A limited number of studies have evaluated sexual functioning in patients with schizophrenia. Most patients show an interest in sex that differs little from the general population. By contrast, psychiatric symptoms, institutionalization, and psychotropic medication contribute to frequently occurring impairments in sexual functioning. Women with schizophrenia have a better social outcome, longer lasting (sexual) relationships, and more offspring than men with schizophrenia. Still, in both sexes social and interpersonal impairments limit the development of stable sexual relationships. Although patients consider sexual problems to be highly relevant, patients and clinicians not easily discuss these spontaneously, leading to an underestimation of their prevalence and contributing to decreased adherence to treatment. Studies using structured interviews or questionnaires result in many more patients reporting sexual dysfunctions. Although sexual functioning can be impaired by different factors, the use of antipsychotic medication seems to be an important factor. A comparison of different antipsychotics showed high frequencies of sexual dysfunction for risperidone and classical antipsychotics, and lower frequencies for clozapine, olanzapine, quetiapine, and aripiprazole. Postsynaptic dopamine antagonism, prolactin elevation, and α₁-receptor blockade may be the most relevant factors in the pathogenesis of antipsychotic-induced sexual dysfunction. Psychosocial strategies to treat antipsychotic-induced sexual dysfunction include psychoeducation and relationship counseling. Pharmacological strategies include lowering the dose or switching to a prolactin sparing antipsychotic. Also, the addition of a dopamine agonist, aripiprazole, or a phosphodiesterase-5 inhibitor has shown some promising results, but evidence is currently scarce.

Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations. Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns. A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus. Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.