Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Doctor of Philosophy

dissertationFunctional magnetic resonance imaging (fMRI) measures the change of oxygen consumption level in the blood vessels of the human brain, hence indirectly detecting the neuronal activity. Resting-state fMRI (rs-fMRI) is used to identify the intrinsic functional patterns of the brain when there is no external stimulus. Accurate estimation of intrinsic activity is important for understanding the functional organization and dynamics of the brain, as well as differences in the functional networks of patients with mental disorders. This dissertation aims to robustly estimate the functional connectivities and networks of the human brain using rs-fMRI data of multiple subjects. We use Markov random field (MRF), an undirected graphical model to represent the statistical dependency among the functional network variables. Graphical models describe multivariate probability distributions that can be factorized and represented by a graph. By defining the nodes and the edges along with their weights according to our assumptions, we build soft constraints into the graph structure as prior information. We explore various approximate optimization methods including variational Bayesian, graph cuts, and Markov chain Monte Carlo sampling (MCMC). We develop the random field models to solve three related problems. In the first problem, the goal is to detect the pairwise connectivity between gray matter voxels in a rs-fMRI dataset of the single subject. We define a six-dimensional graph to represent our prior information that two voxels are more likely to be connected if their spatial neighbors are connected. The posterior mean of the connectivity variables are estimated by variational inference, also known as mean field theory in statistical physics. The proposed method proves to outperform the standard spatial smoothing and is able to detect finer patterns of brain activity. Our second work aims to identify multiple functional systems. We define a Potts model, a special case of MRF, on the network label variables, and define von Mises-Fisher distribution on the normalized fMRI signal. The inference is significantly more difficult than the binary classification in the previous problem. We use MCMC to draw samples from the posterior distribution of network labels. In the third application, we extend the graphical model to the multiple subject scenario. By building a graph including the network labels of both a group map and the subject label maps, we define a hierarchical model that has richer structure than the flat single-subject model, and captures the shared patterns as well as the variation among the subjects. All three solutions are data-driven Bayesian methods, which estimate model parameters from the data. The experiments show that by the regularization of MRF, the functional network maps we estimate are more accurate and more consistent across multiple sessions

DEVELOPMENT OF A CEREBELLAR MEAN FIELD MODEL: THE THEORETICAL FRAMEWORK, THE IMPLEMENTATION AND THE FIRST APPLICATION

Brain modeling constantly evolves to improve the accuracy of the simulated brain dynamics with the ambitious aim to build a digital twin of the brain. Specific models tuned on brain regions specific features empower the brain simulations introducing bottom-up physiology properties into data-driven simulators. Despite the cerebellum contains 80 % of the neurons and is deeply involved in a wide range of functions, from sensorimotor to cognitive ones, a specific cerebellar model is still missing. Furthermore, its quasi-crystalline multi-layer circuitry deeply differs from the cerebral cortical one, therefore is hard to imagine a unique general model suitable for the realistic simulation of both cerebellar and cerebral cortex. The present thesis tackles the challenge of developing a specific model for the cerebellum. Specifically, multi-neuron multi-layer mean field (MF) model of the cerebellar network, including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells, was implemented, and validated against experimental data and the corresponding spiking neural network microcircuit model. The cerebellar MF model was built using a system of interdependent equations, where the single neuronal populations and topological parameters were captured by neuron-specific inter- dependent Transfer Functions. The model time resolution was optimized using Local Field Potentials recorded experimentally with high-density multielectrode array from acute mouse cerebellar slices. The present MF model satisfactorily captured the average discharge of different microcircuit neuronal populations in response to various input patterns and was able to predict the changes in Purkinje Cells firing patterns occurring in specific behavioral conditions: cortical plasticity mapping, which drives learning in associative tasks, and Molecular Layer Interneurons feed-forward inhibition, which controls Purkinje Cells activity patterns. The cerebellar multi-layer MF model thus provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in health and pathological conditions. Furthermore, preliminary attempts to simulate a pathological cerebellum were done in the perspective of introducing our multi-layer cerebellar MF model in whole-brain simulators to realize patient-specific treatments, moving ahead towards personalized medicine. Two preliminary works assessed the relevant impact of the cerebellum on whole-brain dynamics and its role in modulating complex responses in causal connected cerebral regions, confirming that a specific model is required to further investigate the cerebellum-on- cerebrum influence. The framework presented in this thesis allows to develop a multi-layer MF model depicting the features of a specific brain region (e.g., cerebellum, basal ganglia), in order to define a general strategy to build up a pool of biology grounded MF models for computationally feasible simulations. Interconnected bottom-up MF models integrated in large-scale simulators would capture specific features of different brain regions, while the applications of a virtual brain would have a substantial impact on the reality ranging from the characterization of neurobiological processes, subject-specific preoperative plans, and development of neuro-prosthetic devices

Advances of high-order interactions in the human brain: Applications in aging and neurodegeneration.

82 p.The human brain generates a large repertoire of spatio-temporal patterns, which supporta wide variety of motor, cognitive, and behavioral functions. The most acceptedhypothesis in modern neuroscience is that each of these representations is encoded indifferent brain networks. From MRI, networks can be defined anatomically (¿structuralconnectivity¿-SC) or functionally (¿functional connectivity¿-FC). Interestingly, while SCis by definition pairwise (white matter fibers project from one region to another), FC isnot. In this thesis we have focused on the study of high-order interactions (HOI) that occur in functional networks, beyond the existing statistical relationships in pairs of regions.When evaluating the interacting n-plets, from triplets to order n, a novel type of statistical interdependencies appear, namely the synergistic and redundant interactions,which are inaccessible when evaluating interacting pairs. The study of these HOI inthe human brain in aging and neurodegeneration is the purpose of this thesis.Biocruces Bizkai

Neuromodulation-induced prehabilitation to leverage neuroplasticity before brain tumor surgery: a single-cohort feasibility trial protocol

IntroductionNeurosurgery for brain tumors needs to find a complex balance between the effective removal of targeted tissue and the preservation of surrounding brain areas. Neuromodulation-induced cortical prehabilitation (NICP) is a promising strategy that combines temporary inhibition of critical areas (virtual lesion) with intensive behavioral training to foster the activation of alternative brain resources. By progressively reducing the functional relevance of targeted areas, the goal is to facilitate resection with reduced risks of neurological sequelae. However, it is still unclear which modality (invasive vs. non-invasive neuromodulation) and volume of therapy (behavioral training) may be optimal in terms of feasibility and efficacy.Methods and analysisPatients undertake between 10 and 20 daily sessions consisting of neuromodulation coupled with intensive task training, individualized based on the target site and neurological functions at risk of being compromised. The primary outcome of the proposed pilot, single-cohort trial is to investigate the feasibility and potential effectiveness of a non-invasive NICP protocol on neuroplasticity and post-surgical outcomes. Secondary outcomes investigating longitudinal changes (neuroimaging, neurophysiology, and clinical) are measured pre-NICP, post-NICP, and post-surgery.Ethics and disseminationEthics approval was obtained from the Research Ethical Committee of Fundació Unió Catalana d'Hospitals (approval number: CEI 21/65, version 1, 13/07/2021). The results of the study will be submitted to a peer-reviewed journal and presented at scientific congresses.Clinical trial registrationClinicalTrials.gov, identifier NCT05844605

Brain Connectivity After Concussion

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits in the United States each year. While most mTBI patients have normal findings in clinical neuroimaging, alterations in brain structure and functional connectivity have frequently been reported. In this study, we investigated the large-scale brain structural and functional connectivity using diffusion MRI and resting-state fMRI data. Data from 40 mTBI patients was acquired at the acute stage (within 24 hrs after injury). 35 patients returned for data acquisition at a follow-up (4-6 weeks after injury). Data was also collected from a cohort of 58 healthy subjects, 36 of whom returned for data acquisition at the second time point, 4-6 weeks later. All data was collected at Wayne State University, Detroit, Michigan, USA. We also evaluated the relationship between functional connectivity findings at the acute stage and neurocognitive symptoms at follow up to assess the feasibility of using neuroimaging data to predict neurocognitive complications after mTBI. Moreover, we developed the connectivity domain, a new analysis method which can potentially improve reproducibility and ability to compare findings across datasets

Observational Learning of New Movement Sequences Is Reflected in Fronto-Parietal Coherence

Mankind is unique in her ability for observational learning, i.e. the transmission of acquired knowledge and behavioral repertoire through observation of others' actions. In the present study we used electrophysiological measures to investigate brain mechanisms of observational learning. Analysis investigated the possible functional coupling between occipital (alpha) and motor (mu) rhythms operating in the 10Hz frequency range for translating “seeing” into “doing”. Subjects observed movement sequences consisting of six consecutive left or right hand button presses directed at one of two target-buttons for subsequent imitation. Each movement sequence was presented four times, intervened by short pause intervals for sequence rehearsal. During a control task subjects observed the same movement sequences without a requirement for subsequent reproduction. Although both alpha and mu rhythms desynchronized during the imitation task relative to the control task, modulations in alpha and mu power were found to be largely independent from each other over time, arguing against a functional coupling of alpha and mu generators during observational learning. This independence was furthermore reflected in the absence of coherence between occipital and motor electrodes overlaying alpha and mu generators. Instead, coherence analysis revealed a pair of symmetric fronto-parietal networks, one over the left and one over the right hemisphere, reflecting stronger coherence during observation of movements than during pauses. Individual differences in fronto-parietal coherence were furthermore found to predict imitation accuracy. The properties of these networks, i.e. their fronto-parietal distribution, their ipsilateral organization and their sensitivity to the observation of movements, match closely with the known properties of the mirror neuron system (MNS) as studied in the macaque brain. These results indicate a functional dissociation between higher order areas for observational learning (i.e. parts of the MNS as reflected in 10Hz coherence measures) and peripheral structures (i.e. lateral occipital gyrus for alpha; central sulcus for mu) that provide low-level support for observation and motor imagery of action sequences