Dataset Information

White matter network of oral word reading identified by network-based lesion-symptom mapping.

ABSTRACT: Oral word reading is supported by a neural subnetwork that includes gray matter regions and white matter tracts connected by the regions. Traditional methods typically determine the reading-relevant focal gray matter regions or white matter tracts rather than the reading-relevant global subnetwork. The present study developed a network-based lesion-symptom mapping (NLSM) method to identify the reading-relevant global white matter subnetwork in 84 brain-damaged patients. The global subnetwork was selected among all possible subnetworks because its global efficiency exhibited the best explanatory power for patients' reading scores. This reading subnetwork was left lateralized and included 7 gray matter regions and 15 white matter tracts. Moreover, the reading subnetwork had additional explanatory power for the patients' reading performance after eliminating the effects of reading-related local regions and tracts. These findings refine the reading neuroanatomical architecture and indicate that the NLSM can be a better method for revealing behavior-specific subnetworks.

SUBMITTER: Li M

PROVIDER: S-EPMC8346667 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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White matter network of oral word reading identified by network-based lesion-symptom mapping.

Li Mingyang M Song Luping L Zhang Yumei Y Han Zaizhu Z

iScience 20210716 8

Oral word reading is supported by a neural subnetwork that includes gray matter regions and white matter tracts connected by the regions. Traditional methods typically determine the reading-relevant focal gray matter regions or white matter tracts rather than the reading-relevant global subnetwork. The present study developed a network-based lesion-symptom mapping (NLSM) method to identify the reading-relevant global white matter subnetwork in 84 brain-damaged patients. The global subnetwork was ...[more]

PMID: 34386727

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Project description:In the present study, we aimed to test the association between the correct function of the left ventral white matter pathways and semantic processing (dual stream models for language processing, Hickok & Poeppel, 2004), using a new set of language tasks during intraoperative electrical stimulation at white matter level. Additionally, we evaluated brain regions needed for correct performance on the different semantic tasks using lesion-symptom analyses (voxel lesion-symptom mapping and track-wise lesion analysis) in a sample of 62 candidates for the awake brain surgery. We found that electrical stimulation in the vicinity of the inferior longitudinal and inferior fronto-occipital fasciculi disturbed performance on semantic processing tasks. Individuals presented with significantly more semantic paraphasias during brain tumor resection than during the electrical stimulation at the cortex level. Track-wise analyses confirmed the role of these left ventral pathways in semantic processing: a significant relationship was observed between the probability of inferior fronto-occipital fasciculus disconnection/damage and the semantic matching tasks, as well as the number of semantic paraphasias in naming. Importantly, the same analyses for the total score of the Boston Naming Test confirmed significant relationships between this test score and the integrity of the inferior fronto-occipital, inferior longitudinal and uncinate fasciculi. This was further supported by the results of VLSM analyses showing a significant relationship between BNT and the presence of lesion within left middle and inferior temporal gyri. The present findings provide new intraoperative evidence for the role of the white-matter ventral pathways in semantic processing, while at the same time emphasizing the need to include a broader assessment of semantic-conceptual aspects during the awake neurosurgical intervention. This approach will ensure better preservation of functional tissue in the tumoral vicinity and therefore substantially diminish post-surgical language impairments.

Project description:White matter hyperintensities (WMH) are common in acute ischemic stroke patients. Although WMH volume has been reported to influence post-stroke cognition, it is still not clear whether WMH location, independent of acute ischemic lesion (AIL) volume and location, contributes to cognitive impairment after stroke. Here, we proposed a multiple-lesion symptom mapping model that considers both the presence of WMH and AIL to measure the additional contribution of WMH locations to post-stroke cognitive impairment. Seventy-six first-ever stroke patients with AILs in the left hemisphere were examined by Montreal Cognitive Assessment (MoCA) at baseline and 1 year after stroke. The association between the location of AIL and WMH and global cognition was investigated by a multiple-lesion symptom mapping (MLSM) model based on support vector regression (SVR). To explore the relative merits of MLSM over the existing lesion-symptom mapping approaches with only AIL considered (mass-univariate VLSM and SVR-LSM), we measured the contribution of the significant AIL and/or WMH clusters from these models to post-stroke cognitive impairment. In addition, we compared the significant WMH locations identified by the optimal SVR-MLSM model for cognitive impairment at baseline and 1 year post stroke. The identified strategic locations of WMH significantly contributed to the prediction of MoCA at baseline (short-term) and 1 year (long-term) after stroke independent of the strategic locations of AIL. The significant clusters of WMH for short-term and long-term post-stroke cognitive impairment were mainly in the corpus callosum, corona radiata, and posterior thalamic radiation. We noted that in some regions, the AIL clusters that were significant for short-term outcome were no longer significant for long-term outcome, and interestingly more WMH clusters in these regions became significant for long-term outcome compared to short-term outcome. This indicated that there are some regions where local WMH burden has larger impact than AIL burden on the long-term post-stroke cognitive impairment. In consequence, SVR-MLSM was effective in identifying the WMH locations that have additional impact on post-stroke cognition on top of AIL locations. Such a method can also be applied to other lesion-behavior studies where multiple types of lesions may have potential contributions to a specific behavior.

Dataset Information

White matter network of oral word reading identified by network-based lesion-symptom mapping.

Publications

White matter network of oral word reading identified by network-based lesion-symptom mapping.

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