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

Abstract

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.

Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging

“…64 Later, Lu et al proposed an automatic algorithm of component identification to match language networks and achieved a level of sensitivity that was superior to the SCA approach. 36 In 2018, Zacà et al developed a promising toolbox, ReStNeuMap, to automatically extract the RSNs in neurosurgical patients. 71 This toolbox provides the ICA results of different numbers of components and automatically identifies the motor, primary visual, and language networks.…”

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

“…3) The application of the pipeline in 6 patients demonstrated a good degree of spatial concordance between the localization of multiple rs-fMRI networks (motor, language, visual, and speech articulation) and the cortical sites that, when stimulated by intraoperative DES, impaired the corresponding function. 9,21,30 Signal spikes can affect the detection of the rs-fMRI connectivity pattern in the brain. Head motion is one, but not the only, cause of signal spikes.…”

“…Furthermore, its results have been validated only for language mapping. 21 In this study, we introduce a pipeline (resting-state neurosurgical mapping, ReStNeu-Map) for rs-fMRI data analysis designed to meet all of the aforementioned surgical and nonsurgical needs. We also assessed the agreement between its results and those of DES for motor, language, visual, and speech articulation mapping in a series of patients.…”

Optimization of rs‐fMRI parameters in the Seed Correlation Analysis (SCA) in DPARSF toolbox: A preliminary study

“…In this study, we focused on the influence of three parameters that is, ALFF, FWHM kernel, radius of ROI, which can be changed during preprocessing in the DPARSF toolbox, on the resultant connectivity maps. The result of rs‐fMRI analysis can be slightly modified since the used DPARSF toolbox is more and more popular as a tool for analyzing medical research such as epilepsy (Peng et al, ), autism (Chen, Nomi, Uddin, Duan, & Chen, ), neural mechanisms and individual differences in cognitive functions (Long et al, ) as well as surgical planning of brain tumor (Lu et al, ).…”