Daria La Rocca1, Nicolas Zilber2, Patrice Abry3, Virginie van Wassenhove4, Philippe Ciuciu5. 1. CEA/DRF/Joliot, NeuroSpin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France; INRIA, Parietal Team, Université Paris-Saclay, France. Electronic address: daria.la-rocca@inria.fr. 2. CEA/DRF/Joliot, NeuroSpin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France; INRIA, Parietal Team, Université Paris-Saclay, France. Electronic address: nicolas.zilber@hotmail.com. 3. Univ Lyon, Ens de Lyon, Univ Claude Bernard, CNRS, Laboratoire de Physique, F-69342 Lyon, France. Electronic address: patrice.abry@ens-lyon.fr. 4. CEA/DRF/Joliot, NeuroSpin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France; Cognitive Neuroimaging Unit, INSERM, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France. Electronic address: virginie.van.wassenhove@gmail.com. 5. CEA/DRF/Joliot, NeuroSpin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France; INRIA, Parietal Team, Université Paris-Saclay, France. Electronic address: philippe.ciuciu@cea.fr.
Abstract
BACKGROUND: The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. NEW METHOD: Here, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task. RESULTS: First, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scale-free structures during task. COMPARISON WITH EXISTING METHOD(S): We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics. CONCLUSIONS: Altogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.
BACKGROUND: The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. NEW METHOD: Here, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task. RESULTS: First, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scale-free structures during task. COMPARISON WITH EXISTING METHOD(S): We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics. CONCLUSIONS: Altogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.