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Background: Delayed diagnosis of bipolar disorder (BD) is common in adolescents and young adults and the search for biomarkers to help in early diagnosis in BD at-risk populations is an important goal of neuroimaging research. Functional connectivity studies in BD patients suggests that anomalous connectivity between prefrontal and limbic regions could be risk biomarkers for BD. The aim of this review is to provide an overview of the neuroimaging literature that employed functional connectivity techniques in adolescents and young adults at-risk for BD. Methods: A literature search was conducted using PubMed and EMBASE databases, to identify fMRI studies that em- ployed a measure of functional or e ective connectivity or network based statistics and included individuals at-risk for BD who were in the age range of early-mid adolescence (13–18 years old) and/or young adulthood (19–25 years old). Results: Ten studies focusing on 4 functional imaging domains were identi ed, namely emotion processing, a ective cognition, reward processing and resting-state. Altered functional connectivity between amygdala and ventrolateral prefrontal cortex (PFC); amygdala and anterior cingulate cortex and between anterior cingulate cortex, ventrolateral PFC and dorsolateral PFC emerged as putative risk biomarkers. Heterogeneity in BD at-risk samples, tasks and connectivity analysis methods has been identi ed. Conclusions: Youth at-risk for BD have altered functional connectivity in prefrontal-limbic networks supporting emo- tion regulation that might underlie emotion lability and mood dysregulation predisposing to BD. Future longitudinal studies in adolescents and young adults with Bipolar At-Risk criteria are important to establish functional connectivity measures as risk biomarkers.

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. info:eu-repo/semantics/publishedVersion

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 so cial 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 neurocogni tion. 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.