Highly Creative Brains are Better Connected1 year, 5 months ago
Posted on Apr 17, 2017, 6 a.m.
New study reveals that creative people have more connections between the right and left hemispheres of their brains.
Recent scientific research has found those who lack creativity have a different brain structure than those who are highly creative. Though plenty of people like to state that everyone has the potential to be artist, the truth is that creativity is predominantly determined by brain connectivity. It is not possible for one to simply "tap into" the right side of his brain to become creative. The connectivity of the brain's right hemisphere to the left hemisphere is what determines an individual's level of creativity.
White Matter Matters!
Highly creative individuals are blessed with uniquely structured brains. The brain of a creative individual has an abundance of white matter connections between the left and right hemispheres. This conclusion was reached by Duke University researchers who conducted extensive brain analyses. The study was spearheaded by statisticians David Dunson and Daniele Durante. Dunson hails from Duke while Durante represents the University of Padova. The pair studied the white matter connections across 68 distinct brain regions in healthy college-age individuals who were willing to participate in the research effort.
White matter is positioned beneath grey matter on the perimeter of the brain. White matter is made of bundles of axons or wires that connect billions of neurons and transmit electrical signals to one another. This study is a component of the relatively recent field of connectomics that makes use of network science to gain an understanding of the human brain. Rather than keying in on specific brain regions in isolation, this field's researchers utilize complex brain imaging strategies to pinpoint and map the brain's dense and rich web of links.
A research team headed by the University of New Mexico's acclaimed neuroscientist, Rex Jung, gathered data for the above-referenced study. They collected data with an MRI technique referred to as diffusion tensor imaging. This technique empowers researchers to look through the skulls of living human beings and trace the flow of axons. These pathways are traced by following the flow of water along them. Computers are then used to filter through the one-gigabyte scans to convert them to 3D maps. This effort produces wiring diagrams of unique human brains.
Jung's research team utilized an array of tests to determine creativity levels. Problem-solving known as divergent thinking was gauged. This style of thinking measures one's ability to provide an array of answers to a question. Participants were asked to draw as many geometric shapes as they could in a timed session. Participants were asked to list new uses for simple objects like paper clips.
Researchers also asked study participants to complete a questionnaire about their accomplishments in ten specific areas such as cooking, dance, creative writing, visual arts etc. Each individual's responses were used to generate a composite creativity score. Durante and Dunson programmed computers to sort through the data and pinpoint differences in participants' brains.
The research team could not pinpoint statistical differences in connectivity between the hemispheres or between male and female participants. However, when those who scored in the top 15 percent in terms of creativity and those who scored in the bottom 15 percent were compared, the high-scorers brains' had considerably more connections between the left and right hemispheres. The majority of the differences were in the frontal lobe section of the brain.
Dunson believes his approach can prove a person's creativity according to his brain network structure. Dunson's team is working on statistical methods to determine if brain connectivity varies according to I.Q. They are also employing methods to detect Alzheimer's disease as early as possible and distinguish it from regular aging.
Daniele Durante, David B. Dunson. Bayesian Inference and Testing of Group Differences in Brain Networks. Bayesian Analysis, 2016; DOI: 10.1214/16-BA1030