Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial.

There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[18F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei (ß = -2.61e-03 [-0.26, 0.25], p = 0.98), right insula (ß = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex (ß = 0.06 [-0.23, 0.34], p = 0.68) where ß is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN (ß = 0.20 [-0.07, 0.47], p = 0.15), right insula (ß = 0.24 [-0.03, 0.51], p = 0.08), or vPFC (ß = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.

In Vivo Brain Imaging, Biodistribution, and Radiation Dosimetry Estimation of [11C]Celecoxib, a COX-2 PET Ligand, in Nonhuman Primates.

COX-2 selective inhibitors (COXIBs) are non-steroidal anti-inflammatory drugs (NSAIDs), with fewer side effects compared with non-selective NSAIDs, and are used for the treatment of arthritis, headaches, and other inflammatory diseases of the brain and peripheral tissues. Radiolabeled COXIBs may permit positron emission tomography (PET) imaging of COX-2 localization and activity in diseases, enable monitoring of inflammatory processes, and determine target occupancy of COX-2 activity by NSAIDs, thus, accelerating the development of novel CIXIBs. We synthesized [11C]celecoxib, one of the COXIBs and a prescription drug, and here report its in vivo uptake in the brain, whole body biodistribution, and radiation dosimetry in baboons using PET. Brain imaging experiments were performed in one baboon and whole body PET scans were performed in triplicates in two male baboons using an ECAT ACCEL (Siemens Medical Solutions, Inc. Knoxville) under anesthetic conditions. PET studies in baboons show that [11C]celecoxib penetrates the blood brain barrier (BBB) and accumulates in the brain, followed by a washout of radioactivity. The liver has the highest residence time and the gallbladder is the critical organ for [11C]celecoxib. Organ Level Internal Dose Assessment (OLINDA) estimates indicate that the maximum permissible single study dosage of [11C]celecoxib in humans is 1110 MBq (30 mCi) for both males and females under the 21 CFR 361.1 dose limit for research subjects.

Volumetric intraoperative brain deformation compensation: model development and phantom validation.

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

Image-guided intraoperative cortical deformation recovery using game theory: application to neocortical epilepsy surgery.

During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions.

From medical image computing to computer-aided intervention: development of a research interface for image-guided navigation.

BACKGROUND: This paper describes the development and application of a research interface to integrate research image analysis software with a commercial image-guided surgery navigation system. This interface enables bi-directional transfer of data such as images, visualizations and tool positions in real time. METHODS: We describe both the design and the application programming interface of the research interface, as well as showing the function of an example client program. The resulting interface provides a practical and versatile link for bringing image analysis research techniques into the operating room (OR). RESULTS: We present examples from the successful use of this research interface in both phantom experiments and real neurosurgeries. In particular, we demonstrate that the integrated dual-computer system achieves tool-tracking performance that is comparable to the more typical single-computer scenario. CONCLUSIONS: Network interfaces for this type are viable solutions for the integration of commercial image-guided navigation systems and research software.

A comprehensive system for intraoperative 3D brain deformation recovery.

During neurosurgery, brain deformation renders preoperative images unreliable for localizing pathologic structures. In order to visualize the current brain anatomy, it is necessary to nonrigidly warp these preoperative images to reflect the intraoperative brain. This can be accomplished using a biomechanical model driven by sparse intraoperative information. In this paper, a linear elastic model of the brain is developed which can infer volumetric brain deformation given the cortical surface displacement. This model was tested on both a realistic brain phantom and in vivo, proving its ability to account for large brain deformations. Also, an efficient semiautomatic strategy for preoperative cortical feature detection is outlined, since accurate segmentation of cortical features can aid intraoperative cortical surface tracking.

Nonrigid 3D brain registration using intensity/feature information.

The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

Non-invasive assessment of radiation injury with electrical impedance spectroscopy.

A detailed understanding of non-targeted normal tissue response is necessary for the optimization of radiation treatment plans in cancer therapy. In this study, we evaluate the ability of electrical impedance spectroscopy (EIS) to non-invasively determine and quantify the injury response in soft tissue after high dose rate (HDR) irradiation, which is characterized by large localized dose distributions possessing steep spatial gradients. The HDR after-loading technique was employed to irradiate small volumes of muscle tissue with single doses (26-52 Gy targeted 5 mm away from the source). Impedance measurements were performed on 29 rats at 1, 2 and 3 month post-irradiation, employing 31 frequencies in the 1 kHz to 1 MHz range. Over the first 3 months, conductivity increased by 48% and 26% following target doses of 52 Gy and 26 Gy 5 mm from the HDR source, respectively. Injury, assessed independently through a grid-based scoring method showed a quadratic dependence on distance from source. A significant injury (50% of cells atrophied, necrotic or degenerating) in 1.2% of the volume, accompanied by more diffuse injury (25% of cells atrophied, necrotic or degenerating) in 9% of the tissue produced a conductivity increase of 0.02 S m(-1) (8% over a baseline of 0.24 S m(-1)). This was not statistically significant at p = 0.01. Among treatment groups, injury differences in 22% of the volume led to statistically significant differences in conductivity of 0.07 S m(-1) (23% difference in conductivity). Despite limitations, the success of EIS in detecting responses in a fraction of the tissue probed, during these early post-irradiation time-points, is encouraging. Electrical impedance spectroscopy may provide a useful metric of atrophy and the development of fibrosis secondary to radiation that could be further developed into a low-cost imaging method for radiotherapy monitoring and assessment.