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Mapping language function with task-based vs. resting-state functional MRI.

ABSTRACT: BACKGROUND:Use of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization. PURPOSE:To contrast RS-fMRI vs. T-fMRI as techniques for localization of language function. METHODS:We analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses. RESULTS:Both fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system. CONCLUSION:We demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.

SUBMITTER: Park KY

PROVIDER: S-EPMC7394427 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Mapping language function with task-based vs. resting-state functional MRI.

Park Ki Yun KY Lee John J JJ Dierker Donna D Marple Laura M LM Hacker Carl D CD Roland Jarod L JL Marcus Daniel S DS Milchenko Mikhail M Miller-Thomas Michelle M MM Benzinger Tammie L TL Shimony Joshua S JS Snyder Abraham Z AZ Leuthardt Eric C EC

PloS one 20200731 7

<h4>Background</h4>Use of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.<h4>Purpose</h4>To cont ...[more]

PMID: 32735611

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Project description:IntroductionPreoperative language mapping using functional magnetic resonance imaging (fMRI) aims to identify eloquent areas in the vicinity of surgically resectable brain lesions. fMRI methodology relies on the blood-oxygen-level-dependent (BOLD) analysis to identify brain language areas. Task-based fMRI studies the BOLD signal increase in brain areas during a language task to identify brain language areas, which requires patients' cooperation, whereas resting-state fMRI (rsfMRI) allows identification of functional networks without performing any explicit task through the analysis of the synchronicity of spontaneous BOLD signal oscillation between brain areas. The aim of this study was to compare preoperative language mapping using rsfMRI and task fMRI to cortical mapping (CM) during awake craniotomies.MethodsFifty adult patients surgically treated for a brain lesion were enrolled. All patients had a presurgical language mapping with both task fMRI and rsfMRI. Identified language networks were compared to perioperative language mapping using electric cortical stimulation.ResultsResting-state fMRI was able to detect brain language areas during CM with a sensitivity of 100% compared to 65.6% with task fMRI. However, we were not able to perform a specificity analysis and compare task-based and rest fMRI with our perioperative setting in the current study. In second-order analysis, task fMRI imaging included main nodes of the SN and main areas involved in semantics were identified in rsfMRI.ConclusionResting-state fMRI for presurgical language mapping is easy to implement, allowing the identification of functional brain language network with a greater sensitivity than task-based fMRI, at the cost of some precautions and a lower specificity. Further study is required to compare both the sensitivity and the specificity of the two methods and to evaluate the clinical value of rsfMRI as an alternative tool for the presurgical identification of brain language areas.

Project description:Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.

Dataset Information

Mapping language function with task-based vs. resting-state functional MRI.

Publications

Mapping language function with task-based vs. resting-state functional MRI.

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