Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis.

ABSTRACT

OBJECTIVE: Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric connections (ICs) in language restoration remains unclear at the network level. Navigated transcranial magnetic stimulation (nTMS) and diffusion tensor imaging fiber tracking data were used to identify language-eloquent regions and their corresponding subcortical structures, respectively. METHODS: Preoperative image-based IC networks and nTMS mapping data from 30 patients without preoperative and postoperative aphasia as the nonaphasia group, 30 patients with preoperative and postoperative aphasia as the glioma-induced aphasia (GIA) group, and 30 patients without preoperative aphasia but who developed aphasia after the operation as the surgery-related aphasia group were investigated using fully connected layer-based deep learning (FC-DL) analysis to weight ICs. RESULTS: GIA patients had more weighted ICs than the patients in the other groups. Weighted ICs between the left precuneus and right paracentral lobule, and between the left and right cuneus, were significantly different among these three groups. The FC-DL approach for modeling functional and structural connectivity was also tested for its potential to predict postoperative language levels, and both the achieved sensitivity and specificity were greater than 70%. Weighted IC was reorganized more in GIA patients to compensate for language loss. CONCLUSIONS: The authors' method offers a new perspective to investigate brain structural organization and predict functional prognosis.