All humans are unique individuals, some of it due to the differences between our brains. Being able to identify the differences in the structure and activity of our brains may have enormous consequences for neurology and neurosurgery.
While CT and MRI scans can’t yet provide a level of detail to diagnose many neurological conditions, researchers at Purdue University are working on using computational methods to spot biomarkers within imaging data. The team is relying on brain imaging scans obtained from the Human Connectome Project, a research venture to map the human brain. The scans were performed while the individuals were doing different tasks, allowing the researchers to focus on the brains in their active state.
Using complex mathematical techniques, the Purdue team is teasing out what characteristics unique individuals exhibit during various tasks. They’re able to now identify persons from each other by only looking at brain scans while these individuals perform simple tasks.
“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 at Purdue. “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.”
Some details from the study abstract in Nature Scientific Reports:
Here we show that the individual fingerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs [functional connectomes] through connectivity eigenmodes maximizes subject identifiability across resting-state and all seven tasks evaluated.
The identifiability of the optimally reconstructed individual connectivity profiles increases both at the global and edgewise level, also when the reconstruction is imposed on additional functional data of the subjects. Furthermore, reconstructed FC data provide more robust associations with task-behavioral measurements. Finally, we extend this approach to also map the most task-sensitive functional connections. Results show that is possible to maximize individual fingerprinting in the functional connectivity domain regardless of the task, a crucial next step in the area of brain connectivity towards individualized connectomics.
Study in Nature Scientific Reports: The quest for identifiability in human functional connectomes…