Third party stabilization of unstable coordination in systems of coupled oscillators

Metastability demystified - the foundational past, the pragmatic present and the promising future

Healthy brain function depends on balancing stable integration between brain areas for effective coordinated functioning, with coexisting segregation that allows subsystems to express their functional specialization. Metastability, a concept from the dynamical systems literature, has been proposed as a key signature that characterizes this balance. Building on this principle, the neuroscience literature has leveraged the phenomenon of metastability to investigate various aspects of brain function in health and disease. However, this body of work often uses the notion of metastability heuristically, and sometimes inaccurately, making it difficult to navigate the vast literature, interpret findings and foster further development of theoretical and experimental methodologies. Here, we provide a comprehensive review of metastability and its applications in neuroscience, covering its scientific and historical foundations and the practical measures used to assess it in empirical data. We also provide a critical analysis of recent theoretical developments, clarifying common misconceptions and paving the road for future developments.

Virtual Partners Improve Synchronization in Human-Machine Trios

The interplay between auditory and motor processes in sensorimotor synchronization is crucial for achieving a cohesive group performance, particularly in musical groups. This study addressed the impact of virtual partners on synchronization performance in human trios. With a novel methodology, the study utilized virtual partners driven by computational models to simulate real-time synchronization with human participants. Trio synchronization with three synchronization models was compared: linear error-correction, Kuramoto oscillators, and delay-coupled oscillators. Forty-eight musically trained adults performed synchronization tasks in both in-phase and anti-phase rhythms with either a human confederate or one of the three computational models as the third partner, forming 24 trios. Synchronization stability and accuracy were significantly enhanced in trios that contained a virtual partner compared to those with a human confederate. Model optimizations revealed a stronger coupling of participants with each other than with virtual partners for in-phase rhythms, and a stronger coupling of virtual partners with participants than of participants with each other in anti-phase rhythms; these patterns were obtained for the oscillator models but not for the linear model. Additionally, participants reported higher perceived synchronization success, greater control over performance, and stronger social relationships with virtual partners than with the human confederate. These findings highlight the potential of virtual partners for improving synchronization and suggest avenues for further research in the use of adaptive agents in group performance settings.

Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology

The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.