As sports grapple with the emergence of research connecting brain trauma with neurodegenerative disease, one researcher is hoping an important diagnostic measure might lie in algorithms.
While injuries like Alzheimer’s Disease and Chronic Traumatic Encephalopathy (CTE) can only be confirmed by autopsy after death, there may be hope that technology can support more accurate diagnosis of diseases like Parkinson’s disease and motor neurone disease (MND).
Behavioural neuroscientist Dr Lyndsey Collins-Praino is recruiting volunteers for her new study at the University of Adelaide which hopes to begin building a biobank of data to feed a diagnostic algorithm connecting traumatic brain injury (TBI) to longer-term neurodegeneration.
“The link between TBI and those neurodegenerative outcomes [Parkinson’s and MND] is actually stronger than the link between TBI and dementia,” Collins-Praino says.
Even minor brain injuries like concussions can increase the risk of Parkinson’s by 56% according to current literature. A more traumatic injury sees the risk jump by 80%.
That’s why Collins-Praino is seeking 600 people who have endured a TBI to join her study, where they’ll undergo cognitive and motor testing and self-reported surveys to provide details around their lifestyle and circumstances of their injury.
They’ll then undergo an MRI scan, provide blood and saliva samples to tease out potential biomarkers.
This data will be used to build an algorithm that can compare a new patient’s information to those of people with diseases like Parkinson’s, and people without brain injury.
By combining machine learning with biomarker and brain scan analysis, Collins-Praino is hoping to provide finer detail and potentially more patient-specific care at the point of diagnosis.
“We’re certainly incorporating biomarkers using technology. But we’re also including genetic factors, we’re including novel neuroimaging, we’re including really targeted behavioural assessment and cognitive assessment, to really try to look at an individual from a full profile to … compare them to someone without a history of brain injury, age matched.
“And can we compare them to someone with established Parkinson’s disease to identify what factors what combination of factors might predict risk?
“That has utility because then when someone experiences a brain injury, you can look at those factors, feed them into our algorithm and say, ‘Okay, well, based on this profile that you display, we do believe you’re at increased risk’.”
Longer term, Collins-Praino hopes her research study, dubbed FIND-TBI, will assist clinicians with tools to give patients more personalised diagnoses.
More information on the study and volunteer participation is available at http://borrowmybrain.org/tbi.