Predicting Language Deficits After Stroke With Connectome-Based Imaging

Prediction is not the important part of this, the fact that we now can have an objective damage diagnosis is key to being able to map successful interventions to damage. In other words, the starting points needed to write up stroke protocols. But nothing will get done because our fucking failures of stroke associations will not understand the importance of this.
http://dgnews.docguide.com/predicting-language-deficits-after-stroke-connectome-based-imaging?
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, according to a study published in the Journal of Neuroscience.
Loss or impairment of the ability to speak is one of the most feared complications of stroke. 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, 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.
“Imaging the connectome of patients after stroke enables the identification of individual signatures of brain organisation that can be used to predict the nature and severity of language deficits and one day could be used to guide therapy,” said Leonardo Bonilha, MD, Medical University of South Carolina, Charleston, South Carolina.
The current 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.
The study enrolled 90 patients at with aphasia due to a single stroke occurring no less than 6 months prior. They were assessed in 4 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 2 days of behaviour assessment, each of the patients underwent traditional structural magnetic resonance imaging (MRI) 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 analyse the imaging results and make predictions about each patient’s language deficits.
The study demonstrated that damage to the white matter fibre tracts that connect the brain’s regions plays a role beyond cortical damage in language impairment after stroke. The study also showed 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 individualised approach to stroke care.
Because the algorithms developed using these study patients can be generalised to a broader stroke population, connectome-based analysis could one day be used to identify the distinctive features of each patient’s stroke. The algorithms could then be used to predict the type and severity of language impairment and the potential for recovery.
“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 Dr. 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.”
SOURCE: Medical University of South Carolina