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

• Published in: Frontiers in neuroscience Vol. 14; p. 611075
• Main Authors: Cai, Siqi, Shi, Zhifeng, Jiang, Chunxiang, Wang, Kai, Chen, Liang, Ai, Lin, Zhang, Lijuan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13-01-2021; Frontiers Media S.A
• Subjects: Brain cancer; Brain tumors; cerebral glioma; Classification; Datasets; Efficiency; Feature selection; functional connectivity; Functional magnetic resonance imaging; Glioma; Handedness; Hemispheric laterality; Malignancy; multivariate logistic regression; Neuroscience; Normal distribution; resting state functional MRI; Sparsity; Time series; topological feature; Tumors; cerebral glioma; functional connectivity; resting state functional MRI; topological feature; multivariate logistic regression
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2020.611075

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.
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.
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.
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.