Yang Wang1, Ming Zhang2, Chen Niu3,4, Alexander D Cohen5, Xin Liu4, Hongwei Li4, Pan Lin6, Ziyi Chen5, Zhigang Min3, Wenfei Li3, Xiao Ling3, Xin Wen3, Maode Wang7, Hannah P Thompson5. 1. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA. yangwang@mcw.edu. 2. Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China. zhangming01@mail.xjtu.edu.cn. 3. Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China. 4. Department of Computer Science, Technical University of Munich, Munich, Germany. 5. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA. 6. Department of Psychology, Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, 410081, Hunan, China. 7. Department of Neurosurgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
Abstract
OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.
OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumorpatients with anatomical and functional reorganizations. KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.
Entities:
Keywords:
Brain neoplasms; Humans; Magnetic resonance imaging; Motor cortex; Neural networks