A team of researchers has developed the first computer model that effectively simulates how toxic proteins associated with several neurodegenerative diseases spread through the brain over a 30-year period. As well as suggesting a possible new diagnostic tool to catch these diseases early, the model could help researchers better identify the efficacy of new treatments.
Image: The above model shows how toxic tau proteins spread throughout the brains of patients with Alzheimer disease(Credit: Stevens Institute of Technology)
The model initially focused on three main neurodegenerative conditions: Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis (ALS). Particular misfolded proteins associated with the progression of each disease were initially identified by studying the brains of numerous deceased patients. Then, a computer model was developed incorporating knowledge of how different signals pass through the brain.
“Imagine a domino effect,” says Ellen Kuhl, a co-author on the new study. “What our model does is connect the dots between the static data points, mathematically, to slow disease progression in unprecedented detail.”
In the case of Alzheimer’s disease, for example, the team designed two separate simulations modeling the progression of tau and amyloid beta proteins, considered to be the primary pathological signs associated with the disease. Once the toxic proteins were “seeded” in a specific area of the brain, the model effectively simulated the development of the disease that accurately resembled the patterns associated its progression.
The researchers suggest that despite the variety of proteins behind different neurodegenerative diseases, and the different origin points in the brain, the progression of each condition can be generally predicted based on the fundamental neuroanatomy of the brain. Johannes Weickenmeier, a lead author on the study, says with more data and new brain imaging techniques, these disease visualization models should be able to eventually be applied to individual patients, helping doctors predict specific symptom progression.
“Once we have those, we’ll be able to calibrate our models to make accurate patient-specific predictions in the future,” says Weickenmeier.
As well as the three diseases studied, it is also believed the model can easily be applied to other neurodegenerative conditions underpinned by similar mechanisms. Multiple sclerosis and chronic traumatic encephalopathy are both specifically noted as likely targets.
The modeling software being developed is planned to be offered freely to other scientists. It will be known as the Living BrainProject, and the research team hopes making the technology openly accessible for no cost will spur other academics and commercial labs to more rapidly produce better diagnostics and interventions for dementia and associated neurodegenerative conditions.
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