For decades, scientists have believed that mapping the intricate connectivity of the human brain is necessary to understand how the structured patterns of activity defining our thoughts, feelings, and behavior emerge. However, a new study challenges this conventional wisdom by revealing structured patterns of activity across nearly the entire brain that relate to thoughts and sensations in much the same way that a musical note arises from vibrations occurring along the entire length of a violin string, not just an isolated segment.
The study, conducted by researchers at the University of California, Berkeley, uncovered this close relationship between shape and function by examining the natural patterns of excitation that can be supported by the anatomy of the brain. In these patterns, called “eigenmodes,” different parts of the brain are all excited at the same frequency.
In a similar way to the preferred vibrational patterns of a violin string that give rise to musical notes, the brain has its own preferred patterns of excitation, which are determined by its anatomical and physical properties. The researchers set out to identify which specific anatomical properties of the brain most strongly affect these patterns.
The Continuous, Wave-Based View of the Brain
The conventional view of the brain is that specific thoughts or sensations elicit activity in specific parts of the brain. This perspective views the brain as a collection of discrete regions, each specialized for a particular function, such as vision or speech. These regions communicate via interconnecting fibers called axons.
However, an alternative view, embodied by an approach to modeling brain activity called neural field theory, eschews this division of the brain into discrete areas. This view focuses on how waves of cellular excitation move continuously through brain tissue, like the ripples formed by raindrops falling into a pond. Just as the shape of the pond constrains the possible patterns formed by the ripples, wavelike patterns of activity are influenced by the three-dimensional shape of the brain.
To compare the two views of the brain, the researchers tested how easily the conventional, discrete view and the continuous, wave-based view can explain more than 10,000 different maps of brain activity. The activity maps were obtained from thousands of functional magnetic resonance imaging (fMRI) experiments as people performed a wide array of cognitive, emotional, sensory, and motor tasks.
The researchers attempted to describe each activity map using eigenmodes based on the brain’s connectivity and eigenmodes based on the brain’s shape. They found that eigenmodes of brain shape – not connectivity – offer the most accurate account of these different activation patterns.
The researchers used computer simulations to confirm that the close link between brain shape and function is driven by wavelike activity propagating throughout the brain. The simulations relied on a simple wave model that is widely used to study other physical phenomena, such as earthquakes and ocean currents. The model only uses the shape of the brain to constrain how the waves evolve through time and space.
Despite its simplicity, this model explained brain activity better than a more sophisticated, state-of-the-art model that tries to capture key physiological details of neuronal activity and the intricate pattern of connectivity between different brain regions.
The researchers also found that most of the 10,000 different brain maps that they studied were associated with activity patterns spanning nearly the entire brain. This result challenges conventional wisdom that activity during tasks occurs in discrete, isolated regions of the brain and indicates that traditional approaches to brain mapping may only reveal the tip of the iceberg when it comes to understanding how the brain works.
Together, these findings suggest that current models of brain function need to be updated. Rather than focusing solely on how signals pass between discrete regions, researchers should also investigate how waves of excitation travel through the brain. In other words, ripples in a pond may be a more appropriate analogy for large-scale brain function than a telecommunication network.
This new approach, drawing on centuries of work in physics and engineering, offers a way to use physical principles to understand how diverse patterns of activity arise from brain anatomy. It also offers immediate practical benefits, as eigenmodes of brain shape are much simpler to quantify than those of brain connectivity. This new approach opens possibilities for studying how brain shape affects function through evolution, development and aging, and in brain disease.
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