Innovative approaches that advance our understanding of the mechanisms that confer risk for psychiatric illness in youths is the focus.
Research on the mechanisms that give rise to and perpetuate psychiatric symptoms has informed us to be sensitive to critical periods of vulnerability as it relates to the onset of psychopathology in youths. In this article, innovative approaches that advance our understanding of the mechanisms that confer risk for psychiatric illness in youths are discussed.
Biology vs environment
Using epidemiological, behavioral, and multimodal neuroimaging methods, researchers have sought to understand the complex interplay between biological pathways and family and environmental risk factors. Stable burdens, such as genetic risk, may confer a strong influence on specific biological pathways-including causing aberrant cognitive and social development, which may lead to psychiatric illness. These studies pave the path for new treatment approaches that are aimed at preventing the onset or progression of psychiatric illness into adulthood.
MRI elucidates neural mechanisms that may underlie the intergenerational transmission of psychopathology risk. Assessing in vivo structural brain abnormalities in offspring of adults with a particular disorder can determine whether there are intrinsic deficits that precede the onset of observable illness rather than being a consequence of illness burden. MRI permits the evaluation of neurobiological factors associated with risk for progression, independent of illness-associated confounds such as comorbidities, medication exposure, and substance use.
Several studies have made important contributions to models for the development and progression of mood disorders in youths based on familial risk. In a study of youths with familial risk of bipolar disorder, amygdalar hyperactivity during face emotion processing was found to be a potential vulnerability marker for the disorder.1 In other studies, youths at risk for bipolar disorder had neurochemical changes in prefrontal and cerebellar regions that were not seen in healthy cohorts.2,3 Multivariate, rather than univariate, approaches are being used to analyze neuroimaging data to better predict in whom a mood disorder is most likely to develop. MourÃ£o-Miranda and colleagues4 used a machine learning approach that assigns predictive probability of group membership to adolescents at risk for Axis I psychiatric disorders. They found that the predictive probabilities were significantly higher for at-risk teens in whom an Axis I disorder subsequently developed.
Another approach to examine the relationship between biological pathways to psychopathology and environmental risk factors is to study response to certain stimuli after exposure to chronic stress. Liu and colleagues5 looked at the correlation between changes in cortisol levels after the Trier Social Stress Test-Child version (TSST-C) and brain structure and function during face emotion processing in 23 adolescents. Increased stress reactivity by the TSST-C was related to less left hippocampal and rostral prefrontal cortical activation in response to faces depicting fear. This study inferred maladaptive stress reactivity originating in hippocampal and prefrontal brain regions in youths who, because of chronic stress, may be at risk for psychopathology.
Hallmayer and colleagues6 assessed 192 twin pairs and calculated concordance rates for narrow (strict autism) and broad (autism spectrum disorders) definitions of autism. For strict autism, probandwise concordance ranged from 0.58 to 0.60 for male and female monozygotic pairs and 0.21 to 0.27 for dizygotic pairs. For autism spectrum disorders, the probandwise concordance ranged from 0.50 to 0.77 for monozygotic pairs and 0.31 to 0.36 for dizygotic pairs.
Much of the variance in liability was explained by shared environmental factors in addition to moderate genetic heritability. This suggests that the susceptibility to autism spectrum disorders appears to be substantially contributed by a shared twin environment, with a moderate contribution from genetic heritability. This study challenges us to consider more complex and multifactorial models for developing psychiatric illness.
Adolescence is a developmental period that has commonly been associated with increased impulsivity, risk taking, and reward seeking. Lack of adequate control over impulsive behaviors may result in significant morbidity and mortality that have public health implications but are largely preventable.
Whelan and colleagues7 sought to identify the brain networks involved in inhibitory control in early adolescence. They looked at the contributions of individual differences in inhibitory control, ADHD symptoms, substance misuse, and genetics. Because of the large sample size (1896 teenagers), they were able to examine interdependent brain regions that form networks that might explain the multidimensional construct of impulsivity. They identified several distinct cortical and subcortical networks underlying successful inhibitions and inhibition failures.
Some networks were associated with drug use, ADHD symptoms, and genetic variations. Decreased functioning of an orbitofrontal cortical network was associated with the likelihood of initiating drug use in early adolescence. Right inferior frontal activity was related to the speed of the inhibition process and to the use of illegal substances, and it was associated with genetic variation in a norepinephrine transporter gene. These results provide both neural endophenotypes and genetic variation as plausible biological explanations for the various manifestations of impulsivity as a construct.
Brain development occurs within an environmental context. There is evidence that psychosocial factors such as childhood socioeconomic status can influence neural development, especially the systems that subserve language and executive function.8 Factors such as prenatal history, interactions between parents and children, and cognitive stimulation in the home have all been identified as important mediators by which socioeconomic status affects brain development. While these and other behavioral factors driven by socioeconomic disparities have clearly had an impact on cognitive, social, and emotional development, the biological pathways by which socioeconomic status shapes neurodevelopment remain largely unknown.
The correlation between socioeconomic status and brain volumes was examined using a sample of 60 children.9 Significant socioeconomic status differences were observed in the hippocampus and amygdala. A larger hippocampus was correlated with a higher income to needs ratio, and a larger amygdala was associated with fewer years of parental education. Socioeconomic status by age interactions was observed in the left superior temporal gyrus and left inferior frontal gyrus, which suggests that socioeconomic status differences increased with age.
These changes in brain volume were not explained by differences in sex, race, or IQ. Proposed mechanisms for these findings include differences in the home linguistic environment and exposure to stress-possible targets for intervention during high neural plasticity.
One study found that socioeconomic status is significantly associated with hippocampal volume in late life.10 The researchers looked at MRI-derived brain volume measures typical of brain aging and Alzheimer disease without dementia. The significant association between childhood socioeconomic status and hippocampal volume late in life suggests that neurodevelopmental context in early life can have significant lasting effects on structural brain development detectable more than 50 years later.
Recent research highlights how tools previously used to characterize psychiatric illness can be creatively used to determine predictors for psychopathology in youths. These studies suggest that there may be identifiable aberrant biological pathways, in some cases even before the onset of frank psychiatric disorders, which may potentially be targets for future intervention. Longitudinal studies, dimension-based approaches, and application of multivariate analytical techniques will be used to verify current theories about the onset and progression of psychiatric disorders from childhood to adulthood.11-14
Examining youths from a developmental lens will enable us to design more sophisticated studies that track associations that arise between children and their environments over time. This will hopefully bring promise for improved early intervention and prevention strategies for youths who have identifiable risk factors for psychiatric illness.
Dr Singh is Assistant Professor of Psychiatry and Behavioral Sciences at the Lucile Salter Packard Children’s Hospital, Stanford University School of Medicine, Division of Child and Adolescent Psychiatry in Stanford, Calif. She reports no conflicts of interest concerning the subject matter of this article.
1. Olsavsky AK, Brotman MA, Rutenberg JG, et al. Amygdala hyperactivation during face emotion processing in unaffected youth at risk for bipolar disorder. J Am Acad Child Adolesc Psychiatry. 2012;51:294-303.
2. Singh M, Spielman D, Adleman N, et al. Brain glutamatergic characteristics of pediatric offspring of parents with bipolar disorder. Psychiatry Res. 2010;182:165-171.
3. Singh MK, Spielman D, Libby A, et al. Neurochemical deficits in the cerebellar vermis in child offspring of parents with bipolar disorder. Bipolar Disord. 2011;13:189-197.
4. MourÃ£o-Miranda J, Oliveira L, Ladouceur CD, et al. Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS One. 2012;7:e29482.
5. Liu J, Chaplin TM, Wang F, et al. Stress reactivity and corticolimbic response to emotional faces in adolescents. J Am Acad Child Adolesc Psychiatry. 2012;51:304-312.
6. Hallmayer J, Cleveland S, Torres A, et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry. 2011;68:1095-1102.
7. Whelan R, Conrod PJ, Poline JB, et al; IMAGEN Consortium. Adolescent impulsivity phenotypes characterized by distinct brain networks. Nat Neurosci. 2012;15:920-925.
8. Hackman DA, Farah MJ, Meaney MJ. Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat Rev Neurosci. 2010;11:651-659.
9. Noble KG, Houston SM, Kan E, Sowell ER. Neural correlates of socioeconomic status in the developing human brain. Dev Sci. 2012;15:516-527.
10. Staff RT, Murray AD, Ahearn TS, et al. Childhood socioeconomic status and adult brain size: child-hood socioeconomic status influences adult hippocampal size. Ann Neurol. 2012;71:653-660.
11. Andreasen NC, Nopoulos P, Magnotta V, et al. Progressive brain change in schizophrenia: a prospective longitudinal study of first-episode schizophrenia. Biol Psychiatry. 2011;70:672-679.
12. Wakschlag LS, Henry DB, Tolan PH, et al. Putting theory to the test: modeling a multidimensional, developmentally-based approach to preschool disruptive behavior. J Am Acad Child Adolesc Psychiatry. 2012;51:593-604.
13. Moylan S, Maes M, Wray NR, Berk M. The neuroprogressive nature of major depressive disorder: pathways to disease evolution and resistance, and therapeutic implications. Mol Psychiatry. 2013;18:595-606.
14. Schneider MR, DelBello MP, McNamara RK, et al. Neuroprogression in bipolar disorder. Bipolar Disord. 2012;14:356-374.
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