Prior ablation and progression of disease correlate with higher tumor-to-normal liver 99mTc-MAA uptake ratio in hepatocellular carcinoma.

PURPOSE: Factors affecting tumor-to-normal tissue ratio (T:N) have implications for patient selection, dosimetry, and outcomes when considering radioembolization for HCC. This study sought to evaluate patient, disease specific, and technical parameters that predict T:N as measured on planning pre-90Y radioembolization 99mTc-MAA scintigraphy for hepatocellular carcinoma (HCC). METHODS: 99mTc-MAA hepatic angiography procedures with SPECT/CT over a 4-year period were reviewed. Data recorded included patient demographics, details of underlying liver disease, tumor size, history of prior treatments for HCC and technical parameters from angiography. Anatomic-based segmentation was performed in 93 cases for measurement of tumor and perfused liver volumes and SPECT counts. T:N were calculated and correlated with collected variables. RESULTS: Mean calculated T:N was 2.52. History of prior ablation was significantly correlated with higher T:N (mean 3.39 vs 2.24, p = 0.003). Cases in which mapping was being performed for treatment of disease progression was significantly correlated with higher T:N (mean 3.35 vs 2.14, p = 0.001). Larger tumor size trended toward lower T:N (p = 0.052). CONCLUSION: Patients with history of ablation and those undergoing treatment for disease progression have higher T:N and, therefore, could be considered for radioembolization preferentially over alternative treatments.

Gallbladder Cryoablation: A Novel Option for High-Risk Patients with Gallbladder Disease.

Management of high-risk surgical patients with cholecystitis poses a significant clinical problem. These patients are often left with the options of permanent cholecystostomy tube drainage or high-risk surgery. Numerous attempts have been made over the past 4 decades to fulfill the need for a minimally invasive, definitive treatment option for such gallbladder disease. These attempts have largely focused on endoluminal ablation with a variety of sclerosants and have been unable to reliably achieve permanent gallbladder devitalization. The advent of modern percutaneous devices and techniques have provided further opportunity to develop minimally invasive treatment options for high-risk patients. Cryoablation, a thermal ablation modality that induces cell death through tissue freezing, has recently emerged as a promising potential option to treat gallbladder disease. Early studies have demonstrated good technical and clinical success, and a prospective trial is ongoing. This manuscript explains the clinical need for gallbladder cryoablation, briefly revisits historical minimally invasive treatments, describes cryoablation technology and why it is well suited for the gallbladder, and reviews the preclinical and clinical studies evaluating the safety and efficacy of gallbladder cryoablation.

Eliciting naturalistic cortical responses with a sensory prosthesis via optimized microstimulation.

OBJECTIVE: Lost sensations, such as touch, could one day be restored by electrical stimulation along the sensory neural pathways. Such stimulation, when informed by electronic sensors, could provide naturalistic cutaneous and proprioceptive feedback to the user. Perceptually, microstimulation of somatosensory brain regions produces localized, modality-specific sensations, and several spatiotemporal parameters have been studied for their discernibility. However, systematic methods for encoding a wide array of naturally occurring stimuli into biomimetic percepts via multi-channel microstimulation are lacking. More specifically, generating spatiotemporal patterns for explicitly evoking naturalistic neural activation has not yet been explored. APPROACH: We address this problem by first modeling the dynamical input-output relationship between multichannel microstimulation and downstream neural responses, and then optimizing the input pattern to reproduce naturally occurring touch responses as closely as possible. MAIN RESULTS: Here we show that such optimization produces responses in the S1 cortex of the anesthetized rat that are highly similar to natural, tactile-stimulus-evoked counterparts. Furthermore, information on both pressure and location of the touch stimulus was found to be highly preserved. SIGNIFICANCE: Our results suggest that the currently presented stimulus optimization approach holds great promise for restoring naturalistic levels of sensation.

Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task.

Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards. The animal was informed of the impending result before reward delivery by means of a visual cue. Neural data was recorded from primary somatosensory cortex (S1) during these experiments and firing rate histograms were created following the appearance of the visual cue and used as input to a variety of classifiers. Reward valence was decoded with high levels of accuracy from S1 both in the post-cue and post-reward periods. Additionally, the proportion of units showing significant changes in their firing rates was influenced in a predictable way based on reward valence. The existence of a signal within S1 cortex that encodes reward valence could have utility for implementing reinforcement learning algorithms for brain machine interfaces. The ability to decode this reward signal in real time with limited data is paramount to the usability of such a signal in practical applications.

Classifier Performance in Primary Somatosensory Cortex Towards Implementation of a Reinforcement Learning Based Brain Machine Interface.

Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist in cortical structures simultaneously used for BMI control. This work evaluates the ability of several common classifiers to detect impending reward delivery within primary somatosensory (S1) cortex during a grip force match to sample task performed by a nonhuman primate. The accuracy of these classifiers was further evaluated over a range of conditions to identify parameters that provide maximum classification accuracy. S1 cortex was found to provide highly accurate classification of the reinforcement signal across many classifiers and a wide variety of data input parameters. The classification accuracy in S1 cortex between rewarding and non-rewarding trials was apparent when the animal was expecting an impending delivery or an impending withholding of reward following trial completion. The high accuracy of classification in S1 cortex can be used to adapt an RL based BMI towards a user's intent. Real-time implementation of these classifiers in an RL based BMI could be used to adapt control of a prosthesis dynamically to match the intent of its user.

Pilot Study for Grip Force Prediction Using Neural Signals from Different Brain Regions.

The design of brain machine interfaces (BMI) has been improving over the past decade. Such improvements have led to advanced capability in terms of restoring the functionality of a paralyzed/amputated limb and producing fine controlled movements of a robotic arm and hand. However, there is still more to be invested towards producing advanced BMI features such as producing appropriate forces when gripping and carrying an object using an artificial limb. This feature requires direct supervision and control from the brain to produce accurate results. Toward this goal, this work investigates the processing of neural signals from four brain regions in a nonhuman primate to predict maximum grip force. The signals received from each of the primary motor (M1) cortex, primary somatosensory (S1) cortex, dorsal premotor (PmD) cortex, and ventral premotor (PmV) cortex are used to build regression models to predict the applied maximum grip force. Comparisons of model prediction results are presented. The relative prediction accuracy from all brain regions would assist in further investigation to build robust approaches for controlling the force values. The brain regions and their interactions could eventually be summed in a weighted manner to complete the targeted approach.