Ajay D Halai

Tractography is widely used in human studies of connectivity with respect to every brain region, function, and is explored developmentally, in adulthood, ageing, and in disease. However, the core issue of how to systematically threshold, taking into account the inherent differences in connectivity values for different track lengths, and to do this in a comparable way across studies has not been solved. By utilising 54 healthy individuals' diffusion‐weighted image data taken from HCP, this study adopted Monte Carlo derived distance‐dependent distributions (DDDs) to generate distance‐dependent thresholds with various levels of alpha for connections of varying lengths. As a test case, we applied the DDD approach to generate a language connectome. The resulting connectome showed both short‐ and long‐distance structural connectivity in the close and distant regions as expected for the dorsal and ventral language pathways, consistent with the literature. The finding demonstrates that the DDD approach is feasible to generate data‐driven DDDs for common thresholding and can be used for both individual and group thresholding. Critically, it offers a standard method that can be applied to various probabilistic tracking datasets.

Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain–behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.

Although impaired discourse production is one of the prominent features of aphasia, only a handful of investigations have addressed the cognitive, linguistic and neural processes that support the production of coherent discourse. In this study, we investigated the cognitive and neural correlates of discourse coherence in a large mixed cohort of patients with post-stroke aphasia, including the first voxel-based lesion-symptom mapping of coherence deficits. Discourse responses using different tasks were collected from 46 patients with post-stroke aphasia, including a wide range of classifications and severity levels, and 20 matched neuro-typical controls. Global coherence, defined as the degree to which utterances related to the expected topic of discourse, was estimated using a previously validated computational linguistic approach. Coherence was then related to fundamental language and cognitive components in aphasia identified using an extensive neuropsychological battery. Relative to neuro-typical controls, patients with aphasia exhibited impaired coherence, and their ability to maintain coherent discourse was related to their performance on other language components: phonological production, fluency and semantic processing, rather than executive functions or motor speech. These results suggest that impairments in core language components play a role in reducing discourse coherence in post-stroke aphasia. Whole-brain voxel-wise lesion-symptom mapping using univariate and multivariate approaches identified the contribution of the left prefrontal cortex, and particularly the inferior frontal gyrus (pars triangularis), to discourse coherence. These findings provide convergent evidence for the role of the inferior frontal gyrus in maintaining discourse coherence, which is consistent with the established role of this region in producing connected speech and semantic control (organizing and selecting appropriate context-relevant concepts). These results make an important contribution to understanding the root causes of disrupted discourse production in post-stroke aphasia.