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Background Presence of residual neurovascular activity within glioma lesions have been recently demonstrated via functional MRI (fMRI) along with active electrical synapses between glioma cells and healthy neurons that influence survival. In this study, we aimed to investigate whether gliomas demonstrate synchronized neurovascular activity with the rest of the brain, by measuring Blood Oxygen Level Dependent (BOLD) signal synchronization, that is, functional connectivity (FC), while also testing whether the strength of such connectivity might predict patients’ overall survival (OS). Methods Resting-state fMRI scans of patients who underwent pre-surgical brain mapping were analyzed (total sample, n = 54; newly diagnosed patients, n = 18; recurrent glioma group, n = 36). A seed-to-voxel analysis was conducted to estimate the FC signal profile of the tumor mass. A regression model was then built to investigate the potential correlation between tumor FC and individual OS. Finally, an unsupervised, cross-validated clustering analysis was performed including tumor FC and clinical OS predictors (e.g., Karnofsky Performance Status - KPS - score, tumor volume, and genetic profile) to verify the performance of tumor FC in predicting OS with respect to validated radiological, demographic, genetic and clinical prognostic factors. Results In both newly diagnosed and recurrent glioma patients a significant pattern of BOLD synchronization between the solid tumor and distant brain regions was found. Crucially, glioma-brain FC positively correlated with variance in individual survival in both newly diagnosed glioma group (r = 0.90–0.96; P < .001; R2 = 81–92%) and in the recurrent glioma group (r = 0.72; P < .001; R2 = 52%), outperforming standard clinical, radiological and genetic predictors. Conclusions Results suggest glioma’s synchronization with distant brain regions should be further explored as a possible diagnostic and prognostic biomarker.

Background and purpose Functional magnetic resonance imaging (fMRI) is becoming widely recognized as a key component of preoperative neurosurgical planning, although intraoperative electrocortical stimulation (ECS) is considered the gold standard surgical brain mapping method. However, acquiring and interpreting ECS results can sometimes be challenging. This retrospective study assesses whether intraoperative availability of fMRI impacted surgical decision-making when ECS was problematic or unobtainable. Methods Records were reviewed for 191 patients who underwent presurgical fMRI with fMRI loaded into the neuronavigation system. Four patients were excluded as a bur-hole biopsy was performed. Imaging was acquired at 3 Tesla and analyzed using the general linear model with significantly activated pixels determined via individually determined thresholds. fMRI maps were displayed intraoperatively via commercial neuronavigation systems. Results Seventy-one cases were planned ECS; however, 18 (25.35%) of these procedures were either not attempted or aborted/limited due to: seizure (10), patient difficulty cooperating with the ECS mapping (4), scarring/limited dural opening (3), or dural bleeding (1). In all aborted/limited ECS cases, the surgeon continued surgery using fMRI to guide surgical decision-making. There was no significant difference in the incidence of postoperative deficits between cases with completed ECS and those with limited/aborted ECS. Conclusions Preoperative fMRI allowed for continuation of surgery in over one-fourth of patients in which planned ECS was incomplete or impossible, without a significantly different incidence of postoperative deficits compared to the patients with completed ECS. This demonstrates additional value of fMRI beyond presurgical planning, as fMRI data served as a backup method to ECS.

The primary goal of presurgical language mapping is localising critical language areas with high sensitivity (ie, capturing areas in which resection could lead to language deficits) and specificity (ie, excluding non-language areas) and reliably determining language hemispheric dominance, on an individual basis. Language mapping is challenging due to the widely distributed functional organisation of language in the frontal, temporal and parietal lobes, and in neurosurgical patients the possibility of tumour-induced functional reorganisation. A major drawback of conventional task-based functional magnetic resonance imaging (tb-fMRI) recommended for presurgical language mapping1 is the contingency on patient performance of precisely timed tasks (eg, antonym generation—AntGen). Drawbacks of task-free resting-state fMRI (rs-fMRI) include confounding effects of ‘mind wandering’ and sensitivity to motion artefacts. In contrast, movie watching is a rich, stimulating and naturalistic activity, predicted to constrain cognitive processes and engage the distributed, multimodal neural networks supporting language function in real life.2 Our previous study demonstrated individual language mapping using movie-watching fMRI (mw-fMRI) in neurologically healthy subjects.3 Here, we examine mw-fMRI language mapping in presurgical patients with a brain tumour encroaching on putative language cortex, and varying levels of language disruption. We hypothesise that mw-fMRI versus AntGen tb-fMRI, and rs-fMRI, will provide comprehensive language mapping at reduced burden, as determined by metrics of in-scanner head motion, and mapping specificity, sensitivity and lateralisation. Mw-fMRI was compared with clinically indicated AntGen tb-fMRI in 34 patients with brain tumour undergoing presurgical language mapping, and with rs-fMRI in 22 of these patients. See online supplemental methods for exclusion criteria, and online supplemental table S1 for demographic and clinical information. Language maps were generated from tb-fMRI using a general linear model, and from mw-fMRI and rs-fMRI using independent …

PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

BACKGROUND AND PURPOSE: Previous literature has demonstrated disparity in the post-operative recovery of first and second language function of bilingual neurosurgical patients. However, it is unclear to whether pre-operative brain mapping of both languages is needed. In this study, we aimed to evaluate the clinical utility of language task functional MRI (fMRI) implemented in both languages in bilingual patients. METHODS: We retrospectively examined fMRI data of thirteen bilingual brain tumor patients (age: 23 to 59 years) who performed antonym generation task-based fMRIs in English and non-English language. The usefulness of bilingual language mapping was evaluated using a structured survey administered to five neurosurgeons. Additionally, quantitative comparison between the brain activation maps of both languages was performed. RESULTS: Survey responses revealed differences in raters’ surgical approach, including asleep vs. awake surgery and extent of resection, after viewing the language fMRI maps. Additional non-English fMRI led to changes in surgical decision-making and bettered localization of language areas. Quantitative analysis revealed an increase in laterality index (LI) in non-English fMRI compared to English fMRI. The Dice coefficient demonstrated fair overlap (0.458 +/− 0.160) between the activation maps. CONCLUSION: Bilingual fMRI mapping of bilingual patients allows to better appreciate functionally active language areas that may be neglected in single language mapping. Utility of bilingual mapping was supported by changes in both surgical approach and LI measurements, suggesting its benefit on pre-operative language mapping.

BACKGROUND AND PURPOSE Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients’ white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. METHODS Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. RESULTS Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. CONCLUSIONS Quantitative results of mean tract distortions on the order of 1–2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.

Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors., Highlights • Reconstruction of edematous white matter from diffusion MRI is investigated. • The performance of two–tensor unscented Kalman filter (UKF) tractography is assessed. • The two–tensor model in UKF is analyzed in phantom and patient data experiments. • Practical guidance on employing the UKF method in neurosurgical patients is provided

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day to 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

PURPOSE Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. MATERIALS AND METHODS We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. RESULTS The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p

Intraoperative magnetic resonance imaging (IoMRI) was devised to overcome brain shifts during craniotomies. Yet, the acceptance of IoMRI is limited.To evaluate impact of IoMRI on intracranial glioma resection outcome including overall patient survival.A retrospective review of records was performed on a cohort of 164 consecutive patients who underwent resection surgery for newly diagnosed intracranial gliomas either with or without IoMRI technology performed by 2 neurosurgeons in our center. Patient follow-up was at least 5 years. Extent of resection (EOR) was calculated using pre- and postoperative contrast-enhanced and T2-weighted MR-images. Adjusted analysis was performed to compare gross total resection (GTR), EOR, permanent surgery-associated neurologic deficit, and overall survival between the 2 groups.Overall median EOR was 92.1%, and 97.45% with IoMRI use and 89.9% without IoMRI, with crude (unadjusted) P0.005. GTR was achieved in 49.3% of IoMRI cases, versus in only 21.4% of no-IoMRI cases, P0.001. GTR achieved was more with the use of IoMRI among gliomas located in both eloquent and noneloquent brain areas, P = 0.017 and0.001, respectively. Permanent surgery-associated neurologic deficit was not (statistically) more significant with no-IoMRI, P = 0.284 (13.8% vs. 6.7%). In addition, the IoMRI group had better 5-year overall survival, P0.001.This study shows that the use of IoMRI was associated with greater rates of EOR and GTR, and better overall 5-year survival in both eloquent brain areas located and non-eloquent brain areas located gliomas, with no increased risk of neurologic complication.

Background To achieve maximal resection with minimal risk of postoperative neurologic morbidity, different neurosurgical adjuncts are being used during low-grade glioma (LGG) surgery. Objectives To investigate the effect of pre- and intraoperative adjuncts on the extent of resection (EOR) of hemispheric LGGs. Methods Medical records were reviewed to identify patients of any sex, ≥ 18 years of age, who underwent LGG surgery at X Hospital between January 2005 and July 2013. Patients were divided into eight subgroups based on the use of various combinations of a neuronavigation system alone (NN), functional MRI-diffusion tensor imaging (fMRI-DTI) guided neuronavigation (FD), intraoperative MRI (MR), and direct electrical stimulation (DES). Initial and residual tumors were measured, and mean EOR was compared between groups. Results Of all 128 patients, gross total resection was achieved in 23.4%. Overall mean EOR was 81.3% ± 20.5%. Using DES in combination with fMRI-DTI (mean EOR: 86.7% ± 12.4%) on eloquent tumors improved mean EOR significantly after adjustment for potential confounders when compared with NN alone (mean EOR: 76.4% ± 25.5%; p = 0.001). Conclusions Using DES in combination with fMRI and DTI significantly improves EOR when LGGs are located in eloquent areas compared with craniotomies in which only NN was used.

BACKGROUND Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping. METHODS A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects’ language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals’ language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects. RESULTS Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumor patients’ movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier. CONCLUSIONS These results suggest that it is feasible to use this novel “task-free” paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation.

BACKGROUND AND PURPOSE: Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients’ white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. METHODS: Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. RESULTS: Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. CONCLUSIONS: Quantitative results of mean tract distortions on the order of 1–2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.

This work presents an initial exploration of joint cortical surface and diffusion MRI analysis for neurosurgical patient data. We propose a groupwise cortical modeling strategy that performs an embedding of cortical points from a healthy population and a method for transferring the embedding (with associated information of anatomical label) to patient datasets for cortical parcellation prediction. Our proposed method correlates cortical surfaces based on groupwise white matter connectivity characteristics via a fiber clustering scheme. Unlike other parcellation methods, correspondence of cortical surface vertices is not required. Thus the proposed method can be applied to datasets of patients with brain tumors, using an approximate cortical surface such as a white matter/gray matter boundary derived from diffusion anisotropy. Our initial results on patient data showed good overlap of functional ground truth (subject-specific functional MRI activation areas) with predicted cortical parcels, with 10 of 13 activations overlapping an anatomically corresponding prediction.

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.

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., Highlights • Spectral clustering machine learning approach for white matter tract identification • Data-driven white matter parcellation learned from healthy subjects tractography • White matter parcellation applied to 18 consecutive patients with brain tumors • Arcuate fasciculus and corticospinal tracts identified in all patients • All tracts within 3 mm of corresponding patient-specific functional activations

The goal of surgery for brain tumors and epilepsy is maximal resection while respecting surrounding eloquent structures to avoid postoperative deficits. In order to preserve sensorimotor, language, and cognitive functions, delineating the patient-specific locations of structures supporting these networks is essential. Studies have shown that the identification of eloquent cortex in close proximity to the region of tumor or a resectable lesion is a strong predictor of worsened neurological outcome postoperatively or a limited extent of resection. Therefore, successful preoperative functional mapping of these regions, and their connections, can assist the neurosurgeon in preserving these fundamental abilities while maximizing extent of resection. This chapter reviews current evidence for using fMRI and DTI as noninvasive methods for mapping both eloquent cortex and critical white matter tracts in neurosurgical planning. Technical details and limitations of each method are discussed.

Traditional models of the human language circuitry encompass three cortical areas, Broca's, Geschwind's and Wernicke's, and their connectivity through white matter fascicles. The neural connectivity deep to these cortical areas remains poorly understood, as does the macroscopic functional organization of the cortico-subcortical language circuitry. In an effort to expand current knowledge, we combined functional MRI (fMRI) and diffusion tensor imaging to explore subject-specific structural and functional macroscopic connectivity, focusing on Broca's area. Fascicles were studied using diffusion tensor imaging fiber tracking seeded from volumes placed manually within the white matter. White matter fascicles and fMRI-derived clusters (antonym-generation task) of positive and negative blood-oxygen-level-dependent (BOLD) signal were co-registered with 3-D renderings of the brain in 12 healthy subjects. Fascicles connecting BOLD-derived clusters were analyzed within specific cortical areas: Broca's, with the pars triangularis, the pars opercularis, and the pars orbitaris; Geschwind's and Wernicke's; the premotor cortex, the dorsal supplementary motor area, the middle temporal gyrus, the dorsal prefrontal cortex and the frontopolar region. We found a functional connectome divisible into three systems-anterior, superior and inferior-around the insula, more complex than previously thought, particularly with respect to a new extended Broca's area. The extended Broca's area involves two new fascicles: the operculo-premotor fascicle comprised of well-organized U-shaped fibers that connect the pars opercularis with the premotor region; and (2) the triangulo-orbitaris system comprised of intermingled U-shaped fibers that connect the pars triangularis with the pars orbitaris. The findings enhance our understanding of language function.

Background Radiation and chemotherapy targeted to the central nervous system can cause cognitive impairment, including impaired memory. These memory impairments may be referable to damage to hippocampal structures resulting from central nervous system (CNS) treatment.

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.To use diffusion tensor imaging tractography to study the OR and the Meyer loop (ML) anatomy in vivo.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.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.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.

In order to carry out the most effective and safe neurosurgical interventions for patients with brain lesions, precise information about the unique structural and functional anatomy of that patient is essential. Eloquent cortical areas cannot be recognized solely by identifying known anatomical landmarks even in non-lesional brains [1-3]. In patients with mass lesions or longstanding epilepsy, physical distortion or compensatory reorganization can further alter normal relationships [4-6]. In order to select patients for surgery, plan the operative approach, and successfully execute the surgical goal, detailed information regarding the individual structural and functional anatomy and the relationship to the lesion is potentially very useful. Thus a number of brain mapping methods which have been developed for neuroscientific brain mapping efforts have been adapted to serve neurosurgical considerations. There are fundamental differences in how these methods are applied to neurosurgical patients and problems. Unlike group studies for neuroscience in which inferences are made about a population based on a sample, neurosurgical planning requires single-subject specific information, spatial precision, adequate accommodation for impaired task performance, prioritization of sensitivity over specificity, and robustness in the face of mass lesions. In the last decade much progress has been made towards fulfilling these needs using several techniques. In this chapter, we will elaborate on the particular opportunities and challenges associated with brain mapping as it applies to patient selection, presurgical planning and intraoperative guidance. We will discuss the principal methods briefly noting in particular relative strengths and weaknesses. We will then review approaches for mapping cortical motor, sensory and language areas, as well as the associated white matter connections. We will also discuss some practical and technical considerations including patient-specific concerns, issues related to data acquisition, analysis and interpretation, and visualization considerations for planning and intraoperative use. We will then discuss the specific role of brain mapping in patients with specific pathologic processes including brain tumors, vascular malformations, and medically refractory epilepsy.

Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits.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.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.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.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.

Bilateral lesions of primary visual cortex (PVC) sustained early in life induce the visual system to undergo structural and functional reorganization and produce modified neuronal networks capable of mediating visual abilities that would be impaired if the lesions occurred in adulthood. Reorganization after early lesion is also accompanied by degeneration of the lateral geniculate nucleus of the thalamus, and 90% of beta retinal ganglion cells die via retrograde degeneration. It is unclear whether the high potential of the system to reorganize after early lesion could overcome the effects of beta retinal ganglion cell death. Visual acuity, which depends on an intact beta-cell array, was impaired in cats that underwent PVC lesions on postnatal day 1 and indicated that neuroplastic potential was insufficient to overcome early lesion-induced maladaptive plasticity. Animals with lesions made at 1 month of age, a stage accompanied by high levels of neuroplastic potential but no death of beta cells, achieved acuity measures equivalent to intact animals. The authors conclude that visual signals are rerouted to subserve functionality when the lesion is made at 1 month of age, but not at 1 day of age.

The mechanism by which the brain integrates visual and emotional information remains incompletely understood, and can be studied through focal lesions that selectively disrupt this process. To date, three reported cases of visual hypoemotionality, a vision-specific form of derealization, have resulted from lesions of the temporo-occipital junction. We present a fourth case of this rare phenomenon, and investigate the role of the inferior longitudinal fasciculus (ILF) in the underlying pathophysiology. A 50-year-old right-handed male was found to have a right medial temporal lobe tumor following new-onset seizures. Interstitial laser ablation of the lesion was complicated by a right temporo-parieto-occipital intraparenchymal hemorrhage. The patient subsequently experienced emotional estrangement from visual stimuli. A lesion overlap analysis was conducted to assess involvement of the ILF by this patient's lesion and those of the three previously described cases, and diffusion tensor imaging was acquired in our case to further investigate ILF disruption. All four lesions specifically overlapped with the expected trajectory of the right ILF, and diminished structural integrity of the right ILF was observed in our case. These findings implicate the ILF in visual hypoemotionality, suggesting that the ILF is critical for integrating visual information with its emotional content.

Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping.A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects.Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumor patients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier.These results suggest that it is feasible to use this novel "task-free" paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation.

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 Foundation, Klarman Family Foundation, European Commission (FP7/2007–2013) n°257528 (KHRESMOI)), European Commission (330003 (FABRIC)), Austrian Science Fund (P 22578-B19 (PULMARCH))

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.

Radiation and chemotherapy targeted to the central nervous system (CNS) can cause cognitive impairment, including impaired memory. These memory impairments may be referable to damage to hippocampal structures resulting from CNS treatment.In the present study, we explored episodic memory and its neuroimaging correlates in 10 adult survivors of childhood acute lymphoblastic leukemia (ALL) treated with cranial radiation therapy and both systemic and intrathecal chemotherapy and 10 controls matched for age and sex, using a subsequent memory paradigm after episodic encoding of visual scenes.We report behavioral, structural, and functional changes in the brains of the adult survivors. They demonstrated poorer recognition memory, hippocampal atrophy, and altered blood oxygenation level-dependent (BOLD) signal in the hippocampus. Whole brain statistical map analysis revealed increased BOLD signal/activation in several brain regions during unsuccessful encoding in ALL survivors, potentially reflecting ineffective neural recruitment. Individual differences in memory performance in ALL participants were related to magnitude of BOLD response in regions associated with successful encoding.Taken together, these findings describe long term neuroimaging correlates of cognitive dysfunction after childhood exposure to CNS-targeted cancer therapies, suggesting enduring damage to episodic memory systems.

The overall goal of this research is the design of statistical atlas models that can be created from normal subjects, but may generalize to be applicable to abnormal brains. We present a new style of joint modeling of fMRI, DTI, and structural MRI. Motivated by the fact that a white matter tract and related cortical areas are likely to displace together in the presence of a mass lesion (brain tumor), in this work we propose a rotation and translation invariant model that represents the spatial relationship between fiber tracts and anatomic and functional landmarks. This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks. Our results indicate that the measured model is consistent across normal subjects, and thus suitable for atlas building. Our experiments demonstrate that the model is robust to displacement and missing data, and can be successfully applied to a small group of patients with mass lesions.

In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. 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.

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.

BACKGROUND: Diffusion tensor imaging (DTI) tractography enables the graphical reconstruction of the white matter pathways in vivo. The use of this technique to display the key fiber tracts in brain tumor patients for reducing the risk of post-operative neurologic deficit has increased in recent years. However, the effects of peritumoral edema pose a major challenge to the conventional single-tensor streamline fiber tracking method. OBJECTIVE: To present a novel technique using the two-tensor unscented Kalman filter (UKF) algorithm for the tracking of arcuate fasciculus (AF) in brain tumor patients by resolving the effects of peritumoral edema. METHODS: Ten patients with malignant brain tumors in the vicinity of language-related areas underwent 3-T magnetic resonance imaging including a diffusion-weighted data set with 31 gradient directions. DTI data was preprocessed using an automatic quality control pipeline method. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography. A two-regions-of-interest approach was applied to perform delineation of the AF. Results from two different algorithms were compared side-by-side. RESULTS: Single-tensor streamline and two-tensor UKF tractography showed fundamental differences in mapping the fiber tracts in the edema area surrounding the malignant brain tumor. Using single-tensor streamline tractography, 4 of 10 patients showed disrupted and incomplete AF and the remaining six patients demonstrated only a slender fiber bundle. In contrast, the two-tensor UKF tractography could fully delineate the AF within such edematous brain areas in all ten patients. CONCLUSION: The findings suggest that two-tensor UKF tractography provides the ability to trace the AF more accurately than single-tensor streamline tractography particularly in the setting of peritumoral edema in brain tumor patients. UKF tractography may be helpful in the quest to preserve the language function in brain surgery.