<p>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.</p>

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<p>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.</p>

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<p>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.</p>