Brain fingerprint is based on the aperiodic, scale-free, neuronal activity
Résumé
Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.
Mots clés
Neuronal Avalanches Brain Dynamics Brain Differentiability Transition Matrices Magnetoencephalography AAL
Automated Anatomical Labeling ATM
Avalanche Transitions Matrix DM
differentiation matrix EOG
electro-oculogram FC
Functional Connectome ICA
independent component analysis MEG
Magnetoencephalography LMCV
Linearly Constrained Minimum Variance PC
Pearson's Correlation Coefficient PCA
principal component analysis ROIs
Regions of Interest SC
Spearman's Correlation Coefficient sFC
static Functional Connectome SR
Success Rate
Automated Anatomical Labeling
ATM
Avalanche Transitions Matrix
DM
differentiation matrix
EOG
electro-oculogram
FC
Functional Connectome
ICA
independent component analysis
MEG
Magnetoencephalography
LMCV
Linearly Constrained Minimum Variance
PC
Pearson's Correlation Coefficient
PCA
principal component analysis
ROIs
Regions of Interest
SC
Spearman's Correlation Coefficient
sFC
static Functional Connectome
SR
Brain Differentiability
Brain Dynamics
Neuronal Avalanches
Transition Matrices
Domaines
Médecine humaine et pathologieOrigine | Publication financée par une institution |
---|---|
Licence |