Summary: New research from Northwestern University shows that the cellular architecture of brains in humans, mice and fruit flies sits near a structural phase transition — a state known in physics as criticality. The study finds fractal-like patterns and consistent scaling laws across species, pointing to a possible universal organizing principle of brain anatomy.
These findings provide a new perspective on brain complexity and could improve computational and generative models that aim to reproduce how brain structure shapes neural dynamics and emergent behavior.
Key Facts:
- Cellular brain structures in humans, mice and fruit flies exhibit signatures consistent with criticality.
- Neurons display fractal-like, self-similar patterns and broad size distributions that indicate proximity to a phase transition.
- Measured scaling laws and critical exponents are consistent across these species, supporting the idea of universal principles useful for computational modeling of brain anatomy and dynamics.
Source: Northwestern University
In physics, criticality describes the behavior of materials at a phase transition—for example, when a magnet loses its magnetization as it heats past a critical temperature. At such points, systems show high complexity and correlations across many scales. Applying this framework to the brain, researchers found that structural features of neuronal tissue appear to be poised near an analogous transition.
The study, published June 10 in Communications Physics, analyzes publicly available three-dimensional reconstructions of brain tissue from humans, mice and fruit flies. Using methods from statistical physics, the researchers quantified structural properties of neurons and found consistent evidence of criticality across organisms.
“The human brain is one of the most complex systems known, and many of the structural details that govern its function remain unclear,” said István Kovács, senior author and assistant professor of physics and astronomy at Northwestern’s Weinberg College of Arts and Sciences. He added that while prior studies have focused on signals and neural dynamics, this work examines criticality at the structural level to better understand how anatomy constrains and supports the brain’s dynamics.
Helen Ansell, the paper’s first author, explained the analogy: when ice melts into water, the same molecules undergo a transition from one phase to another. The brain’s cellular architecture, she said, appears to sit near a comparable structural boundary. “We are not saying the brain is ‘melting,’” Ansell noted; rather, the brain’s anatomy shows the statistical hallmarks of a system balanced between order and disorder.
The analysis revealed several signatures of critical systems: self-similar, fractal-like neuronal shapes across scales; long-range correlations in cellular arrangement; and broad, heavy-tailed size distributions of neuronal components. These observables give rise to critical exponents — numerical values that characterize how structural measures scale with size — and the team found those exponents to be quantitatively consistent across human, mouse and fly samples.
Finding matching critical exponents is significant because such universal numbers are largely insensitive to microscopic details. “These are things we see in all critical systems in physics,” Kovács said. “It seems the brain is in a delicate balance between two phases.” The fact that critical exponents follow the expected relationships from statistical physics further supports the interpretation of structural criticality.
Despite major differences in scale and organization between species — a whole fly brain is roughly the size of a single human neuron — the shared statistical measures suggest common organizing rules. The compatibility of these structural features across organisms points to a potentially universal principle that could help explain why disparate brains nevertheless display similar functional properties.
These results have practical implications for computational neuroscience and artificial neural network design. Simple physical models constrained by the observed scaling laws could serve as generative templates for realistic cellular-level brain structure. Such templates would improve dynamical models that link anatomy to activity, and they might inspire new architectures for machine learning that better reflect biological constraints.
The authors plan to extend their approach to larger datasets and additional species as new, higher-resolution reconstructions become available. Future work will test whether the observed universality of critical exponents persists at broader scales and across a wider variety of brains.
The study, “Unveiling universal aspects of the cellular anatomy of the brain,” benefited in part from computational resources at Northwestern’s Quest high-performance computing facility.
About this neuroscience research news
Author: Amanda Morris
Source: Northwestern University
Contact: Amanda Morris – Northwestern University
Image: Image credited to Neuroscience News
Original Research: Open access. “Unveiling universal aspects of the cellular anatomy of the brain” by István Kovács et al., Communications Physics.
Abstract
Unveiling universal aspects of the cellular anatomy of the brain
Recent cellular-level volumetric brain reconstructions have revealed high levels of anatomic complexity. Determining which structural aspects of the brain to focus on, particularly for comparisons with computational models and between species, remains a central challenge.
This work quantifies structural complexity and presents evidence that brain anatomy follows universal scaling laws, supporting the notion of structural criticality at the cellular level. Building on the theory of critical systems, the framework identifies informative structural observables and provides estimates of critical exponents for human, mouse and fruit fly brains. Where data permit, these exponents are consistent across organisms.
Because universal quantities are robust to many microscopic differences among brains, these results provide a foundation for generative computational models of cellular brain structure and clarify in which respects one animal’s anatomy may serve as a model for another’s.