Functional geometry alignment and localization of brain areas

Matching functional brain regions across individuals is a challenging task, largely due to the variability in their location and extent. It is particularly difficult, but highly relevant, for patients with pathologies such as brain tumors, which can cause substantial reorganization of functional systems. In such cases spatial registration based on anatomical data is only of limited value if the goal is to establish correspondences of functional areas among different individuals, or to localize potentially displaced active regions. Rather than rely on spatial alignment, we propose to perform registration in an alternative space whose geometry is governed by the functional interaction patterns in the brain. We first embed each brain into a functional map that reflects connectivity patterns during a fMRI experiment. The resulting functional maps are then registered, and the obtained correspondences are propagated back to the two brains. In application to a language fMRI experiment, our preliminary results suggest that the proposed method yields improved functional correspondences across subjects. This advantage is pronounced for subjects with tumors that affect the language areas and thus cause spatial reorganization of the functional regions.National Institutes of Health (U.S.) (P01 CA067165)National Institutes of Health (U.S.) (U41RR019703)National Institutes of Health (U.S.) (NIBIB NAMIC U54- EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218)National Science Foundation (U.S.) (CAREER Grant 0642971)National Science Foundation (U.S.) (Grant IIS/CRCNS 0904625

Decoupling function and anatomy in atlases of functional connectivity patterns: Language mapping in tumor patients

In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.National Science Foundation (U.S.). Division of Information & Intelligent Systems (Collaborative Research in Computational Neuroscience Grant 0904625)National Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) P41-RR13218)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/Neuroimaging Analysis Center (U.S.) P41-EB-015902)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) U54-EB005149)National Institutes of Health (U.S.) (U41RR019703)National Institutes of Health (U.S.) (Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) R01HD067312)National Institutes of Health (U.S.) (P01CA067165)Brain Science FoundationKlarman Family FoundationEuropean Commission (FP7/2007–2013) n°257528 (KHRESMOI))European Commission (330003 (FABRIC))Austrian Science Fund (P 22578-B19 (PULMARCH)

Development of a Clinical Functional Magnetic Resonance Imaging Service

One of the limitations of anatomical based imaging approaches is its relative inability to identify whether specific brain functions may be compromised by the location of brain lesions or contemplated brain surgeries. For this reason, methods for identifying the regions of eloquent brain that should not be disturbed are absolutely critical to the surgeon. By accurately identifying these regions preoperatively, virtually every pre-surgical decision from the surgical approach, operative goals (biopsy, sub-total vs. gross-total resection), and the potential need for awake craniotomy with intraoperative cortical-mapping is affected. Of the many techniques available to the surgeon, functional magnetic resonance imaging (fMRI) has become the primary modality of choice due to the ability of MRI to serve as a “one-stop shop” for assessing both anatomy and functionality of the brain. Given their prevalence, brain tumors serve as the model pathology for the included discussion; however, a similar case can be made for the use of fMRI in other neurological conditions, most notably epilepsy. The value of fMRI was validated in 2007 when the Centers for Medicare and Medicaid Services (CMS) established three new current procedural terminology (CPT) codes for clinical fMRI based upon its use for pre-therapeutic planning. In this article we will discuss the specific requirements for establishing an fMRI program, including specific software and hardware requirements. In addition, the nature of the fMRI CPT codes will be discussed

Visual Pathway Study Using In Vivo Diffusion Tensor Imaging Tractography to Complement Classic Anatomy

BACKGROUND: Knowledge of the individual course of the optic radiations (ORs) is important to avoid postoperative visual deficits. Cadaveric studies of the visual pathways are limited because it has not been possible to separate the OR from neighboring tracts accurately and results may not apply to individual patients. Diffusion tensor imaging studies may be able to demonstrate the relationships between the OR and neighboring fibers in vivo in individual subjects. OBJECTIVE: To use diffusion tensor imaging tractography to study the OR and the Meyer loop (ML) anatomy in vivo. METHODS: Ten healthy subjects underwent magnetic resonance imaging with diffusion imaging at 3 T. With the use of a fiducial-based diffusion tensor imaging tractography tool (Slicer 3.3), seeds were placed near the lateral geniculate nucleus to reconstruct individual visual pathways and neighboring tracts. Projections of the ORs onto 3-dimensional brain models were shown individually to quantify relationships to key landmarks. RESULTS: Two patterns of visual pathways were found. The OR ran more commonly deep in the whole superior and middle temporal gyri and superior temporal sulcus. The OR was closely surrounded in all cases by an inferior longitudinal fascicle and a parieto/occipito/temporo-pontine fascicle. The mean left and right distances between the tip of the OR and temporal pole were 39.8 ± 3.8 and 40.6 ± 5.7 mm, respectively. CONCLUSION: Diffusion tensor imaging tractography provides a practical complementary method to study the OR and the Meyer loop anatomy in vivo with reference to individual 3-dimensional brain anatomy

Intraoperative Real-Time Querying of White Matter Tracts During Frameless Stereotactic Neuronavigation

BACKGROUND: Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits. OBJECTIVE: For surgeons to have optimal intraoperative information concerning white matter (WM) anatomy, we developed a platform that allows the intraoperative real-time querying of tractography data sets during frameless stereotactic neuronavigation. METHODS: Structural magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging were performed on 5 patients before they underwent lesion resection using neuronavigation. During the procedure, the tracked surgical tool tip position was transferred from the navigation system to the 3-dimensional Slicer software package, which used this position to seed the WM tracts around the tool tip location, rendering a geometric visualization of these tracts on the preoperative images previously loaded onto the navigation system. The clinical feasibility of this approach was evaluated in 5 cases of lesion resection. In addition, system performance was evaluated by measuring the latency between surgical tool tracking and visualization of the seeded WM tracts. RESULTS: Lesion resection was performed successfully in all 5 patients. The seeded WM tracts close to the lesion and other critical structures, as defined by the functional and structural images, were interactively visualized during the intervention to determine their spatial relationships relative to the lesion and critical cortical areas. Latency between tracking and visualization of tracts was less than a second for a fiducial radius size of 4 to 5 mm. CONCLUSION: Interactive tractography can provide an intuitive way to inspect critical WM tracts in the vicinity of the surgical region, allowing the surgeon to have increased intraoperative WM information to execute the planned surgical resection

Defining language networks from resting-state fMRI for surgical planning-a feasibility study

Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision-making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood-oxygen-level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task-based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting-state fMRI (rs-fMRI), we propose a “task-free” paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right-handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs-fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi-automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping

The Fiber Laterality Histogram: A New Way to Measure White Matter Asymmetry

The quantification of brain asymmetries may provide biomarkers for presurgical localization of language function and can improve our understanding of neural structure-function relationships in health and disease. We propose a new method for studying the asymmetry of the white matter tracts in the entire brain, and we apply it to a preliminary study of normal subjects across the handedness spectrum. Methods for quantifying white matter asymmetry using diffusion MRI tractography have thus far been based on comparing numbers of fibers or volumes of a single fiber tract across hemispheres. We propose a generalization of such methods, where the “number of fibers” laterality measurement is extended to the entire brain using a soft fiber comparison metric. We summarize the distribution of fiber laterality indices over the whole brain in a histogram, and we measure properties of the distribution such as its skewness, median, and inter-quartile range. The whole-brain fiber laterality histogram can be measured in an exploratory fashion without hypothesizing asymmetries only in particular structures. We demonstrate an overall difference in white matter asymmetry in consistent- and inconsistent-handers: the skewness of the fiber laterality histogram is significantly different across handedness groups

Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography

Background: Diffusion imaging tractography is increasingly used to trace critical fiber tracts in brain tumor patients to reduce the risk of post-operative neurological deficit. However, the effects of peritumoral edema pose a challenge to conventional tractography using the standard diffusion tensor model. The aim of this study was to present a novel technique using a two-tensor unscented Kalman filter (UKF) algorithm to track the arcuate fasciculus (AF) in brain tumor patients with peritumoral edema. Methods: Ten right-handed patients with left-sided brain tumors in the vicinity of language-related cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-Tesla magnetic resonance imaging (MRI) including a diffusion-weighted dataset with 31 directions. Fiber tractography was performed using both single-tensor streamline and two-tensor UKF tractography. A two-regions-of-interest approach was applied to perform the delineation of the AF. Results from the two different tractography algorithms were compared visually and quantitatively. Results: Using single-tensor streamline tractography, the AF appeared disrupted in four patients and contained few fibers in the remaining six patients. Two-tensor UKF tractography delineated an AF that traversed edematous brain areas in all patients. The volume of the AF was significantly larger on two-tensor UKF than on single-tensor streamline tractography (p < 0.01). Conclusions: Two-tensor UKF tractography provides the ability to trace a larger volume AF than single-tensor streamline tractography in the setting of peritumoral edema in brain tumor patients

Automated white matter fiber tract identification in patients with brain tumors

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions

Special Surgical Considerations for Functional Brain Mapping

The development of functional mapping techniques gives neurosurgeons many options for preoperative planning. Integrating functional and anatomic data can inform patient selection and surgical planning and makes functional mapping more accessible than when only invasive studies were available. However, the applications of functional mapping to neurosurgical patients are still evolving. Functional imaging remains complex and requires an understanding of the underlying physiologic and imaging characteristics. Neurosurgeons must be accustomed to interpreting highly processed data. Successful implementation of functional image-guided procedures requires efficient interactions between neurosurgeon, neurologist, radiologist, neuropsychologist, and others, but promises to enhance the care of patients