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Abstract The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB’s goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.

Abstract Motivation Ontology development and the annotation of biological data using ontologies are time-consuming exercises that currently require input from expert curators. Open, collaborative platforms for biological data annotation enable the wider scientific community to become involved in developing and maintaining such resources. However, this openness raises concerns regarding the quality and correctness of the information added to these knowledge bases. The combination of a collaborative web-based platform with logic-based approaches and Semantic Web technology can be used to address some of these challenges and concerns. Results We have developed the BOWiki, a web-based system that includes a biological core ontology. The core ontology provides background knowledge about biological types and relations. Against this background, an automated reasoner assesses the consistency of new information added to the knowledge base. The system provides a platform for research communities to integrate information and annotate data collaboratively. Availability The BOWiki and supplementary material is available at http://www.bowiki.net/. The source code is available under the GNU GPL from http://onto.eva.mpg.de/trac/BoWiki.

Abstract Background In addition to genome sequencing, accurate functional annotation of genomes is required in order to carry out comparative and evolutionary analyses between species. Among primates, the human genome is the most extensively annotated. Human miRNA gene annotation is based on multiple lines of evidence including evidence for expression as well as prediction of the characteristic hairpin structure. In contrast, most miRNA genes in non-human primates are annotated based on homology without any expression evidence. We have sequenced small-RNA libraries from chimpanzee, gorilla, orangutan and rhesus macaque from multiple individuals and tissues. Using patterns of miRNA expression in conjunction with a model of miRNA biogenesis we used these high-throughput sequencing data to identify novel miRNAs in non-human primates. Results We predicted 47 new miRNAs in chimpanzee, 240 in gorilla, 55 in orangutan and 47 in rhesus macaque. The algorithm we used was able to predict 64% of the previously known miRNAs in chimpanzee, 94% in gorilla, 61% in orangutan and 71% in rhesus macaque. We therefore added evidence for expression in between one and five tissues to miRNAs that were previously annotated based only on homology to human miRNAs. We increased from 60 to 175 the number miRNAs that are located in orthologous regions in humans and the four non-human primate species studied here. Conclusions In this study we provide expression evidence for homology-based annotated miRNAs and predict de novo miRNAs in four non-human primate species. We increased the number of annotated miRNA genes and provided evidence for their expression in four non-human primates. Similar approaches using different individuals and tissues would improve annotation in non-human primates and allow for further comparative studies in the future.

Abstract Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with.

Abstract Background Most biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately. Results We developed a method to provide definition patterns for relations between classes using OWL and describe a novel implementation of the RO based on this method. We implemented our extension in software that converts ontologies in the OBO Flatfile Format to OWL, and also provide a prototype to extract relational patterns from OWL ontologies using automated reasoning. The conversion software is freely available at http://bioonto.de/obo2owl, and can be accessed via a web interface. Conclusions Explicitly defining relations permits their use in reasoning software and leads to a more flexible and powerful way of representing biomedical ontologies. Using the extended langua0067e and semantics avoids several mistakes commonly made in formalizing biomedical ontologies, and can be used to automatically detect inconsistencies. The use of our method enables the use of graph-based ontologies in OWL, and makes complex OWL ontologies accessible in a graph-based form. Thereby, our method provides the means to gradually move the representation of biomedical ontologies into formal knowledge representation languages that incorporates an explicit semantics. Our method facilitates the use of OWL-based software in the back-end while ontology curators may continue to develop ontologies with an OBO-style front-end.

Abstract Background Several biomedical ontologies cover the domain of biological functions, including molecular and cellular functions. However, there is currently no publicly available ontology of anatomical functions. Consequently, no explicit relation between anatomical structures and their functions is expressed in the anatomy ontologies that are available for various species. Such an explicit relation between anatomical structures and their functions would be useful both for defining the classes of the anatomy and the phenotype ontologies accurately. Results We provide an ontological analysis of functions and functional abnormalities. From this analysis, we derive an approach to the automatic extraction of anatomical functions from existing ontologies which uses a combination of natural language processing, graph-based analysis of the ontologies and formal inferences. Additionally, we introduce a new relation to link material objects to processes that realize the function of these objects. This relation is introduced to avoid a needless duplication of processes already covered by the Gene Ontology in a new ontology of anatomical functions. Conclusions Ontological considerations on the nature of functional abnormalities and their representation in current phenotype ontologies show that we can extract a skeleton for an ontology of anatomical functions by using a combination of process, phenotype and anatomy ontologies automatically. We identify several limitations of the current ontologies that still need to be addressed to ensure a consistent and complete representation of anatomical functions and their abnormalities. Availability The source code and results of our analysis are available at http://bioonto.de.

Abstract Background Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike. Results We provide both an ontological analysis of biological sequences and a formal representation that can be used in knowledge-based applications and other ontologies. We distinguish three distinct kinds of entities that can be referred to as "biological sequence": chains of molecules, syntactic representations such as those in biological databases, and the abstract information-bearing entities. For use in knowledge-based applications and inclusion in biomedical ontologies, we implemented the developed axiom system for use in automated theorem proving. Conclusion Axioms are necessary to achieve the main goal of ontologies: to formally specify the meaning of terms used within a domain. The axiom system for the ontology of biological sequences is the first elaborate axiom system for an OBO Foundry ontology and can serve as starting point for the development of more formal ontologies and ultimately of knowledge-based applications.

Abstract Background Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies. Results We have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach. Conclusion Biomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.

The aim of the paper is to point out one way of integrating the supposedly incommensurate disciplines investigated in sports science. General, common principles can be found among apparently unrelated disciplines when the focus is put on the dynamics of sports-related phenomena. Dynamical systems approaches that have recently changed research in biological and social sciences among others, offer key concepts to create a common pluricontextual language in sport science. This common language, far from being homogenising, offers key synthesis between diverse fields, respecting and enabling the theoretical and experimental pluralism. It forms a softly integrated sports science characterised by a basic dynamic explanatory backbone as well as context-dependent theoretical flexibility. After defining the dynamic integration in living systems, unable to be captured by structural static approaches, we show the commonalities between the diversity of processes existing on different levels and time scales in biological and social entities. We justify our interpretation by drawing on some recent scientific contributions that use the same general principles and concepts, and diverse methods and techniques of data analysis, to study different types of phenomena in diverse disciplines. We show how the introduction of the dynamic framework in sport science has started to blur the boundaries between physiology, biomechanics, psychology, phenomenology and sociology. The advantages and difficulties of sport science integration and its consequences in research are also discussed.

Many researchers and clinicians in cognitive neuroscience hold to a modular view of cognitive function in which the cerebral cortex operates by the activation of areas with circumscribed elementary cognitive functions. Yet an ongoing paradigm shift to a dynamic network perspective is underway. This new viewpoint treats cortical function as arising from the coordination dynamics within and between cortical regions. Cortical coordination dynamics arises due to the unidirectional influences imposed on a cortical area by inputs from other areas that project to it, combined with the projection reciprocity that characterizes cortical connectivity and gives rise to reentrant processing. As a result, cortical dynamics exhibits both segregative and integrative tendencies and gives rise to both cooperative and competitive relations within and between cortical areas that are hypothesized to underlie the emergence of cognition in brains.

Emotion and motion, though seldom studied in tandem, are complementary aspects of social experience. This study investigates variations in emotional responses during movement coordination between a human and a Virtual Partner (VP), an agent whose virtual finger movements are driven by the Haken-Kelso-Bunz (HKB) equations of Coordination Dynamics. Twenty-one subjects were instructed to coordinate finger movements with the VP in either inphase or antiphase patterns. By adjusting model parameters, we manipulated the 'intention' of VP as cooperative or competitive with the human's instructed goal. Skin potential responses (SPR) were recorded to quantify the intensity of emotional response. At the end of each trial, subjects rated the VP's intention and whether they thought their partner was another human being or a machine. We found greater emotional responses when subjects reported that their partner was human and when coordination was stable. That emotional responses are strongly influenced by dynamic features of the VP's behavior, has implications for mental health, brain disorders and the design of socially cooperative machines., (Copyright © 2016 Elsevier B.V. All rights reserved.)

What we know about infant learning and memory is founded largely on systematic studies by the late Carolyn Rovee-Collier (1942-2014) and her associates of a phenomenon called mobile conjugate reinforcement. Experiments show that when a ribbon is attached from a 3-month-old infant's foot to a mobile suspended overhead the baby quickly realizes it can make the mobile move. The mobile, which offers interesting sights and sounds, responds conjugately to the baby's vigorous kicks which increase in rate by a factor of 3-4. In this paper, using the concepts, methods and tools of coordination dynamics, we present a theoretical model which reproduces the experimental observations of Rovee-Collier and others and predicts a number of additional features that can be experimentally tested. The model is a dynamical system consisting of three equations, one for the baby's leg movements, one for the jiggling motion of the mobile and one for the functional coupling between the two. A key mechanism in the model is positive feedback which is shown to depend sensitively on bifurcation parameters related to the infant's level of attention and inertial properties of the mobile. The implications of our model for the dynamical (and developmental) origins of agency are discussed.

This study investigated interpersonal coordination tendencies in 1vs.1 dyads in rugby union, here expressed by participants' movement velocity towards or away from the sideline as they competed to score or prevent a try. We examined whether coupling tendencies of members of each dyad shaped key performance outcomes (try or successful tackle). Data on movement displacement trajectories of eight male rugby union players (aged 11-12 years) were analysed during performance in 47 trials. To assess coordination tendencies during exploratory behaviours in the dyads, analyses of performance time series data were undertaken using variable time graphs, running correlations and cross-correlations. Results revealed distinct coupling patterns characterised by shifts between synchronous coordination and asynchronous coordination tendencies and uncoordinated actions. Observed behaviours were interpreted as attempts of competing participants to create and perceive possibilities for action while seeking to achieve specific performance goals. Findings also revealed that a variety of patterned relations between participants resulted in different performance outcomes.

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities.

Social behavior is a complex integrative function that entails many aspects of the brain's sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called "neuromarkers" of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be task-specific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction.

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

Social neuroscience has called for new experimental paradigms aimed toward real-time interactions. A distinctive feature of interactions is mutual information exchange: One member of a pair changes in response to the other while simultaneously producing actions that alter the other. Combining mathematical and neurophysiological methods, we introduce a paradigm called the human dynamic clamp (HDC), to directly manipulate the interaction or coupling between a human and a surrogate constructed to behave like a human. Inspired by the dynamic clamp used so productively in cellular neuroscience, the HDC allows a person to interact in real time with a virtual partner itself driven by well-established models of coordination dynamics. People coordinate hand movements with the visually observed movements of a virtual hand, the parameters of which depend on input from the subject's own movements. We demonstrate that HDC can be extended to cover a broad repertoire of human behavior, including rhythmic and discrete movements, adaptation to changes of pacing, and behavioral skill learning as specified by a virtual "teacher." We propose HDC as a general paradigm, best implemented when empirically verified theoretical or mathematical models have been developed in a particular scientific field. The HDC paradigm is powerful because it provides an opportunity to explore parameter ranges and perturbations that are not easily accessible in ordinary human interactions. The HDC not only enables to test the veracity of theoretical models, it also illuminates features that are not always apparent in real-time human social interactions and the brain correlates thereof.

We suggest the authors' endeavor toward a science of intentional change may benefit from recent advances in informationally meaningful self-organizing dynamical systems. Coordination Dynamics, having contributed to an understanding of behavior on several time scales - adaptation, learning, and development - and on different levels of analysis, from the neural to the social, may complement, if not enhance, the authors' insights.

To further advance our understanding of the brain, new concepts and theories are needed. In particular, the ability of the brain to create information flows must be reconciled with its propensity for synchronization and mass action. The theoretical and empirical framework of Coordination Dynamics, a key aspect of which is metastability, are presented as a starting point to study the interplay of integrative and segregative tendencies that are expressed in space and time during the normal course of brain and behavioral function. Some recent shifts in perspective are emphasized, that may ultimately lead to a better understanding of brain complexity.

Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or "bound" together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic cognitive, behavioral, and social functions., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Beamforming offers a way to estimate the solution to the inverse problem in EEG and MEG but is also known to perform poorly in the presence of highly correlated sources, e.g. during binaural auditory stimulation, when both left and right primary auditory cortices are activated simultaneously. Surface Laplacian, or the second spatial derivative calculated from the electric potential, allows for deblurring of EEG potential recordings reducing the effects of low skull conductivity and is independent of the reference electrode location. We show that anatomically constrained beamforming in conjunction with the surface Laplacian allows for detection of both locations and dynamics of temporally correlated sources in EEG. Whole-head 122 channel binaural stimulus EEG data were simulated using a boundary element method (BEM) and realistic geometry forward model. We demonstrate that in contrast to conventional potential-based EEG beamforming, Laplacian beamforming allows to determine locations of correlated source dipoles without any a priori assumption about the number of sources. We also show (by providing simulations of auditory evoked potentials) that the dynamics at the detected source locations can be derived from subsets of electrodes. Deblurring auditory evoked potential maps subdivides EEG signals from each hemisphere and allows for the beamformer to be applied separately for left and right hemispheres., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Much evidence suggests that dynamic laws of neurobehavioral coordination are sui generis: they deal with collective properties that are repeatable from one system to another and emerge from microscopic dynamics but may not (even in principle) be deducible from them. Nevertheless, it is useful to try to understand the relationship between different levels while all the time respecting the autonomy of each. We report a program of research that uses the theoretical concepts of coordination dynamics and quantitative measurements of simple, well-defined experimental model systems to explicitly relate neural and behavioral levels of description in human beings. Our approach is both top-down and bottom-up and aims at ending up in the same place: top-down to derive behavioral patterns from neural fields, and bottom-up to generate neural field patterns from bidirectional coupling between astrocytes and neurons. Much progress can be made by recognizing that the two approaches--reductionism and emergentism--are complementary. A key to understanding is to couch the coordination of very different things--from molecules to thoughts--in the common language of coordination dynamics., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Bimanual coordination engages a distributed network of brain areas, the spatiotemporal organization of which has given rise to intense debates. Do bimanual movements require information processing in the same set of brain areas that are engaged by movements of the individual components (left and right hands)? Or is it necessary that other brain areas are recruited to help in the act of coordination? These two possibilities are often considered as mutually exclusive, with studies yielding support for one or the other depending on techniques and hypotheses. However, as yet there is no account of how the two views may work together dynamically. Using the method of Mode-Level Cognitive Subtraction (MLCS) on high density EEG recorded during unimanual and bimanual movements, we expose spatiotemporal reorganization of large-scale cortical networks during stable inphase and antiphase coordination and transitions between them. During execution of stable bimanual coordination patterns, neural dynamics were dominated by temporal modulation of unimanual networks. At instability and transition, there was evidence for recruitment of additional areas. Our study provides a framework to quantify large-scale network mechanisms underlying complex cognitive tasks often studied with macroscopic neurophysiological recordings., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Objectives: The goal of this research was to investigate sub-band modulations in the mu domain in dyads performing different social coordination tasks., Methods: Dyads of subjects performed rhythmic finger movement under three different task conditions: intrinsic - maintain self-produced movement while ignoring their partner's movement; in-phase - synchronize with partner; and anti-phase - maintain syncopation with partner. Movement profiles of the dyads were used to estimate a synchronization index (SI) to verify differences in coordination according to each task. EEG was recorded during task performance and at baseline (partner's actions hidden from view). Log power ratios of mu band activity (active against baseline) were used to assess the relative levels of synchronization/de-synchronization in both the upper and lower mu bands., Results: Results confirm a functional dissociation of lower (8-10 Hz) and upper (10-12 Hz) mu bands in social coordination tasks. Lower mu band activity was independent of specific modulations across tasks and hemispheric preferences. Upper mu band activity was sensitive to coordination tasks and exhibited marked differences between the hemispheres. Accentuated de-synchronization of right relative to left hemisphere in the anti-phase task appeared related to the greater demand of perceptual-motor discrimination. Left hemisphere de-synchronization in both in-phase and anti-phase coordination was interpreted in terms of successful production of imitation. Right hemisphere synchronization in the intrinsic task was interpreted as inhibition of an imitative response tendency., Conclusions: Functional dissociation of lower and upper mu band and hemispheric preferences exists in real-time social coordination., Significance: This research attests to the merit of analyzing sub-band activity in the alpha-mu domain in order to identify neural correlates of social coordination. Such 'neuromarkers' may be relevant for brain disorders such as apraxia and autism., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape ("intrinsic dynamics"). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task-the usual outcome-but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or "routes": bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change.

Multistable coordination dynamics exists at many levels, from multifunctional neural circuits in vertebrates and invertebrates to large-scale neural circuitry in humans. Moreover, multistability spans (at least) the domains of action and perception, and has been found to place constraints upon, even dictating the nature of, intentional change and the skill-learning process. This paper reviews some of the key evidence for multistability in the aforementioned areas, and illustrates how it has been measured, modelled and theoretically understood. It then suggests how multistability--when combined with essential aspects of coordination dynamics such as instability, transitions and (especially) metastability--provides a platform for understanding coupling and the creative dynamics of complex goal-directed systems, including the brain and the brain-behaviour relation.

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies., (© 2011 IEEE)

This study sought to investigate the effects of manipulating social coordination on brain synchronization/de-synchronization in the mu band. Mu activation is associated with understanding and coordinating motor acts and may play a key role in mediating social interaction. Members of a dyad were required to interact with one another in a rhythmic finger movement coordination task under various instructions: intrinsic where each member of the dyad was instructed to maintain their own and ignore their partner's movement; in-phase where they were asked to synchronize with their partner's movement; and anti-phase where they were instructed to syncopate with their partner's movement. EEG and movement data were recorded simultaneously from both subjects during all three tasks and a control condition. Log power ratios of EEG activity in the active conditions versus control were used to assess the effect of task context on synchronization/de-synchronization in the mu spectral domain. Results showed clear and systematic modulation of mu band activity in the 10-12 Hz range as a function of coordination context. In the left hemisphere general levels of alpha-mu suppression increased progressively as one moved from intrinsic through in-phase to anti-phase contexts but with no specific central-parietal focus. In contrast the right hemisphere displayed context-specific changes in the central-parietal region. The intrinsic condition showed a right synchronization which disappeared with the in-phase context even as de-synchronization remained greater in the left hemisphere. Anti-phase was associated with larger mu suppression in the right in comparison with left at central-parietal region. Such asymmetrical changes were highly correlated with changing behavioral dynamics. These specific patterns of activation and deactivation of mu activity suggest that localized neural circuitry in right central-parietal regions mediates how individuals interpret the movements of others in the context of their own actions. A right sided mechanism in the 10-12 Hz range appears to be involved in integrating the mutual information among the members of a dyad that enables the dynamics of social interaction to unfold in time., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Compared to healthy individuals, schizophrenic patients suffer from sensorimotor disorders including problems when tracking moving targets and perceiving biological motion. Recent advances in embodied cognition and social coordination dynamics have emphasized the important role played by bodily information exchange (e.g. facial expressions, posture, and movements) in the way people interact with and mutually influence each other. These experimental studies on healthy participants provide data on sensorimotor performances of a patient that are recorded at high temporal and spatial resolutions. They should therefore be considered in studies on schizophrenic patients. These functional, quantitive and dynamic aspects of sensorimotor coordination abilities, may offer promising perspectives and could lead to a better understanding of sensorimotor disorders in schizophrenia. The purpose of this article is to introduce a new experimental paradigm in schizophrenia inspired by the field of coordination dynamics, a theoretical and experimental approach born more than 30 years ago that has recently expanded to interpersonal interactions, the so-called social coordination dynamics. In our study, we hypothesize that the sensorimotor deficits associated with schizophrenia in social interaction may be, at least partially, due to a failure to properly pick up information about the movements of other people. We therefore designed a study where healthy individuals and schizophrenic patients were asked to intentionally track the oscillations of visual targets of various social relevance using hand movements. Four different rhythmic visual stimuli varying in degree of biological relevance (form and motion) are used: [1] an oscillating dot; [2] a computer generated hand moving up and down continuously driven by a sine function; [3] pre-recorded oscillatory movements of a real hand; and [4] the hand of a real individual (behind a curtain that occluded vision of the rest of the body). Two distinct dependent variables are computed to quantify the coordination between the movements of the participants and the visual stimuli: the relative phase and the power spectrum overlap between their own movements. In this preliminary study, analyses of kinematic data revealed that schizophrenic patients had trouble synchronizing to (the more) "biological" target unlike control healthy individuals. These results suggest that patients with schizophrenia may suffer from sensorimotor coordination disabilities with socially relevant visual stimuli. The novel paradigm we introduce in research on schizophrenia should allow for a better understanding of the troubles these patients encounter when interacting with other people thanks to an approach rooted and building on social coordination dynamics as well as motor and social cognition., (Copyright © 2011 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.)

Neural activity as measured non-invasively using electroencephalography (EEG) or magnetoencephalography (MEG) originates in the cortical gray matter. In the cortex, pyramidal cells are organized in columns and activated coherently, leading to current flow perpendicular to the cortical surface. In recent years, beamforming algorithms have been developed, which use this property as an anatomical constraint for the locations and directions of potential sources in MEG data analysis. Here, we extend this work to EEG recordings, which require a more sophisticated forward model due to the blurring of the electric current at tissue boundaries where the conductivity changes. Using CT scans, we create a realistic three-layer head model consisting of tessellated surfaces that represent the cerebrospinal fluid-skull, skull-scalp, and scalp-air boundaries. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from MRI scans. EEG beamforming is implemented on simulated sets of EEG data for three different head models: single spherical, multi-shell spherical, and multi-shell realistic. Using the same conditions for simulated EEG and MEG data, it is shown (and quantified by receiver operating characteristic analysis) that EEG beamforming detects radially oriented sources, to which MEG lacks sensitivity. By merging several techniques, such as linearly constrained minimum variance beamforming, realistic geometry forward solutions, and cortical constraints, we demonstrate it is possible to localize and estimate the dynamics of dipolar and spatially extended (distributed) sources of neural activity.

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters.

Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence) as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies.

The theoretical framework of coordination dynamics posits complementary neural mechanisms to maintain complex behavioral patterns under circumstances that may render them unstable and to voluntarily switch between behaviors if changing internal or external conditions so demand. A candidate neural structure known to play a role in both the selection and maintenance of intentional behavior is the basal ganglia. Here, we use functional magnetic resonance imaging to explore the role of basal ganglia in intentional switching between bimanual coordination patterns that are known to differ in their stability as a function of movement rate. Key measures of pattern dynamics and switching were used to map behavior onto the associated neural circuitry to determine the relation between specific behavioral variables and activated brain areas. Results show that putamen activity is highly sensitive to pattern stability: greater activity was observed in bilateral putamen when subjects were required to switch from a more to a less stable pattern than vice versa. Since putamen activity correlated with pattern stability both before and during the switching process, its role may be to select desired actions and inhibit competing ones through parametric modulation of the intrinsic dynamics. Though compatible with recent computational models of basal ganglia function, our results further suggest that pattern stability determines how the basal ganglia efficiently and successfully select among response alternatives.

In coordination dynamics, rate is a nonspecific control parameter that alters the stability of behavioral patterns and leads to spontaneous pattern switching. We used fMRI in conjunction with measures of effective connectivity to investigate the neural basis of behavioral dynamics by examining two coordination patterns known to be differentially stable (synchronization and syncopation) across a range of rates (0.75 to 1.75 Hz). Activity in primary auditory and motor cortices increased linearly with rate, independent of coordination pattern. On the contrary, activity in a premotor-cerebellar circuit varied directly with the stability of the collective variable (relative phase) that specifies coordinated behavioral patterns. Connectivity between premotor and motor cortices was also modulated by the stability of the behavioral pattern indicative of greater reliance on sensorimotor integration as action becomes more variable. By establishing a critical connection between behavioral and large scale brain dynamics, these findings reveal a basic principle for the neural organization underlying coordinated action.

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI -- Virtual Partner Interaction -- a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms.

One difference between the excitator model and other theoretical models of coordination is the mechanism of discrete movement initiation. In addition to an imperative signal common to all discrete movement initiation, the excitator model proposes that movements are initiated when a threshold element in state space, the so-called separatrix, is crossed as a consequence of stimulation or random fluctuations. The existence of a separatrix predicts that false starts will be caused by mechanical perturbations and that they depend on the perturbation's direction. The authors tested this prediction in a reaction-time task to an auditory stimulus. Participants applied perturbations in the direction of motion (i.e., index finger flexion) or opposed to the motion prior to the stimulus on 1/4 of the trials. The authors found false starts in 34% and 9% of trials following flexion perturbations and extension perturbations, respectively, as compared with only 2% of trials without perturbations, confirming a unique prediction of the excitator model.

Understanding the coordination of multiple parts in a complex system such as the brain is a fundamental challenge. We present a theoretical model of cortical coordination dynamics that shows how brain areas may cooperate (integration) and at the same time retain their functional specificity (segregation). This model expresses a range of desirable properties that the brain is known to exhibit, including self-organization, multi-functionality, metastability and switching. Empirically, the model motivates a thorough investigation of collective phase relationships among brain oscillations in neurophysiological data. The most serious obstacle to interpreting coupled oscillations as genuine evidence of inter-areal coordination in the brain stems from volume conduction of electrical fields. Spurious coupling due to volume conduction gives rise to zero-lag (inphase) and antiphase synchronization whose magnitude and persistence obscure the subtle expression of real synchrony. Through forward modeling and the help of a novel colorimetric method, we show how true synchronization can be deciphered from continuous EEG patterns. Developing empirical efforts along the lines of continuous EEG analysis constitutes a major response to the challenge of understanding how different brain areas work together. Key predictions of cortical coordination dynamics can now be tested thereby revealing the essential modus operandi of the intact living brain.

The basic reciprocity between individual parts and collective organization constitutes a key scientific question spanning the biological and social sciences. Such reciprocity is accompanied by the absence of direct linkages between levels of description giving rise to what is often referred to as the aggregation or nonequivalence problem between levels of analysis. This issue is encountered both in neuroscience and economics. So far, in spite of being identified and extensively discussed in various (other) scientific fields, the problem of understanding the nature of the interactions and coordination dynamics between individual (neuron approximately agent) and collective (neural networks approximately population of humans) behaviors has received little, if any attention in the growing field of neuroeconomics. The present contribution focuses on bringing a theoretical perspective to the interpretation of experiments recently published in this field and addressing how the concepts and methods of coordination dynamics may impact future research. First, we very briefly discuss the links between biology and economics. Second, we address the nonequivalence problem between different levels of analysis and the concept of reciprocal causality. Third, neuroeconomics studies that investigate the neural underpinnings of social decision making in the context of two economic games (trust and ultimatum) are reviewed to highlight issues that arise when experimental results exist at multiple scales of observation and description. Finally, in the last two sections, we discuss how coordination dynamics might provide novel routes to studying and modelling the relation between brain activity and decision making.

Contemporary brain theories of cognitive function posit spatial, temporal and spatiotemporal reorganization as mechanisms for neural information processing. Corresponding brain imaging results underwrite this perspective of large-scale reorganization. As we show here, a suitable choice of experimental control tasks allows the disambiguation of the spatial and temporal components of reorganization to a quantifiable degree of certainty. When using electro- or magnetoencephalography (EEG or MEG), our approach relies on the identification of lower dimensional spaces obtained from the high dimensional data of suitably chosen control task conditions. Encephalographic data from task conditions are reconstructed within these control spaces. We show that the residual signal (part of the task signal not captured by the control spaces) allows the quantification of the degree of spatial reorganization, such as recruitment of additional brain networks.

The present paper examines natural language as a dynamical system. The oft-expressed view of language as "a static system of symbols" is here seen as an element of a larger system that embraces the mutuality of symbols and dynamics. Following along the lines of the theoretical biologist H.H. Pattee, the relation between symbolic and dynamic aspects of language is expressed within a more general framework that deals with the role of information in biological systems. In this framework, symbols are seen as information-bearing entities that emerge under pressures of communicative needs and that serve as concrete constraints on development and communication. In an attempt to identify relevant dynamic aspects of such a system, one has to take into account events that happen on different time scales: evolutionary language change (i.e., a diachronic aspect), processes of communication (language use) and language acquisition. Acknowledging the role of dynamic processes in shaping and sustaining the structures of natural language calls for a change in methodology. In particular, a purely synchronic analysis of a system of symbols as "meaning-containing entities" is not sufficient to obtain answers to certain recurring problems in linguistics and the philosophy of language. A more encompassing research framework may be the one designed specifically for studying informationally based coupled dynamical systems (coordination dynamics) in which processes of self-organization take place over different time scales.

Key methodological issues for designing, analyzing, and interpreting neuroimaging experiments are presented from the perspective of the framework of Coordination Dynamics. To this end, a brief overview of Coordination Dynamics is introduced, including the main concepts of control parameters and collective variables, theoretical modeling, novel experimental paradigms, and cardinal empirical findings. Basic conceptual and methodological issues for the design and implementation of coordination experiments in the context of neuroimaging are discussed. The paper concludes with a presentation of neuroimaging findings central to understanding the neural basis of coordination and addresses their relevance for the sport sciences. The latter include but are not restricted to learning and practice-related issues, the role of mental imagery, and the recovery of function following brain injury.

Several conjectures by A. S. Iberall on life and mind are used as a backdrop to sketch a theory of mental activity that respects both the contents of thought and the dynamics of thinking. The dynamics, in this case, refers fundamentally to animated, meaningfully coupled self-organizing processes (coordination dynamics) and exhibit multistability, switching, and, because of symmetry breaking, metastability. The interplay of 2 simultaneously acting forces underlies the metastable mind: the tendency for the coordinating elements to couple together (integration) and the tendency for the elements to express their individual autonomy (segregation). Metrics for metastability are introduced that enable these cooperative and competitive tendencies to be quantified. Whereas bistability is the basis for polarized, either/or thinking, the metastable régime-which contains neither stable nor unstable states, no states at all, in fact-gives rise to a far more fluid, complementary mode of operation in which it is possible for apparent contraries to coexist in the mind at the same time.

This paper examines the influence of context on the processing of category names embedded in sentences. The investigation focuses on the nature of information available immediately after such a word is heard as well as on the dynamics of adaptation to context. An on-line method (Cross Modal Lexical Priming) was used to trace how this process unfolds in time. We found that the information available immediately after a category word is presented is not altered by the sentence context in which the word is immersed. Rather, the structure of availability of particular exemplars of the category resembles the typicality structure of a conceptual representation. The adaptation to context occurs later (between 300 and 450 ms after the category word) and takes the form of a rapid reorganization of the structure rather than a gradual activation of a contextually relevant exemplar. We claim that such data is best accounted for in a dynamical framework, where a coherent global structure emerges through locally guided self-organization.

Niels Bohr's maxim contraria sunt complementa indicated his strong suspicion that the complementarity interpretation of quantum mechanics might someday be expanded into a generalized principle. It now appears that such a principle has been found in metastability which appears at the scale of living things. Metastability has been proposed as a principle of brain~behavior, and is captured in the extended or 'broken-symmetry' version of the HKB model of coordination dynamics. The metastable regime of coordination dynamics reconciles the tendency of specialized brain regions to express autonomy (segregation) and their simultaneous tendency to work together as a synergetic whole (integration). There is growing evidence from recent studies in the brain and behavioral sciences that the complementary nature of integrating and segregating tendencies is essential to the way human brain~minds work.