Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance

Area of Science:

• Neuroscience
• Complexity Science
• Signal Processing

Background:

• Traditional views link health to homeostasis, but loss of complexity often indicates ill-health.
• Monofractals, defined by a single scaling exponent (H), have previously shown associations between increased H and aging, scopolamine, and faster task responses in resting fMRI.
• Previous monofractal analyses may oversimplify complex, non-stationary brain dynamics.

Purpose of the Study:

• To apply a novel multifractal approach to resting-state fMRI data.
• To investigate non-stationary fractal dynamics using a spectrum of local singularity exponents.
• To explore turbulence as a mechanism for multifractal dynamics in the brain.

Main Methods:

• Utilized resting-state functional magnetic resonance imaging (fMRI) time-series data.
• Applied a multifractal analysis to characterize non-stationary fractal dynamics via a spectrum of local singularity exponents.
• Investigated turbulence models, including Carstaing's model, and critical-phase phenomena.

Main Results:

• The multifractal parameterization effectively differentiated effects of age, scopolamine, and task performance on brain signal complexity.
• This approach provided a more refined description of associated signal changes compared to monofractal methods.
• Evidence suggests scale invariance of energy dissipation is better explained by critical-phase phenomena than by Carstaing's turbulent flow model.

Conclusions:

• Brain complexity, analyzed multifractally, is sensitive to aging, pharmacological interventions, and cognitive demands.
• The findings support the hypothesis that the brain operates in a state of self-organized criticality.
• Multifractal analysis offers a powerful tool for understanding dynamic brain states and their relationship to health and disease.