White matter networks dissociate semantic control from semantic knowledge representations: Evidence from voxel-based lesion-symptom mapping

ABSTRACT

Although semantic system is composed of two distinctive processes (i.e., semantic knowledge and semantic control), it remains unknown in which way these two processes dissociate from each other. Investigating the white matter neuroanatomy underlying these processes helps improve understanding of this question. To address this issue, we recruited brain-damaged patients with semantic dementia (SD) and semantic aphasia (SA), who had selective predominant deficits in semantic knowledge and semantic control, respectively. We built regression models to identify the white matter network associated with the semantic performance of each patient group. Semantic knowledge deficits in the SD patients were associated with damage to the left medial temporal network, while semantic control deficits in the SA patients were associated with damage to the other two networks (left frontal-temporal/occipital and frontal-subcortical networks). The further voxel-based analysis revealed additional semantic-relevant white matter tracts. These findings specify different processing principles of the components in semantic system.

KEYWORDS:

• Semantic knowledge
• semantic control
• semantic dementia
• semantic aphasia
• white matter network

Acknowledgments

We thank Yuxing Fang for providing the white matter pathway masks, Jiyuan Li for editing the manuscript and Haoming Liu for drawing the figures. This work was supported by the National Key R&D Program of China under Grant (2017YFF0207400, 2016YFC1306305 & 2018YFC1315200), the National Natural Science Foundation of China under Grants (31872785, 81972144 & 81171019), and the Beijing Natural Science Foundation under Grant (7182088).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under [grant numbers 2017YFF0207400, 2016YFC1306305 & 2018YFC1315200], the National Natural Science Foundation of China under [grant numbers 31872785, 81972144 & 81171019), and the Beijing Natural Science Foundation under [grant number 7182088].