Predicting Language Deficits After Stroke with Connectome-Based Imaging

Newswise — Mapping damage to the brain’s white matter connections after stroke can predict long-term language deficits, improve the understanding of how language is processed in the brain and potentially inform a course of rehabilitative therapy that would be more effective.

In a recent Journal of Neuroscience article, investigators at the Medical University of South Carolina and the University of South Carolina report that imaging all of the brain’s connections after stroke, in addition to imaging only the areas of cortical tissue damage, could better predict which patients will have language deficits and how severe those deficits will be.

Loss or impairment of the ability to speak is one of the most feared complications of stroke, one faced by about 20 percent of stroke patients. Language, as one of the most complex functions of the brain, is not seated in a single brain region but involves connections between many regions. This is referred to as the connectome.

The researchers found that mapping all of the brain’s white matter connections after stroke, in addition to imaging the areas of cortical tissue damage, could better predict which patients will have language deficits and how severe those deficits will be.

The laboratory of MUSC Health neurologist Leonardo Bonilha M.D., Ph.D., senior author on the Journal of Neuroscience article, focuses on connectome imaging, particularly as it relates to language loss after stroke. “Imaging the connectome of patients after stroke enables the identification of individual signatures of brain organization that can be used to predict the nature and severity of language deficits and one day could be used to guide therapy,” he said.

Grigori Yourganov, Ph.D., is the first author on the article. Other authors are Julius Fridriksson, Ph.D., Chris Rorden, Ph.D., and Ezequiel Gleichgerrcht, Ph.D, aphasia researchers at USC who recently received National Institutes of Health funding to establish a Center for the Study of Aphasia Recovery.

This study is the one of the first to use whole-brain connectome imaging to examine how disruptions to white matter connectivity after stroke affect language abilities. White matter fiber tracts are the insulated wires that connect one area of the brain to others. White matter is named for the myelin sheaths (insulation) that cover the many axons (wires) that make up the fiber tracts.

Yourganov said, “If you have two brain areas and both of them have to work together in order to carry out a function and the stroke lesion takes out axons that connect those brain areas — the two areas are intact but the communication between them is disrupted and so there is dysfunction.”

Currently, structural magnetic resonance imaging, or MRI, is used after stroke to assess damage in the cortical tissue — the brain’s gray matter. However, the extent of cortical lesion or damage often does not correlate with the severity of language deficits.

Bonilha said stroke patients sometimes have significant impairments beyond the amount of cortical damage. “It is also hard to predict how well a patient will recover based on the cortical lesion alone.”

The study enrolled 90 patients at MUSC and USC with aphasia, a communication disorder that results from damage to the parts of the brain that contain language, due to a single stroke occurring no less than six months prior. They were assessed in four areas related to speech and language using the Western Aphasia Battery — speech fluency, auditory comprehension, speech repetition and oral naming — as well as a summary score of overall aphasia.

Within two days of behavior assessment, each of the patients underwent traditional structural MRI imaging studies to map cortical damage as well as diffusion imaging, used for connectome mapping. The team then used a type of machine learning algorithm, support vector regression, to analyze the imaging results and make predictions about each patient’s language deficits.

Bonilha said the study demonstrates that damage to the white matter fiber tracts that connect the brain’s regions plays a role beyond cortical damage in language impairment after stroke. The study also shows that connections in the brain’s parietal region are particularly important for language function, especially fluency. This region is less likely to sustain damage after stroke, even in patients who experience aphasia, suggesting that damage or preservation of the brain’s connections in this region could play a key role in determining who will experience aphasia and who will have the best chances for recovery.

The integrity of these connections could not be mapped with conventional structural MRI but can now be assessed through connectome-based analysis. The study findings also suggest that connectome-based analysis could be used to develop a more individualized approach to stroke care.

Because the algorithms developed using these study patients can be generalized to a broader stroke population, connectome-based analysis could one day be used to identify the distinctive features of each patient’s stroke — which connections have been lost and which preserved. The algorithms could then be used to predict the type and severity of language impairment and the potential for recovery.

Bonilha, director of MUSC Health’s Language and Aphasia Clinic, said his team takes a multidisciplinary approach, combining the services of neurology and speech pathology. Its purpose is to diagnose and treat language disorders and aphasia. This study helps further that goal.

“By mapping much more accurately the individual pattern of brain structural connectivity in a stroke survivor, we can determine the integrity of neuronal networks and better understand what was lesioned and how that relates to language abilities that are lost,” said Bonilha.

“This is, broadly stated, a measure of post-stroke brain health. It is the individual signature pattern that could also be used to inform about the personalized potential for recovery with therapy and guide treatments to focus on the deficient components of the network.