WEST LAFAYETTE, Ind. — Patients with dementia and other neural diseases show physical symptoms such as stumbling and confusion, but identifying the problem isn’t as simple as taking an X-ray. A group of researchers at Purdue University are designing data-driven tools that will help clinicians better understand the progression of neurodegenerative diseases by identifying and tracking changes in the brain.

“We’re not to the point where we’re taking X-rays to see if you have a broken bone in your leg, but we’re at least at the stage where we’re saying, ‘Your gait is very funny,’” said Tom Talavage, professor of electrical and computer engineering and biomedical engineering, and a co-investigator for the project. “We can narrow it down to something wrong with your leg, and we can make inferences about what’s wrong with your leg. We can say, ‘You probably have a broken leg because of how you’re walking.’ That’s what we’re really getting at.”

The project is led by Joaquín Goñi, an assistant professor of biomedical engineering who studies the network of neural connections composing the human brain. This network is called the connectome, the focus of an emerging field of study known as brain connectomics. Brain-imaging techniques, such as diffusion weighted imaging and functional magnetic resonance imaging (fMRI), allow neuroscientists to model and examine the connectome to understand communication between different regions of the brain. This helps them see which parts of the brain are functioning normally - and which regions are not - by observing changes over time.
“What we’re really doing is starting to create the means to see symptoms of neurodegenerative diseases; they become physical in the graphical approaches we apply. It’s very visual,” Talavage said.

The team is using data from the Human Connectome Project, a collection of data sets from different projects focused on the connectome, to research and develop their method. Goñi and postdoctoral researcher Enrico Amico recently published a paper in Nature Scientific Reports using data from the Human Connectome Project to propose a data-driven method for assessing the connectome. Their work demonstrates that individual connectomes are unique enough to be identifiable – and could potentially be used to better understand the differences between people’s individual connectomes and how this relates to their health.

Their project uses brain-imaging data that shows both “structural” and “functional” connectivity – physical connections between different regions of the brain, and the communication between those regions as a subject performs a given task. Making sense of an individual person’s network of connections and understanding their cognitive health depends on images of brain activity collected during these tasks.

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