LAUSR

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.