Using a deep generation network reveals neuroanatomical specificity in hemispheres.

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

Neurovascular coupling dysfunction of visual network organization in Parkinson's disease.

Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.

Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems.

The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.

Functional connectivity in people at clinical and familial high risk for schizophrenia.

Patients diagnosed with schizophrenia (SZ) exhibit compromised functional connectivity within extensive brain networks. However, the precise development of this impairment during disease progression in the clinical high-risk (CHR) population and their relatives remains unclear. Our study leveraged data from 128 resting electroencephalography (EEG) channels acquired from 30 SZ patients, 21 CHR individuals, 17 unaffected healthy relatives (RSs; those at heightened SZ risk due to family history), and 31 healthy controls (HCs). These data were harnessed to establish functional connectivity patterns. By calculating the geometric distance between EEG sequences, we unveiled local and global nonlinear relationships within the entire brain. The process of dimensionality reduction led to low-dimensional representations, providing insights into high-dimensional EEG data. Our findings indicated that CHR participants exhibited aberrant functional connectivity across hemispheres, whereas RS individuals showcased anomalies primarily concentrated within hemispheres. In the realm of low-dimensional analysis, RS participants' third-dimensional occipital lobe values lay between those of the CHR individuals and HCs, significantly correlating with scale scores. This low-dimensional approach facilitated the visualization of brain states, potentially offering enhanced comprehension of brain structure, function, and early-stage functional impairment, such as occipital visual deficits, in RS individuals before cognitive decline onset.

Effects of Microstate Dynamic Brain Network Disruption in Different Stages of Schizophrenia.

Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease.

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Altered Neurovascular Coupling for Multidisciplinary Intensive Rehabilitation in Parkinson's Disease.

Effective rehabilitation in Parkinson's disease (PD) is related to brain reorganization with restoration of cortico-subcortical networks and compensation of frontoparietal networks; however, further neural rehabilitation evidence from a multidimensional perspective is needed. To investigate how multidisciplinary intensive rehabilitation treatment affects neurovascular coupling, 31 PD patients (20 female) before and after treatment and 30 healthy controls (17 female) underwent blood oxygenation level-dependent functional magnetic resonance imaging and arterial spin labeling scans. Cerebral blood flow (CBF) was used to measure perfusion, and fractional amplitude of low-frequency fluctuation (fALFF) was used to measure neural activity. The global CBF-fALFF correlation and regional CBF/fALFF ratio were calculated as neurovascular coupling. Dynamic causal modeling (DCM) was used to evaluate treatment-related alterations in the strength and directionality of information flow. Treatment reduced CBF-fALFF correlations. The altered CBF/fALFF exhibited increases in the left angular gyrus and the right inferior parietal gyrus and decreases in the bilateral thalamus and the right superior frontal gyrus. The CBF/fALFF alteration in right superior frontal gyrus showed correlations with motor improvement. Further, DCM indicated increases in connectivity from the superior frontal gyrus and decreases from the thalamus to the inferior parietal gyrus. The benefits of rehabilitation were reflected in the dual mechanism, with restoration of executive control occurring in the initial phase of motor learning and compensation of information integration occurring in the latter phase. These findings may yield multimodal insights into the role of rehabilitation in disease modification and identify the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD.SIGNIFICANCE STATEMENT Although rehabilitation has been proposed as a promising supplemental treatment for PD as it results in brain reorganization, restoring cortico-subcortical networks and eliciting compensatory activation of frontoparietal networks, further multimodal evidence of the neural mechanisms underlying rehabilitation is needed. We measured the ratio of perfusion and neural activity derived from arterial spin labeling and blood oxygenation level-dependent fMRI data and found that benefits of rehabilitation seem to be related to the dual mechanism, restoring executive control in the initial phase of motor learning and compensating for information integration in the latter phase. We also identified the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD patients.

Characterizing cortical responses to short-term multidisciplinary intensive rehabilitation treatment in patients with Parkinson's disease: A transcranial magnetic stimulation and electroencephalography study.

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is a powerful non-invasive tool for qualifying the neurophysiological effects of interventions by recording TMS-induced cortical activation with high temporal resolution and generates reproducible and reliable waves of activity without participant cooperation. Cortical dysfunction contributes to the pathogenesis of the clinical symptoms of Parkinson's disease (PD). Here, we examined changes in cortical activity in patients with PD following multidisciplinary intensive rehabilitation treatment (MIRT). Forty-eight patients with PD received 2 weeks of MIRT. The cortical response was examined following single-pulse TMS over the primary motor cortex by 64-channel EEG, and clinical symptoms were assessed before and after MIRT. TMS-evoked potentials were quantified by the global mean field power, as well as oscillatory power in theta, alpha, beta, and gamma bands, and their clinical correlations were calculated. After MIRT, motor and non-motor symptoms improved in 22 responders, and only non-motor function was enhanced in 26 non-responders. Primary motor cortex stimulation reduced global mean field power amplitudes in responders but not significantly in non-responders. Oscillations exhibited attenuated power in the theta, beta, and gamma bands in responders but only reduced gamma power in non-responders. Associations were observed between beta oscillations and motor function and between gamma oscillations and non-motor symptoms. Our results suggest that motor function enhancement by MIRT may be due to beta oscillatory power modulation and that alterations in cortical plasticity in the primary motor cortex contribute to PD recovery.

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs. METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated. RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%). SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

Resting-State Functional MRI of Healthy Adults: Temporal Dynamic Brain Coactivation Patterns.

Background The aging brain is typically associated with aberrant interactions of large-scale intrinsic networks. However, the dynamic variation of these networks' coactivation or deactivation across the adult lifespan remains unclear. Purpose To promote the interpretation of dynamic brain network variations underlying the complex aging process by quantifying activation levels and obtaining a clear definition of coactivation patterns (CAPs) with resting-state functional MRI (rsfMRI). Materials and Methods In a retrospective study (October 2010 to September 2013), rsfMRI data from healthy participants in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository were used to generate CAPs by applying single-volume temporal clustering analysis. Spatial clustering analysis was then performed to capture dynamic coactivation and deactivation within or between primary sensory networks and high-order cognitive networks (including the default mode network [DMN], attentional network [AN], and frontoparietal network [FPN]). Linear relationships between dynamic metrics and age were revealed with Spearman partial correlations. Results A total of 614 participants (mean age, 54 years ± 18 [SD]; 311 women) ranging in age from 18 to 88 years were evaluated. There was a negative correlation of the CAPs (Spearman correlations: r = -0.98, P < .001) with loss of coactivation (partial correlations: r = -0.17, P < .001) and deactivation (partial correlations: r = 0.216, P < .001) with aging. The CAPs, characterized by negative correlation patterns between the DMN and AN, occurred (partial correlations: r = 0.14, P = .003) and dwelled (partial correlations: r = 0.10, P = .04) more with aging. Moreover, the AN and DMN CAP transitioned more to the AN and FPN CAP with aging (partial correlations: r = 0.17, P < .001). Conclusion The dynamics of the healthy aging brain are characterized mainly by more flexibility of the high-order cognitive networks while maintaining primary sensory functions (networks). Online supplemental material is available for this article. © RSNA, 2022 See also the editorial by Holodny in this issue.

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease.

Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

Development of a Piezoelectric Actuated Tactile Stimulation Device for Population Receptive Field Mapping in Human Somatosensory Cortex With fMRI.

BACKGROUND: Multichannel tactile stimulation devices is need to investigate human finger population receptive field (pRF) characteristics in the primary somatosensory cortex during functional magnetic resonance imaging (fMRI). PURPOSE: To accurately characterize right-hand somatosensory representation based on the Bayesian pRF model. STUDY TYPE: Prospective. POPULATION: A water phantom and six healthy participants (four males, mean 23.8 years old). FIELD STRENGTH/SEQUENCE: T1-weighted magnetization-prepared rapid gradient-echo, T2*-weighted echo planar imaging at 3 T. ASSESSMENT: The piezoelectric actuated tactile stimulation device consisted of execution unit and control unit. The output performance of the device was measured by a laser displacement sensor. The effect of the device on images' signal-to-noise ratio (SNR) was measured by phantom experiments. The activation representation arrangement order, relative volumes, and receptive field size of the right hand were assessed during the along-digits and cross-digits paradigms. STATISTICAL TESTS: The normality of the data was tested by the Shapiro-Wilk method. A paired-sample t test was performed to test pRF characteristics for all digit pairings. The significance level was set to P = 0.05 (false discovery rate [FDR] correct). RESULTS: Percussive stimulation provided by the piezoelectric actuated tactile stimulator had a stable displacement (2.64 mm) over a wide range of vibration frequencies (0-30 Hz). The output delay of the device was 1 millisecond. The device did not affect the image's SNR (without the device: SNR = 138.24 ± 7.87, temporal SNR [TSNR] = 440.03 ± 52.08. With the device: SNR = 138.06 ± 8.44, TSNR = 438.52 ± 56.38. PSNR  = 0.88, PTSNR  = 0.46). Representations of right-hand fingers showed the same arrangement order in both experiments (D1-D5 arranged along the central sulcus). However, the relative volumes of D3 showed significant differences in S1 (P = 0.003). Among four subareas, the relative volumes of D3 were significantly different in area 1 (P = 0.047). DATA CONCLUSION: This developed stimulator, through experimental verification, could play a role in pRF mapping exploration. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis.

Patients with bipolar disorder have deficits in self-referenced information. The brain functional connectivity during social cognitive processing in bipolar disorder is unclear. Electroencephalogram (EEG) was recorded in 23 patients with bipolar disorder and 19 healthy comparison subjects. We analyzed the time-frequency distribution of EEG power for each electrode associated with self, other, and font reflection conditions and used the phase lag index to characterize the functional connectivity between electrode pairs for 4 frequency bands. Then, the network properties were assessed by graph theoretic analysis. The results showed that bipolar disorder induced a weaker response power and phase lag index values over the whole brain in both self and other reflection conditions. Moreover, the characteristic path length was increased in patients during self-reflection processing, whereas the global efficiency and the node degree were decreased. In addition, when discriminating patients from normal controls, we found that the classification accuracy was high. These results suggest that patients have impeded integration of attention, memory, and other resources of the whole brain, resulting in a deficit of efficiency and ability in self-referential processing.

Episodic memory in aspects of brain information transfer by resting-state network topology.

Cognitive functionality emerges due to neural interactions. The interregional signal interactions underlying episodic memory are a complex process. Thus, we need to quantify this process more accurately to understand how brain regions receive information from other regions. Studies suggest that resting-state functional connectivity (FC) conveys cognitive information; additionally, activity flow estimates the contribution of the source region to the activation pattern of the target region, thus decoding the cognitive information transfer. Therefore, we performed a combined analysis of task-evoked activation and resting-state FC voxel-wise by activity flow mapping to estimate the information transfer pattern of episodic memory. We found that the cinguloopercular (CON), frontoparietal (FPN) and default mode networks (DMNs) were the most recruited structures in information transfer. The patterns and functions of information transfer differed between encoding and retrieval. Furthermore, we found that information transfer was a better predictor of memory ability than previous methods. Additional analysis indicated that structural connectivity (SC) had a transportive role in information transfer. Finally, we present the information transfer mechanism of episodic memory from multiple neural perspectives. These findings suggest that information transfer is a better biological indicator that accurately describes signal communication in the brain and strongly influences the function of episodic memory.

Microbubble-Enhanced Heating: Exploring the Effect of Microbubble Concentration and Pressure Amplitude on High-Intensity Focused Ultrasound Treatments.

High-intensity focused ultrasound (HIFU) is a non-invasive tool that can be used for targeted thermal ablation treatments. Currently, HIFU is clinically approved for treatment of uterine fibroids, various cancers, and certain brain applications. However, for brain applications such as essential tremors, HIFU can only be used to treat limited areas confined to the center of the brain because of geometrical limitations (shape of the transducer and skull). A major obstacle to advancing this technology is the inability to treat non-central brain locations without causing damage to the skin and/or skull. Previous research has indicated that cavitation-induced bubbles or microbubble contrast agents can be used to enhance HIFU treatments by increasing ablation regions and shortening acoustic exposures at lower acoustic pressures. However, little research has been done to explore the interplay between microbubble concentration and pressure amplitude on HIFU treatments. We developed an in vitro experimental setup to study lesion formation at three different acoustic pressures and three microbubble concentrations. Real-time ultrasound imaging was integrated to monitor initial microbubble concentration and subsequent behavior during the HIFU treatments. Depending on the pressure used for the HIFU treatment, there was an optimal concentration of microbubbles that led to enhanced heating in the focal area. If the concentration of microbubbles was too high, the treatment was detrimentally affected because of non-linear attenuation by the pre-focal microbubbles. Additionally, the real-time ultrasound imaging provided a reliable method to monitor microbubble activity during the HIFU treatments, which is important for translation to in vivo HIFU applications with microbubbles.

Image-Guided Treatment of Primary Liver Cancer in Mice Leads to Vascular Disruption and Increased Drug Penetration.

Despite advances in interventional procedures and chemotherapeutic drug development, hepatocellular carcinoma (HCC) is still the fourth leading cause of cancer-related deaths worldwide with a <30% 5-year survival rate. This poor prognosis can be attributed to the fact that HCC most commonly occurs in patients with pre-existing liver conditions, rendering many treatment options too aggressive. Patient survival rates could be improved by a more targeted approach. Ultrasound-induced cavitation can provide a means for overcoming traditional barriers defining drug uptake. The goal of this work was to evaluate preclinical efficacy of image-guided, cavitation-enabled drug delivery with a clinical ultrasound scanner. To this end, ultrasound conditions (unique from those used in imaging) were designed and implemented on a Philips EPIQ and S5-1 phased array probe to produced focused ultrasound for cavitation treatment. Sonovue® microbubbles which are clinically approved as an ultrasound contrast agent were used for both imaging and cavitation treatment. A genetically engineered mouse model was bred and used as a physiologically relevant preclinical analog to human HCC. It was observed that image-guided and targeted microbubble cavitation resulted in selective disruption of the tumor blood flow and enhanced doxorubicin uptake and penetration. Histology results indicate that no gross morphological damage occurred as a result of this process. The combination of these effects may be exploited to treat HCC and other challenging malignancies and could be implemented with currently available ultrasound scanners and reagents.

Numerical investigation of the inertial cavitation threshold under multi-frequency ultrasound.

Through the introduction of multi-frequency sonication in High Intensity Focused Ultrasound (HIFU), enhancement of efficiency has been noted in several applications including thrombolysis, tissue ablation, sonochemistry, and sonoluminescence. One key experimental observation is that multi-frequency ultrasound can help lower the inertial cavitation threshold, thereby improving the power efficiency. However, this has not been well corroborated by the theory. In this paper, a numerical investigation on the inertial cavitation threshold of microbubbles (MBs) under multi-frequency ultrasound irradiation is conducted. The relationships between the cavitation threshold and MB size at various frequencies and in different media are investigated. The results of single-, dual and triple frequency sonication show reduced inertial cavitation thresholds by introducing additional frequencies which is consistent with previous experimental work. In addition, no significant difference is observed between dual frequency sonication with various frequency differences. This study, not only reaffirms the benefit of using multi-frequency ultrasound for various applications, but also provides a possible route for optimizing ultrasound excitations for initiating inertial cavitation.

Acoustic Holographic Rendering with Two-dimensional Metamaterial-based Passive Phased Array.

Acoustic holographic rendering in complete analogy with optical holography are useful for various applications, ranging from multi-focal lensing, multiplexed sensing and synthesizing three-dimensional complex sound fields. Conventional approaches rely on a large number of active transducers and phase shifting circuits. In this paper we show that by using passive metamaterials as subwavelength pixels, holographic rendering can be achieved without cumbersome circuitry and with only a single transducer, thus significantly reducing system complexity. Such metamaterial-based holograms can serve as versatile platforms for various advanced acoustic wave manipulation and signal modulation, leading to new possibilities in acoustic sensing, energy deposition and medical diagnostic imaging.

Thrombolysis using multi-frequency high intensity focused ultrasound at MHz range: an in vitro study.

High intensity focused ultrasound (HIFU) based thrombolysis has emerged as a promising drug-free treatment approach for ischemic stroke. The large amount of acoustic power required by this approach, however, poses a critical challenge to the future clinical translation. In this study, multi-frequency acoustic waves at MHz range (near 1.5 MHz) were introduced as HIFU excitations to reduce the required power for treatment as well as the treatment time. In vitro bovine blood clots weighing around 150 mg were treated by single-frequency and multi-frequency HIFU. The pulse length was 2 ms for all experiments except the ones where the duty cycle was changed. It was found that dual-frequency thrombolysis efficiency was statistically better than single-frequency under the same acoustic power and excitation condition. When varying the acoustic power but fixing the duty cycle at 5%, it was found that dual-frequency ultrasound can save almost 30% power in order to achieve the same thrombolysis efficiency. In the experiment where the duty cycle was increased from 0.5% to 10%, it was shown that dual-frequency ultrasound can achieve the same thrombolysis efficiency with only half of the duty cycle of single-frequency. Dual-frequency ultrasound could also accelerate the thrombolysis by a factor of 2-4 as demonstrated in this study. No significant differences were found between dual-frequencies with different frequency differences (0.025, 0.05, and 0.1 MHz) and between dual-frequency and triple-frequency. The measured cavitation doses of dual-frequency and triple-frequency excitations were at about the same level but both were significantly higher than that of single-frequency.