PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI

Huang, Huiyuan; Ding, Zhongxiang; Mao, Dewang; Yuan, Jianhua; Zhu, Fu‐Gang; Chen, Shuda; Xu, Yan; Lou, Lin; Feng, Xiaoyan; Qi, Le; Qiu, Wusi; Zhang, Han; Zang, Yu‐Feng

doi:10.1007/s12021-016-9304-y

Cited by 23 publications

(29 citation statements)

References 59 publications

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“…With respect to similar pipelines that have been proposed, 14 our system represents a significant advancement for the following reasons. 1) The analysis settings depend- ing on DICOM image parameters are automatically read from the raw data, minimizing manual user intervention.…”

Section: Discussionmentioning

confidence: 99%

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

Zacà

Jovicich

Corsini

et al. 2019

Journal of Neurosurgery

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OBJECTIVEResting-state functional MRI (rs-fMRI) represents a promising and cost-effective alternative to task-based fMRI for presurgical mapping. However, the lack of clinically streamlined and reliable rs-fMRI analysis tools has prevented wide adoption of this technique. In this work, the authors introduce an rs-fMRI processing pipeline (ReStNeuMap) for automatic single-patient rs-fMRI network analysis.METHODSThe authors provide a description of the rs-fMRI network analysis steps implemented in ReStNeuMap and report their initial experience with this tool after performing presurgical mapping in 6 patients. They verified the spatial agreement between rs-fMRI networks derived by ReStNeuMap and localization of activation with intraoperative direct electrical stimulation (DES).RESULTSThe authors automatically extracted rs-fMRI networks including eloquent cortex in spatial proximity with the resected lesion in all patients. The distance between DES points and corresponding rs-fMRI networks was less than 1 cm in 78% of cases for motor, 100% of cases for visual, 87.5% of cases for language, and 100% of cases for speech articulation mapping.CONCLUSIONSThe authors’ initial experience with ReStNeuMap showed good spatial agreement between presurgical rs-fMRI predictions and DES findings during awake surgery. The availability of the rs-fMRI analysis tools for clinicians aiming to perform noninvasive mapping of brain functional networks may extend its application beyond surgical practice.

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Section: Discussionmentioning

confidence: 99%

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

Zacà

Jovicich

Corsini

et al. 2019

Journal of Neurosurgery

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“…Advanced functional and metabolic imaging techniques have demonstrated considerable potential for determining prognosis, postoperative deficits, and treatment response in patients with brain tumors (Lambin et al, ; Barone et al, ; Spena et al, ; Bailey et al, ; Huang et al, ; Pillai, ). A particular focus of recent studies has been outcome prediction for high grade gliomas (Q. Li et al, ; Boonzaier et al, ; L. Liu et al, ; Shiroishi et al, ; Marcus et al, ) and the nonenhancing component of the tumor (Jain et al, ).…”

Section: Discussionmentioning

confidence: 99%

Real‐time presurgical resting‐state fMRI in patients with brain tumors: Quality control and comparison with task‐fMRI and intraoperative mapping

Vakamudi

Posse

Jung

et al. 2019

Human Brain Mapping

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Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand (8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with realtime data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.K E Y W O R D S electrocortical mapping, functional MRI, high-speed fMRI, presurgical mapping, real-time fMRI, resting-state fMRI, task-based fMRI Kishore Vakamudi and Stefan Posse are considered as joint first authors.

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“…If the goal is to identify language comprehension-related components, another template should be generated, e.g., a template based on the seed region placed in the temporal parietal area (Wernicke’s area), or on language interpretation-based task-related experimental design (e.g., wording listening, phonological/rhyming, and noun-verb semantic association task.). Therefore, we suggest that in the future, different templates should be prepared for different purposes, depending on the location of the tumor in the language network 48 . From the methodological viewpoint, with our DICI algorithm, we can detect multiple components (i.e., several highly ranking components) according to the two largest DICI values, higher model orders (80, 90, or more), and using templates with both frontal and parietal subdivisions included.…”

Section: Discussionmentioning

confidence: 99%

“…This software is a MATLAB-based toolbox, which is publicly available on a source code management website, GitHub ( https://github.com/missy139/PreSurgMapp ), as well as in the rs-fMRI forum ( http://restfmri.net/forum/node/2382 ). The detailed manual of this toolbox can be referred to in our previous paper 48 .…”

Section: Discussionmentioning

confidence: 99%

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

Zhang

Hameed

et al. 2017

Sci Rep

Self Cite

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As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

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PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI

Cited by 23 publications

References 59 publications

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

Real‐time presurgical resting‐state fMRI in patients with brain tumors: Quality control and comparison with task‐fMRI and intraoperative mapping

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

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