Context Progressive brain volume changes in schizophrenia are thought to be due principally to the disease. However, recent animal studies indicate that antipsychotics, the mainstay of treatment for schizophrenia patients, may also contribute to brain tissue volume decrement. Because antipsychotics are prescribed for long periods for schizophrenia patients and have increasingly widespread use in other psychiatric disorders, it is imperative to determine their long-term effects on the human brain. Objective To evaluate relative contributions of 4 potential predictors (illness duration, antipsychotic treatment, illness severity, and substance abuse) of brain volume change. Design Predictors of brain volume changes were assessed prospectively based on multiple informants. Setting Data from the Iowa Longitudinal Study. Patients Two hundred eleven patients with schizophrenia who underwent repeated neuroimaging beginning soon after illness onset, yielding a total of 674 high-resolution magnetic resonance scans. On average, each patient had 3 scans (≥2 and as many as 5) over 7.2 years (up to 14 years). Main Outcome Measure Brain volumes. Results During longitudinal follow-up, antipsychotic treatment reflected national prescribing practices in 1991 through 2009. Longer follow-up correlated with smaller brain tissue volumes and larger cerebrospinal fluid volumes. Greater intensity of antipsychotic treatment was associated with indicators of generalized and specific brain tissue reduction after controlling for effects of the other 3 predictors. More antipsychotic treatment was associated with smaller gray matter volumes. Progressive decrement in white matter volume was most evident among patients who received more antipsychotic treatment. Illness severity had relatively modest correlations with tissue volume reduction, and alcohol/illicit drug misuse had no significant associations when effects of the other variables were adjusted. Conclusions Viewed together with data from animal studies, our study suggests that antipsychotics have a subtle but measurable influence on brain tissue loss over time, suggesting the importance of careful risk-benefit review of dosage and duration of treatment as well as their off-label use.

Background Schizophrenia has a characteristic onset during adolescence or young adulthood but also tends to persist throughout life. Structural magnetic resonance studies indicate that brain abnormalities are present at onset, but longitudinal studies to assess neuroprogression have been limited by small samples and short or infrequent follow-up intervals. Methods The Iowa Longitudinal Study is a prospective study of 542 first-episode patients who have been followed up to 18 years. In this report, we focus on those patients (n = 202) and control subjects (n = 125) for whom we have adequate structural magnetic resonance data (n = 952 scans) to provide a relatively definitive determination of whether progressive brain change occurs over a time interval of up to 15 years after intake. Results A repeated-measures analysis showed significant age-by-group interaction main effects that represent a significant decrease in multiple gray matter regions (total cerebral, frontal, thalamus), multiple white matter regions (total cerebral, frontal, temporal, parietal), and a corresponding increase in cerebrospinal fluid (lateral ventricles and frontal, temporal, and parietal sulci). These changes were most severe during the early years after onset. They occur at severe levels only in a subset of patients. They are correlated with cognitive impairment but only weakly with other clinical measures. Conclusions Progressive brain change occurs in schizophrenia, affects both gray matter and white matter, is most severe during the early stages of the illness, and occurs only in a subset of patients. Measuring severity of progressive brain change offers a promising new avenue for phenotype definition in genetic studies of schizophrenia.

Objective Longitudinal structural MRI studies have shown that patients with schizophrenia have progressive brain tissue loss after onset. Recurrent relapses are believed to play a role in this loss, but the relationship between relapse and structural MRI measures has not been rigorously assessed. The authors analyzed longitudinal data to examine this question. Methods The authors studied data from 202 patients drawn from the Iowa Longitudinal Study of first-episode schizophrenia for whom adequate structural MRI data were available (N=659 scans) from scans obtained at regular intervals over an average of 7 years. Because clinical follow-up data were obtained at 6-month intervals, the authors were able to compute measures of relapse number and duration and relate them to structural MRI measures. Because higher treatment intensity has been associated with smaller brain tissue volumes, the authors also examined this countereffect in terms of dose-years. Results Relapse duration was related to significant decreases in both general (e.g., total cerebral volume) and regional (e.g., frontal) brain measures. Number of relapses was unrelated to brain measures. Significant effects were also observed for treatment intensity. Conclusions Extended periods of relapse may have a negative effect on brain integrity in schizophrenia, suggesting the importance of implementing proactive measures that may prevent relapse and improve treatment adherence. By examining the relative balance of effects, that is, relapse duration versus antipsychotic treatment intensity, this study sheds light on a troublesome dilemma that clinicians face. Relapse prevention is important, but it should be sustained using the lowest possible medication dosages that will control symptoms.

Marijuana exposure during the critical period of adolescent brain maturation may disrupt neuromodulatory influences of endocannabinoids and increase schizophrenia susceptibility. Cannabinoid receptor 1 (CB1/CNR1) is the principal brain receptor mediating marijuana effects. No study to-date has systematically investigated the impact of CNR1 on quantitative phenotypic features in schizophrenia and inter-relationships with marijuana misuse. We genotyped 235 schizophrenia patients using 12 tag single nucleotide polymorphisms (tSNPs) that account for most of CB1 coding region genetic variability. Patients underwent a high-resolution anatomic brain magnetic resonance scan and cognitive assessment. Almost a quarter of the sample met DSM marijuana abuse (14%) or dependence (8%) criteria. Effects of CNR1 tSNPs and marijuana abuse/dependence on brain volumes and neurocognition were assessed using ANCOVA, including co-morbid alcohol/non-marijuana illicit drug misuse as covariates. Significant main effects of CNR1 tSNPs (rs7766029, rs12720071, and rs9450898) were found in white matter (WM) volumes. Patients with marijuana abuse/dependence had smaller fronto-temporal WM volumes than patients without heavy marijuana use. More interestingly, there were significant rs12720071 genotype-by-marijuana use interaction effects on WM volumes and neurocognitive impairment; suggestive of gene-environment interactions for conferring phenotypic abnormalities in schizophrenia. In this comprehensive evaluation of genetic variants distributed across the CB1 locus, CNR1 genetic polymorphisms were associated with WM brain volume variation among schizophrenia patients. Our findings suggest that heavy cannabis use in the context of specific CNR1 genotypes may contribute to greater WM volume deficits and cognitive impairment, which could in turn increase schizophrenia risk.

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Background Adolescent marijuana use is associated with increased risk for schizophrenia. We previously reported that marijuana misuse in conjunction with specific cannabinoid receptor 1 (CNR1) genetic variants (rs12720071-Gallele carriers) contributed to white-matter (WM) brain volume deficits in schizophrenia patients. In this study, we assessed the influence of another cannabinoid-related gene, mitogen-activated protein kinase 14 (MAPK14), and potential MAPK14–CNR1 gene–gene interactions in conferring brain volume abnormalities among schizophrenia patients with marijuana abuse/dependence. MAPK14 encodes a member of the MAPK family involved in diverse cellular processes, including CNR1-induced apoptosis. Method We genotyped 235 schizophrenia patients on nine MAPK14 tag single nucleotide polymorphisms (tSNPs). Approximately one quarter of the sample had marijuana abuse or dependence. Differential effects of MAPK14 tSNPs on brain volumes across patients with versus without marijuana abuse/dependence were examined using ANCOVA. Results Of the MAPK14 tSNPs, only rs12199654 had significant genotype effects and genotype × marijuana misuse interaction effects on WM volumes. rs12199654-A homozygotes with marijuana abuse/dependence had significantly smaller total cerebral and lobar WM volumes. The effects of MAPK14 rs12199654 on WM volume deficits remained significant even after controlling for the CNR1 rs12720071 genotype. There were significant main effects of the MAPK14 CNR1 diplotype and diplotype × marijuana interaction on WM brain volumes, with both genetic variants having additive contributions to WM volume deficits only in patients with marijuana misuse. Conclusions Given that CNR1-induced apoptosis is preceded by increased MAPK phosphorylation, our study suggests that potential MAPK14–CNR1 gene–gene interactions may mediate brain morphometric features in schizophrenia patients with heavy marijuana use.

Although schizophrenia is generally considered to occur as a consequence of multiple genes that interact with one another, very few methods have been developed to model epistasis. Phenotype definition has also been a major challenge for research on the genetics of schizophrenia. In this report we use novel statistical techniques to address the high dimensionality of genomic data, and we apply a refinement in phenotype definition by basing it on the occurrence of brain changes during the early course of the illness, as measured by repeated MR scans (i.e., an “intermediate phenotype.” The method combines a machine learning algorithm, the ensemble method using stochastic gradient boosting, with traditional general linear model statistics. We began with fourteen genes that are relevant to schizophrenia based on association studies or their role in neurodevelopment and then used statistical techniques to reduce them to five genes and 17 SNPs that had a significant statistical interaction: 5 for PDE4B, 4 for RELN, 4 for ERBB4, 3 for DISC1, and one for NRG1. Five of the SNPs involved in these interactions replicate previous research, in that these five SNPs have previously been identified as schizophrenia vulnerability markers or implicate cognitive processes relevant to schizophrenia. This ability to replicate previous work suggests that our method has potential for detecting a meaningful epistatic relationships among the genes that influence brain abnormalities in schizophrenia.