Comparative transduction efficiency of AAV vector serotypes 1-6 in the substantia nigra and striatum of the primate brain.

Vectors derived from adeno-associated virus (AAV) are promising candidates for neural cell transduction in vivo because they are nonpathogenic and achieve long-term transduction in the central nervous system. AAV serotype 2 (AAV2) is the most widely used AAV vector in clinical trials based largely on its ability to transduce neural cells in the rodent and primate brain. Prior work in rodents suggests that other serotypes might be more efficient; however, a systematic evaluation of vector transduction efficiency has not yet been performed in the primate brain. In this study, AAV viral vectors of serotypes 1-6 with an enhanced green-fluorescent protein (GFP) reporter gene were generated at comparable titers, and injected in equal amounts into the brains of Chlorocebus sabaeus. Vector injections were placed in the substantia nigra (SN) and the caudate nucleus (CD). One month after injection, immunohistochemistry for GFP was performed and the total number of GFP+ cells was calculated using unbiased stereology. AAV5 was the most efficient vector, not only transducing significantly more cells than any other serotype, but also transducing both NeuN+ and glial-fibrillary-acidic protein positive (GFAP+) cells. These results suggest that AAV5 is a more effective vector than AAV2 at delivering potentially therapeutic transgenes to the nigrostriatal system of the primate brain.

Decreased hippocampal volume on MRI is associated with increased extracellular glutamate in epilepsy patients.

PURPOSE: Temporal lobe epilepsy (TLE) is associated with smaller hippocampal volume and with elevated extracellular (EC) glutamate levels. We investigated the relationship between the hippocampal volume and glutamate in refractory TLE patients. METHODS: We used quantitative MRI volumetrics to measure the hippocampal volume and zero-flow microdialysis to measure the interictal glutamate, glutamine, and GABA levels in the epileptogenic hippocampus of 17 patients with medication-resistant epilepsy undergoing intracranial EEG evaluation. The relationships between hippocampal volume, neurochemical levels, and relevant clinical factors were examined. RESULTS: Increased EC glutamate in the epileptogenic hippocampus was significantly related to smaller ipsilateral (R(2)= 0.75, p < 0.0001), but not contralateral hippocampal volume when controlled for glutamine and GABA levels, and for clinical factors known to influence hippocampal volume. Glutamate in the atrophic hippocampus was significantly higher (p = 0.008, n = 9), with the threshold for hippocampal atrophy estimated as 5 microM. GABA and glutamine levels in the atrophic and nonatrophic hippocampus were comparable. Decreased hippocampal volume was related to higher seizure frequency (p = 0.008), but not to disease duration or febrile seizure history. None of these clinical factors were related to the neurochemical levels. CONCLUSIONS: We provide evidence for a significant association between increased EC glutamate and decreased ipsilateral epileptogenic hippocampal volume in TLE. Future work will be needed to determine whether the increase in glutamate has a causal relationship with hippocampal atrophy, or whether another, yet unknown factor results in both. This work has implications for the understanding and treatment of epilepsy as well as other neurodegenerative disorders associated with hippocampal atrophy.

Novel interaction techniques for neurosurgical planning and stereotactic navigation.

Neurosurgical planning and image guided neurosurgery require the visualization of multimodal data obtained from various functional and structural image modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), functional MRI, Single photon emission computed tomography (SPECT) and so on. In the case of epilepsy neurosurgery for example, these images are used to identify brain regions to guide intracranial electrode implantation and resection. Generally, such data is visualized using 2D slices and in some cases using a 3D volume rendering along with the functional imaging results. Visualizing the activation region effectively by still preserving sufficient surrounding brain regions for context is exceedingly important to neurologists and surgeons. We present novel interaction techniques for visualization of multimodal data to facilitate improved exploration and planning for neurosurgery. We extended the line widget from VTK to allow surgeons to control the shape of the region of the brain that they can visually crop away during exploration and surgery. We allow simple spherical, cubical, ellipsoidal and cylindrical (probe aligned cuts) for exploration purposes. In addition we integrate the cropping tool with the image-guided navigation system used for epilepsy neurosurgery. We are currently investigating the use of these new tools in surgical planning and based on further feedback from our neurosurgeons we will integrate them into the setup used for image-guided neurosurgery.

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.

REGISTRATION OF BRAIN RESECTION MRI WITH INTENSITY AND LOCATION PRIORS.

Images with missing correspondences are difficult to align using standard registration methods due to the assumption that the same features appear in both images. To address this problem in brain resection images, we have recently proposed an algorithm in which the registration process is aided by an indicator map that is simultaneously estimated to distinguish between missing and valid tissue. We now extend our method to include both intensity and location information for the missing data. We introduce a prior on the indicator map using a Markov random field (MRF) framework to incorporate map smoothness and spatial knowledge of the missing correspondences. The parameters for the indicator map prior are automatically estimated along with the transformation and indicator map. The new method improves both segmentation and registration accuracy as demonstrated using synthetic and real patient 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.