Fernando Montani

Memory consolidation depends critically on brain state transitions, particularly between wakefulness and

Sleep is known to support memory consolidation through a complex interplay of neural dynamics across multiple timescales. Using intracranial EEG (iEEG) recordings from patients undergoing clinical monitoring, we characterize spectral activity, neuronal avalanche dynamics, and temporal correlations across sleep-wake states, with a focus on their spatial distribution and potential functional relevance. We observe increased low-frequency power, larger avalanches, and enhanced long-range temporal correlations—quantified via Detrended Fluctuation Analysis—during N2 and N3 sleep. In contrast, REM sleep and wakefulness show reduced temporal persistence and fewer large-scale cascades, suggesting a shift toward more fragmented and flexible dynamics. These signatures vary across cortical regions, with distinctive patterns emerging in medial temporal and frontal areas—regions implicated in memory processing. Rather than providing direct evidence of consolidation, our results point to a functional neural landscape that may favor both stabilization and reconfiguration of internal representations during sleep. Overall, our findings highlight the utility of iEEG in revealing the multiscale spatio-temporal structure of sleep-related brain dynamics, offering insights into the physiological conditions that support memory-related processing.

Nonlinear dynamics is the study of dynamical systems in finite dimensions, whether in discrete or continuous time, in which the evolution equation (a difference or differential equation, respectively) is not linear in the state variables

The involvement of the neocortex in memory processes depends on neuronal plasticity, the ability to restructure inter-neuronal connections, which is essential for learning and long-term memory. Understanding these mechanisms is crucial for advancing early diagnosis and treatment of cognitive disorders such as Parkinson’s, epilepsy, and Alzheimer’s disease. This study explores a neuronal model with expanded populations, using information-theoretic cues to uncover dynamics underlying plasticity. By employing Bandt–Pompe’s entropy-complexity (H×C) and Fisher entropy-information (H×F) planes, hidden patterns in neuronal activity are revealed. These methodologies are particularly suitable for analyzing nonlinear dynamics and causal relationships in time series. In addition, the Hénon map is applied to capture nonlinear behaviors, such as neural firing, highlighting the trade-off between stability and unpredictability in neural networks. Our approach integrates local field potential and intracranial electroencephalograms’ data in multiple frequency bands, connecting computational models with experimental evidence. By addressing higher-order interactions, such as action potential triplets, this work advances the understanding of synaptic adjustments and their implications for neuronal complexity and cognitive disorders.

The processes involved in encoding and decoding signals in the human brain are a continually studied topic, as neuronal information flow involves complex nonlinear dynamics. This study examines awake human intracranial electroencephalography (iEEG) data from normal brain regions to explore how biological sex influences these dynamics. The iEEG data were analyzed using permutation entropy and statistical complexity in the time domain and power spectrum calculations in the frequency domain. The Bandt and Pompe method was used to assess time series causality by associating probability distributions based on ordinal patterns with the signals. Due to the invasive nature of data acquisition, the study encountered limitations such as small sample sizes and potential sources of error. Nevertheless, the high spatial resolution of iEEG allows detailed analysis and comparison of specific brain regions. The results reveal differences between sexes in brain regions, observed through power spectrum, entropy, and complexity analyses. Significant differences were found in the left supramarginal gyrus, posterior cingulate, supplementary motor cortex, middle temporal gyrus, and right superior temporal gyrus. This study emphasizes the importance of considering sex as a biological variable in brain dynamics research, which is essential for improving the diagnosis and treatment of neurological and psychiatric disorders.