AUTHOR=Ren Weijing , Jia Chunying , Zhou Ying , Zhao Jingdu , Wang Bo , Yu Weiyong , Li Shiyi , Hu Yiru , Zhang Hao 

TITLE=A precise language network revealed by the independent component-based lesion mapping in post-stroke aphasia

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.981653

DOI=10.3389/fneur.2022.981653

ISSN=1664-2295

ABSTRACT=Brain lesion mapping studies have provided the strongest evidence regarding the neural basis of cognition. However, it remained a problem to identify the symptom-specific brain networks accounting for the observed clinical and neuroanatomical heterogeneity. Independent component analysis (ICA) is a statistical method that decomposes the mixed signals into multiple independent components. We aimed to solve this issue by proposing an Independent Component-based Lesion Mapping (ICLM) method to identify the language network in patients with moderate-to-severe post-stroke aphasia. Lesions were first extracted from 49 post-stroke aphasia patients as masks applied to the fMRI data in a cohort of healthy participants to calculate the functional connectivity (FC) within the masks and those non-mask brain voxels. ICA was further performed on the reformatted FC matrix to extract multiple independent networks. Specifically, we found that one of the lesion-related independent components (ICs) highly resembled classical language networks. Moreover, the damaged level within the language-related lesioned network is strongly associated with language deficits, including aphasia quotient, naming, and auditory comprehension scores. In comparison, none of the other two traditional lesion mapping methods found any regions responsible for language dysfunction. The language-related lesioned network extracted by the ICLM method showed high specificity in detecting the aphasia symptoms compared with the performance of the resting ICs and classical language networks. In total, we detected a precise language network in aphasia patients and proved its efficiency in the relationships with language symptoms. To be generalized, our ICLM could successfully identify multiple lesion-related networks from the complicated brain diseases, used as an effective tool to study the brain-behavior relationships and to provide potential biomarkers of particular clinical behavioral deficits.