Comparison between resting state fMRI networks and responsive cortical stimulations in glioma patients

Cochereau, Jérôme; Deverdun, Jérémy; Herbet, Guillaume; Charroud, Céline; Boyer, Anthony; Moritz‐Gasser, Sylvie; Bars, Emmanuelle Le; Molino, François; Bonafé, Alain; Champfleur, Nicolas Menjot de; Duffau, Hugues

doi:10.1002/hbm.23270

Cited by 65 publications

(45 citation statements)

References 57 publications

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“…Similarly, gliomas may also lead to topographical alterations to the whole-brain FC connectomics (Hart et al 2016b(Hart et al , 2016aAerts et al 2016;Ghumman et al 2016;Bartolomei et al 2006;Bosma et al 2009;Cochereau et al 2016). It is probable that different gliomas could cause distinct influences to the brain functional connectomics, resulting in different aftermaths from multi-dimensional behavioral abnormalities to varied survival time.…”

Section: Introductionmentioning

confidence: 99%

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Liu

Zhang

et al. 2018

Brain Imaging and Behavior

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High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.

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Section: Introductionmentioning

confidence: 99%

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Liu

Zhang

et al. 2018

Brain Imaging and Behavior

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“…The potential added value of rsfMRI will be much more difficult to establish for language mapping due to the heterogenous representation of different language networks in the brain. However, rsfMRI for language mapping showed some initial promise [42] and warrants further research. Additionally, by analyzing all patients with available imaging data at two sites, potential selection biases based on tumor pathological grade, surgical selection or motor performance outcome were minimized and the likely generalizability of the findings to other glioma populations were maximized.…”

Section: Discussionmentioning

confidence: 98%

Presurgical Localization of the Primary Sensorimotor Cortex in Gliomas

et al. 2020

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Purpose Functional magnetic resonance imaging (fMRI) has an established role in neurosurgical planning; however, ambiguity surrounds the comparative value of resting and task-based fMRI relative to anatomical localization of the sensorimotor cortex. This study was carried out to determine: 1) how often fMRI adds to prediction of motor risks beyond expert neuroradiological review, 2) success rates of presurgical resting and task-based sensorimotor mapping, and 3) the impact of accelerated resting fMRI acquisitions on network detectability. Methods Data were collected at 2 centers from 71 patients with a primary brain tumor (31 women; mean age 41.9 ± 13.9 years) and 14 healthy individuals (6 women; mean age 37.9 ± 12.7 years). Preoperative 3T MRI included anatomical scans and resting fMRI using unaccelerated (TR = 3.5 s), intermediate (TR = 1.56 s) or high temporal resolution (TR = 0.72 s) sequences. Task fMRI finger tapping data were acquired in 45 patients. Group differences in fMRI reproducibility, spatial overlap and success frequencies were assessed with t-tests and χ 2-tests. Results Radiological review identified the central sulcus in 98.6% (70/71) patients. Task-fMRI succeeded in 100% (45/45). Resting fMRI failed to identify a sensorimotor network in up to 10 patients; it succeeded in 97.9% (47/48) of accelerated fMRIs, compared to only 60.9% (14/23) of unaccelerated fMRIs ((2) = 17.84, p < 0.001). Of the patients 12 experienced postoperative deterioration, largely predicted by anatomical proximity to the central sulcus. Conclusion The use of fMRI in patients with residual or intact presurgical motor function added value to uncertain anatomical localization in just a single peri-Rolandic glioma case. Resting fMRI showed high correspondence to task localization when acquired with accelerated sequences but offered limited success at standard acquisitions.

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“…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.…”

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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Comparison between resting state fMRI networks and responsive cortical stimulations in glioma patients

Cited by 65 publications

References 57 publications

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Presurgical Localization of the Primary Sensorimotor Cortex in Gliomas

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

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