Predicting language outcome and recovery after stroke: the PLORAS system

Abstract

The ability to comprehend and produce speech after stroke depends on whether the areas of the brain that support language have been damaged. Here, we review two different ways to predict language outcome after stroke. The first depends on understanding the neural circuits that support language. This model-based approach is a challenging endeavor because language is a complex cognitive function that involves the interaction of many different brain areas. The second approach, by contrast, does not require an understanding of why a lesion impairs language; instead, predictions are made on the basis of the recovery of previous patients with the same lesion. This approach requires a database that records the speech and language capabilities of a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions. In addition, a system is required that converts an MRI scan from a new patient into a three-dimensional description of the lesion and compares this lesion against all others on the database. The outputs of this system are the longitudinal language outcomes of corresponding patients in the database. This approach will provide the patient with a range of probable recovery patterns over a variety of language measures.

Figure 1. Brain activation during speech processing and the predicted effects of lesions

A. Left Schematic illustration of Broca’s area and Wernicke’s area. Right: fMRI activation for reciting the phrase “1,2,3.” This articulation task activates the same set of regions in the left and right hemisphere (right hemisphere not shown). B. fMRI activation in each of 6 healthy subjects listening to stories relative to listening to meaningless reversed speech. During this speech comprehension task, all 6 subjects consistently activate Wernicke’s area in the posterior superior temporal cortex. In addition, activation is consistently observed in the anterior superior temporal cortex and inconsistently observed in Broca’s area. C. Schematic and hypothetical illustration of the effects of lesions (grey hatched areas) when there are two alternative pathways (orange and pink) to the same output (red). The effect of the lesion depends on whether one of the pathways remains intact. Although this is more likely after a small lesion, it is not so much the size of the lesion that matters but where the lesion occurs. For example, the middle configuration has a small lesion that knocks out an area that is critical to all both pathways whereas the left configuration has a large lesion that leaves one pathway intact. The lesion in the right configuration illustrates how damage to two pathways can have a much more devastating effect than damage to one pathway. The effect of the lesion on recovery will influence the most appropriate type of therapy.

Figure 2. Schematic illustration of the procedures and database needed to estimate recovery of language after brain damage

The input is a high resolution structural MRI scan of a new patient. This is converted to a 3D image that indexes the degree of damage at every voxel (2mm³) in the brain. The lesion image is compared to those from all the other patients already in the database. Patients in the database are then selected if they have a very similar lesion to the new patient. The outputs are the language scores for the new patient plotted, over time to estimate the time course of recovery for the new patient. The outputs differ with the type of language function and will also depend on patient demographics, co-morbidity and degree and type of therapeutic intervention.