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Reduced synaptic plasticity and E/I imbalance drive peripersonal space boundaries expansion in schizophrenia
(Elsevier B.V., 2026-09-01)
Abnormal encoding of peripersonal space (PPS) is believed to affect bodily self disruptions in schizophrenia (SCZ). Empirical studies show that SCZ patients exhibit a narrower PPS than controls but maintain its plasticity. Computational research links this smaller PPS to increased excitation of sensory neurons and reduced feedforward synaptic density. However, it is unclear how such differences influence learning during the expansion of PPS boundaries. We hypothesise that Hebbian plasticity can account for PPS expansion after active tool use training. To explore the effect of such mechanisms on PPS plasticity, we developed a SCZ network model which was fit to behavioural data before and after tool manipulation. We found that PPS expansion occurs in spite of E/I imbalance or reduced synaptic density, but does not match the post-training PPS representation of patients. A better fit was obtained after altering plasticity by either reducing the learning rate, increasing the forgetting rate or increasing the plasticity threshold. We discuss our findings in terms of dysfunctional plasticity in SCZ and highlight the key challenges in identifying the neurobiological correlates of reduced plasticity within PPS networks. Because current empirical data supports multiple viable mechanisms, we propose experiments to distinguish between the proposed plasticity accounts and clarify mixed findings on PPS representation in SCZ.
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Increased excitation enhances the sound-induced flash illusion by impairing multisensory causal inference in the schizophrenia spectrum
(Elsevier B.V., 2025-09-01)
The spectrum of schizophrenia is characterised by an altered sense of self with known impairments in tactile sensitivity, proprioception, body-self boundaries, and self-recognition. These are thought to be produced by failures in multisensory integration mechanisms, commonly observed as enlarged temporal binding windows during audiovisual illusion tasks. To our knowledge, there is an absence of computational explanations for multisensory integration deficits in patients with schizophrenia and individuals with high schizotypy, particularly at the neurobiological level. We implemented a multisensory causal inference network to reproduce the responses of individuals who scored low in schizotypy in a simulated double flash illusion task. Next, we explored the effects of recurrent excitation, cross-modal and feedback weights, and synaptic density on the visual illusory responses of the network. Using quantitative fitting to empirical data, we found that an increase in the weights of the recurrent excitatory connectivity in the network enlarges the temporal binding window and increases the overall proneness to experience the illusion, matching the responses of individuals scoring high in schizotypy. Moreover, we found that an increase in excitation increases the probability of inferring a common cause from the stimuli. We propose an E/I imbalance account of reduced temporal discrimination in the SCZ spectrum and discuss possible links with Bayesian theories of schizophrenia. We highlight the importance of adopting a multisensory causal inference perspective to address body-related symptomatology of schizophrenia.
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Scikit-neuromsi: a generalized framework for modeling multisensory integration
(Springer, 2025)
Multisensory integration is a fundamental neural mechanism crucial for understanding cognition. Multiple theoretical models exist to account for the computational processes underpinning this mechanism. However, there is an absence of a consolidated framework that facilitates the examination of multisensory integration across diverse experimental and computational contexts. We introduce Scikit-NeuroMSI, an accessible Python-based open-source framework designed to streamline the implementation and evaluation of computational models of multisensory integration. The capabilities of Scikit-NeuroMSI were demonstrated in enabling the implementation of multiple models of multisensory integration at different levels of analysis. Furthermore, we illustrate the utility of the software in systematically exploring the model’s behavior in spatiotemporal causal inference tasks through parameter sweeps in simulations. Particularly, we conducted a comparative analysis of Bayesian and network models of multisensory integration to identify commonalities that may enable to bridge both levels of description, addressing a key research question within the field. We discuss the significance of this approach in generating computationally informed hypotheses in multisensory research. Recommendations for the improvement of this software and directions for future research using this framework are presented.
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