Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

Yuan, Binke; Zhang, Nan; Yan, Jing; Cheng, Jingliang; Lu, Junfeng; Wu, Jinsong

doi:10.1016/j.nicl.2019.102023

Cited by 37 publications

(27 citation statements)

References 85 publications

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“…The ability of several rsFC ROI pairs to predict OS further extends our previous report that intratumor rsFC may be a prognostic marker for overall survival ( 18 ). This finding aligned with previous reports that brain tumors can lead to various cognitive deficits and are related to alterations in local and interhemispheric rsFC across functional networks ( 49 , 50 , 55 , 56 ). Here we showed that particular ROI-pair dependent reductions and increases in rsFC are associated with OS prediction, suggesting that connectivity alterations at specific cortical locations play an important role in influencing outcomes.…”

Section: Discussionsupporting

confidence: 92%

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

et al. 2021

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Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

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Section: Discussionsupporting

confidence: 92%

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

et al. 2021

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“…One potential limitation of our study is the generalization of the neuroplasticity strategy we described to other neurological conditions. The slow-growing feature of low-grade gliomas gives the brain the best opportunities to reorganize its networks over time, in parallel to tumor infiltration ( Desmurget et al, 2007 , Yuan et al, 2019 ). Whether homotopic plasticity may occur in sudden neurological diseases such as stroke injury or other neurological events remains to date unclear.…”

Section: Discussionmentioning

confidence: 99%

Homotopic redistribution of functional connectivity in insula-centered diffuse low-grade glioma

Almairac

Deverdun

Cochereau

et al. 2021

NeuroImage: Clinical

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Highlights Large and homogenous sample of patients with insula-centered low-grade gliomas. Functional connectivity of the contralesional insula compared with healthy controls. Inter-insular connectivity decreased. Functional connectivity increased towards bilateral visual and sensorimotor networks. Homotopic redistribution of the functional connectivity in brain-damaged patients.

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“…Interestingly, different patterns of neuroplasticity based upon various mechanisms of FC changes were correlated with different levels of cognitive disturbances: Cerebellar activity in regions with increased alteration was significantly associated with language (especially naming and comprehension scores) and MMSE Mini Mental State Examination scores [ 34 ]. Therefore, Yuan et al [ 72 ] concluded that individual language processing investigated in patients with a left hemispheric tumor using rsfMRI relied on large-scale, bilateral, cortical-subcortical, and cerebro-cerebellar circuits with various network reallocation mechanisms underpinning the different levels of language worsening in LGG and HGG patients.…”

Section: The Contribution Of Fni and Tms Mapping To Investigate Inmentioning

confidence: 99%

Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients

Duffau

2020

Cancers

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Intraoperative direct electrostimulation mapping (DEM) is currently the gold-standard for glioma surgery, since functional-based resection allows an optimization of the onco-functional balance (increased resection with preserved quality of life). Besides intrasurgical awake mapping of conation, cognition, and behavior, preoperative mapping by means of functional neuroimaging (FNI) and transcranial magnetic stimulation (TMS) has increasingly been utilized for surgical selection and planning. However, because these techniques suffer from several limitations, particularly for direct functional mapping of subcortical white matter pathways, DEM remains crucial to map neural connectivity. On the other hand, non-invasive FNI and TMS can be repeated before and after surgical resection(s), enabling longitudinal investigation of brain reorganization, especially in slow-growing tumors like low-grade gliomas. Indeed, these neoplasms generate neuroplastic phenomena in patients with usually no or only slight neurological deficits at diagnosis, despite gliomas involving the so-called “eloquent” structures. Here, data gained from perioperative FNI/TMS mapping methods are reviewed, in order to decipher mechanisms underpinning functional cerebral reshaping induced by the tumor and its possible relapse, (re)operation(s), and postoperative rehabilitation. Heterogeneous spatiotemporal patterns of rearrangement across patients and in a single patient over time have been evidenced, with structural changes as well as modifications of intra-hemispheric (in the ipsi-lesional and/or contra-lesional hemisphere) and inter-hemispheric functional connectivity. Such various fingerprints of neural reconfiguration were correlated to different levels of cognitive compensation. Serial multimodal studies exploring neuroplasticity might lead to new management strategies based upon multistage therapeutic approaches adapted to the individual profile of functional reallocation.

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Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

Cited by 37 publications

References 85 publications

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Homotopic redistribution of functional connectivity in insula-centered diffuse low-grade glioma

Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients

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