Increased excursions to functional networks in schizophrenia in the absence of task

Brain activity during rest has been demonstrated to evolve through a repertoire of functional connectivity (FC) patterns, whose alterations may provide biomarkers of schizophrenia - a psychotic disorder characterized by dysfunctional brain connectivity. In this study, differences between the dynamic exploration of resting-state networks using functional magnetic resonance imaging (fMRI) data from 71 schizophrenia patients and 74 healthy controls were investigated using a method focusing on the dominant fMRI signal phase coherence pattern at each time point. Through the lens of dynamical systems theory, brain activity in the form of temporal FC state trajectories was examined for intergroup differences by calculating the fractional occupancy, dwell time, limiting probability of each state and the transition probabilities between states. Results showed reduced fractional occupancy of a globally synchronized state in schizophrenia. Conversely, FC states overlapping with canonical functional subsystems exhibited increased fractional occupancy and limiting probability in schizophrenia. Furthermore, state-to-state transition probabilities were altered in schizophrenia. This revealed a reduced probability of remaining in a global integrative state, increased probability of switching from this state to functionally meaningful networks and reduced probability of remaining in a state related to the Default Mode network. These results revealed medium to large effect sizes. Finally, this study showed that using K-medoids clustering did not influence the observed intergroup differences - highlighting the utility of dynamical systems theory to better understand brain activity. Combined, these findings expose pronounced differences between schizophrenia patients and healthy controls - supporting and extending current knowledge regarding disrupted brain dynamics in schizophrenia.

On Smoother Attributions using Neural Stochastic Differential Equations

Several methods have recently been developed for computing attributions of a neural network's prediction over the input features. However, these existing approaches for computing attributions are noisy and not robust to small perturbations of the input. This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. Using dynamical systems theory, we theoretically analyze the robustness of these attributions. We also experimentally demonstrate the efficacy of our approach in providing smoother, visually sharper and quantitatively robust attributions by computing attributions for ImageNet images using ResNet-50, WideResNet-101 models and ResNeXt-101 models.

Divided mind – divided brain. The neurobiology of dissociative identity disorder from the perspective of dynamical systems theory

Explaining the biology of dissociative identity disorder and its clinical aspects is one of the major challenges in modern neuroscience. The complexity and the unique nature of this disorder, coupled with insufficient biopsychological models needed for creating universal therapeutic procedures, make the treatment difficult. The vast majority of neuroimaging studies in dissociative identity disorder patients proved to be inconclusive. Abnormalities of particular brain structures do not explain the wide range of symptoms in dissociative identity disorder. Moreover, the findings are partly contradictory. Collecting adequate representative study samples is difficult and most reports are in fact single case studies. On top of that, meta-analyses show that patients with dissociative identity disorder usually present with additional mental problems, which makes interpretation of neuroimaging data extremely difficult. Nowadays, scientific research on dissociative identity disorder focuses on child trauma and its influence on the development of the central nervous system. This article is an overview of recent research on the neurobiology of dissociative identity disorder, with particular emphasis on the impact of childhood trauma on the development of the central nervous system. It focuses on hypotheses attempting to capture the specific dynamics of neural activity leading to neural network fragmentation, and uses the dynamical systems theory to describe this phenomenon.

The Dynamics of Musical Participation

In this paper we argue that our comprehension of musical participation—the complex network of interactive dynamics involved in collaborative musical experience—can benefit from an analysis inspired by the existing frameworks of dynamical systems theory and coordination dynamics. These approaches can offer novel theoretical tools to help music researchers describe a number of central aspects of joint musical experience in greater detail, such as prediction, adaptivity, social cohesion, reciprocity, and reward. While most musicians involved in collective forms of musicking already have some familiarity with these terms and their associated experiences, we currently lack an analytical vocabulary to approach them in a more targeted way. To fill this gap, we adopt insights from these frameworks to suggest that musical participation may be advantageously characterized as an open, non-equilibrium, dynamical system. In particular, we suggest that research informed by dynamical systems theory might stimulate new interdisciplinary scholarship at the crossroads of musicology, psychology, philosophy, and cognitive (neuro)science, pointing toward new understandings of the core features of musical participation.