The dual-stream model of speech processing describes a cortical network involved in speech processing. However, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI data sets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors collected at another site. We successfully identified an intrinsic functional network among the dual-stream model's regions in the control group using functional connectivity. We then used both standard functional connectivity analyses and graph theory approaches to determine how this connectivity may predict performance on clinical aphasia assessments. Our findings provide evidence that the dual-stream model of speech processing is an intrinsic network as measured via resting-state MRI and that functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. In addition, the functional connectivity of the hub nodes predicted linguistic impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs, versus to the right ventral stream hubs, is a particularly strong predictor of poststroke aphasia severity and symptomology.

Understanding the neurobiology of semantic knowledge is a major goal of cognitive neuroscience. Taxonomic and thematic semantic knowledge are represented differently within the brain's conceptual networks, but the specific neural mechanisms remain unclear. Some neurobiological models propose that the anterior temporal lobe is an important hub for taxonomic knowledge, whereas the TPJ is especially involved in the representation of thematic knowledge. However, recent studies have provided divergent evidence. In this context, we investigated the neural correlates of taxonomic and thematic confrontation naming errors in 79 people with aphasia. We used three complementary lesion-symptom mapping (LSM) methods to investigate how structure and function in both spared and impaired brain regions relate to taxonomic and thematic naming errors. Voxel-based LSM mapped brain damage, activation-based LSM mapped BOLD signal in surviving tissue, and network-based LSM mapped white matter subnetwork integrity to error type. Voxel- and network-based lesion symptom mapping provided converging evidence that damage/disruption of the left mid-to-anterior temporal lobe was associated with a greater proportion of thematic naming errors. Activation-based lesion symptom mapping revealed that higher BOLD signal in the left anterior temporal lobe during an in-house naming task was associated with a greater proportion of taxonomic errors on the Philadelphia Naming Test administered outside of the scanner. A lower BOLD signal in the bilateral angular gyrus, precuneus, and right inferior frontal cortex was associated with a greater proportion of taxonomic errors. These findings provide novel evidence that damage to the anterior temporal lobe is especially related to thematic naming errors.

Neuroimaging studies suggest that a fronto-parietal network is activated when we expect visual information to appear at a specific spatial location. Here we examined whether a similar network is involved for auditory stimuli. We used sparse fMRI to infer brain activation while participants performed analogous visual and auditory tasks. On some trials, participants were asked to discriminate the elevation of a peripheral target. On other trials, participants made a nonspatial judgment. We contrasted trials where the participants expected a peripheral spatial target to those where they were cued to expect a central target. Crucially, our statistical analyses were based on trials where stimuli were anticipated but not presented, allowing us to directly infer perceptual orienting independent of perceptual processing. This is the first neuroimaging study to use an orthogonal-cuing paradigm (with cues predicting azimuth and responses involving elevation discrimination). This aspect of our paradigm is important, as behavioral cueing effects in audition are classically only observed when participants are asked to make spatial judgments. We observed similar fronto-parietal activation for both vision and audition. In a second experiment that controlled for stimulus properties and task difficulty, participants made spatial and temporal discriminations about musical instruments. We found that the pattern of brain activation for spatial selection of auditory stimuli was remarkably similar to what we found in our first experiment. Collectively, these results suggest that the neural mechanisms supporting spatial attention are largely similar across both visual and auditory modalities.

Measures of brain activation (e.g., changes in scalp electrical potentials) have become the most popular method for inferring brain function. However, examining brain disruption (e.g., examining behavior after brain injury) can complement activation studies. Activation techniques identify regions involved with a task, whereas disruption techniques are able to discover which regions are crucial for a task. Voxel-based lesion mapping can be used to determine relationships between behavioral measures and the location of brain injury, revealing the function of brain regions. Lesion mapping can also correlate the effectiveness of neurosurgery with the location of brain resection, identifying optimal surgical targets. Traditionally, voxel-based lesion mapping has employed the chi-square test when the clinical measure is binomial and the Student's t test when measures are continuous. Here we suggest that the Liebermeister approach for binomial data is more sensitive than the chi-square test. We also suggest that a test described by Brunner and Munzel is more appropriate than the t test for nonbinomial data because clinical and neuropsychological data often violate the assumptions of the t test. We test our hypotheses comparing statistical tests using both simulated data and data obtained from a sample of stroke patients with disturbed spatial perception. We also developed software to implement these tests (MRIcron), made freely available to the scientific community.