Sydney, June 2 The shape of brain, not interactions between different regions, is crucial in how we think, feel and behave, finds a new study that may pave the way for new dementia and stroke treatments.
For over a century, researchers have thought that the patterns of brain activity that define our experiences, hopes, and dreams are determined by how different brain regions communicate with each other through a complex web of trillions of cellular connections.
Now, researchers at Australia's Monash University examined more than 10,000 different maps of human brain activity and found that the overall shape of a person's brain exerts a far greater influence on how we think, feel and behave than its intricate neuronal connectivity.
The study, published in the journal Nature, draws together approaches from physics, neuroscience, and psychology to overturn the century-old paradigm emphasising the importance of complex brain connectivity, instead identifying a previously unappreciated relationship between brain shape and activity.
The findings are significant because they greatly simplified the way that we can study how the brain functions, develops and ages, said lead author Dr James Pang, from the varsity's Turner Institute.
"The work opens opportunities to understand the effects of diseases like dementia and stroke by considering models of brain shape, which are far easier to deal with than models of the brain's full array of connections," he said.
The team used magnetic resonance imaging (MRI) to study eigenmodes, which are the natural patterns of vibration or excitation in a system, where different parts of the system are all excited at the same frequency.
Eigenmodes are normally used to study physical systems in areas such as physics and engineering and have only recently been adapted to study the brain.
The study focused on developing the best way to efficiently construct the eigenmodes of the brain.
The team compared how well eigenmodes obtained from models of the shape of the brain could account for different patterns of activity when compared to eigenmodes obtained from models of brain connectivity.
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