Search

Frontal and temporal white matter pathways play key roles in language processing, but the specific computations supported by different tracts remain a matter of study. A role in speech planning has been proposed for a recently described pathway, the frontal aslant tract (FAT), which connects the posterior inferior frontal gyrus to the pre-SMA. Here, we use longitudinal functional and structural MRI and behavioral testing to evaluate the behavioral consequences of a lesion to the left FAT that was incurred during surgical resection of a frontal glioma in a 60-year-old woman, Patient AF. The pattern of performance in AF is compared, using the same measures, with that in a 37-year-old individual who underwent a left anterior temporal resection and hippocampectomy (Patient AG). AF and AG were both cognitively intact preoperatively but exhibited specific and doubly dissociable behavioral deficits postoperatively: AF had dysfluent speech but no word finding difficulty, whereas AG had word finding difficulty but otherwise fluent speech. Probabilistic tractography showed that the left FAT was lesioned postoperatively in AF (but not AG) whereas the inferior longitudinal fasciculus was lesioned in AG (but not AF). Those structural changes were supported by corresponding changes in functional connectivity to the posterior inferior frontal gyrus: decreased functional connectivity postoperatively between the posterior inferior frontal gyrus and pre-SMA in AF (but not AG) and decreased functional connectivity between the posterior inferior frontal gyrus and the middle temporal gyrus in AG (but not AF). We suggest from these findings that the left FAT serves as a key communicative link between sentence planning and lexical access processes.

The format of high-level object representations in temporal-occipital cortex is a fundamental and as yet unresolved issue. Here we use fMRI to show that human lateral occipital cortex (LOC) encodes novel 3-D objects in a multisensory and part-based format. We show that visual and haptic exploration of objects leads to similar patterns of neural activity in human LOC and that the shared variance between visually and haptically induced patterns of BOLD contrast in LOC reflects the part structure of the objects. We also show that linear classifiers trained on neural data from LOC on a subset of the objects successfully predict a novel object based on its component part structure. These data demonstrate a multisensory code for object representations in LOC that specifies the part structure of objects.