Hemisphere-Specific Functional Remodeling and Its Relevance to Tumor Malignancy of Cerebral Glioma Based on Resting-State Functional Network Analysis

Abstract

Background: Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis. Subjects and Methods: One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation. Results: Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas. Conclusion: Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

“…Grade II gliomas exhibit a higher peak position in the distribution profile of FC than grades III and IV [34], indicating that FC tends to be stronger in the former, and the ABI positively correlates with the tumor grade [36]. It was also proven that FC is a good predictor of tumor grade, achieving ROC area under the curve between 0.85 and 1 in classifying low-grade vs. high-grade gliomas in the left and the right side, respectively [37]. Moreover, patients with wildtype IDH display reduced FC [38] and greater ABI [36] versus controls at the whole-brain level.…”

“…[22][23][24][25][26][27][28][29][30]32,33,35,[38][39][40][43][44][45][46][47][49][50][51][52][53][54][55][56], grouped by field of application and reported in order of citation. This information was not available for the other 7 studies [31,34,36,37,41,42,48]. reviewed for this work [22,24,25,27,[29][30][31][32][33][34][35][36][37][38][40][41][42]…”

“…This information was not available for the other 7 studies [31,34,36,37,41,42,48]. reviewed for this work [22,24,25,27,[29][30][31][32][33][34][35][36][37][38][40][41][42][43][44][45][46][47][48][49][50][51][52][54][55][56], grouped by field of application, and reported in order of citation. This information was not available for the other 5 studies [23,26,28,39,53].…”

“…Number of patients (pt) with frontal, insular, parietal, and temporal glioma locations among the studies (N = 28) reviewed for this work[22][23][24][25][26][27][28][29][30]32,33,35,[38][39][40][43][44][45][46][47][49][50][51][52][53][54][55][56], grouped by field of application and reported in order of citation. This information was not available for the other 7 studies[31,34,36,37,41,42,48].…”

The dynamic functional connectivity fingerprint of high-grade gliomas

“…While most of these studies have focused their attention on speci c RSNs, such as language network (LANG) [14][15][16] , default-mode network (DMN) 12,[17][18][19][20][21] and fronto-parietal network (FPN) [16][17][18] , only a few number of papers have investigated these alterations at the whole-brain level 19,[22][23][24][25][26][27] .…”