Browsing by Author "Caldeira, S"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Social Cognition, Negative Symptoms and Psychosocial Functioning in Schizophrenia.Publication . Madeira, N; Caldeira, S; Bajouco, M; Pereira, AT; Martins, MJ; Macedo, AAlthough 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.
- The quest for biomarkers in schizophrenia: from neuroimaging to machine learning.Publication . Bajouco, M; Mota, D; Coroa, M; Caldeira, S; Santos, V; Madeira, NSchizophrenia 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.