Schizophrenia is a major mental illness characterized by positive and negative symptoms,
and by cognitive deficit. Although cognitive impairment is disabling for patients, it has been …

The imaging community has increasingly adopted machine learning (ML) methods to
provide individualized imaging signatures related to disease diagnosis, prognosis, and …

Disease heterogeneity is a significant obstacle to understanding pathological processes and
delivering precision diagnostics and treatment. Clustering methods have gained popularity …

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of
established schizophrenia to discover two volumetric subgroups—a 'lower brain …

Background Grey matter (GM) reduction is a consistent observation in established late
stages of schizophrenia, but patients in the untreated early stages of illness display an …

Background Cortical thinning is a well-known feature in schizophrenia. The considerable
variation in the spatial distribution of thickness changes has been used to parse …

Schizophrenia and bipolar disorder include patients with different characteristics, which may
hamper the definition of biomarkers. One of the dimensions with greater heterogeneity …

BackgroundRecent genetic evidence implicates glutamatergic-receptor variations in
schizophrenia. Glutamatergic excess during early life in people with schizophrenia may …

Importance Approaches are needed to stratify individuals in early psychosis stages beyond
positive symptom severity to investigate specificity related to affective and normative …

Abstract Machine learning has been increasingly used to obtain individualized
neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in …