Brain fingerprint is based on the aperiodic, scale-free, neuronal activity
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 ca...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923004111 |
| _version_ | 1797787177561620480 |
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| author | Pierpaolo Sorrentino Emahnuel Troisi Lopez Antonella Romano Carmine Granata Marie Constance Corsi Giuseppe Sorrentino Viktor Jirsa |
| author_facet | Pierpaolo Sorrentino Emahnuel Troisi Lopez Antonella Romano Carmine Granata Marie Constance Corsi Giuseppe Sorrentino Viktor Jirsa |
| author_sort | Pierpaolo Sorrentino |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T01:19:16Z |
| format | Article |
| id | doaj.art-352b1d43ffb94939ba3397f658726615 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-13T01:19:16Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-352b1d43ffb94939ba3397f6587266152023-07-05T05:15:36ZengElsevierNeuroImage1095-95722023-08-01277120260Brain fingerprint is based on the aperiodic, scale-free, neuronal activityPierpaolo Sorrentino0Emahnuel Troisi Lopez1Antonella Romano2Carmine Granata3Marie Constance Corsi4Giuseppe Sorrentino5Viktor Jirsa6Institut 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; Correspondence to: Dr. Pierpaolo Sorrentino, Institut de Neurosciences des Systèmes, Aix-Marseille Universitè, 27, Boulevard Jean Moulin, 13005 Marseille, FranceInstitute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy; Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, ItalyDepartment of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, ItalyInstitute of Applied Sciences and Intelligent Systems, CNR, Naples, ItalySorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, FranceInstitute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy; Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy; Institute of Diagnosis and Treatment Hermitage Capodimonte, Naples, ItalyInstitut de Neurosciences des Systèmes, Aix-Marseille Universitè, Marseille, FranceSubject 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.http://www.sciencedirect.com/science/article/pii/S1053811923004111Neuronal AvalanchesBrain DynamicsBrain DifferentiabilityTransition MatricesMagnetoencephalography |
| spellingShingle | Pierpaolo Sorrentino Emahnuel Troisi Lopez Antonella Romano Carmine Granata Marie Constance Corsi Giuseppe Sorrentino Viktor Jirsa Brain fingerprint is based on the aperiodic, scale-free, neuronal activity NeuroImage Neuronal Avalanches Brain Dynamics Brain Differentiability Transition Matrices Magnetoencephalography |
| title | Brain fingerprint is based on the aperiodic, scale-free, neuronal activity |
| title_full | Brain fingerprint is based on the aperiodic, scale-free, neuronal activity |
| title_fullStr | Brain fingerprint is based on the aperiodic, scale-free, neuronal activity |
| title_full_unstemmed | Brain fingerprint is based on the aperiodic, scale-free, neuronal activity |
| title_short | Brain fingerprint is based on the aperiodic, scale-free, neuronal activity |
| title_sort | brain fingerprint is based on the aperiodic scale free neuronal activity |
| topic | Neuronal Avalanches Brain Dynamics Brain Differentiability Transition Matrices Magnetoencephalography |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923004111 |
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