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La Rocca, D.

Publications and source records attributed to La Rocca, D..

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Emergence of β and γ networks following multisensory training

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent perceptual task, and investigated oscillatory activity and functional connectivity as a function of sensory history in training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography (MEG). Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional inter-actions following training. One main finding is the emergence of a large-scale synchronization in the high{gamma} (gamma: 60-120Hz) and {beta} (beta:15-30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.

neuroscience↗

Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics

BackgroundThe 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.\n\nNew MethodHere, 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 task.\n\nResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scalefree 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 higherorder 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 scalefree structures during task.\n\nComparison 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.\n\nConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.\n\nHighlightsO_LIWe estimated scale-free human brain dynamics using wavelet-leader formalism.\nC_LIO_LIHigh-to-low self-similarity defined a fronto-occipital gradient.\nC_LIO_LIThe gradient was enhanced in task compared to resting-state.\nC_LIO_LIScale-free brain dynamics showed multifractal properties.\nC_LIO_LISelf-similarity decreased whereas multifractality increased from rest to task.\nC_LI

neuroscience↗