From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia.

The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological, clinical and psychopathological data to better investigate functional outcome still constitutes a challenge. Artificial neural network-based modeling might help to better capture clinical heterogeneity by analyzing the non-linear relationships among multiple variables. Two hundred and fourteen clinically stabilized patients with schizophrenia were recruited and assessed for neurocognition, psychopathology and functioning. Artificial neural network analyses were conducted to yield significant predictors of functional outcome among clinical and cognitive variables and to build distinct functional Profiles, each characterized by a different medley of cognitive and clinical features. Twenty-two key predictors of daily functioning emerged, encompassing neurocognitive and clinical domains, with major roles for processing speed and attention. Four Profiles were constructed based on specific levels of functioning, each characterized by a distinct distribution of key clinical and neurocognitve measures. This study highlights the importance of a more in-depth investigation of cognitive and clinical heterogeneity. A better understanding of the building blocks of these Profiles would lead to more individualized rehabilitation treatments.

Addiction and empathy: a preliminary analysis.

Addicted patients show impaired social functioning. Chronic drug consumption may lead to impairments in decoding empathic cues. The aim of the study is to explore empathy abilities in addicted patients and the hypothesis of a differential impairment between affective and cognitive empathy. 62 addicted patients and 40 healthy volunteers were evaluated using the empathy quotient (EQ) and its subscales cognitive empathy (factor 1), emotional empathy (factor 2), social skills (factor 3). Patients scored statistically significantly lower than controls in EQ total score, in particular in factor 2. No difference was found in factor 1 and in factor 3. Consistent with previous findings, our study suggests specific impairment in emotional empathy combined with preserved cognitive empathy. These findings show important clinical implication in the development of specific rehabilitative programmes for the empowerment of empathy abilities and interpersonal skills that constitute important components in the prevention of relapse.

Supporting tools in psychiatric treatment decision-making: sertraline outcome investigation with artificial neural network method.

Controlled trials in clinical psychopharmacology may fail to provide reliable information about the benefit of treatment for the patient when considered in a real-life setting rather than as a part of a well-defined sampling procedure. Previously, we applied the mathematical model of an artificial neural network (ANN) to a pool of clinical information gathered through case descriptions provided by senior psychiatrists in clinical charts of patients receiving their first exposure to sertraline. In the present study, we applied the same mathematical model to a larger sample. The performance of the ANN model in forecasting successful and unsuccessful treatment showed an overall accuracy of classification of 97.12%. This result supports our previous finding about the potential application of this method as a reliable predictor of a given psychiatric patient's outcome during a specific psychopharmacological therapy.