Guidelines for successful motor cortex ultrasonic neurostimulation in mice.

BACKGROUND: Ultrasound neurostimulation (USNS) is a non-invasive neuromodulation technique that might hold promise for treating neuropsychiatric disorders with regards to its noninvasiveness, penetration depth, and high resolution. OBJECTIVE: We sought in this experimental study to provide detailed and optimized protocol and methodology for a successful ultrasonic neurostimulation of the Primary Motor Cortex (M1) in mice addressed to young researchers/students beginning their research in the field of ultrasonic neurostimulation and encountering practical challenges. METHODS: A 500 kHz single-element transducer was used for stimulating the primary motor cortex at different acoustic pressures in C57BL/6 mice at various anesthesia levels. To further illustrate the effect of anesthesia, real time visual observations of motor responses validated with video recordings as well as electromyography were employed for evaluating the success and reliability of the stimulations. RESULTS: Detailed experimental procedure for a successful stimulations including targeting and anesthesia is presented. Our study demonstrates that we can achieve high stimulation success rates (91 % to 100 %) at acoustic pressures ranging from 330 kPa to 550 kPa at anesthesia washout period. CONCLUSIONS: This study shows a reliable and detailed methodology for successful USNS in mice addressed to beginners in ultrasonic brain stimulation topic. We showed an effective USNS protocol. We offered a simple and consistent non-invasive technique for locating and targeting brain zones. Moreover, we illustrated the acoustic pressure and stimulation success relationship and focused on the effect of anesthesia level for successful stimulation.

Optimization of catheter's implementation in the mold, in the case of vaginal HDR brachytherapy treatment.

Background: The purpose of this study was to evaluate and compare results obtained in high dose rate (HDR) brachytherapy treatment of vaginal cancer. Different catheters distributions inside the custom mold were explored. The difference between those distributions is the position of the posterior catheter located near the rectum in the actual custom mold applicator used in different hospitals, each one having a catheter displacement of 0.5 which is equal to the length of a step position. The best catheters distribution offering an optimal dose distribution: better coverage of the clinical target volume (CTV), while reducing the dose received by organs at risk (OARs), were discussed. Materials and methods: A group of 60 patients treated with HDR brachytherapy, alone or in combination with external radiotherapy, was investigated. A custom mold is normally used for HDR brachytherapy vaginal cancer treatment. Three different geometrical positions of the catheters (G1, G2 and G3) and, consequently, 3 different dosimetries were simulated out for each patient on the CT images, using the Oncentra planning system. The coverage of the CTV was studied. Results: The average volume treated was 30.46 cc (min = 9.8 cc, max = 70.86 cc). The total prescribed dose, including external and internal radiotherapy, was 80 Gy. We evaluated conformity index (CI), dose homogeneity index (DHI) and conformality index (COIN) indices for the three implantation geometries to reach the same coverage criteria of the CTV. The D2cc parameter allowed the evaluation of the dose received by the OARs. For the rectum, a dose reduction of 9.67% (range 0.29-32.86) was obtained with the second geometry of implantation compared to 10.14% (range 1.43-28.33) with the third geometry. For the bladder, the second geometry of implantation showed a better preservation for this organ [15.93% (range 0.86-58.71) vs. 8.35% (range 0.33-30.43) with the third geometry]. The sigmoid was more protected using the second plan of implantation as well [6.33% (range 0.14-40.71) for the second implantation compared to 5.95% (range 0.33-36) for the third implantation]. Conclusions: G2 and G3 catheters' distribution, having catheter position farther from the mold wall and so from the vaginal wall compared to the catheter position applied showed a better protection for the OARs while giving the same prescribed dose for the CTV.

Detection of Doppler Microembolic Signals Using High Order Statistics.

Robust detection of the smallest circulating cerebral microemboli is an efficient way of preventing strokes, which is second cause of mortality worldwide. Transcranial Doppler ultrasound is widely considered the most convenient system for the detection of microemboli. The most common standard detection is achieved through the Doppler energy signal and depends on an empirically set constant threshold. On the other hand, in the past few years, higher order statistics have been an extensive field of research as they represent descriptive statistics that can be used to detect signal outliers. In this study, we propose new types of microembolic detectors based on the windowed calculation of the third moment skewness and fourth moment kurtosis of the energy signal. During energy embolus-free periods the distribution of the energy is not altered and the skewness and kurtosis signals do not exhibit any peak values. In the presence of emboli, the energy distribution is distorted and the skewness and kurtosis signals exhibit peaks, corresponding to the latter emboli. Applied on real signals, the detection of microemboli through the skewness and kurtosis signals outperformed the detection through standard methods. The sensitivities and specificities reached 78% and 91% and 80% and 90% for the skewness and kurtosis detectors, respectively.

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals.

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

Reducing sojourn points from recurrence plots to improve transition detection: Application to fetal heart rate transitions.

The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state.

Contrast improvement in sub- and ultraharmonic ultrasound contrast imaging by combining several hammerstein models.

Sub- and ultraharmonic (SUH) ultrasound contrast imaging is an alternative modality to the second harmonic imaging, since, in specific conditions it could produce high quality echographic images. This modality enables the contrast enhancement of echographic images by using SUH present in the contrast agent response but absent from the nonperfused tissue. For a better access to the components generated by the ultrasound contrast agents, nonlinear techniques based on Hammerstein model are preferred. As the major limitation of Hammerstein model is its capacity of modeling harmonic components only, in this work we propose two methods allowing to model SUH. These new methods use several Hammerstein models to identify contrast agent signals having SUH components and to separate these components from harmonic components. The application of the proposed methods for modeling simulated contrast agent signals shows their efficiency in modeling these signals and in separating SUH components. The achieved gain with respect to the standard Hammerstein model was 26.8 dB and 22.8 dB for the two proposed methods, respectively.

A general framework for modeling sub- and ultraharmonics of ultrasound contrast agent signals with MISO volterra series.

Sub- and ultraharmonics generation by ultrasound contrast agents makes possible sub- and ultraharmonics imaging to enhance the contrast of ultrasound images and overcome the limitations of harmonic imaging. In order to separate different frequency components of ultrasound contrast agents signals, nonlinear models like single-input single-output (SISO) Volterra model are used. One important limitation of this model is its incapacity to model sub- and ultraharmonic components. Many attempts are made to model sub- and ultraharmonics using Volterra model. It led to the design of mutiple-input singe-output (MISO) Volterra model instead of SISO Volterra model. The key idea of MISO modeling was to decompose the input signal of the nonlinear system into periodic subsignals at the subharmonic frequency. In this paper, sub- and ultraharmonics modeling with MISO Volterra model is presented in a general framework that details and explains the required conditions to optimally model sub- and ultraharmonics. A new decomposition of the input signal in periodic orthogonal basis functions is presented. Results of application of different MISO Volterra methods to model simulated ultrasound contrast agents signals show its efficiency in sub- and ultraharmonics imaging.