Estimation of brain dynamics under visuomotor task using functional connectivity analysis based on graph theory

Network studies of brain connectivity have demonstrated that the highly connected area, or hub, is a vital feature of human functional and structural brain organization. Hubs identify which region plays an important role in cognitive/sensorimotor tasks. In addition, a complex visuomotor learning skill causes specific changes of neuronal activation across brain regions. Accordingly, this study utilizes the hub as one of the features to map the visuomotor learning tasks and their dynamic functional connectivity (dFC). The electroencephalogram (EEG) data recorded under three different behavior conditions were investigated: motion only (MO), vision only (VO), and tracking (Tra) conditions. Here, we used the phase locking value (PLV) with a sliding window (50 ms) to calculate the dFC at four distinct frequency bands: 8-12 Hz (alpha), 18-22 Hz (low beta), 26-30 Hz (high beta) and 38-42 Hz (gamma), and the eigenvector centrality to evaluate the hub identification. The Gaussian Mixture Model (GMM) was applied to investigate the dFC patterns. The results showed that the dFC patterns with the hub feature represent the characteristic of neuronal activities under visuomotor coordination

Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach

Imagined activities could actually be a cognitive basis for creative thinking. However, it is still unknown how they might be related with the architecture of the brain. A recent study has proved the relevance of the imagined activity when investigating neuronal diseases by comparing variations in the neuronal activity of patients with brain diseases and healthy subjects. One important aspect of the scientific methodologies focused on neuronal diseases is therefore to provide a trustable methodology that could allow us to distinguish between realized and imagined activities in the brain. The electroencephalogram is the result of synchronized action of the cerebrum, and our end is portraying the network dynamics through the neuronal responses when the subjects perform visuomotor and specific imaginary assignments. We use a subtle information theoretical approach accounting for the time causality of the signal and the closeness centrality of the different nodes. More specifically we perform estimations of the probability distribution of the data associated to each node using the Bandt and Pompe approach to account for the causality of the electroencephalographic signals. We calculate the Jensen-Shannon distance across different nodes, and then we quantify how fast the information flow would be through a given node to other nodes computing the closeness centrality. We perform a statistical analysis to compare the closeness centrality considering the different rhythmic oscillation bands for each node taking into account imagined and visuomotor tasks. Our discoveries stress the pertinence of the alpha band while performing and distinguishing the specific imaginary or visuomotor assignments.Fil: Baravalle, Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Guisande, Natalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Granado, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Rosso, Osvaldo Anibal. Hospital Italiano. Departamento de Informática en Salud; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentin

Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. View Full-Text Keywords: neuropsychiatric systemic lupus erythematosus; rs-fMRI; graph theory; functional connectivity; surrogate data; machine learning; visuomotor ability; mental flexibilit

Investigating Brain Functional Networks in a Riemannian Framework

The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain. The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects. In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them. Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices. Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning

Characterization of visuomotor/imaginary movements in EEG: an information theory and complex network approach

Imagined activities could actually be a cognitive basis for creative thinking. However, it is still unknown how they might be related with the architecture of the brain. A recent study has proved the relevance of the imagined activity when investigating neuronal diseases by comparing variations in the neuronal activity of patients with brain diseases and healthy subjects. One important aspect of the scientific methodologies focused on neuronal diseases is therefore to provide a trustablemethodology that could allow us to distinguish between realized and imagined activities in the brain. The electroencephalogram is the result of synchronized action of the cerebrum, and our end is portraying the network dynamics through the neuronal responses when the subjects perform visuomotor and specific imaginary assignments. We use a subtle information theoretical approach accounting for the time causality of the signal and the closeness centrality of the different nodes. More specifically we perform estimations of the probability distribution of the data associated to each node using the Bandt and Pompe approach to account for the causality of the electroencephalographic signals. We calculate the Jensen-Shannon distance across different nodes, and then we quantify how fast the information flow would be through a given node to other nodes computing the closeness centrality.We performa statistical analysis to compare the closeness centrality considering the different rhythmic oscillation bands for each node taking into account imagined and visuomotor tasks. Our discoveries stress the pertinence of the alpha band while performing and distinguishing the specific imaginary or visuomotor assignments.Instituto de Física La Plat

The effect of concurrent cognitive-visuomotor multitasking and task difficulty on dynamic functional connectivity in the brain

This thesis investigated the effect of visuomotor and working memory 1) task difficulty and 2) multitasking on dynamic functional connectivity in the brain. Studies have only recently begun to investigate functional connectivity within the scope of concurrent dual task or varying task difficulty conditions (Cocchi, Zalesky, et al. 2011; Rietschel et al. 2012). A series of EEG recordings were conducted during execution of visuomotor or working memory tasks within a novel paradigm using BCI2VR custom MATLAB toolbox. Functional connectivity was correlated with task-related coherence (TRCoh) analysis between two task conditions involving either variation in task difficulty or concurrent execution during multitasking within the delta (0 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 12 Hz), beta1 (12-16 Hz), beta2 (16 – 20 Hz) and beta3 (20 – 24 Hz) frequency bands. An increase in coherence was observed with increased cognitive load, during both increased task difficulty and multitasking, in all frequency bands except beta1 and beta2. This may suggest that the psychomotor efficiency hypothesis also applies to multitasking as well as task difficulty. Decreases in beta coherence were observed with increased performance error, indicating that interregional beta coherence may not follow the PEH trend. The increased coherence between brain regions in the alpha, delta and theta bands contributes to the growing volume of research on quantifying cognitive workload and may serve as a future basis on increasing multitasking efficiency during high stress environments. Further research recording multitasking effects on individuals over regular intervals during an extended period of time (months or years) will be required to better understand changes in functional connectivity within the brain

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics

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

Multi-neuroimaging model of identifying neuroplasticity under motor cognitive learning condition: MRI based study.

Motor learning is a fundamental ability and one of the most robust models to study neural plasticity. The majority of human motor learning imaging studies focused on either short-term or long-term learning using one single imaging modality. These studies were thus not able to systematically investigate the dynamic process of motor learning from a multimodal perspective. The current project combined both short-term and long-term motor learning to comprehensively characterize neural plasticity at multiple phenotypic levels of the brain: functional activation, functional connectivity, grey matter volume, and glutamate concentration. To this end, this project involved a cross-sectional and a longitudinal study with multimodal brain imaging techniques (task fMRI, resting-state fMRI, gray matter structural fMRI, pharmacological fMRI, and MRS). Short-term motor learning was significantly correlated with brain network features related to network efficiency. It was also associated with a highly reliable cerebellum-centered network which was significantly modulated by the NMDA antagonist ketamine. Long-term motor learning was associated with increased activation in premotor / SMA and parietal regions and with increased gray matter volume of the SMA and the hippocampus. In addition, long-term motor learning was accompanied by a decrease in the functional connectivity of a network centered on the sensorimotor cortex which was related to handknob glutamate concentration levels and which involved regions that were highlighted by our activation and structural analyses. Taken together, this thesis contributes important evidence to the neurofunctional and neurostructural underpinnings of motor learning and points to the critical roles of the cerebellum, the hippocampus and the relevance of glutamate for motor learning in humans