Biology subjects

Pischedda, D.

Publications and source records attributed to Pischedda, D..

3 recordsLinked to original sources

bioRxiv2026

Virtual Brain Modelling Reveals Altered Network Dynamics and Predicts Symptom Severity in Schizophrenia: Relevance to Antipsychotic Treatment Response and Resistance.

Background: Schizophrenia (SZ) is a heterogeneous disorder characterized by positive, negative, cognitive and disorganisation symptoms, with pleomorphic phenotype and poorly understood aetiology. Dopamine dysregulation represents the most replicated in vivo pathophysiology finding. However, accumulating evidence suggests altered large-scale brain connectivity and excitation/inhibition balance disruption. While quantifying these alterations would help clarify SZ neurophysiology and guide therapy, single-patient identification remains impractical. This is particularly relevant for treatment-resistant SZ (TRS) whose pathophysiology remains unclear. Methods: Using virtual brain modelling, we simulated large-scale brain dynamics from functional and structural MRI data in 43 healthy controls and 33 SZ patients, stratified into TRS and non-TRS. We used the Wong-Wang model to derive four network parameters reflecting GABAergic and NMDA receptor-mediated glutamatergic transmission: global coupling (G), local inhibitory (Ji) and excitatory (JNMDA) coupling, and recurrent excitation (w+). We performed simulations in the whole brain and in resting-state networks, compared model parameters between groups and tested their association with Positive and Negative Syndrome Scale (PANSS) scores. Results: Compared to non-TRS, TRS patients showed increased JNMDA and decreased w+ at the whole-brain level, increased JNMDA and G in the dorsal attention network, lower G in the limbic network and increased w+ in the visual network. Moreover, Ji in the default mode network predicted negative symptom severity while JNMDA in the limbic and ventral attention networks predicted total PANSS scores and disorganisation. Conclusions: These findings suggest that TRS is characterized by network-specific alterations in excitation/inhibition profiles and support the use of personalised multiscale modelling to identify symptom biomarkers.

neuroscience↗

bioRxiv2020

The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code.

Adaptive coding of stimuli in visual cortex is well documented in perception, where it supports efficient encoding over a broad range of possible percepts. Recently, a similar neural mechanism has been reported also in value-based decision, where it allows optimal encoding of vast ranges of values in PFC: neuronal response to value depends on the choice context (relative coding), rather than being invariant across contexts (absolute coding). Additionally, value learning is sensitive to the amount of feedback information: providing complete feedback (both obtained and forgone outcomes) instead of partial feedback (only obtained outcome) improves learning. However, it is unclear whether relative coding occurs in all PFC regions and how it is affected by feedback information. We systematically investigated univariate and multivariate feedback encoding in various PFC regions and compared three modes of neural coding: absolute, partially-adaptive and fully-adaptive. Twenty-eight human participants (both sexes) performed a learning task while undergoing fMRI scanning. On each trial, they chose between two symbols associated with a certain outcome. Then, the decision outcome was revealed. Notably, in half of the trials participants received partial feedback, while in the other half they got complete feedback. We used univariate and multivariate analysis to explore value encoding in different feedback conditions. We found that both obtained and forgone outcomes were encoded in mPFC, but with opposite sign in ventral and dorsal subdivisions. Moreover, we showed that increasing feedback information induced a switch from absolute to relative coding. Our results suggest that complete feedback information promotes context-dependent outcome encoding. SIGNIFICANCE STATEMENTThis study offers a systematic investigation of the effect of the amount of feedback information (partial vs. complete) on univariate and multivariate outcome value encoding, within multiple regions in mPFC/ACC critical for value-based decisions and behavioural adaptation. Moreover, we provide the first comparison of three possible models of neural coding (i.e., absolute, partially-adaptive, and fully-adaptive coding) of value signal in mPFC by using commensurable measures of model accuracy. Taken together, our results help build a more comprehensive picture of how the human brain encodes and processes outcome value. In particular, our results suggest that simultaneous presentation of obtained and foregone outcomes promotes relative value representation.

neuroscience↗

bioRxiv2018

The context-dependent nature of the neural implementation of intentions

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, knowledge about what these networks exactly encoded is still scarce. In this study we look into the content of those processes. We ask whether the neural representations of intentions are context- and reason-invariant, or whether these processes depend on the context we are in, and the reasons we have for choosing an action. We use a combination of functional magnetic resonance imaging and multivariate decoding to directly assess the context- and reason-dependency of the processes underlying intentional action. We were able to decode action decisions in the same context and for the same reasons from the fMRI data, in line with previous decoding studies. Furthermore, we could decode action decisions across different reasons for choosing an action. Importantly, though, decoding decisions across different contexts was at chance level. These results suggest that for voluntary action, there is considerable context-dependency in intention representations. This suggests that established invariance in neural processes may not reflect an essential feature of a certain process, but that this stable character could be dependent on invariance in the experimental setup, in line with predictions from situated cognition theory.

neuroscience↗