Could also be useful
for football and hockey
for football and hockey
Two MR techniques may predict cognitive impairment in professional fighters
July 27, 2017
by Lauren Dubinsky, Senior Reporter
A new Radiology study revealed that two different MR techniques could help predict cognitive impairment in professional fighters.
“There has been a plethora of research articles, looking at either gray matter or white matter in isolation, [that] have found changes due to repetitive head trauma in fighters,” Dr. Virendra Mishra of Cleveland Clinic Lou Ruvo Center for Brain Health, told HCB News. “Unfortunately, it has been very difficult to translate these findings into clinical practice and classify those fighters who are vulnerable to cognitive decline.”
Mishra and his team hypothesized that if they evaluate both types of tissue with a multivariate approach, they may be able to classify the vulnerable fighters early on. That approach involved T1-weighted MR and diffusion-tensor imaging.
The team used data from the Professional Fighters Brain Health Study on 273 male fighters who underwent baseline imaging exams, and 56 who returned for a follow-up scan. Neuropsychological testing, and measures from processing speed and psychomotor speed were used to categorize the fighters as cognitively impaired or non-impaired.
All fighters underwent T1-weighted MR and DTI and the team found a set of seven imaging predictors, including regions of gray and white matter that were associated with cognitive function in fighters. For example, T1-weighted volumetric measurements of the left thalamus, which is gray matter, helped distinguish cognitively impaired and non-impaired fighters.
Fractional anisotropy values, a measure of white matter integrity, along two different white matter tracts, were also found to be potential predictors of cognitive impairment. The team concluded that the seven imaging predictors may become biomarkers for cognitive impairment in fighters.
“The MR techniques employed in our research definitely have the potential to become the standard for predicting cognitive impairment in fighters,” said Mishra. “Combining information from multiple MRI modalities in an intelligent fashion, using the techniques of machine learning, will certainly improve the diagnostic ability in routine clinical practice.”
The approach has several potential applications — to help predict later cognitive changes in fighters and to track change in clinical trials of therapies that reduce the risk of cognitive impairment. It could also be used to study the impact of other contact sports in which head injuries are common, including football and hockey.
“We are currently working on validating our technique across different sites and populations, and hope to refine the predictors of cognitive decline in fighters due to repetitive head trauma,” said Mishra.
“There has been a plethora of research articles, looking at either gray matter or white matter in isolation, [that] have found changes due to repetitive head trauma in fighters,” Dr. Virendra Mishra of Cleveland Clinic Lou Ruvo Center for Brain Health, told HCB News. “Unfortunately, it has been very difficult to translate these findings into clinical practice and classify those fighters who are vulnerable to cognitive decline.”
Mishra and his team hypothesized that if they evaluate both types of tissue with a multivariate approach, they may be able to classify the vulnerable fighters early on. That approach involved T1-weighted MR and diffusion-tensor imaging.
The team used data from the Professional Fighters Brain Health Study on 273 male fighters who underwent baseline imaging exams, and 56 who returned for a follow-up scan. Neuropsychological testing, and measures from processing speed and psychomotor speed were used to categorize the fighters as cognitively impaired or non-impaired.
All fighters underwent T1-weighted MR and DTI and the team found a set of seven imaging predictors, including regions of gray and white matter that were associated with cognitive function in fighters. For example, T1-weighted volumetric measurements of the left thalamus, which is gray matter, helped distinguish cognitively impaired and non-impaired fighters.
Fractional anisotropy values, a measure of white matter integrity, along two different white matter tracts, were also found to be potential predictors of cognitive impairment. The team concluded that the seven imaging predictors may become biomarkers for cognitive impairment in fighters.
“The MR techniques employed in our research definitely have the potential to become the standard for predicting cognitive impairment in fighters,” said Mishra. “Combining information from multiple MRI modalities in an intelligent fashion, using the techniques of machine learning, will certainly improve the diagnostic ability in routine clinical practice.”
The approach has several potential applications — to help predict later cognitive changes in fighters and to track change in clinical trials of therapies that reduce the risk of cognitive impairment. It could also be used to study the impact of other contact sports in which head injuries are common, including football and hockey.
“We are currently working on validating our technique across different sites and populations, and hope to refine the predictors of cognitive decline in fighters due to repetitive head trauma,” said Mishra.