Antipsychotics represent the mainstay of schizophrenia pharmacological therapy, and their
role has been expanded in the last years to mood disorders treatment. Although introduced …

For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state
functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in …

Schizophrenia is characterized by dysconnectivity syndrome. Evidence of widespread
impairment of structural and functional integration has been demonstrated in schizophrenia …

In recent years, machine learning (ML) has been a promising approach in the research of
treatment outcome prediction in psychosis. In this study, we reviewed ML studies using …

PDE10A, a phosphodiesterase that inactivates both cAMP and cGMP, is a unique signaling
molecule in being highly and nearly exclusively expressed in striatal medium spiny neurons …

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ)
patients from normal controls (NCs) and for detecting abnormal brain regions in …

Background: Prior studies have separately demonstrated that magnetic resonance imaging
(MRI) and schizophrenia polygenic risk score (PRS) are predictive of antipsychotic …

Convergent evidence has suggested a significant effect of antipsychotic exposure on brain
structure and function in patients with schizophrenia, yet the characteristics of favorable …

With new data about different aspects of schizophrenia being continually generated, it
becomes necessary to periodically revisit exactly what we know. Along with a need to review …