Motor deficits in schizophrenia quantified by nonlinear analysis of postural sway.

Motor dysfunction is a consistently reported but understudied aspect of schizophrenia. Postural sway area was examined in individuals with schizophrenia under four conditions with different amounts of visual and proprioceptive feedback: eyes open or closed and feet together or shoulder width apart. The nonlinear complexity of postural sway was assessed by detrended fluctuation analysis (DFA). The schizophrenia group (n = 27) exhibited greater sway area compared to controls (n = 37). Participants with schizophrenia showed increased sway area following the removal of visual input, while this pattern was absent in controls. Examination of DFA revealed decreased complexity of postural sway and abnormal changes in complexity upon removal of visual input in individuals with schizophrenia. Additionally, less complex postural sway was associated with increased symptom severity in participants with schizophrenia. Given the critical involvement of the cerebellum and related circuits in postural stability and sensorimotor integration, these results are consistent with growing evidence of motor, cerebellar, and sensory integration dysfunction in the disorder, and with theoretical models that implicate cerebellar deficits and more general disconnection of function in schizophrenia

Spontaneous Prediction Error Generation in Schizophrenia

Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks

The *subjectivity* of subjective experience - A representationalist analysis of the first-person perspective

This is a brief and accessible English summary of the "Self-model Theory of Subjectivity" (SMT), which is only available as German book in this archive. It introduces two new theoretical entities, the "phenomenal self-model" (PSM) and the "phenomenal model of the intentionality-relation" PMIR. A representationalist analysis of the phenomenal first-person persepctive is offered. This is a revised version, including two pictures

Altered predictive capability of the brain network EEG model in schizophrenia during cognition

Producción CientíficaThe study of the mechanisms involved in cognition is of paramount importance for the understanding of the neurobiological substrates in psychiatric disorders. Hence, this research is aimed at exploring the brain network dynamics during a cognitive task. Specifically, we analyze the predictive capability of the pre-stimulus theta activity to ascertain the functional brain dynamics during cognition in both healthy and schizophrenia subjects. Firstly, EEG recordings were acquired during a three-tone oddball task from fifty-one healthy subjects and thirty-five schizophrenia patients. Secondly, phase-based coupling measures were used to generate the time-varying functional network for each subject. Finally, pre-stimulus network connections were iteratively modified according to different models of network reorganization. This adjustment was applied by minimizing the prediction error through recurrent iterations, following the predictive coding approach. Both controls and schizophrenia patients follow a reinforcement of the secondary neural pathways (i.e., pathways between cortical brain regions weakly connected during pre-stimulus) for most of the subjects, though the ratio of controls that exhibited this behavior was statistically significant higher than for patients. These findings suggest that schizophrenia is associated with an impaired ability to modify brain network configuration during cognition. Furthermore, we provide direct evidence that the changes in phase-based brain network parameters from pre-stimulus to cognitive response in the theta band are closely related to the performance in important cognitive domains. Our findings not only contribute to the understanding of healthy brain dynamics, but also shed light on the altered predictive neuronal substrates in schizophrenia.This research project was supported in part by “Ministerio de Economía y Competitividad” and FEDER under project TEC2014-53196-R, by ‘European Commission’ (POCTEP 0378_AD_EEGWA_2_P), by ‘Consejería de Educación de la Junta de Castilla y León’ (VA037U16), by “Fondo de Investigaciones Sanitarias (Instituto de Salud Carlos III)” under projects FIS PI11/02203 and PI15/00299, and by “Gerencia Regional de Salud de Castilla y León” under projects GRS 932/A/14 and GRS 1134/A/15. A. Lubeiro was in receipt of a grant from the Consejería de Educación (Junta de Castilla y León). J. Gomez-Pilar was in receipt of a grant from University of Valladolid

The Potential of the Human Connectome as a Biomarker of Brain Disease

The human connectome at the level of fiber tracts between brain regions has been shown to differ in patients with brain disorders compared to healthy control groups. Nonetheless, there is a potentially large number of different network organizations for individual patients that could lead to cognitive deficits prohibiting correct diagnosis. Therefore changes that can distinguish groups might not be sufficient to diagnose the disease that an individual patient suffers from and to indicate the best treatment option for that patient. We describe the challenges introduced by the large variability of connectomes within healthy subjects and patients and outline three common strategies to use connectomes as biomarkers of brain diseases. Finally, we propose a fourth option in using models of simulated brain activity (the dynamic connectome) based on structural connectivity rather than the structure (connectome) itself as a biomarker of disease. Dynamic connectomes, in addition to currently used structural, functional, or effective connectivity, could be an important future biomarker for clinical applications.Comment: Perspective Article for special issue on Magnetic Resonance Imaging of Healthy and Diseased Brain Network

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

Altered predictive capability of the brain network EEG model in schizophrenia during cognition

[EN]The study of the mechanisms involved in cognition is of paramount importance for the understanding of the neurobiological substrates in psychiatric disorders. Hence, this research is aimed at exploring the brain network dynamics during a cognitive task. Specifically, we analyze the predictive capability of the pre-stimulus theta activity to ascertain the functional brain dynamics during cognition in both healthy and schizophrenia subjects. Firstly, EEG recordings were acquired during a three-tone oddball task from fifty-one healthy subjects and thirty-five schizophrenia patients. Secondly, phase-based coupling measures were used to generate the time-varying functional network for each subject. Finally, pre-stimulus network connections were iteratively modified according to different models of network reorganization. This adjustment was applied by minimizing the prediction error through recurrent iterations, following the predictive coding approach. Both controls and schizophrenia patients follow a reinforcement of the secondary neural pathways (i.e., pathways between cortical brain regions weakly connected during pre-stimulus) for most of the subjects, though the ratio of controls that exhibited this behavior was statistically significant higher than for patients. These findings suggest that schizophrenia is associated with an impaired ability to modify brain network configuration during cognition

Wiring optimization explanation in neuroscience: What is Special about it?

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system