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Background: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm 2 and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN., Objective: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN., Methods: We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm 2 , b = 2000 s/mm 2 and b = 3000 s/mm 2 , using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result., Results: The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result., Conclusions: Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Rapid and flexible learning during behavioral choices is critical to our daily endeavors and constitutes a hallmark of dynamic reasoning. An important paradigm to examine flexible behavior involves learning new arbitrary associations mapping visual inputs to motor outputs. We conjectured that visuomotor rules are instantiated by translating visual signals into actions through dynamic interactions between visual, frontal and motor cortex. We evaluated the neural representation of such visuomotor rules by performing intracranial field potential recordings in epilepsy subjects during a rule-learning delayed match-to-behavior task. Learning new visuomotor mappings led to the emergence of specific responses associating visual signals with motor outputs in 3 anatomical clusters in frontal, anteroventral temporal and posterior parietal cortex. After learning, mapping selective signals during the delay period showed interactions with visual and motor signals. These observations provide initial steps towards elucidating the dynamic circuits underlying flexible behavior and how communication between subregions of frontal, temporal, and parietal cortex leads to rapid learning of task-relevant choices., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method., (© 2019 Wiley Periodicals, Inc.)

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., (Copyright © 2018 by the American Society of Neuroimaging.)

Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.

Objective: To assess the diagnostic accuracy and prognostic value of functional MRI (fMRI) in determining lateralization and predicting postsurgical language and memory outcomes., Methods: An 11-member panel evaluated and rated available evidence according to the 2004 American Academy of Neurology process. At least 2 panelists reviewed the full text of 172 articles and selected 37 for data extraction. Case reports, reports with <15 cases, meta-analyses, and editorials were excluded., Results and Recommendations: The use of fMRI may be considered an option for lateralizing language functions in place of intracarotid amobarbital procedure (IAP) in patients with medial temporal lobe epilepsy (MTLE; Level C), temporal epilepsy in general (Level C), or extratemporal epilepsy (Level C). For patients with temporal neocortical epilepsy or temporal tumors, the evidence is insufficient (Level U). fMRI may be considered to predict postsurgical language deficits after anterior temporal lobe resection (Level C). The use of fMRI may be considered for lateralizing memory functions in place of IAP in patients with MTLE (Level C) but is of unclear utility in other epilepsy types (Level U). fMRI of verbal memory or language encoding should be considered for predicting verbal memory outcome (Level B). fMRI using nonverbal memory encoding may be considered for predicting visuospatial memory outcomes (Level C). Presurgical fMRI could be an adequate alternative to IAP memory testing for predicting verbal memory outcome (Level C). Clinicians should carefully advise patients of the risks and benefits of fMRI vs IAP during discussions concerning choice of specific modality in each case., (© 2017 American Academy of Neurology.)

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies., (Copyright © 2016 by the American Society of Neuroimaging.)

Purpose To develop an electrocorticography (ECoG) grid by using deposition of conductive nanoparticles in a polymer thick film on an organic substrate (PTFOS) that induces minimal, if any, artifacts on computed tomographic (CT) and magnetic resonance (MR) images and is safe in terms of tissue reactivity and MR heating. Materials and Methods All procedures were approved by the Animal Care and Use Committee and complied with the Public Health Services Guide for the Care and Use of Animals. Electrical functioning of PTFOS for cortical recording and stimulation was tested in two mice. PTFOS disks were implanted in two mice; after 30 days, the tissues surrounding the implants were harvested, and tissue injury was studied by using immunostaining. Five neurosurgeons rated mechanical properties of PTFOS compared with conventional grids by using a three-level Likert scale. Temperature increases during 30 minutes of 3-T MR imaging were measured in a head phantom with no grid, a conventional grid, and a PTFOS grid. Two neuroradiologists rated artifacts on CT and MR images of a cadaveric head specimen with no grid, a conventional grid, and a PTFOS grid by using a four-level Likert scale, and the mean ratings were compared between grids. Results Oscillatory local field potentials were captured with cortical recordings. Cortical stimulations in motor cortex elicited muscle contractions. PTFOS implants caused no adverse tissue reaction. Mechanical properties were rated superior to conventional grids (χ(2) test, P < .05). The temperature increase during MR imaging for the three cases of no grid, PTFOS grid, and conventional grid was 3.84°C, 4.05°C, and 10.13°C, respectively. PTFOS induced no appreciable artifacts on CT and MR images, and PTFOS image quality was rated significantly higher than that with conventional grids (two-tailed t test, P < .05). Conclusion PTFOS grids may be an attractive alternative to conventional ECoG grids with regard to mechanical properties, 3-T MR heating profile, and CT and MR imaging artifacts. (©) RSNA, 2016 Online supplemental material is available for this article.

Objective: To compare the diagnostic yield and safety profiles of intraoperative magnetic resonance imaging (MRI)-guided needle brain biopsy with 2 traditional brain biopsy methods: frame-based and frameless stereotactic brain biopsy., Methods: A retrospective analysis was performed of 288 consecutive needle brain biopsies in 277 patients undergoing stereotactic brain biopsy with any of the 3 biopsy methods at Brigham and Women's Hospital from 2000-2008. Variables including age, sex, history of radiation and previous surgery, pathology results, complications, and postoperative length of hospital stay were analyzed., Results: Over the course of 8 years, 288 brain biopsies were performed. Of these, 253 (87.8%) biopsies yielded positive diagnostic tissue. Young age (<40 years old) and history of brain radiation or surgery were significant negative predictors for a positive biopsy diagnostic yield. Excluding patients with prior radiation or surgeries, no significant difference in diagnostic yield was detected among the 3 groups, with frame-based biopsies yielding 96.9%, frameless biopsies yielding 91.8%, and intraoperative MRI-guided needle biopsies yielding 89.9% positive diagnostic yield. Serious adverse events occurred 19 biopsies (6.6%). Intraoperative MRI-guided brain biopsies were associated with less serious adverse events and the shortest postoperative hospital stay., Conclusions: Frame-based, frameless stereotactic, and intraoperative MRI-guided brain needle biopsy techniques have comparable diagnostic yield for patients with no prior treatments (either radiation or surgery). Intraoperative MRI-guided brain biopsy is associated with fewer serious adverse events and shorter hospital stay., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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., (Copyright © 2014. Published by Elsevier Inc.)

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., (Copyright © 2013 Wiley Periodicals, Inc.)

We compare two strategies for modeling the connections of the brain's white matter: fiber clustering and the parcellation-based connectome. Both methods analyze diffusion magnetic resonance imaging fiber tractography to produce a quantitative description of the brain's connections. Fiber clustering is designed to reconstruct anatomically-defined white matter tracts, while the parcellation-based white matter segmentation enables the study of the brain as a network. From the perspective of white matter segmentation, we compare and contrast the goals and methods of the parcellation-based and clustering approaches, with special focus on reviewing the field of fiber clustering. We also propose a third category of new hybrid methods that combine the aspects of parcellation and clustering, for joint analysis of connection structure and anatomy or function. We conclude that these different approaches for segmentation and modeling of the white matter can advance the neuroscientific study of the brain's connectivity in complementary ways., (Copyright © 2013 Elsevier Inc. All rights reserved.)

First described for use in mapping the human visual cortex in 1991, functional magnetic resonance imaging (fMRI) is based on blood-oxygen level dependent (BOLD) changes in cortical regions that occur during specific tasks. Typically, an overabundance of oxygenated (arterial) blood is supplied during activation of brain areas. Consequently, the venous outflow from the activated areas contains a higher concentration of oxyhemoglobin, which changes the paramagnetic properties of the tissue that can be detected during a T2-star acquisition. fMRI data can be acquired in response to specific tasks or in the resting state. fMRI has been widely applied to studying physiologic and pathophysiologic diseases of the brain. This review will discuss the most common current clinical applications of fMRI as well as emerging directions., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

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., (Copyright © 2011 Elsevier Inc. All rights reserved.)

We present what we believe to be the first investigation into unbiased multi-subject registration of whole brain diffusion tractography of the white matter. To our knowledge, this is also the first entropy-based objective function applied to fiber tract registration. To define the probability of fiber trajectories for the computation of entropy, we take advantage of a pairwise fiber distance used as the basis for a Gaussian-like kernel. By employing several values of the kernel's scale parameter, the method is inherently multi-scale. Results of experiments using synthetic and real datasets demonstrate the potential of the method for simultaneous joint registration of tractography.

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.

The present study examined the relationship between hand preference degree and direction, functional language lateralization in Broca's and Wernicke's areas, and structural measures of the arcuate fasciculus. Results revealed an effect of degree of hand preference on arcuate fasciculus structure, such that consistently-handed individuals, regardless of the direction of hand preference, demonstrated the most asymmetric arcuate fasciculus, with larger left versus right arcuate, as measured by DTI. Functional language lateralization in Wernicke's area, measured via fMRI, was related to arcuate fasciculus volume in consistent-left-handers only, and only in people who were not right hemisphere lateralized for language; given the small sample size for this finding, future investigation is warranted. Results suggest handedness degree may be an important variable to investigate in the context of neuroanatomical asymmetries., (Copyright 2010 Elsevier Inc. All rights reserved.)

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.

Functional MRI (fMRI) is often used for presurgical language lateralization. In the most common approach, a laterality index (LI) is calculated on the basis of suprathreshold voxels. However, strong dependencies between LI and threshold can diminish the effectiveness of this technique; in this study we investigated an original methodology that is independent of threshold. We compared this threshold-independent method against the common threshold-dependent method in 14 patients with epilepsy who underwent Wada testing. In addition, clinical results from electrocortical language mapping and postoperative language findings were used to assess the validity of the fMRI lateralization method. The threshold-dependent methodology yielded ambiguous or incongruent lateralization outcomes in 4 of 14 patients in the inferior frontal gyrus (IFG) and in 6 of 14 patients in the supramarginal gyrus (SMG). Conversely, the threshold-independent method yielded unambiguous lateralization in all the patients tested, and demonstrated lateralization outcomes incongruent with clinical standards in 2 of 14 patients in IFG and in 1 of 14 patients in SMG. This validation study demonstrates that the threshold-dependent LI calculation is prone to significant within-patient variability that could render results unreliable; the threshold-independent method can generate distinct LIs that are more concordant with gold standard clinical findings.

Language functional magnetic resonance imaging (fMRI) is a promising non-invasive technique for pre-surgical planning in patients whose lesions are adjacent to or within critical language areas. Most language fMRI studies in patients use blocked experimental design. In this study, we compared a blocked design and a rapid event-related design with a jittered inter-stimulus-interval (ISI) (or stochastic design) for language fMRI in six healthy controls, and eight brain tumor patients, using a vocalized antonym generation task. Comparisons were based on visual inspection of fMRI activation maps and degree of language lateralization, both of which were assessed at a constant statistical threshold for each design. The results indicated a relatively high degree of discordance between the two task designs. In general, the event-related design provided maps with more robust activations in the putative language areas than the blocked design, especially for brain tumor patients. Our results suggest that the rapid event-related design has potential for providing comparable or even higher detection power over the blocked design for localizing language function in brain tumor patients, and therefore may be able to generate more sensitive language maps. More patient studies, and further investigation and optimization of language fMRI paradigms will be needed to determine the utility and validity of this approach for pre-surgical planning.

Purpose: To investigate the utility of a proposed clinical diffusion imaging scheme for rapidly generating multiple b-value diffusion contrast in brain magnetic resonance imaging (MRI) with high signal-to-noise ratio (SNR)., Materials and Methods: Our strategy for efficient image acquisition relies on the invariance property of the diffusion tensor eigenvectors to b-value. A simple addition to the conventional diffusion tensor MR imaging (DTI) data acquisition scheme used for tractography yields diffusion-weighted images at twice and three times the conventional b-value. An example from a neurosurgical brain tumor is shown. Apparent diffusion-weighted (ADW) images were calculated for b-values 800, 1600, and 2400 s/mm(2), and a map of excess diffusive kurtosis was computed from the three ADWs., Results: High b-value ADW images demonstrated decreased contrast between normal gray and white matter, while the heterogeneity and contrast of the lesion was emphasized relative to conventional b-value data. Kurtosis maps indicated the deviation from Gaussian diffusive behavior., Conclusion: DTI data with multiple b-values and good SNR can be acquired in clinically reasonable times. High b-value ADW images show increased contrast and add information to conventional DWI. Ambiguity in conventional b-value images over whether hyperintense signal results from abnormally low diffusion, or abnormally long T(2), is better resolved in high b-value images., ((c) 2009 Wiley-Liss, Inc.)

We introduce an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts. Using subject-specific tractography bundle segmentations, we generate an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this paper we present a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements. We propose a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. We report TBM results of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower lambda(3), the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher lambda(1), the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, we demonstrate the anatomical locations of the interhemispheric differences. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy.

Objective: Various methods of intraoperative structural monitoring during neurosurgery are used to localize lesions after brain shift and to guide surgically introduced probes such as biopsy needles or stimulation electrodes. With its high temporal resolution, portability, and nonionizing mode of radiation, ultrasound has potential advantages over other existing imaging modalities for intraoperative monitoring, yet ultrasound is rarely used during neurosurgery largely because of the craniotomy requirement to achieve sufficiently useful signals., Methods: Prompted by results from recent studies on transcranial ultrasound, a prototype device that aims to use the shear mode of transcranial ultrasound transmission for intraoperative monitoring was designed, constructed, and tested with 10 human participants. Magnetic resonance images were then obtained with the device spatially registered to the magnetic resonance imaging (MRI) reference coordinates. Peaks in both the ultrasound and MRI signals were identified and analyzed for both spatial localization and signal-to-noise ratio (SNR)., Results: The first results aimed toward validating the prototype device with MRI showed an excellent correlation (n = 38; R(2) = 0.9962) between the structural localization abilities of the two modalities. In addition, the overall SNR of the ultrasound backscatter signals (n = 38; SNR = 25.4 +/- 5.2 dB, mean +/- SD) was statistically equivalent to that of the MRI data (n = 38; SNR = 22.5 +/- 4.8 dB)., Conclusions: A statistically significant correlation of localized intracranial structures between intraoperative transcranial ultrasound monitoring and MRI data was achieved with 10 human participants. We have shown and validated a prototype device incorporating transcranial shear mode ultrasound for clinical monitoring applications.

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.

We describe a method for correcting the distortions present in echo planar images (EPI) and registering the EPI to structural MRI. A fieldmap is predicted from an air / tissue segmentation of the MRI using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmaps, however neither fieldmaps, nor knowledge of shim coefficients is required.

Objectives: The determination of eloquent cortex is essential when planning neurosurgical approaches to brain lesions. This study examined the abilities of medical personnel of various backgrounds to predict the location of functional cortex using anatomical information provided by MR imaging., Patients and Methods: Neurosurgeons, neuroscientists, neuroradiologists, medical students and MR technologists viewed anatomical MR images acquired from patients with brain tumors and healthy controls. These five groups of raters were then asked to locate the primary motor hand, supplementary motor and primary auditory areas and their predictions were compared to fMRI data acquired from the same subjects., Results: The overall mean distance from the center of the fMRI activation was 2.38 cm. The neuroscientists performed the best and MR technologists performed the worst (mean distance from center of 1.83 and 3.04 cm, respectively, p<0.05). The difference between patients and controls was not significant. The mean distance by ROI was primary motor hand 2.03 cm, auditory area 2.06 cm and supplementary motor area 3.18 cm (p<0.05). Raters also performed best in the medial-lateral direction, compared to superior-inferior and anterior-posterior directions (mean distances from center 0.42, 1.04 and 1.81 cm, respectively). Finally, the approximate minimum fields of view necessary to capture the entire fMRI activations using the raters' predictions ranged from 5 to 15 cm, or 3 to 12 cm larger than the fMRI activations., Conclusion: Medical personnel of various training perform poorly when using only anatomical information to predict the location of functional areas of cortex.

Multisubject statistical analyses of diffusion tensor images in regions of specific white matter tracts have commonly measured only the mean value of a scalar invariant such as the fractional anisotropy (FA), ignoring the spatial variation of FA along the length of fiber tracts. We propose to instead perform tract-based morphometry (TBM), or the statistical analysis of diffusion MRI data in an anatomical tract-based coordinate system. We present a method for automatic generation of white matter tract arc length parameterizations, based on learning a fiber bundle model from tractography from multiple subjects. Our tract-based coordinate system enables TBM for the detection of white matter differences in groups of subjects. We present example TBM results from a study of interhemispheric differences in FA.

The unambiguous localization of eloquent functional areas is necessary to decrease the neurological morbidity of neurosurgical procedures. We explored the minimum spatial resolution requirements for functional magnetic resonance imaging (fMRI) data acquisition when brain mapping is used in neurosurgical planning and navigation. Using a 1.5 Tesla clinical MRI scanner, eight patients with brain tumors underwent fMRI scans using spatial resolution of approximately 4 x 4 x 4 mm(3) to map the eloquent motor and language areas during the performance of cognitive/sensorimotor tasks. The fMRI results were then used intra-operatively in an open MRI system to delineate eloquent areas. Retrospectively, activation patterns were visually inspected by a neurosurgeon to determine qualitatively whether ambiguity with respect to the activation boundaries, due to low spatial resolution, could be of potential significance for surgical guidance. A significant degree of ambiguity in both the extent and shape of activation was judged to be present in data from six of the eight patients. Analysis of fMRI data at multiple resolutions from a normal volunteer showed that at 3 mm isotropic resolution, eloquent areas were better localized within the gray matter although there was still some potential for ambiguity caused by activations appearing to cross a sulcus. The data acquired with 2-mm isotropic voxels significantly enhanced the spatial localization of activation to within the gray matter. Thus, isotropic spatial resolution on the order of 2 x 2 x 2 mm(3), which is much higher than the resolutions used in typical fMRI examinations, may be needed for the unambiguous identification of cortical activation with respect to tumors and important anatomical landmarks., (Copyright 2003 Wiley-Liss, Inc.)