Childhood onset schizophrenia: Cortical brain abnormalities as young adults

Child Psychiatry Branch, NIMH/NIH, Bethesda, MD 20892, USA.
Journal of Child Psychology and Psychiatry (Impact Factor: 6.46). 11/2006; 47(10):1003-12. DOI: 10.1111/j.1469-7610.2006.01658.x
Source: PubMed

ABSTRACT Childhood onset schizophrenia (COS) is a rare but severe form of the adult onset disorder. While structural brain imaging studies show robust, widespread, and progressive gray matter loss in COS during adolescence, there have been no longitudinal studies of sufficient duration to examine comparability with the more common adult onset illness.
Neuro-anatomic magnetic resonance scans were obtained prospectively from ages 7 through 26 in 70 children diagnosed with COS and age and sex matched healthy controls. Cortical thickness was measured at 40,962 points across the cerebral hemispheres using a novel, fully automated, validated method. Patterns of patient-control differences in cortical development were compared over a 19-year period.
Throughout the age range, the COS group had significantly smaller mean cortical thickness compared to controls. However, the COS brain developmental trajectory appeared to normalize in posterior (parietal) regions, and remained divergent in the anterior regions (frontal and temporal) regions, and the pattern of loss became more like that seen in adults.
Cortical thickness loss in COS appears to localize with age to prefrontal and temporal regions that are seen for both medication naïve and medicated adult onset patients.

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    • "Our results are consistent with decreased frontal cortical thickness and gyrification in childhood-and early-onset schizophrenia, adultonset first-episode psychosis and chronic schizophrenia (White et al., 2003; Narr et al., 2005; Greenstein et al., 2006; Voets et al., 2008; Rimol et al., 2010; Palaniyappan et al., 2011). To the best of our knowledge , this is the first study that assessed sulcal span in childhood and adolescent psychotic populations. "
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    ABSTRACT: Introduction: Recent evidence points to overlapping decreases in cortical thickness and gyrification in the frontal lobe of patients with adult-onset schizophrenia and bipolar disorder with psychotic symptoms, but it is not clear if these findings generalize to patients with a disease onset during adolescence and what may be the mechanisms underlying a decrease in gyrification. Method: This study analyzed cortical morphology using surface-based morphometry in 92 subjects (age range 11-18 years, 52 healthy controls and 40 adolescents with early-onset first-episode psychosis diagnosed with schizophrenia (n=20) or bipolar disorder with psychotic symptoms (n=20) based on a two year clinical follow up). Average lobar cortical thickness, surface area, gyrification index (GI) and sulcal width were compared between groups, and the relationship between the GI and sulcal width was assessed in the patient group. Results: Both patients groups showed decreased cortical thickness and increased sulcal width in the frontal cortex when compared to healthy controls. The schizophrenia subgroup also had increased sulcal width in all other lobes. In the frontal cortex of the combined patient group sulcal width was negatively correlated (r=-0.58, p<0.001) with the GI. Conclusions: In adolescents with schizophrenia and bipolar disorder with psychotic symptoms there is cortical thinning, decreased GI and increased sulcal width of the frontal cortex present at the time of the first psychotic episode. Decreased frontal GI is associated with the widening of the frontal sulci which may reduce sulcal surface area. These results suggest that abnormal growth (or more pronounced shrinkage during adolescence) of the frontal cortex represents a shared endophenotype for psychosis.
    Schizophrenia Research 07/2014; 158(1-3). DOI:10.1016/j.schres.2014.06.040 · 3.92 Impact Factor
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    • "In the NIMH-COS sample (46), a combination of cross-sectional and longitudinal data from 70 patients compared to controls revealed diffuse decreases in mean cortical thickness in childhood (~7.5% smaller), which became localized specifically to the frontal and temporal lobes with increasing age. Statistical significance survived correction for covariates such as sex, socioeconomic status, and IQ. "
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    ABSTRACT: Background: Autism spectrum disorder (ASD) and childhood onset schizophrenia (COS) are pediatric neurodevelopmental disorders associated with significant morbidity. Both conditions are thought to share an underlying genetic architecture. A comparison of neuroimaging findings across ASD and COS with a focus on altered neurodevelopmental trajectories can shed light on potential clinical biomarkers and may highlight an underlying etiopathogenesis. Methods: A comprehensive review of the medical literature was conducted to summarize neuroimaging data with respect to both conditions in terms of structural imaging (including volumetric analysis, cortical thickness and morphology, and region of interest studies), white matter analysis (include volumetric analysis and diffusion tensor imaging) and functional connectivity. Results: In ASD, a pattern of early brain overgrowth in the first few years of life is followed by dysmaturation in adolescence. Functional analyses have suggested impaired long-range connectivity as well as increased local and/or subcortical connectivity in this condition. In COS, deficits in cerebral volume, cortical thickness, and white matter maturation seem most pronounced in childhood and adolescence, and may level off in adulthood. Deficits in local connectivity, with increased long-range connectivity have been proposed, in keeping with exaggerated cortical thinning. Conclusion: The neuroimaging literature supports a neurodevelopmental origin of both ASD and COS and provides evidence for dynamic changes in both conditions that vary across space and time in the developing brain. Looking forward, imaging studies which capture the early post natal period, which are longitudinal and prospective, and which maximize the signal to noise ratio across heterogeneous conditions will be required to translate research findings into a clinical environment.
    Frontiers in Psychiatry 12/2013; 4:175. DOI:10.3389/fpsyt.2013.00175
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    • "Structural brain magnetic resonance imaging (MRI) studies of schizophrenia indicate widespread neuroanatomic abnormalities in cortical thickness, hippocampus, subcortical structures, and total brain measures (Shenton et al., 2001; Narr et al., 2005; Greenstein et al., 2006; Steen et al., 2006; Nesvag et al., 2008; Byne et al., 2009; Mattai et al., 2011; van Haren et al., 2011). Functional MRI and diffusion tensor imaging studies of schizophrenia also support brain dysfunction in schizophrenia involving multiple brain systems, emphasizing networks, and connectivity dysfunction rather than brain regions acting in isolation (Meyer- Lindenberg et al., 2005; Bassett et al., 2008; Lynall et al., 2010; Repovs et al., 2011). "
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    ABSTRACT: Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI). However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays, and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest (RF), a machine learning method, we classified 98 childhood onset schizophrenia (COS) patients and 99 age, sex, and ethnicity-matched healthy controls. We also used RF to estimate the probability of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation (CNV) associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p = 0.0004) and fewer developmental delays (p = 0.02). Presence of CNV was associated with lower probability of being classified as schizophrenia (p = 0.001). The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusion: Schizophrenia and control groups can be well classified using RF and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.
    Frontiers in Psychiatry 06/2012; 3:53. DOI:10.3389/fpsyt.2012.00053
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