Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy 1

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1 This copy is for personal use only. To order printed copies, contact Original Research n Technical Developments Matthew N. DeSalvo, MD Naoaki Tanaka, MD, PhD Linda Douw, PhD Catherine L. Leveroni, PhD Bradley R. Buchbinder, MD Douglas N. Greve, PhD Steven M. Stufflebeam, MD Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy 1 Purpose: Materials and Methods: To measure the accuracy of resting-state functional magnetic resonance (MR) imaging in determining hemispheric language dominance in patients with medically intractable focal epilepsies against the results of an intracarotid amobarbital procedure (IAP). This study was approved by the institutional review board, and all subjects gave signed informed consent. Data in 23 patients with medically intractable focal epilepsy were retrospectively analyzed. All 23 patients were candidates for epilepsy surgery and underwent both IAP and resting-state functional MR imaging as part of presurgical evaluation. Language dominance was determined from functional MR imaging data by calculating a laterality index (LI) after using independent component analysis. The accuracy of this method was assessed against that of IAP by using a variety of thresholds. Sensitivity and specificity were calculated by using leave-one-out cross validation. Spatial maps of language components were qualitatively compared among each hemispheric language dominance group. 1 From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass. Received April 29, 2014; revision requested June 10; revision received August 19; accepted September 10; final version accepted June 3, Address correspondence to M.N.D. ( mndesalvo@mgh.harvard.edu). Supported by the National Institutes of Health (grants P41-RR14075 [S.M.S.] and 5R01-NS [C.L.L., L.D., D.N.G., S.M.S.]), the National Center for Research Resources (5P41-EB [S.M.S.]), the National Institutes of Health Human Connectome Project (5U01MH [S.M.S., D.N.G.]), the Mental Illness and Neuroscience Discovery Institute, the NWO (Dutch Organization for Scientific Research) Rubicon grant (L.D.), the NOW Veni grant, the Society in Science Branco Weiss Fellowship (L.D.), the Epilepsy Foundation, the Japan Epilepsy Research Foundation (N.T.), and the Radiological Society of North America Research Medical Student Grant (M.N.D.). Results: Conclusion: Measurement of hemispheric language dominance with resting-state functional MR imaging was highly concordant with IAP results, with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity. Composite language component maps in patients with typical language laterality consistently included classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality also included non-classical language areas such as the superior and middle frontal gyri, the insula, and the occipital cortex. Resting-state functional MR imaging can be used to measure language laterality in patients with medically intractable focal epilepsy. q RSNA, 2016 Online supplemental material is available for this article. q RSNA, radiology.rsna.org n Radiology: Volume 281: Number 1 October 2016

2 Although the majority of patients with focal epilepsy can remain seizure free by using antiepileptic drugs, up to 30% of these patients continue to experience seizures despite optimal medical therapy and may need surgical treatment (1). The determination of hemispheric language dominance is a critical part of presurgical evaluation of these patients. The traditional test for measuring hemispheric language dominance is the intracarotid amobarbital procedure (IAP), also known as the Wada test. Because of the invasiveness and associated risks of the IAP, taskbased functional magnetic resonance (MR) imaging has been studied and shown to be an excellent noninvasive alternative to the IAP for presurgical determination of language laterality (2,3). However, the usefulness and reliability of a task-based approach to measuring hemispheric language dominance are limited in some patients because of age, language barriers, disability, or states of altered consciousness (eg, anesthesia or coma). Therefore a task-free approach to measuring hemispheric language dominance might be useful for patients who cannot perform a language task. A task-free protocol may also have logistic benefits, including ease of implementation and repeatability in the clinical setting. In this approach, networks of functionally Advances in Knowledge nn We determined hemispheric language dominance with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity in patients with intractable focal epilepsy using resting-state functional MR imaging. nn Language maps in patients with typical language laterality primarily comprised classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality were more heterogeneous. connected brain regions are defined by using mathematic techniques to compare approximately 0.1-Hz fluctuations in blood oxygen level dependent (BOLD) signal throughout the brain. Recent studies have demonstrated the feasibility of defining language networks in both healthy subjects (4,5) and patients with intractable epilepsy (6) by using resting-state functional connectivity. Furthermore, this technique has been used to the study cerebral lateralization of several aspects of brain function, including language, in healthy subjects (7). However, to our knowledge, a direct comparison between hemispheric language dominance testing with IAP and that with resting-state functional MR imaging has not been performed in this patient population. The goal of this study was to assess the accuracy of an independent component analysis (ICA) of resting-state functional MR imaging data against IAP for measuring hemispheric language dominance in patients with intractable focal epilepsy. Materials and Methods Patients This study was approved by our institutional review board, and all subjects gave signed informed consent. Data in 23 patients (mean age, 27.2 years [standard deviation]; age range, years; 12 male patients) with medically refractory focal epilepsy who were evaluated between August 2009 and April 2013 were retrospectively analyzed. Patients were included if they had been given a diagnosis of medically refractory focal epilepsy and referred for advanced neuroimaging, including resting-state functional MR imaging and Implication for Patient Care nn Resting-state functional MR imaging may be a noninvasive yet accurate alternative method to determine hemispheric language dominance in patients with intractable focal epilepsy who cannot perform a task-based procedure. IAP as part of presurgical evaluation. Patients who had undergone prior neurosurgery, those who did not meet all inclusion criteria, and those in whom data were unavailable were excluded. No patients were excluded because of these criteria. Diagnoses were based on comprehensive evaluation, including long-term video-electroencephalographic monitoring, MR imaging, neuropsychologic testing, ictal/interictal cerebral blood flow-based single photon emission computed tomography, and fluorine 18 fluorodeoxyglucose positron emission tomography. Detailed clinical data are shown in the Table. All study patients were assessed clinically by a single epileptologist (N.T., with 12 years of experience) who was unaware of imaging results at the time of assessment. Data Acquisition MR imaging was performed with a 3.0- T system (TimTrio; Siemens, Erlangen, Germany) with a vendor-produced 32-channel head coil. High-resolution T1- weighted magnetization-prepared rapid acquisition gradient-echo sequences were performed with repetition time msec/echo time msec, 2530/1.74; and flip angle, 7 ; resulting in a matrix of isotropic 1-mm voxels. Published online before print /radiol Content codes: Radiology 2016; 281: Abbreviations: BOLD = blood oxygen level dependent IAP = intracarotid amobarbital procedure ICA = independent component analysis LI = laterality index Author contributions: Guarantors of integrity of entire study, M.N.D., N.T.; study concepts/study design or data acquisition or data analysis/ interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, M.N.D., B.R.B., S.M.S.; clinical studies, M.N.D., N.T., S.M.S.; experimental studies, N.T., S.M.S.; statistical analysis, M.N.D., D.N.G., S.M.S.; and manuscript editing, M.N.D., N.T., L.D., B.R.B., D.N.G., S.M.S. Conflicts of interest are listed at the end of this article. Radiology: Volume 281: Number 1 October 2016 n radiology.rsna.org 265

3 Patient Characteristics Patient No./Age (y)/sex Handedness Resting-state functional data were acquired by using a gradient-echo echoplanar pulse sequence sensitive to BOLD contrast with the following parameters: 3000/30; flip angle, 85 ; and 47 axial sections of voxels at 3-mm isotropic resolution, resulting in 160 frames over 480 seconds. Experimental Paradigm Each patient underwent one restingstate run during which they passively fixated on a visual cross-hair centered on a screen. Patients were instructed to stay awake and remain as still as possible while passively fixing on a visual cross-hair, without any additional task instruction. MR Imaging Preprocessing MR imaging preprocessing procedures for functional data were based on those applied by Buckner et al (8). These Laterality according to IAP Diagnosis Lesion (if present) 1/38/F Right Left Right TLE Encephalitis 2/18/M Right Left Right TLE 3/27/F Right Left Right TLE 4/21/M Right Left Left TLE 5/18/M Ambidextrous Left Left OLE Occipitotemporal polymicrogyria 6/46/F Right Left Left TLE 7/14/F Right Left Right OLE 8/28/F Right Left Right TLE 9/25/F Left Left Right TLE 10/30/F Right Left Left TLE 11/50/M Right Left Left TLE 12/33/M Right Left Right TLE 13/18/F Right Left Left TLE Cortical dysplasia 14/27/M Right Left FLE 15/23/M Right Left Right TLE MTS; cortical dysplasia 16/14/F Right Left Left TLE 17/44/M Right Left Left FLE Cortical dysplasia 18/15/M Right Left Left TLE 19/14/M Right Bilateral Left FLE 20/59/M Right Left Left TLE Left MTS 21/17/F Right Bilateral Left TLE 22/10/M Left Right Left TLE Left MTS; left hemispheric atrophy 23/36/F Left Right Left FLE or TLE Note. FLE = frontal lobe epilepsy, MTS = mesial temporal sclerosis, OLE = occipital lobe epilepsy, TLE = temporal lobe epilepsy. procedures, performed by using FSL ( included removal of the first four volumes to allow for T1-equilibration effects, compensation for section-dependent time shifts, motion correction, and projection to FreeSurfer fsaverage5 space (9,10). Temporal filtering removed constant and linear trends over each run while retaining frequencies lower than 0.08 Hz. Several sources of spurious variance were regressed: six parameters of rigid body head motion, whole-brain average signal, signal averaged over the lateral ventricles, and signal averaged over the deep cerebral white matter. Finally, data were smoothed by using a Gaussian kernel at 6 mm full width at half maximum. ICA Procedures Single-subject probabilistic ICA was performed in surface space by using the MELODIC toolbox (11). First, each patient s data were reduced to 20 dimensions by using a probabilistic principal component analysis based method; then, 20 components were estimated by using the FastICA algorithm. We chose this number of components to avoid inconsistently subdividing our broadly defined network of classic language areas (12) and because the feasibility of selecting individual language networks using fixed ICA parameters with resting-state functional MR imaging data has been previously demonstrated (4). These components were then converted into z-score maps and were thresholded at z 1, 2, and 3 for separate analyses. Language Component Selection A putative language component was selected for each patient by using a template-matching method. A broadly defined language template consisting of classic language areas that is, the bilateral frontal opercula (pars opercularis, orbitalis, and triangularis), the inferior parietal lobules, the superior temporal gyri, and the supramarginal gyri was used (Fig E1 [online]) (13,14). The spatial overlap between each component and the template was measured in surface space by using the Dice coefficient, as follows: ( ) ( ) Dice coefficient = 2* S T S + T, where S is one of a single patient s ICAderived components and T is the language template. The single component with the greatest Dice coefficient was selected as the candidate language component. Laterality Index Calculation Finally, the laterality index (LI) of each patient s candidate language component was calculated by using the conventional formula: LI = ( QL - QR)( QL + Q R), where Q L and Q R are the number of voxels within the left and right language areas, respectively. 266 radiology.rsna.org n Radiology: Volume 281: Number 1 October 2016

4 Categorization and Accuracy Calculation On the basis of the LI, each patient s hemispheric language dominance was categorized as left, right, or bihemispheric. This categorization was performed by using a variety of LI thresholds ranging from 0 to 1 at intervals of Patients with an LI greater than the LI threshold were considered to have left dominance. Patients with an LI less than the negative LI threshold were considered to have right dominance. Patients with an absolute value of LI that was less than the LI threshold were considered to have bihemispheric dominance. Each patient s functional MR imaging and IAP results were compared such that a match (eg, left vs left) was scored as 100% accurate, while a mismatch (eg, left vs right) was scored as 0% accurate. Language Maps Composite language maps were created for each functional MR imaging based language laterality group by using the LI and z-score thresholds that were most accurate, 0.15 and 2, respectively. Within each laterality group, patients language components were overlaid, and an intensity value was calculated for each voxel representing the proportion of language components within that group that contained that voxel. These maps were thresholded to exclude voxels that were present in fewer than two patients. Figure 2 Figure 1 Figure 1: Summary of accuracy results. Accuracy versus LI threshold at z-score thresholds of z 1, z 2, and z 3. For each patient, accuracy was computed as either 100% (eg, left functional MR imaging vs left IAP) or 0% (eg, left functional MR imaging vs right IAP). Accuracy values are averaged across subjects. Statistical Analysis Accuracy values comparing language laterality between functional MR imaging and IAP for each patient were averaged across all patients and were calculated for multiple LI and z-score thresholds (Figs 1, 2). Sensitivity and specificity were calculated by using leave-one-out cross validation as follows: Optimal thresholds were set on the basis of results in 22 patients, and the remaining patient was then classified (15). This was repeated for all patients to calculate the true-positive rate (sensitivity) and true-negative rate (specificity). The number of brain regions included in each candidate language map, as defined by the FreeSurfer anatomic Figure 2: LIs for each patient. Values shown are at a z-score threshold of z 2. Colors represent IAP-based laterality. Patients are shown here in the same order as in the Table. = The patient with discordant functional MR imaging and IAP results. atlas, was compared between patients with typical language laterality and those with atypical language laterality by using a two-sample t test. MR image preprocessing, ICA analysis, and statistical analysis were performed by three experts in computational neuroimaging: M.N.D. (with 5 years of experience), L.D. (with 7 years of experience) and D.N.G. (with 20 years of experience). Results The results of this ICA-based analysis of resting-state functional MR imaging data were consistent with IAP results, with up to 96% (22 of 23) accuracy, Radiology: Volume 281: Number 1 October 2016 n radiology.rsna.org 267

5 Figure 3 Figure 3: Composite language maps. Maps of resting-state functional MR imaging language components at z 2 are shown. Patients were grouped as having left (L), right (R), or bihemispheric (B) dominance according to functional MR imaging based classification. Color maps = proportions and are displayed over a common anatomic template. Warmer colors = greater values. Voxels present in fewer than two patients were excluded. Images are displayed in radiologic convention. IFG = inferior frontal gyrus, IPL = inferior parietal lobule, SFG = superior frontal gyrus, STG = superior temporal gyrus. 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity (Fig 1). An accuracy of 96% was achieved by using an LI threshold of 0.15 and z 2, wherein only a single partial match occurred: One patient was classified as having bilateral dominance at functional MR imaging (LI = 0.04) but as having left dominance according to the IAP (Fig 2). Among z-score thresholds, z 2 was the threshold that achieved the single greatest overall accuracy value. On the other hand, z 3 was moderately accurate across a wide range of LI thresholds. For all LI thresholds greater than 0.2, accuracy results were strictly decreasing with increasing LI threshold. One candidate language network was identified for each patient by using overlap to a common template of classic language areas (mean Dice coefficient, ). Language networks showed variable within-group consistency among patients on the basis of language laterality classification. Language components of patients with typical language laterality mostly comprised classical language areas, as well as part of the superior frontal gyrus. On the other hand, language components of patients with atypical laterality included a greater number of brain regions, including nonclassical language areas such as the superior frontal gyrus, the orbitofrontal cortex, the insula, and the occipital cortex (P,.05; Fig 3). Discussion In this study, we evaluated the accuracy of resting-state functional MR imaging compared with that of IAP in classifying hemispheric language dominance in patients with intractable focal epilepsy. Our results demonstrate that restingstate functional MR imaging can accurately determine hemispheric language dominance in patients with intractable focal epilepsy and may be a clinically useful noninvasive task-free alternative test to determine language laterality. Although language laterality was accurately classified for all but one patient, this method did not achieve 100% accuracy. Language networks included fewer brain regions among patients with typical language laterality (P,.05). This observation may reflect true heterogeneity among patients with atypical language networks, which has been previously observed (16). Although language networks of patients with atypical language dominance included more brain regions, their classification by using this method was robust, at 100% (four of four), assuming that the patient with discordant classification had typical language laterality. These results suggest that an ICA-based approach with resting-state functional MR imaging may be sufficient to determine hemispheric language dominance. This study adds to the growing body of evidence to suggest that resting-state functional MR imaging can be potentially useful for presurgical mapping of eloquent cortices (4,6,17 23) but that it is limited by methodologic differences among prior studies, small sample sizes of these studies, and the use of various reference techniques (eg, task-based functional MR imaging, electrocortical stimulation, IAP). Although prior studies have demonstrated that resting-state functional MR imaging can potentially be used for language mapping in both healthy subjects (4,5) and patients with epilepsy (6), this study provides evidence that 268 radiology.rsna.org n Radiology: Volume 281: Number 1 October 2016

6 language lateralization methods involving resting-state functional MR imaging can achieve reasonable accuracy when compared with an IAP-based approach. Furthermore, in contrast to prior studies that have used seed-based methods for presurgical mapping of sensorimotor (17) and language (18) functions with resting state functional MR imaging, this study used a model-free ICA approach. There were several limitations to this study that merit further research. First, the study was retrospective with small numbers of patients, especially patients with atypical language laterality. Second, we acknowledge that this method may suffer from technical issues for patients who have undergone surgery patients or patients with major structural lesions that may affect cortical surface reconstruction and the consistency of anatomic templates. Although this method was very accurate overall, results were more inconsistent for the fewer patients with atypical language laterality, so this method may not be completely generalizable. We acknowledge that the definition of the language template used in this study is not universally established. We also acknowledge that although a lateralized resting-state language network has not been consistently demonstrated in prior task functional MR imaging studies, it has been shown that ICA analysis of resting-state functional MR imaging data can produce maps of language-related brain areas at the individual-subject level (4). It has been demonstrated that specific methodologic parameters such as number of ICA components (12) and choice of language template (4) can influence the results of ICAbased functional MR imaging analyses. Finally, in this study we did not establish that individual resting-state functional MR imaging data are sufficient to create reliable maps of language networks with comparable spatial extent to those derived from task-based functional MR imaging data. Acknowledgment: The authors thank Behroze Vaccha, MD, who helped with data analysis. Disclosures of Conflicts of Interest: M.N.D. disclosed no relevant relationships. N.T. disclosed no relevant relationships. L.D. disclosed no relevant relationships. C.L.L. disclosed no relevant relationships. B.R.B. disclosed no relevant relationships. D.N.G. disclosed no relevant relationships. S.M.S. disclosed no relevant relationships. References 1. Arroyo S. Evaluation of drug-resistant epilepsy [in Spanish]. Rev Neurol 2000;30(9): Dym RJ, Burns J, Freeman K, Lipton ML. Is functional MR imaging assessment of hemispheric language dominance as good as the Wada test? a meta-analysis. Radiology 2011;261(2): Janecek JK, Swanson SJ, Sabsevitz DS, et al. 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