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This cross-sectional study assesses distinct neuroanatomical dimensions of autism spectrum disorder using novel semisupervised machine learning methods and tests whether the dimensions can serve as endophenotypes also in individuals without autism spectrum disorder. Key Points: Question: Are there multiple distinct dimensions/clusters that coexist to account for the underlying neuroanatomical, clinical, and genetic heterogeneity in autism spectrum disorder (ASD)? Findings: In this cross-sectional study that included 669 adolescents to adults, a 3-dimensional endophenotypic representation to capture the neuroanatomical heterogeneity of ASD was discovered. The 3 clinically distinct dimensions showed partially overlapping imaging and genetic signatures compared with schizophrenia and were present also in the general population. Meaning: This dimensional representation of ASD may quantify disease susceptibility at the individual level and highlight potentially high-risk individuals in the general population and serves as a prototype for precision diagnostics and treatment. Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10−6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate–adjusted P =.048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10−4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10−6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses. [ABSTRACT FROM AUTHOR]

Objective: This study examined the effect of neuroleptic treatment on indexes of dopamine and serotonin function in schizophrenic patients. We hypothesized that neuroleptic treatment would be effective by changing dopamine and serotonin function and/or by altering their interaction. Design: Lumbar cerebrospinal fluid (CSF) concentrations of the metabolites of dopamine (homovanillic acid, [HVA]) and serotonin (5-hydroxyindoleacetic acid, [5-HIAA]) were measured after a minimum drug-free period of two weeks and again after five weeks of treatment with haloperidol, 20 mg/d orally. Psychiatric symptoms were rated within one day of CSF sampling. Patients: Sixteen schizophrenic and three schizoaffective male inpatients. Results: Neuroleptic treatment significantly raised HVA concentrations and significantly increased the ratio between HVA and 5-HIAA. The increase in HVA was not related to symptomatic improvement, whereas the increase in the HVA/5-HIAA ratio was significantly correlated with reduction in overall symptomatology. Conclusions: These findings suggest that it is the increase in HVA relative to 5-HIAA, and not the absolute increase in HVA, that is related to symptomatic improvement. This, in turn, suggests that changing dopamine function relative to serotonin function, rather than changing dopamine per se, is associated with the therapeutic effect of haloperidol. Exploring serotonin-dopamine interactions in schizophrenia may be more informative than examining each system in isolation. (1993;50:599-605) Rene S. Kahn et al, Department of Psychiatry (116A), Bronx Veterans Affairs Hospital, 130 W Kingsburg Rd, Bronx, NY 10468.

The article discusses the study which seeks to identify single-nucleotide polymorphisms associated with second-generation antipsychotic drug-induced weight gain. Patients undergoing first exposure to antipsychotic drugs, and subjects assessed for antipsychotic drug-induced weight gain are presented to have been genotyped. The 20 single-nucleotide polymorphisms yielded at a single locus is described to be near the melanocortin 4 receptor gene and overlaps the region identified in obesity.

The article discusses a study on twins that investigates whether patients with schizophrenia or bipolar disorder (BD) display overlapping abnormalities in brain volumes and cortical thickness and whether these are caused by shared genetic and environmental influences. Magnetic resonance imaging studies of patients' brains showed structures having overlapping and segregating genetic liabilities for schizophrenia and BD. This suggests that both disorders share genetic neurodevelopmental roots.

A response by Heleen B. M. Boos, Andre Aleman, Wiepke Cahn, Hilleke Hulshoff, and Rene S. Kahn to a letter to the editor about their article "Brain Volumes in Relatives of Patients with Schizophrenia: A Meta Analysis," in the 2007 issue is presented.