Synopsis: Picking the Brain

A mathematical analysis explains why brain dynamics can be modeled using only a few key regions of interest.

The activity of the human brain is both “localized” and “delocalized.” On a local level, different functions (e.g., vision) are associated with specific regions of the brain—a view supported by studies of the effects of local brain injuries. But the brain is also a “small-world” network, in which each part is only a few neural connections away from any other part: any local response is integrated into a global perception affecting the entire brain activity. Current brain models attempt to capture this dual nature by choosing, within a global network, a small set of local “regions of interest” (ROIs): the most active brain areas that need to be modeled to capture the behavior of the entire brain, for instance, during the execution of a specific task.

Such ROIs are typically picked in an empirical way (e.g., based on functional-magnetic-resonance-imaging observations of correlated fluctuations in brain activity) but the work of Peter Robinson at the University of Sydney, Australia, reported in Physical Review E, suggests a more systematic approach. The author analyzes a set of equations that describe neural response based on certain sensory input stimuli and on a neural connectivity matrix. Using techniques similar to those that relate spatially extended and localized eigenstates in quantum mechanics, he shows that the complex dynamics can be reduced to a representation based on sampling a few dominant eigenmodes at localized points (the ROIs). The results explain why the dynamics at the ROIs alone provide a good approximation of brain behavior and suggest a way to select ROIs on a first-principles basis, which may lead to better brain models. – Matteo Rini


Features

More Features »

Announcements

More Announcements »

Subject Areas

Nonlinear DynamicsInterdisciplinary Physics

Previous Synopsis

Next Synopsis

Related Articles

Synopsis: Social Determinants of Epidemic Growth
Complex Systems

Synopsis: Social Determinants of Epidemic Growth

A new network model reveals that social mixing and mobility can determine the areas of a city that are critical in provoking an epidemic outbreak. Read More »

Viewpoint: No Synchronization for Qubits
Nonlinear Dynamics

Viewpoint: No Synchronization for Qubits

Theorists have determined that a quantum oscillator needs at least three energy levels in order to sync up with another oscillator. Read More »

Focus: Identifying Early Signs of Online Extremist Groups
Complex Systems

Focus: Identifying Early Signs of Online Extremist Groups

An analogy between the growth of online networks and the formation of gels suggests ways to detect extremist groups before they become influential. Read More »

More Articles