Team coordination—members of a group acting together rather than performing specific actions individually—is essential for success in many real-world tasks such as military missions, sports, workplace, or school interactions. However, team coordination is highly variable, which is one reason why its underlying neural processes are largely unknown. Here we used dual electroencephalography (EEG) in dyads to study the neurobehavioral dynamics of team coordination in an ecologically valid task that places intensive demands on joint performance. We present a novel conceptual framework to interpret neurobehavioral variability in terms of degeneracy, a fundamental property of complex biological systems said to enhance flexibility and robustness. We characterize degeneracy conceptually in terms of a manifold representing the geometric locus of the dynamics in the high dimensional state-space of neurobehavioral signals. The geometry and dimensionality of the manifold are determined by task constraints and team coordination requirements which restrict the manifold to trajectories that are conducive to successful task performance. Our results indicate that team coordination is associated with dimensionality reduction of the manifold as evident in increased inter-brain phase coherence of beta and gamma rhythms during critical phases of task performance where subjects exchange information. Team coordination was also found to affect the shape of the manifold manifested as a symmetry breaking of centro-parietal wavelet power patterns across subjects in trials with high team coordination. These results open a conceptual and empirical path to identifying the mechanisms underlying team performance in complex tasks.

Performing a task as a team requires that team members mutually coordinate their actions. It is this coordination that distinguishes the performance of a team from the same actions performed independently. Here we set out to identify signatures of team coordination in behavioral and brain dynamics. We use dual electroencephalography (EEG) to measure brain dynamics of dyadic teams performing a virtual room clearing task. Such complex tasks often exhibit high variability of behavioral and brain dynamics. Although such variability is often considered to impede identification of the behavior or brain dynamics of interest here we present a conceptual and empirical framework which explains variability in geometrical terms and classifies its sources into those that are detrimental and non-detrimental to performing the task at hand. Using our framework we found that behaviorally team coordination is reflected in terms of role dependent behavior. Furthermore we identified a low-dimensional subspace of the brain dynamics in the frequency domain which is specific for team behavior and correlated with successful team coordination. Moreover, successful team coordination was positively correlated with the inter- but not intra-brain coherence in the gamma band. Our results hence indicate that successful team coordination is associated with increased team cognition, particularly readiness to engage in the task.

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

We report results from a dual electroencephalography (EEG) study, in which two-member teams performed a simulated combat scenario. Our aim was to distinguish expert from novice teams by their brain dynamics. Our findings suggest that dimensionality increases in the joint brain dynamics of the team members is a signature of increased task demand, both objective, e.g. increased task difficulty, and subjective, e.g. lack of experience in performing the task. Furthermore in each team we identified a subspace of joint brain dynamics related to team coordination. Our approach identifies signatures specific to team coordination by introducing surrogate team data as a baseline for joint brain dynamics without team coordination. This revealed that team coordination affects the subspace itself in which the joint brain dynamics of the team members are evolving, but not its dimensionality. Our results confirm the possibility to identify signatures of team coordination from the team members' brain dynamics.

Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of random matrices. We study data reduction based on the comparison of empirical correlation matrices with a suitably chosen ensemble of random positive matrices. Accordingly, data dimensions can be discarded if the quality of fit of the data spectrum deviates locally from the theoretical result, which is derived here analytically. Further, more complex quantities such as the number variance are discussed and shown to be potentially useful in an analogous manner.

This paper introduces a method to study the variation of brain functional connectivity networks with respect to experimental conditions in fMRI data. It is related to the psychophysiological interaction technique introduced by Friston et al . and extends to networks of correlation modulation (CM networks). Extended networks containing several dozens of nodes are determined in which the links correspond to consistent correlation modulation across subjects. In addition, we assess inter-subject variability and determine networks in which the condition-dependent functional interactions can be explained by a subject-dependent variable. We applied the technique to data from a study on syntactical production in bilinguals and analysed functional interactions differentially across tasks (word reading or sentence production) and across languages. We find an extended network of consistent functional interaction modulation across tasks, whereas the network comparing languages shows fewer links. Interestingly, there is evidence for a specific network in which the differences in functional interaction across subjects can be explained by differences in the subjects' syntactical proficiency. Specifically, we find that regions, including ones that have previously been shown to be involved in syntax and in language production, such as the left inferior frontal gyrus, putamen, insula, precentral gyrus, as well as the supplementary motor area, are more functionally linked during sentence production in the second, compared with the first, language in syntactically more proficient bilinguals than in syntactically less proficient ones. Our approach extends conventional activation analyses to the notion of networks, emphasizing functional interactions between regions independently of whether or not they are activated. On the one hand, it gives rise to testable hypotheses and allows an interpretation of the results in terms of the previous literature, and on the other hand, it provides a basis for studying the structure of functional interactions as a whole, and hence represents a further step towards the notion of large-scale networks in functional imaging.

In this paper, we propose a generic framework for the analysis of steady-state fMRI datasets, applied here to resting-state datasets. Our approach avoids the introduction of user-defined seed regions for the study of spontaneous activity. Unlike existing techniques, it yields a sparse representation of resting-state activity networks which can be characterized and investigated fairly easily in a semi-interactive fashion. We proceed in several steps, based on the idea that spectral coherence of the fMRI time courses in the low frequency band carries the information of interest. In particular, we address the question of building adapted representations of the data from the spectral coherence matrix. We analyze nine datasets taken from three subjects and show resting-state networks validated by EEG-fMRI simultaneous acquisition literature, with low intra-subject variability; we also discuss the merits of different (rapid/slow) fMRI acquisition schemes.

Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.

Clusters of correlated activity in fMRI data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on Hopfield networks. It allows to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. Further we propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations.

We investigate the influence of physiological measures like heart beat and respiration on functional connectivity networks from fMRI. Cardiac and respiratory effects were measured simultaneously during high rate MRI data acquisition and the functional connectivity networks were determined in a data driven manner using graph theory. One of our findings is that removing the physiological effects from the data leads to disappearance of a considerable part of the functional connectivity networks and to the appearance of small, but consistent networks. We found further that high signal variance loss due to physiological effect removal does not coincide with a high correlation loss, on the contrary, a considerable part of the networks appears preferably at locations with high variance loss.

Correlation analysis has been widely used in the study of functional connectivity based on fMRI data. It assumes that the relevant information about the interactions of brain regions is reflected by a linear relationship between the values of two signals at the same time. However, this hypothesis has not been thoroughly investigated yet. In this work, we study in depth the information shared by BOLD signals of pairs of brain regions. In particular, we assess the amount of nonlinear and/or nonsynchronous interactions present in data. This is achieved by testing models reflecting linear, synchronous interactions against more general models, encompassing nonlinear, nonsynchronous interactions. Many factors influencing measured BOLD signals are critical for the study of connectivity, such as paradigm-induced BOLD responses, preprocessing, motion artifacts, and geometrical distortions. Interactions are also influenced by the proximity of brain regions. The influence of all these factors is taken into account and the nature of the interactions is studied using various experimental conditions such that the conclusions reached are robust with respect to variation of these factors. After defining nonlinear and/or nonsynchronous interaction models in the framework of general linear models, statistical tests are performed on different fMRI data sets to infer the nature of the interactions. Finally, a new connectivity metric is proposed which takes these inferences into account. We find that BOLD signal interactions are statistically more significant when taking into account the history of the distant signal, i.e., the signal from the interacting region, than when using a model of linear instantaneous interaction. Moreover, about 75% of the interactions are symmetric, as assessed with the proposed connectivity metric. The history-dependent part of the coupling between brain regions can explain a high percentage of the variance in the data sets studied. As these results are robust with respect to various confounding factors, this work suggests that models used to study the functional connectivity between brain areas should in general take the BOLD signal history into account.

In this thesis the perspectives of statistical and graph-theoretical methods for the analysis of fMRI data are investigated. This involves preprocessing, extraction of components, functional connectivity and analytical derivations.The main emphasis is on functional connectivity for which we develop a graph theoretical framework based on correlation matrices. The framework is completely data driven and does not rely on previously defined regions of interest. Furthermore, it takes into account both undelayed and delayed correlations, thereby identifying temporal relationships between functional units. The approach is not restricted to fMRI but can be used also in other imaging modalities, including EEG and MEG. It is shown that by using this approach interesting network structures of functional units can be extracted, which could provide a basis for large scale modeling of brain function.The graph theoretical approach is based on local properties of the data, in contrast to global multivariate data driven methods such as principal and independent component analysis, which are described earlier in this thesis. These methods are analyzed with respect to their ability to separate identifiable brain activity from noise and compared with commonly used stimulus-driven methods. Furthermore, the analysis based on image vectors is compared with the analysis based on time course vectors, and in the case of PCA analytical conditions for equality of the two aspects are derived. In addition, the intrinsic dimension of the data is estimated by using random matrix theory.Other contributions include a semi-automatized algorithm to extract the brain area from MR images, an approach to quantify head movements and brain pulsations, and an analytical derivation of the probability density of the sample correlation of a finite Gaussian independent identically distributed (i.i.d.) sample with a fixed reference using high dimensional spherical coordinates.

In addition to information on localization of brain functions, data from fMRI experiments contain also cues about the functional connectivity among modular units. We propose a data-driven deterministic clustering algorithm based on temporal cross-correlations and elements of graph theory to detect functionally connected regions. The cluster concept can be changed in a controlled manner to reveal the functional connectivity structure in detail. The algorithm is applied to data from a motor task and shows to successfully determine clusters related to the stimulus. Furthermore, the method can be extended to include the analysis of temporal relations between different brain regions.

We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus-independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabilities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.

Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, e.g. in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.

Two inverse algorithms were applied for solving the EEG inverse problem assuming a single dipole as a source model. For increasing the efficiency of the forward computations the lead field approach based on the reciprocity theorem was applied. This method provides a procedure to calculate the computationally heavy forward problem by a single solution for each EEG lead. A realistically shaped volume conductor model with five major tissue compartments was employed to obtain the lead fields of the standard 10–20 EEG electrode system and the scalp potentials generated by simulated dipole sources. A least-squares method and a probability-based method were compared in their performance to reproduce the dipole source based on the reciprocal forward solution. The dipole localization errors were 0 to 9 mm and 2 to 22 mm without and with added noise in the simulated data, respectively. The two different inverse algorithms operated mainly very similarly. The lead field method appeared applicable for the solution of the inverse problem and especially useful when a number of sources, e.g., multiple EEG time instances, must be solved.

Functional magnetic resonance imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity in a given situation, e.g. in response to a stimulus or during task solving. The fMRI signal is very small and often cannot be identified from the anatomical images. Thus data analysis methods are required to localize the activity. We discuss different data analysis methods, a simple correlation analysis, principal component analysis (PCA) and independent component analysis (ICA), in the context of a motor task experiment with predefined stimulus time course. We show how it is possible to detect even weak activity without prior knowledge about the stimulus time course with PCA and ICA. The stimulus time course is extracted and major components of the signal, e.g. head movements are also identified.