The role of deep cerebral tracts in predicting postoperative aphasia: an nTMS-based investigation of the corticothalamic fibers - Bao, Zixu; Zhang, Haosu; Schwendner, Maximilian; Schröder, Axel; Meyer, Bernhard; Krieg, Sandro; Ille, Sebastian (Human brain mapping, 2025)
Postoperative aphasia (POA) is a common complication in patients undergoing surgery for language-eloquent lesions. This study aimed to enhance the prediction of POA by leveraging preoperative navigated transcranial magnetic stimulation (nTMS) language mapping and diffusion tensor imaging (DTI)-based tractography, incorporating deep learning (DL) algorithms. One hundred patients with left-hemispheric lesions were retrospectively enrolled (43 developed postoperative aphasia, as the POA group; 57 did not, as the non-aphasia (NA) group). Fiber tracking was performed at fractional anisotropy threshold (FAthres) of 0.10 and 0.15, analyzing the total fiber volume (Vfibertotal), average fractional anisotropy (FAwhole), and number of visualized tracts. Additionally, the visualization ratio (VR) and FA-sensitive visualization were assessed for individual tractography. The NA group demonstrated significantly higher Vfibertotal (FAthres = 0.10: 61.1 vs. 51.7 cm3, p = 0.029; FAthres = 0.15: 36.9 vs. 29.6 cm3, p = 0.008), higher FAwhole (FAthres = 0.10: 0.38 vs. 0.35, p = 0.006; FAthres = 0.15: 0.42 vs. 0.39, p = 0.006), and greater tract numbers (FAthres = 0.10: 6.1 vs. 5.7, p = 0.111; FAthres = 0.15: 5.6 vs. 4.8, p = 0.004). Among individual fiber tracts, the corticothalamic fibers (CtF) showed significantly higher VR in the NA group (86.0% vs. 58.1%, p = 0.003), whereas FA-sensitive visualization of CtF was higher in the POA group (11.6% vs. 0.0%, p = 0.013). A binary DL model developed to predict POA achieved a sensitivity of 72.3% and specificity of 85.3%, with an area underthecurve (AUC) of 0.82. Our findings demonstrate the potential of nTMS-based tractography to predict POA by integrating DL. The CtF showed the most significant potential in predicting aphasia risk and understanding the complexity of the language network, whereas their individual predictive contribution within the model remained limited.
Veröffentlicht: 11 September 2025
Gesehen am 19.01.2026