Science Briefs

Biological Pathways to Psychopathology

Ongoing efforts are beginning to shed light on the mechanisms that cause differences in behavioral traits and neuropsychiatric diseases.

By Ahmad R. Hariri

Ahmad R. Hariri, PhD, is Professor of Psychology & Neuroscience at Duke University, where he is also an Investigator in the Institute for Genome Sciences & Policy.  Dr. Hariri’s program of research encompasses magnetic resonance imaging, positron emission tomography, pharmacology and molecular genetics.  Through the integration of complementary technologies Dr. Hariri’s research has begun to illuminate the neurobiological mechanisms mediating individual differences in complex behavioral traits.  This work represents a critical foundation for the identification of risk markers that interact with unique environmental factors to predict neuropsychiatric disorders as well as for the development of more effective and individually tailored treatments for these same disorders.  Findings from Dr. Hariri’s program of research have been published in Science, Nature, Nature Neuroscience, Journal of Neuroscience, Archives of General Psychiatry, Biological Psychiatry, Trends in Cognitive Sciences and the Annual Review of Neuroscience.  In August 2009, Dr. Hariri’s contributions to the science of individual differences were recognized by the American Psychological Association who presented him with the Distinguished Scientific Award for Early Career Contribution to Psychology.


Individual differences in trait affect, personality and temperament are critical in shaping complex human behaviors, such as those involved in successfully navigating social interactions and overcoming challenges from our ever changing environments. Such individual differences may also serve as important predictors of vulnerability to neuropsychiatric disorders including depression, anxiety and addiction, especially upon exposure to environmental adversity. Accordingly, identifying the biological mechanisms that give rise to these differences can afford a unique opportunity to develop a deeper understanding of complex human behaviors, disease liability and treatment. Having established a number of neural processes that support complex behavioral processes, human neuroimaging studies, especially those employing BOLD fMRI, have now begun to reveal the neural substrates of inter-individual variability in these and related constructs. Moreover, recent studies have established that BOLD fMRI measures represent temporally stable and reliable indices of brain function. Thus, much like their behavioral counterparts, such patterns of brain activation are increasingly thought to represent enduring, trait-like phenomenon, which in and of themselves may serve as important markers of individual differences related to disease liability and pathophysiology.

As neuroimaging studies continue to illustrate the predictive relation between regional brain activation and trait-like behaviors (e.g., increased amygdala reactivity predicts trait anxiety), an important next step is to systematically identify the underlying mechanisms driving variability in brain circuit function. In this regard, recent neuroimaging studies employing pharmacological challenge paradigms, principally targeting monoamine neurotransmission, have revealed that even subtle alterations in dopaminergic, noradrenergic and serotonergic signaling can have a profound impact on the functional response of brain circuitries supporting affect, personality and temperament. Similarly, multimodal neuroimaging approaches have provided evidence for directionally specific relations between key components of monoaminergic signaling cascades, assessed with radiotracer PET, and brain function, assessed with BOLD fMRI. Together, pharmacological challenge neuroimaging and multimodal PET/fMRI are revealing how variability in behaviorally-relevant brain activation emerges as a function of underlying variability in key brain signaling pathways (e.g., increased serotonin signaling predicting increased amygdala reactivity). One key next step is to identify the sources of inter-individual variability in these key neurochemical signaling mechanisms.

In the modern era of human molecular genetics, this step includes identifying common variation in the genes that influence the functioning or availability of components in these pathways. As DNA sequence variation across individuals represents the ultimate wellspring of variability in emergent molecular, neurobiological and related behavioral processes, understanding the relations between genes, brain and behavior is important for establishing a mechanistic foundation for individual differences in behavior and related psychiatric disease. Moreover, such genetic polymorphisms can be readily identified from DNA collected via cells from individual blood or even saliva samples using relatively well-tolerated, inexpensive and standardized laboratory protocols. Once collected and isolated, an individual’s DNA can be amplified repeatedly providing an almost endless reservoir of material for genotyping of additional candidate polymorphisms as they are identified. When precise cascades of related neurobiological and behavioral effects are clearly established, common polymorphisms could become powerful, readily accessible predictive markers of such emergent properties. DNA samples can be collected in doctor’s offices from everyone, even newborns, and genetic assays cost only tens of dollars per sample in comparison to the hundreds and even thousands required for fMRI, PET and drug studies. Of course, arriving at this ultimate reduction requires intensive and expansive efforts wherein all these technologies as well as epidemiological and clinical studies are first brought to bear on explicating the detailed biological mechanisms mediating individual differences in trait behaviors and related risk for neuropsychiatric disease.

In the last five years, significant progress has been made in describing the contributions of multiple common genetic polymorphisms to individual differences in complex behavioral phenotypes and disease liability – in particular, by identifying effects of functional genetic variation on the neural processes that mediate behavioral responses to environmental challenge (Caspi & Moffitt, 2006; Hariri & Holmes, 2006). The potential of this approach is highlighted by recent studies demonstrating how common polymorphisms affecting brain chemistry bias brain circuitry that helps shape individual differences in behaviors such as temperamental anxiety and impulsivity (Figure 1). With increased utilization and continued expansion each level of analysis in this integrative strategy - brain circuit function, neural signaling cascades and molecular genetics – also has the potential to uniquely illuminate clinically relevant information that can be used in efforts to devise individually tailored treatment regimes and establish predictive disease markers. Three specific examples, summarized in Table 1, illustrate the effectiveness of this integrated strategy to parse biological mechanisms mediating individual differences in complex behaviors. In each, subjects were retrospectively genotyped for the candidate functional polymorphisms of interest from stored samples of DNA and this information was used to group subjects based on their individual genotypes. Notably, the behavioral assessments in all three examples were conducted as a component of a larger parent protocol that preceded measurement of task-related regional brain function with BOLD fMRI by an average interval of 29 weeks. The fact that robust brain-behavior correlations were observed despite the separation in time is consistent with the suggestion that both metrics (i.e., brain function and behavior) are remarkably stable, possibly indicative of trait-related variation. Such a relation further underscores the likelihood that inter-individual variability in brain-behavior associations are influenced by functional genetic polymorphisms.

Figure 1. Integration of complementary technologies can be used to reveal the neurobiology of individual differences in complex behavioral traits. a. Individual differences in personality and temperament are critical in shaping complex human behaviors and may serve as important predictors of vulnerability to neuropsychiatric disorders. b. Neuroimaging technologies, especially BOLD fMRI, can identify relationships between variability in brain circuit function and individual differences in personality and temperament. c. Multimodal PET/fMRI (or pharmacological fMRI) can map individual differences in behaviorally relevant brain circuit function to variability in specific molecular signaling pathways. d. Variability in specific molecular signaling pathways can be mapped to functional genetic polymorphisms which inform their ultimate biological origins and can be used to efficiently model how such emergent variability impacts behaviorally relevant brain function. e. Each level of analysis can potentially inform clinically relevant issues, provide guiding principles for the development of more effective and personalized treatment options and represent predictive risk markers that interact with unique environmental factors to precipitate disease.

Note: data are created for illustration only. Measures of brain circuit function could be derived from fMRI, EEG, MEG.  Those for molecular signaling could be derived from PET or Drug Challenge studies.

As detailed in the three studies summarized in Table 1, neuroimaging technologies, especially BOLD fMRI, have begun to identify how variability in the neural substrates underlying information processing contribute to emergent individual differences in stable and enduring aspects of human behaviors such as personality and temperament. In parallel, the application of pharmacological fMRI and multimodal PET/fMRI is improving our understanding of how variability in specific molecular signaling pathways influences individual differences in the function of these behaviorally relevant brain circuitries. Moreover, information on DNA sequence variation in humans (and related identification of functional genetic polymorphisms) is now being utilized to understand the biological origins of variability in component processes of molecular signaling pathways. Furthermore, this information is being used to efficiently model how such emergent variability impacts behaviorally relevant brain function. Such efforts have the potential to inform clinically relevant issues and provide guiding principles for the development of more effective and individually tailored treatment regimes. In addition, mechanisms that have been elucidated, especially those mapped to functional genetic polymorphisms, can lead to identification of predictive risk markers that interact with unique environmental factors to precipitate disease.

Table 1. Summary of studies linking individual differences in complex behavioral traits with underlying variability in brain circuit function, molecular signaling pathways and functional genetic polymorphisms.

Abbreviations: 5-HT – serotonin; DA – dopamine; eCB – endocannabinoid; HTR1A – serotonin 1A receptor gene; DAT1 – dopamine transporter polymorphism; FAAH – fatty acid amide hydrolase gene.

While the three examples highlighted here are evidence for the potential of this integrated research strategy, much work is left to be done. First, to allow for tractable experimental designs and testable hypotheses in existing samples, the studies highlighted above have focused on the effects of a single signaling pathway on behaviorally relevant brain circuitry. Of course, it is very clear there are numerous complex interactions between signaling pathways and that more than one pathway contributes to the regulation of any brain circuitry. However, existing studies lack the power and sophistication to model such complex interactions while effectively controlling for other important modulatory factors (e.g., age, sex) in the context of BOLD fMRI, pharmacological MRI or multimodal PET/fMRI protocols. To do so, we must aggressively expand the scale and scope of our studies to include hundreds and, preferably, thousands of subjects.

A second important consideration is that existing studies have been largely conducted in small ethnically and racially homogenous populations. Thus, the observed effects may not generalize to other populations. The potential effect of any single genetic variant on a complex biological and behavioral phenotype is likely small against the background of the approximately 20,000-25,000 human genes and the multitude of other neurobiologically relevant functional variants they likely harbor. Thus, it is important to explicitly test the independence of functional polymorphisms through rigorous statistical modeling in larger samples and also to test the validity of any associations derived in one sample population (e.g., Caucasian) to populations with different genetic backgrounds (e.g., Asian or African).

A third important consideration for the future of this research is the need to conduct large-scale prospective studies beginning in childhood to determine any developmental shifts in neurogenetic pathways mediating individual differences in behavior as well as their predictive utility in identifying neuropsychiatric disease risk as a function of environmental or other stressors. All of the studies described above and most of the studies available in the literature as a whole have been conducted in adults carefully screened for the absence of psychopathology. Because of this, these findings identify mechanisms contributing to variability in the normative range of behavior only. As such, the utility of these neural, molecular or genetic markers in predicting vulnerability to neuropsychiatric disorder is unclear. Such predictive utility is ideally tested through prospective studies beginning with premorbid populations that account for the moderating effects of environmental stress in the emergence of clinical disorder over time (Caspi & Moffitt, 2006; Viding, Williamson, & Hariri, 2006).

Finally, there is tremendous potential in developing large databases (again preferably thousands of subjects) with detailed measures of behavioral traits, neuroimaging based measures of multiple brain circuitries, and extensive genotyping. One of the most exciting applications of molecular genetics is in identifying novel biological pathways contributing to the emergence of complex traits (Gibson & Goldstein, 2007; McCarthy et al., 2008). The continued refinement of a detailed map of sequence variation across the entire human genome (i.e., single nucleotide polymorphisms [SNPs] that “tag” every gene) and production of technologies supporting cost-efficient identification of such variation have dramatically accelerated the discovery of genes involved in the emergence of complex disease processes (Fellay et al., 2007; Link et al., 2008) as well as normal variability in continuous traits (Lettre et al., 2008). Many of the genes identified in such studies have illuminated novel pathways not previously implicated in these processes or traits, spurring intensive efforts to understand the potential biological effects of the proteins produced by these genes. As such, these “genome-wide” screens represent an opportunity to leap forward beyond the available pool of candidate molecules and pathways in parsing the mechanisms of complex biological processes. Because neuroimaging based measures of brain function reveal key mechanisms involved in the emergence of individual differences in behavioral traits and are closer to the biological effects of functional genetic polymorphisms, they are ideal substrates for genome-wide screens. For example, BOLD fMRI estimates of amygdala reactivity predicting variability in temperamental anxiety can be used as the continuous trait in a genome-wide screen. Any significant associations that emerge between genetic variation and amygdala reactivity may confirm existing relations (e.g., the importance of genes biasing 5-HT signaling) or, perhaps more importantly, reveal unexpected candidate molecules or pathways (e.g., a gene producing a molecule that is expressed in the brain and may function in second-messenger signaling cascades). Once identified and, ideally, replicated in large-scale databases that effectively address confounds common to genome-wide screens (e.g., controlling for multiple comparisons resulting from testing the association of a phenotype with hundreds of thousands or millions of SNPs), the impact of variation in novel genes associated with amygdala reactivity can be explored at each level of the biological cascade leading to trait anxiety (i.e., be fed back into the discovery loop outlined in Figure 1). In addition to exponentially improving our understanding of neurobiological pathways leading to individual differences in complex behavioral traits these efforts may lead to the discovery of novel therapeutic strategies targeting related disease processes.

In summary, ongoing efforts are beginning to shed light on detailed mechanisms that mediate individual differences in complex behavioral traits and, potentially, related neuropsychiatric diseases. Elaboration of these mechanisms at the level of brain circuit function, molecular signaling pathways and functional genetic polymorphisms could one day inform clinically relevant issues and provide guiding principles for the development of more effective and individually tailored treatment regimes. In addition, an understanding of such mechanisms, especially those mapped to functional genetic polymorphisms, may lead to identification of predictive risk markers that interact with unique environmental factors to predict disease risk.

Adapted, with permission, from the Annual Review of Neuroscience, Volume 32 (c) 2009 by Annual Reviews. www.annualreviews.org

 

References

Caspi, A., & Moffitt, T. E. (2006). Gene-environment interactions in psychiatry: joining forces with neuroscience. Nature Reviews Neuroscience, 7(7), 583-590.

Fakra, E., Hyde, L. W., Gorka, A., Fisher, P. M., Muñoz, K. E., Kimak, M., Halder, I., Ferrell, R. E., Manuck, S. B., & Hariri , A.R. (2009) Effects of HTR1A C(-1019)G on amygdala reactivity and trait anxiety. Archives of General Psychiatry . 66(1):33-40.

Fellay, J., Shianna, K. V., Ge, D., Colombo, S., Ledergerber, B., Weale, M., et al. (2007). A whole-genome association study of major determinants for host control of HIV-1. Science, 317( 5840), 944-947.

Forbes, E. E., Brown, S. M., Kimak, M., Ferrell, R. E., Manuck, S. B., & Hariri AR. (2009) Genetic variation in components of dopamine neurotransmission impacts ventral striatal reactivity associated with impulsivity. Molecular Psychiatry. 14(1):60-70.

Gibson, G., & Goldstein, D. B. (2007). Human genetics: the hidden text of genome-wide associations. Current Biology, 17(21), R929-932.

Hariri, A. R., & Holmes, A. (2006). Genetics of emotional regulation: the role of the serotonin transporter in neural function. Trends in Cognitive Sciences, 10(4), 182-191.

Hariri, A. R. (2009) The neurobiology of individual differences in complex behavioral traits. Annual Review of Neuroscience. 32:225-47.

Hariri , A. R., Gorka, A., Hyde, L. W., Kimak, M., Halder, I., Ducci, F., Ferrell, R. E., Goldman, D., & Manuck, S.B. (2009) Divergent effects of genetic variation in endocannabinoid signaling on human threat- and reward-related brain function. Biological Psychiatry, .66(1):9-16.

Lettre, G., Jackson, A. U., Gieger, C., Schumacher, F. R., Berndt, S. I., Sanna, S., et al. (2008). Identification of ten loci associated with height highlights new biological pathways in human growth. Nature Genetics, 40(5), 584-591.

Link, E., Parish, S., Armitage, J., Bowman, L., Heath, S., Matsuda, F., et al. (2008). SLCO1B1 variants and statin-induced myopathy--a genomewide study. New England Journal of Medicine, 359(8), 789-799.

McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., et al. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.

Viding, E., Williamson, D. E., & Hariri, A. R. (2006). Developmental imaging genetics: challenges and promises for translational research. Development and Psychopathology, 18(3), 877-892.