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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
Published on: March 8, 2024
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Brain fingerprint is based on the aperiodic, scale-free, neuronal activity
Pierpaolo Sorrentino1, Emahnuel Troisi Lopez2, Antonella Romano3
1Institut de Neurosciences des Systèmes, Aix-Marseille Universitè, Marseille, France; Deparment of Biomedical Science, University of Sassari, Sassari, Italy; Institute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy.
Neuroimage
|July 1, 2023
Summary
Non-linear brain activity, specifically neuronal avalanches, carries unique individual information. Analyzing these dynamic events significantly improves subject differentiation in brain analysis compared to traditional methods.
Area of Science:
- Neuroscience
- Complex Systems
- Statistical Mechanics
Background:
- Individualized brain analysis requires understanding subject-specific neural features.
- Current methods often assume stationarity, potentially missing non-linear dynamics.
- The nature of processes generating individual brain differences remains largely unknown.
Purpose of the Study:
- To investigate if non-linear perturbations, termed neuronal avalanches, carry subject-specific information.
- To test the hypothesis that neuronal avalanches are key to brain differentiability.
- To compare differentiability using avalanche dynamics versus stationary methods.
Main Methods:
- Computed the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic (MEG) data.
- Characterized subject-specific fast neural dynamics using ATMs.
- Performed differentiability analysis based on ATMs and compared with Pearson's correlation.
Main Results:
- Selecting moments and locations of neuronal avalanche spread significantly improved subject differentiation (P < 0.0001).
- This improvement was achieved despite discarding the majority of the linear signal.
- Non-linear components of brain signals were found to contain the most subject-specific information.
Conclusions:
- Non-linear dynamics, specifically neuronal avalanches, are crucial for individual brain differentiation.
- This study clarifies that the non-linear aspects of brain signals are primary drivers of personalization.
- A principled method is proposed to link large-scale personalized brain activity to underlying microscopic processes.
