Acoustic Droplet Vaporization of Perfluorohexane Emulsions Induced by Heterogeneous Nucleation at an Ultrasonic Frequency of 1.1 MHz.

Droplets made of liquid perfluorocarbon undergo a phase transition and transform into microbubbles when triggered by ultrasound of intensity beyond a critical threshold; this mechanism is called acoustic droplet vaporization (ADV). It has been shown that if the intensity of the signal coming from high ultrasonic harmonics are sufficiently high, superharmonic focusing is the mechanism leading to ADV for large droplets (>3 µm) and high frequencies (>1.5 MHz). In such a scenario, ADV is initiated due to a nucleus occurring at a specific location inside the droplet volume. But the question on what induces ADV in the case of nanometer-sized droplets and/or at low ultrasonic frequencies (<1.5 MHz) still remains. We investigated ADV of perfluorohexane (PFH) nano- and microdroplets at a frequency of 1.1 MHz and at conditions where there is no superharmonic focusing. Three types of droplets produced by microfluidics were studied: plain PFH droplets, PFH droplets containing many nanometer-sized water droplets, and droplets made of a PFH corona encapsulating a single micron-sized water droplet. The probability to observe a vaporization event was measured as a function of acoustic pressure. As our experiments were performed on droplet suspensions containing a population of monodisperse droplets, we developed a statistical model to extrapolate, from our experimental curves, the ADV pressure thresholds in the case where only one droplet would be insonified. We observed that the value of ADV pressure threshold decreases as the radius of a plain PFH droplet increases. This value was further reduced when a PFH droplet encapsulates a micron-sized water droplet, while the encapsulation of many nanometer-sized water droplets did not modify the threshold. These results cannot be explained by a model of homogeneous nucleation. However, we developed a heterogeneous nucleation model, where the nucleus appears at the surface in contact with PFH, that successfully predicts our experimental ADV results.

Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI.

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.

Contribution of exploratory methods to the investigation of extended large-scale brain networks in functional MRI: methodologies, results, and challenges.

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). We here review the rationale for such an investigation, the methods used, the results obtained, and also discuss some issues that have to be faced for an efficient exploration.