Network-based statistics distinguish anomic and Broca's aphasia.

Abstract

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI). We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM. NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest. We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.