Background. Cognitive interventions (either restorative or compensatory) developed for

Although functional recovery could be advocated as an achievable treatment goal, many effective interventions for the treatment of psychotic symptoms, such as antipsychotic drugs, may not improve functioning. The last two decades of cognitive and clinical research on schizophrenia were a turning point for the firm acknowledgment of how relevant social cognitive deficits and negative symptoms could be in predicting psychosocial functioning. The relevance of social cognition dysfunction in schizophrenia patients' daily living is now unabated. In fact, social cognition deficits could be the most significant predictor of functionality in patients with schizophrenia, non-redundantly with neurocognition. Emerging evidence suggests that negative symptoms appear to play an indirect role, mediating the relationship between neurocognition and social cognition with functional outcomes. Further explorations of this mediating role of negative symptoms have revealed that motivational deficits appear to be particularly important in explaining the relationship between both neurocognitive and social cognitive dysfunction and functional outcomes in schizophrenia. In this paper we will address the relative contribution of two key constructs-social cognitive deficits and negative symptoms, namely how intertwined they could be in daily life functioning of patients with schizophrenia.

Schizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected individuals, and is associated with substantial societal and economical costs.The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs.Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute 'organic surrogates' of this disorder. However, the neuroimaging field, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.

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Day-to-day we have to make choices. Even simple decisions involve complex cognitive processes that are still not clearly understood. When it comes to critical decisions which may lead to impactful and irreversible consequences, the understanding of the decision-making process is highly relevant. Dysfunctional coping responses may lead to stress and ultimately to trauma. In the present project, we aim to understand decision-making in the context of firefighting and to study how neurocognitive control and stress management strategies affect decision-making. We present here a functional brain imaging paradigm coupled with biosensors while firefighters play a decision-making task. In this computerized task, the firefighter faces the dilemma of entering or not in a burning house, with variable risk of house collapsing, variable number of victims in danger and variable probability of the victims saving themselves. We present here the preliminary results from a cohort of 13 firefighters, of which none presented clinically significant symptoms of post-traumatic stress disorder. We found brain regions, including the ventromedial and dorsolateral prefrontal cortices and angular gyrus, showing a parametric pattern of activation during the decision phase, i.e. the lower the risk of collapsing, the higher the neural activity in these areas. This suggests that these regions are processing that risk information and signalling the chances of being successful in the rescue phase. We also found that the power in high frequencies of the pulse rate variability of this cohort of firefighters is higher when they decide to enter for the rescue. It is suggestive that deciding not to enter for rescue causes an increase in arousal, which may be related to the expectation about the victims’ outcome (whether they survived or not). Concerning coping strategies, we found that active coping used in personal context was significantly correlated with the age and the years of experience in our sample of firefighters. We aim to understand if this pattern of brain activity reflects the coping strategies and if, in turn, it is reflected in the physiological signals we measure. Concurrently, we are recruiting firefighters with post-traumatic stress disorder and non-firefighters to perform the same task. We will investigate whether the decision processes in firefighters are different from non-firefighters, and we will address if post-traumatic stress disorder impairs decision-making in this context. Biosignals will also be used in a second phase of the project to inform training systems about the physiological state of the firefighter during virtual reality simulation. This may help developing optimal neurocognitive control and better coping strategies to deal with stress.