Understanding how the human brain produces complex thought is daunting given its intricacy and scale. The brain is home to around 100 billion neurons. These neurons coordinate their activity through 100 trillion connections. These connections are typically similar between people.
A Dartmouth study has found an innovative way to look at brain networks by using mathematical concepts of fractals. This concept can be used to communicate patterns of communication between brain regions while listening to an audio story. The results are published in Nature Communications.
Our brains create amazing connections patterns to create our thoughts. The patterns are beautiful however, they are extremely complicated. Our mathematical framework allows us to quantify how those patterns relate to different scales and how they change with time.”
Jeremy R. Manning, Study Senior Author and Assistant Professor of Psychological and Brain Sciences, Dartmouth College
In the field of geometry, fractals are shapes that appear similar at different sizes. Within a fractal, patterns and patterns are repeated in an endless cascade, like spirals made up of smaller spirals which are then surrounded by still-smaller spirals, and further. Dartmouth’s research has revealed that brain networks are structured in a similar fashion patterns of brain interactions can be mirrored at different sizes. When people engage in complex thoughts, their brain networks seem to spontaneously organize into patterns that resemble fractals. If the thoughts are disturbed the fractal patterns could be chaotic and lose their integrity.
Researchers have developed mathematical models to discover patterns in network interactions on different scales or “orders”. The team called this a “zero-order” pattern in which brain structures don’t display consistent patterns of interaction.
A “first-order” pattern is when two brain structures interact. “Second order” patterns refers to similar patterns of interaction in different brain structures at various scales. When patterns of interaction develop into “fractal(fractal)” or “first-order” the order refers to the number of times they are repeated on different scales.
The study shows that when listeners listened to an audio recording of a 10-minute story their brain networks spontaneously organized into fourth-order network patterns. The pattern was disrupted after the recording was altered. The brains of the participants displayed only second-order patterns when the story’s paragraphs were randomly shuffled. This retained some but not all the story’s meaning. If every word of the story was shuffled this caused disruption of all but the most basic (zero-order) patterns.
“The more finely the story was shuffled, the more fractal patterns of the patterns of the network were disorganized,” said first author Lucy Owen, a graduate student in psychological and brain sciences at Dartmouth. “Since the disruptions in those patterns were directly associated with the degree to which people were able to comprehend the story, this research could shed light on how our brain structures work together to understand what’s happening in the story.”
The fractal patterns were surprisingly similar across people: patterns from one group could be used to accurately estimate what portion of the story another group was listening to.
The team also examined which brain structures were interconnected to create these fractal patterns. The results revealed that the smaller (first-order) interactions took place in brain regions processing raw sounds. Second-order interactions connected these sounds with speech processing regions and third-order interactions linked to sound and speech regions that are connected to the visual processing regions.
The most extensive scale (fourth-order) interactions linked these sensory networks with brain structures that facilitate high-level thinking. Researchers believe that when these networks are organized at various scales, it could reveal how the brain processes sensory information into complex thinking. This could be the case with raw sounds and speech, as well as visualisation and full-on understanding.
The researchers’ computational framework can also be applied to other areas that go beyond neuroscience. The team has already started using a similar method to study the interplay between prices of stock and animal movement patterns.
Owen, L. L. W., et al. (2021) High-level cognition during listening to stories is reflected in high-order dynamic correlations in neural activity patterns. Nature Communications. doi.org/10.1038/s41467-021-25876-x.
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