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
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