British Medical Bulletin

This Article Abstract FREE Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Search for citing articles in: ISI Web of Science (112) Request Permissions Disclaimer Google Scholar Articles by Hariri, A. R Articles by Weinberger, D. R Search for Related Content PubMed PubMed Citation Articles by Hariri, A. R Articles by Weinberger, D. R Related Collections Neurology Genetics Radiology Social Bookmarking          What's this?

British Medical Bulletin 65:259-270 (2003)
© 2003 The British Council

Imaging genomics

Ahmad R Hariri and Daniel R Weinberger

Clinical Brain Disorders Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA

	Abstract

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
The recent completion of a working draft of the human genome sequence promises to provide unprecedented opportunities to explore the genetic basis of individual differences in complex behaviours and vulnerability to neuropsychiatric illness. Functional neuroimaging, because of its unique ability to assay information processing at the level of brain within individuals, provides a powerful approach to such functional genomics. Recent fMRI studies have established important physiological links between functional genetic polymorphisms and robust differences in information processing within distinct brain regions and circuits that have been linked to the manifestation of various disease states such as Alzheimer's disease, schizophrenia and anxiety disorders. Importantly, all of these biological relationships have been revealed in relatively small samples of healthy volunteers and in the absence of observable differences at the level of behaviour, underscoring the power of a direct assay of brain physiology like fMRI in exploring the functional impact of genetic variation.

	Introduction

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
Identifying the biological mechanisms that contribute to complex cognitive and emotional behaviours is paramount to our understanding of how individual differences in these behaviours emerge and how such differences may confer vulnerability to psychiatric disease. Advances in both molecular genetics and non-invasive functional neuroimaging have begun to provide the tools necessary to explore these as well as other behaviourally relevant biological mechanisms. With completion of a rough draft of the reference human genome sequence^1,2, a major effort is underway to identify common variations in this sequence that impact on gene function (i.e. functional polymorphisms) and subsequently to understand how such functional variations alter human biology. Since approximately 70% of all genes are expressed in brain, many of these functional polymorphisms will effect how the brain processes information. Functional neuroimaging (i.e. PET, fMRI, EEG/MEG), because of its capacity to assay within individuals information processing in discreet brain circuits, has unique potential as a tool for characterizing functional genomics in brain. In this review, we: (i) describe the conceptual basis for, and potential of, this synthetic approach, referred to here as imaging genomics; (ii) propose several guiding principles for its implementation and advancement; and (iii) highlight recent studies that exemplify these principles.

	Why study genes?

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
Genes represent the ‘go’ square on the Monopoly board of life. They are the biological toolbox with which one negotiates the environment. While most human behaviours cannot be explained by genes alone, and certainly much variance in aspects of brain information processing will not be genetically determined, variations in genetic sequence that impact gene function will contribute some variance to these more complex phenomena. This conclusion is implicit in the results of studies of twins that have revealed heritabilities of 40–70% for various aspects of cognition, temperament, and personality³.

Genes have unparalleled potential impact on all levels of biology. In the context of disease states, particularly behavioural disorders, genes not only transcend phenomenological diagnosis, they represent mechanisms of disease. Moreover, genes offer the potential to identify at-risk individuals and biological pathways for the development of new treatments. In the case of psychiatric illness, genes appear to be the only consistent risk factors that have been identified across populations and the lions' share of susceptibility to major psychiatric disorders is accounted for by inheritance⁴. While the strategy for finding susceptibility genes for complex disorders, by traditional linkage and association methods, may seem relatively straightforward (albeit not easily achieved), developing a useful and comprehensive understanding of the mechanisms by which such genes increase biological risk is a much more daunting challenge. How many genes contribute to a particular complex behaviour or complex disease state? What genetic overlap exists across behaviours and diseases? How large are the effects of candidate genes on particular brain functions? And, perhaps most importantly, how does a gene affect brain information processing to increase risk for a disorder of behaviour?

The ‘candidate gene association approach’ has been a particularly popular strategy for attempting to answer these questions. Genetic association is a test of a relationship between a particular phenotype and a specific allele of a gene. This approach usually begins with selecting a biological aspect of a particular condition or disease, then identifying variants in genes thought to impact on the candidate biological process, and next searching for evidence that the frequency of a particular variant (‘allele’) is increased in populations having the disease or condition. A significant increase in allele frequency in the selected population is evidence of association. When a particular allele is significantly associated with a particular phenotype, it is potentially a causative factor in determining that phenotype. There are caveats to the design and interpretation of genetic association studies, such as linkage disequilibrium with other loci and ancestral stratification, that are beyond the scope of this review and have been discussed at length elsewhere⁵.

Imaging genomics is a form of genetic association analysis, where the phenotype is not a disease phenotype but a physiological response of the brain during specific information processing. The protocol for imaging genomics involves first identifying a meaningful variation in the DNA sequence within a candidate gene. For the variant to be meaningful, it should have an impact at the molecular and cellular level in gene or protein function (i.e. be a functional variation) and the distribution of such effects at the level of brain systems involved in specific forms of information processing should be predictable. For example, a genetic variation in the gene for the serotonin transporter that impacts the availability of synaptic serotonin would be expected to affect amygdala function because serotonin is important in amygdala physiology (see below). Finally, the contributions of abnormalities in these systems to complex behaviours and emergent phenomena, possibly including psychiatric syndromes, can then be understood from a more biological perspective. It is within the context of this ‘candidate gene association approach’ that imaging genomics provides an ideal opportunity to further our understanding of disease mechanisms.

	Why functional neuroimaging?

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
Traditionally, the impact of genetic polymorphisms on human behaviour has been examined using indirect assays such as personality questionnaires and neuropsychological batteries. While a few such studies have reported significant associations between specific genetic polymorphisms and behaviours, their collective results have been weak and inconsistent⁶. This is not surprising given the considerable individual variability and subjectivity of such behavioural measures. Because such behavioural assays are vague and imprecise, it has been necessary to use very large samples, often exceeding several hundred subjects, to identify even small gene effects⁷. In addition, behavioural probes and neuropsychological tests allow for the use of alternative task strategies by different individuals that may obscure potential gene effects on the underlying neural substrates meant to be engaged by the tests.

Because the response of brain regions subserving specific cognitive and emotional processes may be more objectively measurable than the subjective experience of these same processes, functional genetic polymorphisms may have a more robust impact at the level of brain than at the level of behaviour. Thus, functional polymorphisms in genes weakly related to behaviours and, in an extended fashion, psychiatric syndromes may be strongly related to the function of neural systems involved in processing cognitive and emotional information in brain. This is the underlying assumption of imaging genomics. The potential for marked differences at the neurobiological level in the absence of significant differences in behavioural measures underscores the need for a direct assay of brain function. Accordingly, imaging genomics provides a unique opportunity to explore and evaluate the functional impact of brain-relevant genetic polymorphisms potentially more incisively and with greater sensitivity than existing behavioural assays.

Functional neuroimaging techniques, especially those that are non-invasive like fMRI, typically require no more than a few minutes of subject participation to acquire substantial data sets, reflecting the acquisition of many hundreds of repeated measures of brain function within a single subject. This is analogous to the signal detection power of EEG and MEG approaches, which also have been used to identify physiological signals that are highly heritable⁸. Thus, these techniques, in contrast to their behavioural counterparts, may require considerably fewer subjects (tens versus hundreds) to identify significant gene effects on the response characteristics of the brain. Moreover, the efficiency of these techniques allows for the ability to investigate the specificity of gene effects by examining their influence on multiple functional systems (e.g. prefrontal, striatal, limbic) in a single subject in one experimental session. This capacity to assay rapidly differences in the brain responses of different information processing systems with enhanced power and sensitivity places functional neuroimaging at the forefront of available tools for the in vivo study of functional genetic variation.

	Imaging genomics: three basic principles

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
Selection of candidate genes

Ideally, the application of functional neuroimaging techniques towards the study of genetic effects should start where studying gene effects on behaviour would also start (i.e. from well-defined functional polymorphisms, such as those reported for APOE, COMT, and 5-HTT, highlighted below). Because the genetic variation in such genes has been associated with specific physiological effects at the cellular level and their impact has been described in distinct brain regions and circuits, imaging paradigms can be developed to explore their effects on local information processing in both normal and impaired populations.

Short of well-defined functional polymorphisms, candidate genes with identified single nucleotide polymorphisms (SNPs) or other allele variants in coding or promoter regions with likely functional implications (e.g. non-conservative amino acid substitution or missense mutation in a promoter consensus sequence) involving circumscribed neuro-anatomical systems would also be attractive substrates for imaging genomics. The investigation of genes and variations without well-established functional implications in brain, however, necessarily requires greater caution not only in the design of imaging tasks but also in the interpretation of differential brain responses.

Control for non-genetic factors

The contribution of single genes to the response characteristics of brain systems, while putatively more substantial than that to emergent behavioural phenomena, is still presumably small. Furthermore, typically large effects of age, gender and IQ as well as environmental factors such as illness, injury, or substance abuse on phenotypic variance can easily obscure these small potential gene effects. Since association studies in imaging genomics are susceptible to population stratification artefacts, as in any case-control association study, ethnic matching within genotype groups is also potentially critical. Thus, the identification and contribution of genetic variation to specific phenotypes should be limited to studies where other potential contributing and confounding factors are carefully matched across genotype groups. If the imaging protocol involves performance of a task, the groups should also be matched for level of performance or, at least, any variability in performance should be considered in the analysis and interpretation of the imaging data. This is because task performance and imaging responses are linked pari passu, and systematic differences in performance between genotype groups could either obscure a true gene effect or masquerade for one.

Task selection

The last 5 years have been witness to a tremendous proliferation of functional neuroimaging studies and, with them, behavioural tasks designed specifically for this experimental setting. Many of these are modified versions of classic behavioural and neuropsychological tests (e.g. the Wisconsin Card Sorting Task⁹) designed to tap neural systems critical to particular behaviours. More recent paradigms have emerged that focus on interactions of specific behaviours and disease states as these questions have become newly accessible with non-invasive imaging (e.g. the emotion Stroop and OCD¹⁰).

Because of the relatively small effects of single genes, even after having controlled for non-genetic and other confounder variables, imaging tasks must maximize sensitivity and inferential value. As the interpretation of potential gene effects depends on the validity of the information processing paradigm, it is best to select well-characterized paradigms that are effective at engaging circumscribed brain regions and systems, produce robust signals in every individual and show variance across individuals (see below). In short, imaging genomics studies are probably not the appropriate venue to design and test new functional tasks, and to do so might undermine their tremendous potential.

	Imaging genomics: applications of the principles

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
The following three sections provide examples of how the application of the principles of imaging genomics outlined above can lead to insights about the biological mechanisms underlying complex behavioural traits. In each study, functional neuroimaging was employed to identify at the systems' level the effects of functional genetic polymorphisms that had been previously associated with alterations at the molecular and cellular level as well as specific behaviours and/or disease states. In addition, each study implemented rigorous controls for non-genetic factors such as age, gender, IQ and performance on the experimental task. They also capitalized on existing functional paradigms designed to explore physiological aspects of distinct neural systems.

Apolipoprotein E and memory systems

A common allelic variant of the apolipoprotein E (APOE) gene has been associated with late-onset familial Alzheimer's disease¹¹. Specifically, the APOE 4 allele has a dose-dependent effect on risk and age of onset for the disease¹². PET studies have reported deficits in cortical resting glucose metabolism in cognitively normal middle-aged subjects with the APOE 4 allele^13,14. Despite these associations, the APOE genotype alone has not been a viable predictor of disease development in non-demented subjects¹⁵.

Bookheimer et al¹⁶ used fMRI during a challenging memory task to explore the genetic effect of the APOE 4 allele on memory-related brain activity. In their landmark study, 16 subjects carrying the APOE 4 allele and 14 subjects homozygous for the APOE 3 allele, which is not associated with increased risk for Alzheimer's disease, were asked to memorize and recall unrelated word pairs, a demanding memory task previously used to identify damage to the medial temporal lobe memory system¹⁷, while undergoing fMRI.

While all subjects were cognitively intact and performed the memory task equally well, the pattern of brain activation between the two groups was strikingly different. In comparison to subjects with the APOE 3 allele, those with the high-risk APOE 4 allele exhibited significantly greater activation (both magnitude and extent) in memory-related brain regions such as the prefrontal cortex and left hippocampus. Such relatively increased neural activity in those with the at-risk allele was interpreted by the authors as reflecting possible compensatory phenomena through the recruitment of additional cognitive resources in the face of greater task difficulty and demand. Interestingly, the magnitude of task-related brain activity was significantly correlated with subsequent memory decline. These data suggest that changes in cortical information processing during declarative memory are associated with the biological effects of APOE 4 even if compensation is made at the level of observable behaviour (i.e. task performance). Thus, the authors concluded that observed differences in memory-related brain activity associated with variation in the APOE gene in the absence of behavioural impairments may provide a useful tool for predicting the course of cognitive decline.

Catechol-o-methyltransferase and the prefrontal cortex

Catechol-o-methyltransferase (COMT), a methylation enzyme that converts released dopamine to inactive 3-methoxytyramine, is believed to play an important role in prefrontal dopamine neurotransmission¹⁸. Because dopamine transporters are virtually absent at cortical synapses, dopamine regulation in the prefrontal cortex is uniquely coupled to inactivation mechanisms in postsynaptic neurons and glia, such as catabolism by COMT¹⁹. A common polymorphism (val^108/158met) in the COMT gene has been associated with most of the human variation in enzyme activity, with the thermolabile met allele having one-fourth the activity of the thermostable val allele. Thus, the val^108/158met COMT polymorphism may impact dopamine regulated prefrontal cortical activity during executive and working memory tasks that tax this functional circuitry and that are affected by variations in dopamine signalling²⁰. In fact, this polymorphism has been linked to impairments in executive function and working memory in val carriers²¹, suggesting that genetically driven alterations in COMT enzymatic activity and subsequent synaptic prefrontal dopamine concentrations may lead to diminished prefrontal function.

To assay directly the impact of the COMT val^108/158met functional polymorphism on prefrontal physiology, Egan et al²¹ used fMRI during the performance of a well-characterized working memory test (the n-back task) that has been effective at engaging the dorsolateral prefrontal cortex in prior imaging studies^22,23. The authors found that in two separate cohorts of healthy volunteers (n = 11–16), all matched for age, gender, education and task performance, the load of the high-activity val allele consistently predicted a relatively exaggerated prefrontal response during the working memory task.

These imaging results provide direct evidence that the effects of the COMT val^108/158met polymorphism on executive tasks such as working memory may reflect alterations in prefrontal dopamine catabolism related to COMT enzymatic activity. Similar to the interpretation of Bookheimer et al¹⁶ for the effects of the APOE polymorphism on task-related brain responses, these authors suggested that decreased concentrations of prefrontal dopamine associated with the high-activity val allele led to an inefficient and thus exaggerated cortical response, perhaps in an effort to maintain task performance or as a reflection of diminished prefrontal signal to noise. These data, much like those of Bookheimer and colleagues, also illustrate the ability of functional neuroimaging to reveal biological effects of genetic variation at the level of brain in the absence of significant behavioural differences.

The COMT fMRI results also illuminate other evidence that COMT val is a susceptibility allele for schizophrenia and possibly other psychoses^24,25. Schizophrenia has long been known to involve abnormal prefrontal function, and COMT val inheritance appears to be a genetic mechanism contributing to this aspect of the disease. The study by Egan and co-workers also highlights the statistical power of imaging genomics. The small effect size of genotype on executive cognition in terms of working memory test performance, in which COMT genotype predicts approximately 3–4% of the variance, required several hundred subjects to achieve statistical significance. In contrast, powerful statistical differences were observed in imaging samples of less than 15 subjects.

5-HTT and the amygdala

A common polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) has been associated with alterations in both 5-HTT transcription and 5-HT uptake. Cultured lymphoblasts homozygous for the long (l) promoter allelic variant having increased levels of 5-HTT mRNA expression as well as 5-HT uptake in comparison to cells possessing either one or two copies of the short (s) promoter variant²⁶. Similar differences in 5-HTT binding levels between the l/l and s/l or s/s genotypes have been detected in the human brain^27,28.

At the behavioural level, possession of either one or two copies of the s allele has been associated with abnormal levels of anxiety^26,29,30, fear³¹ and development of affective illness³². These differences most likely reflect relative differences in 5-HTT expression and subsequent levels of synaptic 5-HT, a potent modulator of emotional behaviour³³. However, the relationship between the 5-HTT promoter polymorphism and subjective measures of emotion and personality has been weak and inconsistent^34–38, likely reflecting the vagueness and subjectivity of the behavioural measurements, but also casting doubt on the validity of the relationship³⁹.

In order to explore the neural underpinnings of the apparent relationship between the 5-HTT promoter polymorphism and emotional behaviour, Hariri et al⁴⁰ used fMRI to evaluate the response of the amygdala, a brain region critical to both normal and abnormal fear behaviour, in healthy volunteers. Because the response of the amygdala may be more objectively measurable than the subjective experience of emotionality, the investigators reasoned that the effects of the 5-HTT polymorphism might be more apparent at the level of amygdala biology than at the level of individual responses to questionnaires or ratings of emotional symptoms. Specifically, they hypothesized that subjects carrying the s allele, who presumably have relatively lower 5-HTT expression and higher synaptic 5-HT and who are typically more anxious and fearful, would exhibit a greater amygdala response than those homozygous for the l allele.

In their study, subjects from two independent cohorts (n = 14) were divided into two equal groups based on their 5-HTT genotype, with the groups matched for age, gender, IQ and task performance. During scanning, the subjects performed a perceptual processing task that required them to match the expression of fearful and angry human faces, stimuli that have consistently engaged the amygdala in imaging studies^41–43. Consistent with their hypothesis, Hariri and colleagues found that subjects carrying the less efficient s allele of the 5-HTT promoter gene had an increased amygdala response to fearful stimuli in comparison to subjects homozygous for the l allele. This finding led the investigators to suggest that the increased anxiety and fear associated with individuals possessing the s allele may reflect the hyper-responsiveness of their amygdala to relevant environmental stimuli. These results are striking not only because they provide the first evidence for a genetically driven difference in the response of brain regions underlying emotional behaviour, but also because these differences at the neurobiological level were marked in a relatively small sample population in the absence of significant differences in behavioural measures. Moreover, as in the other examples, the results of this imaging genomics study help elucidate a potential biological mechanism for the genetic association of this polymorphism with vague psychiatric disturbances, namely various dimensions of anxiety and neuroticism.

	Conclusions

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 
The results of these studies underscore the power of a direct assay of brain function like fMRI to identify phenotypes in brain related to functional polymorphisms in genes likely important for human behaviour and neuropsychiatric illness. They also provide compelling evidence that the application of imaging genomics in light of the basic principles outlined above promises a unique opportunity to explore and evaluate the functional impact of brain-relevant genetic polymorphisms more rapidly and with greater sensitivity than existing behavioural assays. In turn, our appreciation of the biological mechanisms that contribute to complex behaviours and the variations in these mechanisms that lead to both individual differences and vulnerability to disease is likely to expand in a manner previously unattainable.

	Footnotes

 
Correspondence to: Daniel R Weinberger MD, Clinical Brain Disorders Branch, National Institute of Mental Health, 10 Center Drive, Room 4S235, Bethesda, MD 20892-1384, USA

	References

Top Abstract Introduction Why study genes? Why functional neuroimaging? Imaging genomics: three basic... Imaging genomics: applications... Conclusions References

 

1. Venter JC, Adams MD, Myers EW et al. The sequence of the human genome. Science 2001; 291: 1304–51[Abstract/Free Full Text]
2. Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860–921[CrossRef][Medline]
3. McGuffin P, Riley B, Plomin R. Genomics and behavior. Toward behavioral genomics. Science 2001; 291: 1232–49[Free Full Text]
4. Moldin SO, Gottesman II. At issue: genes, experience, and chance in schizophrenia – positioning for the 21st century. Schizophr Bull 1997; 23: 547–61[Abstract/Free Full Text]
5. Emahazion T, Feuk L, Jobs M et al. SNP association studies in Alzheimer's disease highlight problems for complex disease analysis. Trends Genet 2001; 17: 407–13[CrossRef][Web of Science][Medline]
6. Malhotra AK, Goldman D. Benefits and pitfalls encountered in psychiatric genetic association studies. Biol Psychiatry 1999; 45: 544–50[CrossRef][Web of Science][Medline]
7. Glatt CE, Freimer NB. Association analysis of candidate genes for neuropsychiatric disease: the perpetual campaign. Trends Genet 2002; 18: 307–12[CrossRef][Web of Science][Medline]
8. Vogel F, Schalt E, Kruger J, Propping P, Lehnert KF. The electroencephalogram (EEG) as a research tool in human behavior genetics: psychological examinations in healthy males with various inherited EEG variants. Hum Genet 1979; 47: 1–45[CrossRef][Web of Science][Medline]
9. Axelrod BN. Are normative data from the 64-card version of the WCST comparable to the full WCST? Clin Neuropsychol 2002; 16: 7–11[Web of Science][Medline]
10. Whalen PJ, Bush G, McNally RJ et al. The emotional counting Stroop paradigm: a functional magnetic resonance imaging probe of the anterior cingulate affective division. Biol Psychiatry 1998; 44: 1219–28[CrossRef][Web of Science][Medline]
11. Saunders AM, Strittmatter WJ, Schmechel D et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology 1993; 43: 1467–72[Abstract/Free Full Text]
12. Corder EH, Saunders AM, Strittmatter WJ et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 1993; 261: 921–3[Abstract/Free Full Text]
13. Small GW, Mazziotta JC, Collins MT et al. Apolipoprotein E type 4 allele and cerebral glucose metabolism in relatives at risk for familial Alzheimer disease. JAMA 1995; 273: 942–7[Abstract/Free Full Text]
14. Reiman EM, Caselli RJ, Yun LS et al. Preclinical evidence of Alzheimer's disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. N Engl J Med 1996; 334: 752–8[Abstract/Free Full Text]
15. National Institute on Aging/Alzheimer's Association Working Group. Apolipoprotein E genotyping in Alzheimer's disease. Lancet 1996; 347: 1091–5[Web of Science][Medline]
16. Bookheimer SY, Strojwas MH, Cohen MS et al. Patterns of brain activation in people at risk for Alzheimer's disease. N Engl J Med 2000; 343: 450–6[Abstract/Free Full Text]
17. Rausch R, Babb TL. Hippocampal neuron loss and memory scores before and after temporal lobe surgery for epilepsy. Arch Neurol 1993; 50: 812–7[Abstract/Free Full Text]
18. Weinshilboum RM, Otterness DM, Szumlanski CL. Methylation pharmacogenetics: catechol O-methyltransferase, thiopurine methyltransferase, and histamine N-methyltransferase. Annu Rev Pharmacol Toxicol 1999; 39: 19–52[CrossRef][Web of Science][Medline]
19. Lewis DA, Sesack SR, Levey AI, Rosenberg DR. Dopamine axons in primate prefrontal cortex: specificity of distribution, synaptic targets, and development. Adv Pharmacol 1998; 42: 703–6
20. Goldman-Rakic PS. Regional and cellular fractionation of working memory. Proc Natl Acad Sci USA 1996; 93: 13473–80[Abstract/Free Full Text]
21. Egan MF, Goldberg TE, Kolachana BS et al. Effect of COMT Val108/158Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA 2001; 98: 6917–22[Abstract/Free Full Text]
22. Callicott JH, Mattay VS, Bertolino A et al. Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cereb Cortex 1999; 9: 20–6[Abstract/Free Full Text]
23. Cohen JD, Perlstein WM, Braver TS et al. Temporal dynamics of brain activation during a working memory task. Nature 1997; 386: 604–8[CrossRef][Medline]
24. Eberhard G, Ross S, Saaf J, Wahlund B, Wetterberg L. Psychoses in twins. A 10 year clinical and biochemical follow-up study. Schizophr Res 1989; 2: 367–74[CrossRef][Web of Science][Medline]
25. Nurnberger Jr JI, Foroud T. Genetics of bipolar affective disorder. Curr Psychiatry Rep 2000; 2: 147–57[Medline]
26. Lesch KP, Bengel D, Heils A et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 1996; 274: 1527–31[Abstract/Free Full Text]
27. Heinz A, Jones DW, Mazzanti C et al. A relationship between serotonin transporter genotype and in vivo protein expression and alcohol neurotoxicity. Biol Psychiatry 2000; 47: 643–9[CrossRef][Web of Science][Medline]
28. Little KY, McLaughlin DP, Zhang L et al. Cocaine, ethanol, and genotype effects on human midbrain serotonin transporter binding sites and mRNA levels. Am J Psychiatry 1998; 155: 207–13[Abstract/Free Full Text]
29. Melke J, Landen M, Baghei F et al. Serotonin transporter gene polymorphisms are associated with anxiety-related personality traits in women. Am J Med Genet 2001; 105: 458–63[CrossRef][Web of Science][Medline]
30. Du L, Bakish D, Hrdina PD. Gender differences in association between serotonin transporter gene polymorphism and personality traits. Psychiatr Genet 2000; 10: 159–64[Web of Science][Medline]
31. Garpenstrand H, Annas P, Ekblom J, Oreland L, Fredrikson M. Human fear conditioning is related to dopaminergic and serotonergic biological markers. Behav Neurosci 2001; 115: 358–64[CrossRef][Web of Science][Medline]
32. Lesch KP, Mossner R. Genetically driven variation in serotonin uptake: is there a link to affective spectrum, neurodevelopmental, and neurodegenerative disorders? Biol Psychiatry 1998; 44: 179–92[CrossRef][Web of Science][Medline]
33. Ressler KJ, Nemeroff CB. Role of serotonergic and noradrenergic systems in the pathophysiology of depression and anxiety disorders. Depress Anxiety 2000; 12 Suppl 1: 2–19[CrossRef][Medline]
34. Ball D, Hill L, Freeman B et al. The serotonin transporter gene and peer-rated neuroticism. Neuroreport 1997; 8: 1301–4[Web of Science][Medline]
35. Ebstein RP, Segman R, Benjamin J et al. 5-HT2C (HTR2C) serotonin receptor gene polymorphism associated with the human personality trait of reward dependence: interaction with dopamine D4 receptor (D4DR) and dopamine D3 receptor (D3DR) polymorphisms. Am J Med Genet 1997; 74: 65–72[CrossRef][Web of Science][Medline]
36. Katsuragi S, Kunugi H, Sano A et al. Association between serotonin transporter gene polymorphism and anxiety-related traits. Biol Psychiatry 1999; 45: 368–70[CrossRef][Web of Science][Medline]
37. Flory JD, Manuck SB, Ferrell RE et al. Neuroticism is not associated with the serotonin transporter (5-HTTLPR) polymorphism. Mol Psychiatry 1999; 4: 93–6[CrossRef][Web of Science][Medline]
38. Deary IJ, Battersby S, Whiteman MC et al. Neuroticism and polymorphisms in the serotonin transporter gene. Psychol Med 1999; 29: 735–9[CrossRef][Web of Science][Medline]
39. Ohara K, Nagai M, Tsukamoto T, Tani K, Suzuki Y. Functional polymorphism in the serotonin transporter promoter at the SLC6A4 locus and mood disorders. Biol Psychiatry 1998; 44: 550–4[CrossRef][Web of Science][Medline]
40. Hariri AR, Mattay VS, Tessitore A et al. Serotonin transporter genetic variation and the response of the human amygdala. Science 2002; 297: 400–3[Abstract/Free Full Text]
41. Hariri AR, Tessitore A, Mattay VS, Fera F, Weinberger DR. The amygdala response to emotional stimuli: a comparison of faces and scenes. Neuroimage 2002; 17: 317–23[CrossRef][Web of Science][Medline]
42. Hariri AR, Mattay VS, Tessitore A et al. Dextroamphetamine modulates the response of the human amygdala. Neuropsychopharmacology 2002; 27: 1037–41
43. Davis M, Whalen PJ. The amygdala: vigilance and emotion. Mol Psychiatry 2001; 6: 13–34[CrossRef][Web of Science][Medline]

CiteULike    Connotea    Del.icio.us    What's this?

This article has been cited by other articles:

				B. Rasch, K. Spalek, S. Buholzer, R. Luechinger, P. Boesiger, A. Papassotiropoulos, and D. J.-F. de Quervain From the Cover: A genetic variation of the noradrenergic system is related to differential amygdala activation during encoding of emotional memories PNAS, November 10, 2009; 106(45): 19191 - 19196. [Abstract] [Full Text] [PDF]

				R. D. Lane, S. R. Waldstein, M. A. Chesney, J. R. Jennings, W. R. Lovallo, P. J. Kozel, R. M. Rose, D. A. Drossman, N. Schneiderman, J. F. Thayer, et al. The Rebirth of Neuroscience in Psychosomatic Medicine, Part I: Historical Context, Methods, and Relevant Basic Science Psychosom Med, February 1, 2009; 71(2): 117 - 134. [Abstract] [Full Text] [PDF]

				E. Fakra, L. W. Hyde, A. Gorka, P. M. Fisher, K. E. Munoz, M. Kimak, I. Halder, R. E. Ferrell, S. B. Manuck, and A. R. Hariri Effects of HTR1A C(-1019)G on Amygdala Reactivity and Trait Anxiety Arch Gen Psychiatry, January 1, 2009; 66(1): 33 - 40. [Abstract] [Full Text] [PDF]

				B. Cheeran, P. Talelli, F. Mori, G. Koch, A. Suppa, M. Edwards, H. Houlden, K. Bhatia, R. Greenwood, and J. C. Rothwell A common polymorphism in the brain-derived neurotrophic factor gene (BDNF) modulates human cortical plasticity and the response to rTMS J. Physiol., December 1, 2008; 586(23): 5717 - 5725. [Abstract] [Full Text] [PDF]

				C. C.Y Wong and G. Schumann Genetics of addictions: strategies for addressing heterogeneity and polygenicity of substance use disorders Phil Trans R Soc B, October 12, 2008; 363(1507): 3213 - 3222. [Abstract] [Full Text] [PDF]

				S. J. Bishop, J. Fossella, C. J. Croucher, and J. Duncan COMT val158met Genotype Affects Recruitment of Neural Mechanisms Supporting Fluid Intelligence Cereb Cortex, September 1, 2008; 18(9): 2132 - 2140. [Abstract] [Full Text] [PDF]

				K. M. Prasad and M. S. Keshavan Structural Cerebral Variations as Useful Endophenotypes in Schizophrenia: Do They Help Construct "Extended Endophenotypes"? Schizophr Bull, July 1, 2008; 34(4): 774 - 790. [Abstract] [Full Text] [PDF]

				E. M. Drabant, A. R. Hariri, A. Meyer-Lindenberg, K. E. Munoz, V. S. Mattay, B. S. Kolachana, M. F. Egan, and D. R. Weinberger Catechol O-methyltransferase Val158Met Genotype and Neural Mechanisms Related to Affective Arousal and Regulation Arch Gen Psychiatry, December 1, 2006; 63(12): 1396 - 1406. [Abstract] [Full Text] [PDF]

				J. Lind, J. Persson, M. Ingvar, A. Larsson, M. Cruts, C. Van Broeckhoven, R. Adolfsson, L. Backman, L.-G. Nilsson, K. M. Petersson, et al. Reduced functional brain activity response in cognitively intact apolipoprotein E {varepsilon}4 carriers Brain, May 1, 2006; 129(5): 1240 - 1248. [Abstract] [Full Text] [PDF]

				E. Viding and U. Frith Genes for susceptibility to violence lurk in the brain PNAS, April 18, 2006; 103(16): 6085 - 6086. [Full Text] [PDF]

				M. C. Rowbotham Mechanisms of neuropathic pain and their implications for the design of clinical trials Neurology, December 29, 2005; 65(12_suppl_4): S66 - S73. [Abstract] [Full Text] [PDF]

				E. Congdon and T. Canli The endophenotype of impulsivity: reaching consilience through behavioral, genetic, and neuroimaging approaches. Behav Cogn Neurosci Rev, December 1, 2005; 4(4): 262 - 281. [Abstract] [PDF]

				T. Iidaka, N. Ozaki, A. Matsumoto, J. Nogawa, Y. Kinoshita, T. Suzuki, N. Iwata, Y. Yamamoto, T. Okada, and N. Sadato A Variant C178T in the Regulatory Region of the Serotonin Receptor Gene HTR3A Modulates Neural Activation in the Human Amygdala J. Neurosci., July 6, 2005; 25(27): 6460 - 6466. [Abstract] [Full Text] [PDF]

				J. Woolley and P. McGuire Neuroimaging in schizophrenia: what does it tell the clinician? Adv. Psychiatr. Treat., May 1, 2005; 11(3): 195 - 202. [Abstract] [Full Text] [PDF]

				K. Koshibu, E. T. Ahrens, and P. Levitt Postpubertal Sex Differentiation of Forebrain Structures and Functions Depend on Transforming Growth Factor-{alpha} J. Neurosci., April 13, 2005; 25(15): 3870 - 3880. [Abstract] [Full Text] [PDF]

				M. N. Smolka, G. Schumann, J. Wrase, S. M. Grusser, H. Flor, K. Mann, D. F. Braus, D. Goldman, C. Buchel, and A. Heinz Catechol-O-Methyltransferase val158met Genotype Affects Processing of Emotional Stimuli in the Amygdala and Prefrontal Cortex J. Neurosci., January 26, 2005; 25(4): 836 - 842. [Abstract] [Full Text] [PDF]

				M. Battaglia, A. Ogliari, A. Zanoni, A. Citterio, U. Pozzoli, R. Giorda, C. Maffei, and C. Marino Influence of the Serotonin Transporter Promoter Gene and Shyness on Children's Cerebral Responses to Facial Expressions Arch Gen Psychiatry, January 1, 2005; 62(1): 85 - 94. [Abstract] [Full Text] [PDF]

				W. Nobis, X. Ren, S. P. Suchyta, T. R. Suchyta, A. J. Zanella, and P. M. Coussens Development of a porcine brain cDNA library, EST database, and microarray resource Physiol Genomics, December 16, 2003; 16(1): 153 - 159. [Abstract] [Full Text] [PDF]

This Article Abstract FREE Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Search for citing articles in: ISI Web of Science (112) Request Permissions Disclaimer Google Scholar Articles by Hariri, A. R Articles by Weinberger, D. R Search for Related Content PubMed PubMed Citation Articles by Hariri, A. R Articles by Weinberger, D. R Related Collections Neurology Genetics Radiology Social Bookmarking          What's this?

Online ISSN 1471-8391 - Print ISSN 0007-1420

Copyright © 2010 Oxford University Press

Oxford Journals Oxford University Press

• Site Map
• Privacy Policy
• Frequently Asked Questions

Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan American National Biography Booksellers' Information Service Children's Fiction and Poetry Children's Reference Corporate & Special Sales Dictionaries Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks Humanities International Education Unit Law Medicine Music Online Products Oxford English Dictionary Reference Rights and Permissions Science School Books Social Sciences Very Short Introductions World's Classics