A simulation study of transcranial magnetoacoustic stimulation of the basal ganglia thalamic neural network to improve pathological beta oscillations in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. OBJECTIVE: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. METHODS: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. RESULTS: When the stimulating effect of ultrasound is ignored, TMAS-STN simultaneously inhibits the beta-band amplitude of the GPi nucleus, whereas TMAS-GPi fails to simultaneously have an inhibitory effect on the STN. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe suppressed the PD pathologic beta band amplitude of each nucleus to a greater extent. When considering the stimulatory effect of ultrasound, lower sound pressures of ultrasound do not affect the neuronal firing state, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. CONCLUSIONS: At 9 T static magnetic field, 0.5-1.5 MPa and 1.5-2.0 MPa ultrasound had synergistic effects on individual STN and GPi neurons. TMAS multinuclear stimulation with appropriate ultrasound intensity was the most effective in suppressing the amplitude of pathological beta oscillations in PD and may be clinically useful.

Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC. METHODS: In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis. RESULTS: The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632). CONCLUSION: The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC.

Transcranial dipole localization and decoding study based on ultrasonic phased array for acoustoelectric brain imaging.

Objective. Neuroimaging is one of the effective tools to understand the functional activities of the brain, but traditional non-invasive neuroimaging techniques are difficult to combine both high temporal and spatial resolution to satisfy clinical needs. Acoustoelectric brain imaging (ABI) can combine the millimeter spatial resolution advantage of focused ultrasound with the millisecond temporal resolution advantage of electroencephalogram signals.Approach. In this study, we first explored the transcranial modulated acoustic field distribution based on ABI, and further localized and decoded single and double dipoles signals.Main results. The results show that the simulation-guided acoustic field modulation results are significantly better than those of self-focusing, which can realize precise modulation focusing of intracranial target focusing. The single dipole transcranial localization error is less than 0.4 mm and the decoding accuracy is greater than 0.93. The double dipoles transcranial localization error is less than 0.2 mm and the decoding accuracy is greater than 0.89.Significance. This study enables precise focusing of transcranial acoustic field modulation, high-precision localization of source signals and decoding of their waveforms, which provides a technical method for ABI in localizing evoked excitatory neuron areas and epileptic focus.

Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study.

RATIONALE AND OBJECTIVES: Accurate prediction neoadjuvant chemotherapy (NACT) response in ovarian cancer (OC) is essential for personalized medicine. We aimed to develop and validate a deep learning (DL) model based on pretreatment contrast-enhanced CT (CECT) images for predicting NACT responses and classifying high-grade serous ovarian cancer (HGSOC) to identify patients who may benefit from NACT. MATERIALS AND METHODS: This multicenter study, which contained both retrospective and prospective studies, included consecutive OC patients (n = 757) from three hospitals. Using WHO RECIST 1.1 for the reference standard, a total of 587 women with 1761 images were included in the training and validation sets, 67 women with 201 images were included in the prospective sets, and 103 women with 309 images were included in the external sets. A multitask DL model based on the multiperiod CT image was developed to predict NACT response and HGSOC. RESULTS: Logistic regression analysis showed that peritoneal invasion, retinal invasion, and inguinal lymph node metastasis were independent predictors. The DL achieved promising segmentation performances with DICEmean= 0.83 (range: 0.78-0.87). For predicting NACT response, the DL model combined with clinical risk factors obtained area under the receiver operating characteristic curve (AUCs) of 0.87 (0.83-0.89), 0.88 (0.86-0.91), 0.86 (0.82-0.89), and 0.79 (0.75-0.82) in the training, validation, prospective, and external sets, respectively. The AUCs were 0.91 (0.87-0.94), 0.89 (0.86-0.91), 0.80 (0.76-0.84), and 0.80 (0.75-0.85) in four sets in HGSOC classification. CONCLUSION: The multitask DL model developed using multiperiod CT images exhibited a promising performance for predicting NACT response and HGSOC with OC, which could provide valuable information for individualized treatment.

Numerical Simulation Study of Firing Threshold of STN Neuron Stimulated by Focused Ultrasound.

OBJECTIVE: Electroencephalography (EEG) is one of the functional brain imaging techniques to effectively measure neuronal activity, but its low spatial resolution makes it difficult to localize evoked excitatory neurons or areas of abnormal firing. Multimodal imaging techniques are expected to combine the high spatial resolution (mm level) of focused ultrasound (FUS) with the high temporal resolution (ms level) of EEG. The technique must be performed under the premise that ultrasound stimulation does not affect neuronal firing, and there is an urgent need to determine the threshold of this ultrasound stimulation parameter. METHODS: In this paper, the subthalamic nucleus neuronal firing model and the bilayer sonophore model are combined to numerically simulate the neuronal firing rhythm under the conditions of different stimulation parameters. The correlation and frequency differences of neuronal firing rhythms with and without ultrasound stimulation were compared and used as an index to evaluate the degree of change, and the final range of effective threshold parameters for ultrasound stimulation of neurons but not inducing neuronal firing was obtained. RESULTS: The results showed that the correlation of neuronal firing rhythms in both conditions with and without stimulation decreased and the frequency difference increased with increasing ultrasound parameters such as duty cycle, intensity, center frequency and pulse repetition frequency. CONCLUSION: An effective range of stimulation threshold parameters can be obtained based on the correlation coefficients and frequency difference matrices under different parameter combinations. SIGNIFICANCE: The threshold can further promote the safe and effective application of FUS for multimodal electrophysiological imaging.

Experimental and simulation studies of localization and decoding of single and double dipoles.

Objective. Electroencephalography is a technique for measuring normal or abnormal neuronal activity in the human brain, but its low spatial resolution makes it difficult to locate the precise locations of neurons due to the volume conduction effect of brain tissue.Approach. The acoustoelectric (AE) effect has the advantage of detecting electrical signals with high temporal resolution and focused ultrasound with high spatial resolution. In this paper, we use dipoles to simulate real single and double neurons, and further investigate the localization and decoding of single and double dipoles based on AE effects from numerical simulations, brain tissue phantom experiments, and fresh porcine brain tissue experiments.Main results. The results show that the localization error of a single dipole is less than 0.3 mm, the decoding signal is highly correlated with the source signal, and the decoding accuracy is greater than 0.94; the location of double dipoles with an interval of 0.4 mm or more can be localized, the localization error tends to increase as the interval of dipoles decreases, and the decoding accuracy tends to decrease as the frequency of dipoles decreases.Significance. This study localizes and decodes dipole signals with high accuracy, and provides a technical method for the development of EEG.

The Influence of Transcranial Magnetoacoustic Stimulation Parameters on the Basal Ganglia-Thalamus Neural Network in Parkinson's Disease.

Objective: Parkinson's disease (PD) is a degenerative disease of the nervous system that frequently occurs in the aged. Transcranial magnetoacoustic stimulation (TMAS) is a neuronal adjustment method that combines sound fields and magnetic fields. It has the characteristics of high spatial resolution and noninvasive deep brain focusing. Methods: This paper constructed a simulation model of TMAS based on volunteer's skull computer tomography, phased controlled transducer and permanent magnet. It simulates a transcranial focused sound pressure field with the Westervelt equation and builds a basal ganglia and thalamus neural network model in the PD state based on the Hodgkin-Huxley model. Results: A biased sinusoidal pulsed ultrasonic TMAS induced current with 0.3 T static magnetic field induction and 0.2 W⋅cm-2 sound intensity can effectively modulate PD states with RI ≥ 0.633. The magnitude of magnetic induction strength was changed to 0.2 and 0.4 T. The induced current was the same when the sound intensity was 0.4 and 0.1 W⋅cm-2. And the sound pressure level is in the range of -1 dB (the induced current difference is less than or equal to 0.019 µA⋅cm-2). TMAS with a duty cycle of approximately 50% can effectively modulates the error firings in the PD neural network with a relay reliability not less than 0.633. Conclusion: TMAS can modulates the state of PD.

The Effects of the Structural and Acoustic Parameters of the Skull Model on Transcranial Focused Ultrasound.

Transcranial focused ultrasound (tFUS) has great potential in brain imaging and therapy. However, the structural and acoustic differences of the skull will cause a large number of technical problems in the application of tFUS, such as low focus energy, focal shift, and defocusing. To have a comprehensive understanding of the skull effect on tFUS, this study investigated the effects of the structural parameters (thickness, radius of curvature, and distance from the transducer) and acoustic parameters (density, acoustic speed, and absorption coefficient) of the skull model on tFUS based on acrylic plates and two simulation methods (self-programming and COMSOL). For structural parameters, our research shows that as the three factors increase the unit distance, the attenuation caused from large to small is the thickness (0.357 dB/mm), the distance to transducer (0.048 dB/mm), and the radius of curvature (0.027 dB/mm). For acoustic parameters, the attenuation caused by density (0.024 dB/30 kg/m3) and acoustic speed (0.021 dB/30 m/s) are basically the same. Additionally, as the absorption coefficient increases, the focus acoustic pressure decays exponentially. The thickness of the structural parameters and the absorption coefficient of the acoustic parameters are the most important factors leading to the attenuation of tFUS. The experimental and simulation trends are highly consistent. This work contributes to the comprehensive and quantitative understanding of how the skull influences tFUS, which further enhances the application of tFUS in neuromodulation research and treatment.

Magnetic resonance imaging semantic and quantitative features analyses: an additional diagnostic tool for breast phyllodes tumors.

OBJECTIVE: This study aimed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors of the breast by the semantic and quantitative features in magnetic resonance imaging (MRI). METHODS: The female patients, diagnosed with phyllodes tumors by MRI and pathological test, were retrospectively selected from December, 2006 to April, 2019. The MRI of benign, borderline and malignant phyllodes tumors was analyzed using 8 semantic features and 20 computed quantitative features from diffuse contrast-enhanced magnetic resonance imaging (DCE-MRI). The semantic features were analyzed by univariate analysis. The least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal subset of MRI quantitative features. According to the results from multivariate logistic regression for the semantic and quantitative features, the model was constructed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors. RESULTS: Thirty-two benign (58.18%), 13 borderline (23.64%) and 10 malignant (18.18%) phyllodes tumors were identified in 54 patients. Five semantic features were proved to be significantly correlated with pathologic grade, including size, the T1 weighted image signal intensity, fat-saturated T2-weighted image signal intensity, enhanced signal intensity, and kinetic curve pattern. With the analysis of LASSO method, three quantitative texture features with significant predictive ability were selected. The model combining both the semantic and quantitative features was proved to have good performance in differentiation on phyllodes tumors, yielding an area under receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.893, 0.933, 1.000, and 0.818, respectively. CONCLUSION: The constructed model based on the semantic and quantitative features of DCE-MRI can significantly improve the differential diagnosis of phyllodes tumors in breast.

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features.

RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy. RESULTS: The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non-triple-negative, 0.784 (0.748) for HER2-enriched vs non-HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< .05) in the subtype classification. CONCLUSIONS: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.

Dual-Targeting Nanoparticles: Codelivery of Curcumin and 5-Fluorouracil for Synergistic Treatment of Hepatocarcinoma.

Chemotherapy has been the standard for cancer therapy, but the nonspecific cytotoxicity of chemotherapeutic agents and drug resistance of tumor cells has limited its efficacy. However, multidrug combination therapy and targeting therapy have resulted in enhanced anticancer effects and have become increasingly important strategies in clinical applications. In this study, a biotin-/lactobionic acid-modified poly(ethylene glycol)-poly(lactic-co-glycolic acid)-poly(ethylene glycol) (BLPP) copolymer was synthesized, and curcumin- and 5-fluorouracil-loaded nanoparticles (BLPPNPs/C + F) were prepared to enhance the treatment of hepatocellular carcinoma. Blank BLPPNPs were shown to have great biocompatibility via both in vitro and in vivo studies. Good targeting of tumor cells of BLPPNPs was confirmed by flow cytometry, fluorescence microscopy, and biodistribution. The synergistic anticancer effects of BLPPNPs/C + F were demonstrated by cytotoxicity and animal studies, while western blotting was used to further verify the synergistic effect of curcumin and 5-fluorouracil. The dual-targeting and drug-loaded codelivery nanosystem demonstrated higher cellular uptake and stronger cytotoxicity for tumor cells. Therefore, these dual-targeting NPs are a promising codelivery carrier that could be made available for cellular targeting of anticancer drugs to achieve better intracellular delivery and synergistic anticancer efficacy.

[Numerical simulation of the focal region modulation to realize uniform temperature distribution during high-intensity focused ultrasound brain tumor therapy].

The temperature during the brain tumor therapy using high-intensity focused ultrasound (HIFU) should be controlled strictly. This research aimed at realizing uniform temperature distribution in the focal region by adjusting driving signals of phased array transducer. The three-dimensional simulation model imitating craniotomy HIFU brain tumor treatment was established based on an 82-element transducer and the computed tomography (CT) data of a volunteer's head was used to calculate and modulate the temperature distributions using the finite difference in time domain (FDTD) method. Two signals which focus at two preset targets with a certain distance were superimposed to emit each transducer element. Then the temperature distribution was modulated by changing the triggering time delay and amplitudes of the two signals. The results showed that when the distance between the two targets was within a certain range, a focal region with uniform temperature distribution could be created. And also the volume of focal region formed by one irradiation could be adjusted. The simulation results would provide theoretical method and reference for HIFU applying in clinical brain tumor treatment safely and effectively.

Modulation of transcranial focusing thermal deposition in nonlinear HIFU brain surgery by numerical simulation.

As the skull induces strong aberrations in phase and amplitude during transcranial treatment of brain surgery, high-intensity focused ultrasound suffers degradation in beam shape and deposits significant heat in the skull which may cause thermal damage to the bone and surrounding tissue. The goal of this study is to optimize the transcranial pressure and thermal fields to reduce thermal damage to the skull and simultaneously concentrate more energy in the focal region and make its size controllable during transcranial brain tumor treatment by modulating the excitation signals of the transducer array (including the phase and amplitude) and superposing the signals used to reduce peak pressure in the skull. A 3D numerical model was developed based on the reconstructed images from high-resolution CT scans of a human skull and a 64-element phased array to simulate acoustic propagation and thermal behavior calculated by the finite-difference time domain method. The simulation showed that more energy was focused at the setting target with little temperature elevation in the skull after correcting phase and amplitude and reducing peak pressure in the skull; through modulating the input intensity of arrays, the volume of focal regions located off-axis could be made equal to the volume achieved with on-axis focusing.

[Research on numerical simulation of temperature distribution during transcranial tumor therapy with high intensity focused ultrasound].

Numerical simulation is one of the most significant methods to predict the temperature distribution in high-intensity focused ultrasound (HIFU) therapy. In this study, the adopted numerical simulation was used based on a transcranial ultrasound therapy model taking a human skull as a reference. The approximation of the Westervelt formula and the Pennes bio-heat conduction equation were applied to the simulation of the transcranial temperature distribution. According to the temperature distribution and the Time Reversal theory, the position of the treatable focal region was corrected and the hot spot existing in the skull was eliminated. Furthermore, the influence of the exposure time, input power and the distance between transducer and skull on the temperature distribution was analyzed. The results showed that the position of the focal region could be corrected and the hot spot was eliminated using the Time Reversal theory without affecting the focus. The focal region above 60 degrees C could be formed at the superficial tis sue located from the skull of 20 mm using the hot spot elimination method and the volume of the focal region increases with the exposure time and the input power in a nonlinear form. When the same volume of the focal region was obtained, the more power was inputted, the less the exposure time was needed. Moreover, the volume of the focal region was influenced by the distance between the transducer and the skull.