtained in the GEO cohort indicated that this gene signature could differentiate between LC patients with and without recurrence. CONCLUSION: To our knowledge, this study is the first to report that the 13 metabolic genes could serve as an independent biomarker for LC, which could provide vital prognostic information and prediction for personalized treatment of LC.

[Three-dimensional reconstruction of carp brain tissue and brain electrodes for biological control].

In order to accurately implant the brain electrodes of carp robot for positioning and navigation, the three-dimensional model of brain structure and brain electrodes is to be proposed in the study. In this study, the tungsten electrodes were implanted into the cerebellum of a carp with the aid of brain stereotaxic instrument. The brain motor areas were found and their three-dimensional coordinate values were obtained by the aquatic electricity stimulation experiments and the underwater control experiments. The carp brain and the brain electrodes were imaged by 3.0 T magnetic resonance imaging instrument, and the three-dimensional reconstruction of carp brain and brain electrodes was carried out by the 3D-DOCTOR software and the Mimics software. The results showed that the brain motor areas and their coordinate values were accurate. The relative spatial position relationships between brain electrodes and brain tissue, brain tissue and skull surface could be observed by the three-dimensional reconstruction map of brain tissue and brain electrodes which reconstructed the three-dimensional structure of brain. The anatomical position of the three-dimensional reconstructed brain tissue in magnetic resonance image and the relationship between brain tissue and skull surface could be observed through the three-dimensional reconstruction comprehensive display map of brain tissue. The three-dimensional reconstruction model in this study can provide a navigation tool for brain electrodes implantation.

Neuroprotective effect of low-intensity transcranial ultrasound stimulation in endothelin-1-induced middle cerebral artery occlusion in rats.

BACKGROUND: Ischemic stroke is one of the leading causes of death and disability worldwide. Low-intensity transcranial ultrasound stimulation (LITUS) is a promising neuroprotective treatment for ischemic stroke. Diffusion-weighted imaging (DWI) can be highly sensitive in the detection of ischemic brain injury. Relative apparent diffusion coefficient (rADC) values can be used to evaluate the effect of LITUS on ischemic stroke. PURPOSE: The aim of this study was to determine the neuroprotective effect of LITUS at different time points using endothelin-1-induced middle cerebral artery occlusion in rats as a model of ischemic stroke. METHODS: Endothelin-1 (ET-1) was injected into the cerebral parenchyma near the middle cerebral artery, which induced focal, reversible, low-flow ischemia in rats. After occlusion of the middle cerebral artery for 30 min, 120 min, and 240 min, LITUS stimulation was used respectively. DWI was performed at 1, 3, 6, 12, 18, 24, 48, and 72 h after ischemia using a 3 T scanner. The rADC values were calculated, and functional outcomes assessed using neurobehavioral scores after ischemia. Nissl staining and estimation of Na+-K+-ATPase activity were used to assess the neuropathology after completing the last Magnetic Resonance Imaging (MRI) examination. RESULTS: Endothelin-1-induced occlusion of the middle cerebral artery resulted in significant dysfunction and neuronal damage in rats. Rats that received LITUS exhibited reduced damage of the affected brain tissue after cerebral ischemia. The greatest protective effect was found when LITUS stimulation occurred 30 min after cerebral ischemia. CONCLUSIONS: Imaging, behavioral, and histological results suggested that LITUS stimulation after an ischemic stroke produced significant neuroprotective effects.

Neuroprotective Effect of Low-Intensity Transcranial Ultrasound Stimulation in Moderate Traumatic Brain Injury Rats.

Traumatic brain injury (TBI) is a kind of severe brain injury characterized with a high incidence rate and a high disability rate. Low-intensity transcranial ultrasound stimulation (LITUS) is a promising neuroprotective method for improving the functional prognosis of TBI. The fractional anisotropy (FA) value and mean diffusivity (MD) value can be sensitive to abnormal brain structure and function and can thus be used to evaluate the effect of LITUS on TBI. Our purpose was to evaluate the therapeutic effect of LITUS in a moderate TBI rat model with FA and MD values. For our method, we used 45 male Sprague Dawley rats (15 sham normal, 15 TBI, and 15 LITUS treatment rats). We used single-shot spin echo echo-planar imaging sequences at 3.0T to obtain the DTI parameters. Parameters of FA and MD on the treated side of the injury cortex were measured to evaluate the therapeutic effect of LITUS in a TBI rat model. For FA and MD values, groups were compared by using a two-way analysis of variance for repeated measures, and this was followed by Tukey's post hoc test. Differences were considered significant at P < 0.05. The results were that the FA value in the LITUS treatment group at 1 day after TBI was significantly higher than that in the control group (adjusted P = 0.0422) and significantly lower than that in the TBI group at 14, 21, and 35 days after TBI (adjusted P = 0.0015, 0.0064, and 0.0173, respectively). At the end of the scan time point, the differences between the two groups were not significant (adjusted P = 0.3242). The MD values in the LITUS treatment group were significantly higher in the early stage than that in the TBI group (adjusted P = 0.0167) and significantly lower at the following time points than in the TBI group. In conclusion, daily treatment with LITUS for 10 min effectively improved the brain damage in the Controlled Cortical Impact (CCI)-caused TBI model. FA and MD values can serve as evaluation indicators for the neuro-protective effect of LITUS.

Evaluating the Therapeutic Effect of Low-Intensity Transcranial Ultrasound on Traumatic Brain Injury With Diffusion Kurtosis Imaging.

BACKGROUND: Low-intensity transcranial ultrasound (LITUS) has a therapeutic effect on traumatic brain injury (TBI). Diffusion kurtosis imaging (DKI) might be able to evaluate the effect changes of injured brain microstructure. PURPOSE: To evaluate the therapeutic effect of LITUS in a moderate TBI rat model with DKI parameters. STUDY TYPE: Prospective case-control animal study. ANIMAL MODEL: Forty-five rats were randomly divided into sham control, TBI, and LITUS treatment groups (n = 15). FIELD STRENGTH/SEQUENCE: Single-shot spin echo echo-planar imaging and fast T2 WI sequences at 3.0T. ASSESSMENT: DKI parameters were obtained on days 1, 7, 14, 21, 28, 35, and 42 after TBI. STATISTICAL TESTS: For the mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr) values, groups were compared using a two-way analysis of variance (ANOVA). RESULTS: LITUS inhibited TBI and caused MK values to increase significantly during the early stage (LITUS vs. TBI, day 7, adjusted P < 0.0001) and decrease during the late stage (LITUS vs. TBI, day 42, adjusted P = 0.0156) in the damaged cortex. In the thalamus, the MK value of the TBI group began to rise on day 7, with no change observed in the LITUS group. TBI increases Ka value during the early stage in the cortex and decreases during the late stage in the cortex and thalamus. LITUS inhibited these Ka changes (LITUS vs. TBI, day 7, adjusted P = 0.0014; LITUS vs. TBI, day 42, adjusted P = 0.0026 and 0.0478, respectively, for cortex and thalamus). The Kr value increased slightly during the early stage in the cortex (TBI vs. Sham, day 1, adjusted P = 0.0016). DATA CONCLUSION: The DKI parameter, particularly the MK value, evaluates primary cortical injury as well as the secondary brain injury that could not be detected by conventional T2 WI. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;52:520-531.