Genetic algorithms can now be used to generate connections between the brain’s nuclei and measure their strengths. Much like our own genetic mutations over time, a genetic algorithm generates large numbers of potential solutions and then combines and adapts them until a desired genetic product is reached. Scientists at Jülich Research Center, Germany, investigated how the strength of connections in the brain’s basal ganglia change when someone develops Parkinson’s disease. Since every human brain is different, what is true for one Parkinsonian patient might not be true for another’s. The basal ganglia are a system of neuron clusters (nuclei) that are connected to each other and to other parts of the brain. These ganglia are associated with control of voluntary movements, procedural learning, and habit formation. When they malfunction, neurological disorders such as Parkinson’s disease and Tourette’s syndrome result. The team found a broad overlap between the strength of individual neural connections in healthy and Parkinsonian brains. But when they looked at the global network activity in response to stimulus, it was easy to determine whether a network configuration was healthy or Parkinsonian. A detailed analysis of these networks, especially the ones which lie on the boundaries between healthy and diseased, may shed light on how a brain transitions from a healthy to a Parkinsonian state. More importantly, it could help identify therapeutically feasible options that would allow transition from a Parkinsonian state back to a healthy state.