Connectome imaging may help predict the severity of language deficits after stoke -
loss or impairment of the ability to speak is one of the most feared complications of race - a face of about 20% of stroke patients. Language, as one of the most complex functions of the brain, are not sitting in a region of the brain only, but involves linkages between many areas.
In an article published in the June 22, 2016 issue of Journal of Neuroscience , researchers at the Medical University of South Carolina (MUSC) and the University of Carolina South (USC) report that the mapping of all connections of white matter of the brain after a stroke, in addition to the imaging of areas of cortical tissue lesions, could better predict which patients will have language deficits and severity of these deficits will. All brain connections is called connectome.
"Imaging the connectome patients after stroke allows identification of individual signatures brain organization that can be used to predict the nature and severity of language deficits and could one day be used to guide the treatment, "said neurologist MUSC Health Leonardo Bonilha MD, Ph.D., lead author of Journal of Neuroscience article, whose laboratory focuses on connectome imaging, in particular as regards loss language after a stroke. Grigory Yourganov, Ph.D., is the first author of the article. Julius Fridriksson Dr. Chris Rorden, Ph.D., and Ezequiel Gleichgerrcht, Ph.D., researchers from aphasia at USC who recently received funding from the NIH to establish a center for the study aphasia recovery and which are longtime collaborators laboratory Bonilha, are also authors of the paper.
This study is one of the first to use the whole brain connectome imaging to examine how disruption of white matter connectivity after stroke affect language abilities. White beam material fibers are insulated son that connect an area of the brain other. The white matter is referred to the myelin sheath (insulation) covering many axons (son) that make up the fiber bundles.
"If you have two areas of the brain and both of them have to work together to perform a function and the injury of the race takes the axons that connect these areas of the brain - the two areas are intact but the communication between them is disturbed and therefore there is a malfunction, "said Yourganov
Currently, structural magnetic. resonance imaging (MRI) is used after stroke to assess the damage in the cortical tissue - the gray matter of the brain. However, the extent of cortical damage often does not match the severity of language deficits.
"stroke patients often have significant impairments in excess of the amount of cortical damage," said Bonilha. "It is also difficult to predict how a patient will recover according to the single cortical lesion. "
-based Connectome imaging could be a useful addition to the evaluation of damage to brain connections after stroke and to guide rehabilitation therapy?
the study by Bonilha has taken an important first step towards answering these questions. the study, which enrolled 0 patients to MUSC and USC aphasic because suddenly occurring not less than six months before, evaluated four areas related to speech / language using the Western aphasia Battery - speech fluency, listening comprehension, repetition word, and the designation orally. - as well as an executive summary of the global aphasia Within two days of evaluation behaviors, each of the patients underwent imaging studies -. Both T1- and T2-weighted MRI, generally used after stroke to map cortical damage and diffusion imaging, used for mapping connectome
The team then used a type of machine learning algorithm - Support vector regression (SVR) - for analyzing imaging results and make predictions about the language deficits of each patient. In essence, an algorithm was created that could make the WAB score is a relevant characteristic imaging damage to the gray matter by structural MRI or relevant feature for imaging Connectome white bundles of fibers of the material brain . The team used 89 of the 0 patients as training sets for SVR and then used the algorithm to predict default language / conservation at the 0th patient. This was done for each of the 0 patients and each patient, for both structural features identified by MRI and imaging connectome.
The prediction accuracy of the WAB scoring algorithm for each patient was then evaluated by comparing the result determined by the WAB behavioral tests. Connectome-comparative analysis was as accurate as the mapping of the cortical lesion to predict the scores of WAB. In fact, it was better predict the auditory comprehension scores than was based imaging lesions using structural MRI, and only slightly less accurate in predicting speech fluency, word repetition, and naming scores .
The study demonstrates that the damage to the white matter bundles of fibers that connect brain regions play a role beyond cortical lesions in language disorders following stroke. In addition, this study also found that the connections in the parietal region of the brain are particularly important for the function of language, in particular mastery. This region is less likely to suffer damage after a stroke, even in patients who suffer from aphasia, suggesting that damage or preservation of brain connections in the region could play a key role in determining who experience aphasia and who will have the best chances of recovery. The integrity of these connections can not be mapped to the vector structural MRIs, but can now be evaluated using a connectome comparative analysis. Suggests
The results of the study that the connectome comparative analysis could be used to inform a more individualized approach to stroke care. Because the algorithms developed using these patients in the study as a whole training are generalizable to a larger population of the race, benchmarking connectome-could one day be used to identify the distinctive features of the race each patient - which connections were lost and preserved-- and the algorithm can be used to predict the type and severity of language and the recovery potential problems. This information could then be used to direct rehabilitation therapy to improve outcomes.
"For much more accurately mapping the individual pattern of structural brain connectivity in a stroke survivor, we can determine the integrity of neural networks and to better understand what has been wronged and how it refers to language skills that are lost, "said Bonilha." This is, broadly stated, a measure of brain stroke health. It is the reason individual signature could also be used to inform the Custom potential for recovery with treatment and therapy guidance to focus on deficient network components. "
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