Skip to main content

Multivariate machine learning‐based language mapping in glioma patients based on lesion topography

Abstract

Diffusive and progressive tumor infiltration within language-related areas of the brain induces functional reorganization. However, the macrostructural basis of subsequent language deficits is less clear. To address this issue, lesion topography data from 137 preoperative patients with left cerebral language-network gliomas (81 low-grade gliomas and 56 high-grade gliomas), were adopted for multivariate machine-learning-based lesion-language mapping analysis. We found that tumor location in the left posterior middle temporal gyrus—a bottleneck where both dorsal and ventral language pathways travel—predicted deficits of spontaneous speech (cluster size = 1356 mm3, false discovery rate corrected P < 0.05) and naming scores (cluster size = 1491 mm3, false discovery rate corrected P < 0.05) in the high-grade glioma group. In contrast, no significant lesion-language mapping results were observed in the low-grade glioma group, suggesting a large functional reorganization. These findings suggest that in patients with gliomas, the macrostructural plasticity mechanisms that modulate brain-behavior relationships depend on glioma grade.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Abbreviations

LGG:

low-grade glioma

HGG:

high-grade glioma

ABC:

Aphasia Battery of Chinese

AQ:

aphasia quotient

SS:

spontaneous speech

References

Download references

Acknowledgements

The authors wish to thank Dr. Xiuyi Wang and Jianfeng Zhang for their advice in drafting the manuscript.

Funding

This work was supported by the Shenzhen Double Chain Grant [2018]256, the Fundamental Research Funds for the Central Universities (No. WK9110000133), Guangdong Key Basic Research Grant (No. 2018B030332001), Guangdong Pearl River Talents Plan (No. 2016ZT06S220), China Postdoctoral Science Foundation (No. 2018M640825), National Natural Science Foundation of China (No. 81401395, No.82001794), Natural Science Foundation of Anhui Province (No. 2008085QH380), Shanghai Young Talents in Health (No. 2017YQ014), Key Program of Medical Science and Technique Foundation of Henan Province (No. SBGJ202002062), the Joint Construction Program of Medical Science and Technique Foundation of Henan Province (No. LHGJ20190156), Research Projects of Henan Higher Education (No.18A320077), the Scientific and Technological Research Projects of Henan Province (No.192102310123). J.S.W is supported by Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX03) and ZJ Lab. The funding agencies took no part in the design or implementation of the research.

Author information

Authors and Affiliations

Authors

Contributions

Conception and study design (JSW and JLC), data collection or acquisition (NZ, JFL and JY), statistical analysis (BKY), interpretation of results (NZ, BKY, JSW and JFL), drafting the manuscript work or revising it critically for important intellectual content (NZ, BKY and JFL) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Corresponding author

Correspondence to Junfeng Lu.

Ethics declarations

All processes strictly followed the requirements of the Declaration of Helsinki. This study was approved and supervised by the Ethics Committees of Huashan Hospital and First Affiliated Hospital of Zhengzhou University. Written informed consent was obtained from the legal guardians of all patients.

Competing financial interests

The authors declare no competing financial interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 285 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, N., Yuan, B., Yan, J. et al. Multivariate machine learning‐based language mapping in glioma patients based on lesion topography. Brain Imaging and Behavior 15, 2552–2562 (2021). https://doi.org/10.1007/s11682-021-00457-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-021-00457-0

Keywords

  • Low‐grade glioma
  • High‐grade glioma
  • Structural MRI
  • Language
  • Machine learning