Ariosky Areces Gonzalez

Complex networks often exhibit emergent behaviors, where simple dyadic interactions yield collective dynamics that cannot be explained by examining the system’s units individually or in pairs. Understanding how redundant and synergistic interaction emerges from elementary connectivity patterns is important in characterizing the behavior of physical, biological, and engineering systems. In this study, the information-theoretic framework of Partial Information Decomposition (PID) is employed to investigate how pairs of signals measured at the nodes of large network systems contribute individually and jointly to determine the overall state of the network. The analyzed systems are networks of numerically simulated Rössler oscillators and physical single-transistor electronic chaotic oscillators, reproducing purely pairwise and symmetric links in biological neuronal cultures, and cortical electroencephalographic networks assessed in humans. In the two settings, PID was extensively applied to decompose the information brought by pairs of signals to the overall state of the system, which was assessed respectively as the dynamic regime (from asynchronous chaos to synchronization) and the subject condition (open vs closed eyes). Our approach highlights the coexistence of redundant and synergistic interplays in determining the effect of the pairwise dynamics on the system’s state. Specifically, we demonstrate how, in a highly synchronized system, where units act following overlapped redundant behaviors, their joint consideration helps determine the system’s state though their synergistic interactions. Our findings reveal the emergence of non-trivial behaviors in networked systems based on pairwise connections and straightforward neural states, highlighting the need for using appropriate analytical tools to capture complex phenomena.

We introduce ξ − α Net, a mesoscale generative model of cortical activity that models the EEG aperiodic ( ξ ) and α -rhythm ( α ) components via Hida-Matérn processes constrained by anatomical connectivity and interareal delays. This framework integrates fractional differential equations (modeling damping/resonance), decomposition of Spectral Granger Causality, and lifespan quantification. Bayesian inversion of the model, on cross-spectral rsEEG data from 1,965 participants aged 5-100 years (HarMNqEEG dataset), allows us to estimate cortical activity and effective connectivity patterns at 8,003-voxel resolution. ξ processes showed extended cortical networks, with occurrence probability increasing with age but amplitude peaking midlife (inverted-U trajectory). α processes exhibited a localization in the posterior areas of the cortex and two specialized networks (parieto-occipital, sensorimotor), with increasing localization probability and peak amplitude/frequency showing age-linked inverted-U trajectory. The model uniquely estimates global conduction delays, which are negatively correlated with α frequency and independent myelin concentration profiles, mechanistically linking neural propagation times to α regulation.

Extracting cortical features, which are the most relevant at characterizing structure and function for normal or abnormal brain conditions, would greatly benefit from multimodal neuroimage processing following the surface-based style. This style recognizes the natural definition space for such features due to the layered (surface-based) Cortex structural and functional organization. It may therefore be more sensitive and specific than the former volume-based style. The Human Connectome Project (HCP) multimodal pipelines render high-quality surface-based processing for some of the most consistently acquired neuroimaging modalities, with the quality too reliant on their precise acquisition requirements. Relevant international brain initiatives are espoused to develop an HCP-compatible neuroinformatic facility for the quality-ensured processing of international neuroimaging datasets, which may not follow the specific HCP acquisition requirements, also coined as legacy datasets. We appointed some initiatives to introduce multimodal pipelines in two HCP-compatible processing branches. a) Structural: forward- modeling with geometry (sources and head) and Lead Fields defined for legacy MEG, or EEG, in the HCP individual cortical space (Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages a more diverse neuroinformatic repository than the HCP structural or MEG pipelines. Ciftify-MEEG produces substantial processing illustrated here with EEG examples, incorporating alternative processing paths and corrections based on a quality control loop and upon qualitative and quantitative indicators. b) Functional: identifying spectral topographies and connectomes for the cortical oscillatory activity observed in the MEG or EEG frequency bands. We leverage a novel repository of Bayesian sparse inverse methods that target identifying the topographies and connectomes with actual statistical guarantees, the brain connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG outputs and with Bayesian sparse priors structured upon Cifti space information. Ciftify-bcVARETA identification is less biased to forward models and more biased to the observations than the HCP pipeline illustrated here with topographies obtained for MEG and EEG legacy databases.

Identifying the connectivity of functional networks underpinning undirectly observed phenomena for neurosciences or other fields poses a Bayesian inverse-problem. Electromagnetic (EEG or MEG) inverse-solutions unveil the cortical oscillatory networks that strongly correlate to brain function with a spectral transparency that no other in vivo neuroimage may provide. Simulations of such an inverse-problem also reveal distortions of the connectivity determined by most common state-of-the-art solutions. We disclose the origin of distortions and remedy them via a Hidden Gaussian Graphical Spectral (HIGGS) model, the Bayesian formalism for the inverse-problem in identifying such networks. In human EEG alpha rhythm simulations, distortions measured as ROC performance do not surpass the 2% in our HIGGS solution and reach 20% in state-of-the-art approaches. Congruence in macaque simultaneous EEG/ECoG recordings provides experimental confirmation for our solution with 1/3 more congruence than state-of-the-art methods.