Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology


A fundamental function of the brain is to evaluate the emotional and motivational significance of stimuli and to adapt behaviour accordingly. The IMAGEN study is the first multicentre genetic-neuroimaging study aimed at identifying the genetic and neurobiological basis of individual variability in impulsivity, reinforcer sensitivity and emotional reactivity, and determining their predictive value for the development of frequent psychiatric disorders. Comprehensive behavioural and neuropsychological characterization, functional and structural neuroimaging and genome-wide association analyses of 2000 14-year-old adolescents are combined with functional genetics in animal and human models. Results will be validated in 1000 adolescents from the Canadian Saguenay Youth Study. The sample will be followed up longitudinally at the age of 16 years to investigate the predictive value of genetics and intermediate phenotypes for the development of frequent psychiatric disorders. This review describes the strategies the IMAGEN consortium used to meet the challenges posed by large-scale multicentre imaging–genomics investigations. We provide detailed methods and Standard Operating Procedures that we hope will be helpful for the design of future studies. These include standardization of the clinical, psychometric and neuroimaging-acquisition protocols, development of a central database for efficient analyses of large multimodal data sets and new analytic approaches to large-scale genetic neuroimaging analyses.


Over the past decade, rapid technological advances in the fields of cognitive neuroscience and genetics have brought the possibility of identifying the neurobiological and genetic basis of frequent psychiatric disorders within closer reach than ever before. Understanding the complex mechanisms through which genes impact on brain and behaviour will be vital in finding biomarkers that aid in earlier diagnosis and in developing individualized treatment approaches. However, these developments have also resulted in new challenges. One of the most consistent findings is that frequent psychiatric disorders are the result of multiple genes interacting with one another and with the environment in a complex manner.1, 2 The ability to identify such complex genetic mechanisms (for example, genetic heterogeneity, polygenicity3, 4) is further complicated by phenotypic variability, such as individual differences in symptom manifestation, severity and comorbidity.

In this article, we describe the integrative approach and methods the IMAGEN project has applied to address these challenges. The main text is complemented by Supplementary Tables 1–6 and Standard Operation Procedures (SOPs) (, which give detailed information on our methods and implementation procedures to provide directions for future genetic–neuroimaging investigations.

IMAGEN is the first European multicentre, multidisciplinary collaboration that combines comprehensive behavioural and neuropsychological characterization, functional and structural neuroimaging and genome-wide association (GWA) analyses in 2000 14-year-old adolescents with functional genetic analyses and animal experimentation. IMAGEN aims to identify the genetic and neurobiological basis of individual variability in quantitative psychological traits, and to determine their predictive value for the development of frequent neuropsychiatric disorders. The rationale behind this project is based on the following five key tenets.

(1) Abnormalities in reinforcer sensitivity, cognitive control and emotional reactivity are implicated in frequent neuropsychiatric disorders: To reduce phenotypic complexity and elucidate the neurobiological basis of frequent psychiatric disorders, we need to move from simple dichotomous disease classifications to investigating ‘intermediate phenotypes’, that is, quantitative, heritable vulnerability markers at the behavioural, neuropsychological or neurobiological levels.

Of particular relevance are individual differences in quantitative psychological traits, such as impulsivity, reward sensitivity and emotional reactivity, which form an essential part of personality differences, but are also implicated in most frequent psychiatric disorders, including alcoholism and other addiction disorders,5 affective and anxiety disorders,6 eating disorders,7 attention-deficit hyperactivity disorders,8 schizophrenia,9, 10 autism spectrum disorders11 and antisocial personality disorder.12

(2) Population studies with adolescents are needed to determine the predictive value of psychological or neurobiological intermediate phenotypes: Many frequent psychiatric disorders develop or have their onset in early adulthood, suggesting that extreme differences in one or more core psychological processes during adolescence may have a causal or modulatory role in the development of psychopathology. Adolescence is a particularly vulnerable period throughout which behavioural changes are related to several brain maturation processes impacting the regulation of motivational and other cognitive processes.13, 14 Case–control and quantitative trait studies with adults are both unsuited to infer causality. For example, by studying individuals with an addiction disorder, it is difficult to determine whether impulsivity or impaired cognitive control are causal factors in the onset of drug use, emerge as a consequence of long-term drug use or are related to co-occurring factors. To infer causality from temporal order, prospective longitudinal population studies with adolescents are required, comparing individual differences in the relevant intermediate phenotype before the onset of the disorder with the manifestation of clinical symptoms in early adulthood.

(3) Neuroimaging–intermediate phenotypes may link behavioural and genetic variation: One potential psychobiological risk marker is inter-individual variability in regional brain ‘reactivity’ during the processing of emotional or cognitive information, which can be assessed using functional magnetic resonance imaging (fMRI). Neuroimaging phenotypes have been shown to be more sensitive in discriminating individuals with clinical disorders from healthy controls than behavioural tests15, 16, 17 (but see18) and they can be dimensionally linked to personality/behavioural measures19, 20 and genetic variation.21

The first generation of ‘imaging genetic’ studies began by selecting a particular genetic polymorphism with a known or putative functional effect on a specific neurotransmitter system, and examined its influence on task-related regional brain activity.22 Although initial reports may have overestimated the magnitude of effect sizes observed in neuroimaging phenotypes,23 they are still many times higher than those obtained in association studies with clinical phenotypes24, 25 supporting the hypothesis that brain reactivity may be more directly influenced by genetic factors. Recent extensions of this approach include the investigation of combined gene–gene effects on regional brain activation,26, 27 and on functional connectivity between different brain areas.28

Nevertheless, the following considerable challenges remain: First, with few exceptions,29 the tacit assumption that neuroimaging phenotypes carry predictive value for vulnerability/resilience of clinical symptoms remains to be established.30 Second, as illustrated in Figure 1, novel approaches and methodologies are needed to move from candidate gene–neuroimaging studies with a limited number of known polymorphisms to the identification and functional characterization of new genes implicated in cognitive, motivational and emotional processes. Third, only by comparing patterns of gene–neuroimaging associations between different behavioural/clinical phenotypes will it be possible to identify risk markers conferring common vulnerability versus risk factors more unique to a particular disorder. Finally, considerably larger samples are required to have sufficient power to detect small gene effects, and additive and epistatic effects of multiple genes and environmental factors in pertinent brain regions.

Figure 1

Brain systems, neurosignalling pathways and complexity of gene effects on brain activation involved in social cognition, reward sensitivity and inhibitory control. Genome-wide gene–neuroimaging approaches are needed to identify the complex mechanisms through which multiple genetic variants can influence variability in task-related regional and supra-regional brain function. Some genetic variations may be shared between versus unique to particular psychological/subclinical traits. (a) Overlapping and distinct brain regions implicated in the ‘social brain’,14, 68 ‘brain reward system’,69 and right lateralized inhibitory control network.70 (b) Different neurotransmitter systems (for example, DA, Sert, GABA, Glutamate ) contribute in complex interactions to the regulation of one brain region (for example, amygdala) or brain circuitry (for example, DA and Sert independently contribute to prefrontal-amygdala coupling). (c) Different polymorphisms simultaneously impact on one neurotransmitter system, by influencing, for example, vesicular release, reuptake, metabolic degradation, pre- and post-synaptic receptor density, while single polymorphisms (for example, MAOA) can act on more than one neurotransmitter system (for example Sert and DA).

(4) Genome-wide genetic–neuroimaging studies extend candidate gene approaches: Recently, the combination of high-throughput genotyping technologies to genotype, simultaneously up to one million genes, large single-nucleotide polymorphism (SNP) databases and the HapMap program has revolutionized genetic analysis. However, despite GWA analysis of large samples, the degree of phenotypic heterogeneity present in samples selected according to psychiatric diagnoses often results in insufficient power to detect unambiguously genome-wide effects.31

Recent genome-wide meta-analyses in schizophrenia have overcome this lack of power by analyzing samples of tens of thousands of individuals.32 In the IMAGEN project, we are pursuing a complementary approach by selecting intermediate phenotypes with higher effect sizes33 and resulting increased statistical power for GWA analysis.

(5) Animal models of core neuropsychological traits: Animal models provide an excellent opportunity to study neurobiological mechanisms contributing to trait variability.34 By means of various genetic tools, pharmacological manipulations, lesion studies and many other interventions the neuroanatomical, neurochemical, molecular and genetic basis of behavioural traits can be examined. This is exemplified by a recent study in rats selected for high impulsivity showing that trait impulsivity and vulnerability to cocaine-taking is mediated in part by Dopamine D2/3 receptor density in the nucleus accumbens.35 Selective breeding of animals with extreme behavioural phenotypes, analysis of transcriptional activation in selected brain regions and genotyping of these animals will be supported by the IMAGEN project.


Below we summarize novel strategies adopted in the IMAGEN study to meet the challenges posed by large-scale multicentre imaging–genomic investigations. They include standardization methods for multicentre acquisition of neuroimaging data and behavioural/neuropsychological assessment in eight study centres in England, Ireland, France and Germany, a central database for storage and efficient statistical analysis of large, complex data sets and new data analysis approaches. Detailed information is provided in Supplementary Materials and SOPs to provide directions for future imaging–genomic studies.

Recruitment and behavioural/clinical phenotype

Two key recruitment criteria are maximization of (1) ethnic homogeneity to reduce stratification effects for genetic analyses, and (2) sample diversity in terms of socioeconomic status, academic achievement and behavioural/emotional functioning. A summary of the recruitment and assessment procedures is shown in Figure 2.

Figure 2

Overview of assessment procedures. Participants are recruited from high schools. To obtain a diverse sample in terms of SES, emotional and cognitive development, private, state-funded and special units are equally targeted. To maximize ethnic (Caucasian) homogeneity, at each study centre recruitment focuses on geographical areas with minimal ethnic diversity. Study participation includes ‘home assessments’ and 1–2 study centre visits. Home assessments comprise personality measures and cognitive/neuropsychological tests carried out through a web-based coordinated system (‘Psytools’) developed for the purpose of multisite, multilingual projects (Delosis, London, UK). Study centre visit(s) include adolescent and parent assessments.

Selection of clinical and self-report measures for phenotypic characterization was based on three criteria: validation across three languages, validation for use with adolescents and suitability for electronic assessment. A pilot study was conducted in three centres to develop and test measures, where one of these criteria was not fulfilled. The test battery assesses (i) demographic variables, (ii) quantitative psychopathological symptoms (on the basis of DSM-IV and ICD-10 criteria,36, 37) (iii) broad dimensions of personality38 as well as several more specific personality risk factors for psychopathology and substance abuse (for example, novelty seeking, impulsivity, anxiety sensitivity39, 40, 41, 42) (iv) familial history of psychopathology43 (v) cognitive/neuropsychological functions related to reward sensitivity, cognitive control and emotional reactivity,44, 45, 46, 47 (vi) current and past substance/drug use behaviours48, 49, 50, 51, 52 (vii) environmental risk factors for frequent psychiatric disorders (for example, stressful life events,53 domestic violence54 and prenatal exposure to potentially harmful conditions and substances55) and (viii) physical development.56, 57 Task specifications and participant inclusion/exclusion criteria are detailed in Supplementary Materials 1+2 and SOP 1.


Structural and fMRI is performed on 3T scanners from a range of manufacturers (Siemens, Munich, Germany; Philips, Best, The Netherlands; General Electrics, Chalfont St Giles, UK; Bruker, Ettlingen, Germany). A key challenge for the ability to pool data acquired on MR scanners of different manufacturers relates to their variation in availability and implementation of particular image-acquisition techniques. To address this problem, for each technique, a set of parameters compatible with all scanners, particularly those directly affecting image contrast or signal-to-noise, was devised and held constant across sites. Where manufacturer-specific choices had to be made (for example the design of head coil), the best manufacturer-specific option was used at all sites with the same scanner type. Two quality control procedures are regularly implemented at each site: (1) The American College of Radiology phantom is scanned to provide information about geometric distortions and signal uniformity related to hardware differences in radiofrequency coils and gradient systems, image contrast and temporal stability, and a custom phantom58 is scanned for diffusion-related parameters. (2) Several healthy volunteers are regularly scanned at each site to assess factors that cannot be measured using phantoms alone and at multiple sites to determine inter-site variability in structural and functional measures (for example, tissue contrast in raw MRI signal, tissue relaxation properties). The details of both quality control procedures are shown in Supplementary Tables 3 and 4.

Structural MRI: High-resolution anatomical MRIs are acquired, including a three-dimensional (T1 weighted (T1W)) magnetization prepared gradient echo sequence (MPRAGE) based on the ADNI protocol (, and T2 weighted fast- (turbo-) spin echo and FLAIR scans for visual assessment. To assess structural properties of white matter, we use an optimized diffusion tensor imaging acquisition sequence based on the study by Jones et al.59

Functional MRI: Four fMRI paradigms were chosen that reliably elicit a strong activation in known functional networks underlying inhibitory control,60 emotional reactivity to social stimuli,61 reward anticipation/outcome62 and global cognition63). Standardized hardware for visual and auditory stimulus presentation (Nordic Neurolabs, Bergen Norway) is used at all sites. BOLD functional images are acquired with a gradient-echo echoplanar imaging (EPI) sequence and using a relatively short echo-time to optimize reliably imaging of subcortical areas (Details of sequence parameters for structural and functional imaging, and the neuroimaging tasks are given in Supplementary Table 5 and in SOP 2).

Preprocessing: Preprocessing of the T1W, fMRI and diffusion tensor imaging data is performed centrally using an automated pipeline that processes the continuously incoming data, and accounts for inter-site variability. fMRI BOLD images are first realigned, corrected for slice timing and co-registered to T1W (MPRAGE) images. T1W images were spatially normalized to the MNI T1W template using SPM8. A subset of 200 T1W scans from all sites that showed good normalization, as measured by the overlap of brain mask images, was selected and this normalization was applied to the EPI. EPIs were then averaged to form an EPI template that had data from all centres and with the image characteristics (contrast, distortions) of the consortium EPI data. fMRI runs will be analyzed centrally to provide a coherent resource that can then be further used in specific projects. Diffusion images will be spatially normalized with the MNI template using the T2W images, and apparent diffusion coefficient and fractional anisotropy images will be constructed before tractography analyses.

Genotyping methods

Blood samples are collected onsite and sent to the DNA bank at regular intervals for processing to allow analyses of DNA, RNA, protein and generation of immortalized B cells (see SOP 3). DNA extraction is performed by a semiautomated process to ensure high quality and sufficient quantity. Genome-wide genotyping of 600 000 autosomic SNPs is performed using the Illumina Quad 610 chips (Illumina, San Diego, CA, USA). Although the primary genetic analysis will be performed in the Caucasian sample, population stratification (for example, related to geographic origin) will be examined using principle component approaches. Statistical GWA–intermediate phenotype analyses are described below. Genes associated with a phenotype of interest will be analyzed for functional SNPs leading to amino-acid exchanges or alteration of transcription-binding sites in regulatory regions of the gene using pertinent databases or capillary sequencing for further functional in vitro analysis. In selected candidate gene variations, functional studies, including in vitro studies using reporter gene constructs, expression analyses and biochemical analyses will be performed.

Behavioural animal models of human neuropsychological tests and functional genetics

Selective breeding of extreme impulsive phenotypes in an animal model of impulsivity, the 5-choice serial reaction time task (5-CSRTT)64 is used to ascertain trait heritability, as indexed by the increase in frequency of the extreme phenotype in successive generations. As soon as these traits become genetically fixed, brain tissue from rats showing extreme phenotypes is examined for differential gene expression in brain regions known to be involved in mediating impulsivity.65 Transcriptomic information will be merged with data from the GWA analysis in a convergent translational genomic approach to obtain a candidate gene list. Prioritized genes will finally be functionally validated by using virus-mediated gene transfer technology to overexpress or knockdown (RNA interference) the gene of interest. The validity of genes identified in animal models will further be tested in the human IMAGEN sample.

Data transfer, central database and data analysis

At each study centre, a customized collection procedure (Nordic Neurolabs) and data-transfer pipeline has been setup (Scito, Paris, France), which enables secure transfer of behavioural and neuroimaging data to a central database (Neurospin, Commisariat à l'Energie Atomique, CEA, France) (see Figure 3). The database uses XNAT technology and provides sufficient storage capacity for raw and summary scores of all behavioural, clinical and neuropsychological measures (5000 variables), raw (2TBytes) and preprocessed neuroimaging data (for example, contrast maps), and information on 600 000 SNP (+CNV). A web-based interface enables users to access the database using personalized login details to search, quality check, download and share heterogeneous data.

Figure 3

Data anonymization and transfer procedures. (a) Data anonymization. During assessment, families are identified by a 5-digit DAWBA family code, which is centrally linked to a 11-digit Pseudocode. (b) Data transfer: DICOM image files are sent from the MR scanners to local routers. Here, a graphical user interface (Nordic Neurolabs) guides users through an export protocol, including (i) a first visual quality check, (ii) manual tagging of behavioural log-files of the fMRI tasks and neuropyschological data, (iii) quality reports of all behavioural and psychometric measures and (iv) details on the blood sample. Data packages are then exported to the central database using a secure transfer protocol. (b) Behavioural data collected through the Psytools platform and DAWBA clinical information are transferred through separate pipelines to the central database. Here, a second level of coding occurs such that pseudocode1 is exchanged by pseudocode2, thus ensuring data anonymity both in terms of personal information and of site-origin.

Over a 20-month period, 1108 data sets have been acquired and assessed, and 1039 transferred to the central database. A summary of quality control procedures of behavioural, neuroimaging and GWA data is shown in Supplementary Table 6. Data exploitation and statistical analyses will proceed over three phases: The first analysis-wave will include 700 quality-controlled data sets. Table 1 provides an overview of the demographic/descriptive characterization of the first-wave sample for whom phenotypic quality control procedures had been completed at the time of writing.

Table 1 Demographic details of N=572 volunteers from all study centres, for whom phenotypic quality control procedures had been completed at the time of writing

(1) Characterization of the behavioural phenotype: Structural equation modelling of the trait, behavioural and neuropsychological data will be performed to derive latent constructs representing common and unique variance for psychiatric symptoms, trait-level psychological constructs and cognitive/behavioural measures.

(2) Brain–behaviour relationships: On all fMRI tasks brain maps will be created for each subject. Univariate and multivariate analyses approaches (for example, machine-learning techniques) will then be used to examine (i) relationships between individual variability in structural measures and task-related fMRI BOLD activity, behavioural performances on cognitive/neuropsychological tasks tapping related processes and personality/preclinical symptom variables, and (ii) to compare common/unique variablility of regional and supra-regional brain activation for different contrasts on different tasks across subjects and between psychological/clinical phenotypes. In all analyses, centre effects and other confounding variables will be modelled.

(3) GWA–neuroimaging analyses: To identify SNPs contributing to individual variability in structural measures (global and regional volumes of grey and white matter, cortical thickness and microstructural properties of white-matter tracts) and task-related regional fMRI BOLD activity, QT regression analyses with all individual SNPs will be performed. To control for the risk of false positives for each SNP, the empirical P-value for association significance will be determined with permutations.66 In addition, cross-validation techniques will be used to limit the risk of overfitting the data. SNPs will be ranked based on their GEE P-values, and those with genome-wide significance (P<5 × 10−2 corrected for multiple comparisons) or candidates for replication will be selected and mapped to closest genes. Phenotypic and environmental measures will be entered as covariates to explore shared and unique patterns of gene–neuroimaging associations. A subsample of N=700 in the exploratory phase of a two-stage design will have power to detect 2.7% variance in continuous traits under an additive model (P<1 × 10−7), assuming that 50% of samples will be genotyped at stage 1 and 1% markers at stage 2. The same procedures will be applied in two replication samples, including N=1300 adolescents acquired during the second wave, and for GWA analyses with structural, cognitive and behavioural measures, 1000 adolescents from the Saguenay Youth Study.29 The entire sample (N=2000) will provide additional power for further exploratory gene–neuroimaging phenotype analyses. The third analyses wave will focus on the predictor value of the genetic profile and neuroimaging–intermediate phenotypes at the age of 14 years in the manifestation of clinical symptoms at the age of 16–18 years. Moreover, the comprehensive data set provides a rich resource for the development of new multivariate methods aimed at reducing and integrating heterogeneous genomic–neuroimaging data sets.


To optimize standardized data acquisition and recruitment strategies, regular training workshops for scientists of all centres are held. In addition, within a yearly summer school, about 15 young neuroscientists and psychiatrists from IMAGEN centres and eastern European countries are trained in concepts and techniques in genetic neuroimaging, including new strategies of data handling and analyses of large-scale and complex data sets.


A multidisciplinary ethics group has been setup to monitor assessment procedures (for example, consent, confidentiality, data protection) involving vulnerable groups (adolescents) and to develop new strategies to deal with sensitive issues related to novel findings of the contribution of genetic, biological and environmental factors in personality and psychopathology in an ethical manner.

Discussion: anticipated outcome of the project

The integration of technological and methodological advances in the fields of cognitive neuroscience, human and molecular genetics, as well as the size of the cohort are expected to reveal new insight into gene × environmental contributions to individual variability in brain structure and function, and complex psychological traits.67 It will be one of the first studies that combine GWA–neuroimaging approaches and animal models, and that have the necessary power to systematically identify and characterize genes of small effect and combined gene effects. The longitudinal design will enable us to ascertain the utility of neuroimaging–intermediate phenotypes as neurobiological risk factors that precipitate frequent neuropsychiatric disorders. In addition, the study carries significant technological and methodological innovation, including behavioural testing batteries for rats and mice, comparable with human neuropsychological tasks, standardization methods for the acquisition and analysis of large-scale structural and functional neuroimaging data sets and the development of novel multivariate methods of neuroimaging–genomics analyses.

In summary, it is anticipated that the IMAGEN study will make a significant contribution to bridging behavioural phenotypes and genetic and neural function, thus laying the groundwork for the development of treatments that target specific pathological processes across frequent psychiatric disorders, and set new standards for future multicentre, integrative genetic–neuroimaging studies.


  1. 1

    Plomin R, McGuffin P . Psychopathology in the postgenomic era. Annu Rev Psychol 2003; 54: 205–228.

    Article  Google Scholar 

  2. 2

    Caspi A, Moffitt TE . Gene-environment interactions in psychiatry: joining forces with neuroscience. Nat Rev Neurosci 2006; 7: 583–590.

    CAS  Article  Google Scholar 

  3. 3

    Goldman D, Oroszi G, Ducci F . The genetics of addictions: uncovering the genes. Nat Rev Genet 2005; 6: 521–532.

    CAS  Article  Google Scholar 

  4. 4

    Wong CCY, Schumann G . Genetics of addictions: strategies for addressing heterogeneity and polygenicity of substance use disorders. Philosophical Transl R Soc B series 2008; 363: 3213–3222.

    Article  Google Scholar 

  5. 5

    Kalivas PW, Volkow ND . The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry 2005; 162: 1403–1413.

    Article  Google Scholar 

  6. 6

    O'Brien BS, Frick PJ . Reward dominance: associations with anxiety, conduct problems, and psychopathy in children. J Abnorm Child Psychol 1996; 24: 223–240.

    CAS  Article  Google Scholar 

  7. 7

    Bergh C, Soedersten P . Anorexia nervosa, self-starvation and the reward of stress. Nat Med 1996; 2: 21–22.

    CAS  Article  Google Scholar 

  8. 8

    Cardinal RN, Winstanley CA, Robbins TW, Everitt BJ . Limbic corticostriatal systems and delayed reinforcement. Ann NY Acad Sci 2004; 1021: 33–50.

    Article  Google Scholar 

  9. 9

    Juckel G, Schlagenhauf F, Koslowski M, Wuestenberg T, Villringer A, Knutson B et al. Dysfunction of ventral striatal reward prediction in schizophrenia. Neuroimage 2009; 29: 409–416.

    Article  Google Scholar 

  10. 10

    Murray GK, Corlett PR, Clark L, Pessiglione M, Blackwell AD, Honey G et al. Substantia nigra/ ventral tegmental reward prediction error disruption in psychosis. Mol Psychiatry 2008; 13: 267–276.

    CAS  Article  Google Scholar 

  11. 11

    Schultz RT . Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. Int J Dev Neurosci 2005; 23: 125–141.

    Article  Google Scholar 

  12. 12

    Birbaumer N, Veit R, Lotze M, Erb M, Hermann C, Grodd W et al. Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study. Arch Gen Psychiatry 2005; 62: 799–805.

    Article  Google Scholar 

  13. 13

    Paus T, Keshavan M, Giedd JN . Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci 2008; 9: 947–957.

    CAS  Article  Google Scholar 

  14. 14

    Blakemore SJ . The social brain in adolescence. Nat Rev Neurosci 2008; 9: 267–277.

    CAS  Article  Google Scholar 

  15. 15

    Whalen PJ, Shin LM, Somerville LH, McLean AA, Kim H . Functional neuroimaging studies of the amygdala in depression. Semin Clin Neuropsychiatry 2002; 7: 234–242.

    Article  Google Scholar 

  16. 16

    Phillips ML, Drevets WC, Rauch SL, Lane R . Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol Psychiatry 2003; 54: 515–528.

    Article  Google Scholar 

  17. 17

    Mier D, Kirsch P, Meyer-Lindenberg A . Neural substrates of pleiotropic action of genetic variation in COMT: a meta-analysis. Mol Psychiatry, published online 5 May 2009; doi:10.1038/mp.2009.36.

  18. 18

    Flint J, Munafo MR . The endophenotype concept in psychiatric genetics. Psychol Med 2007; 37: 163–180.

    Article  Google Scholar 

  19. 19

    Etkin A, Klemenhagen KC, Dudmna JT, Rogan MT, Hen R, Kandel ER et al. Individual differences in trait anxiety predict the response of the basolateral amygdala to unconsciously processed fearful faces. Neuron 2004; 44: 1043–1055.

    CAS  Article  Google Scholar 

  20. 20

    Haas BW, Omura K, Constable RT, Canli T . Emotional conflict and neuroticism: personality-dependent activation in the amygdala and subgenual anterior cingulate. Behav Neurosci 2007; 121: 249–256.

    Article  Google Scholar 

  21. 21

    Hariri AR, Weinberger DR . Imaging genomics. Br Med Bull 2003; 65: 259–270.

    CAS  Article  Google Scholar 

  22. 22

    Hariri AR . The neurobiology of individual differences in complex behavioural traits. Annu Rev Neurosci 2009; 32: 225–247.

    CAS  Article  Google Scholar 

  23. 23

    Munafo MR, Brown SM, Hariri AR . Serotonin transporter (5-HTTLPR) genotype and amygdala activation: a meta-analysis. Biol Psychiatry 2008; 63: 852–857.

    CAS  Article  Google Scholar 

  24. 24

    Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 1996; 274: 1527–1531.

    CAS  Article  Google Scholar 

  25. 25

    Collier DA, Stoeber G, Li T, Heils A, Catalano M, Di Bella D et al. A novel functional polymorphism within the promoter of the serotonin transporter gene: possible role in susceptibility to affective disorders. Mol Psychiatry 1996; 6: 453–460.

    Google Scholar 

  26. 26

    Smolka MN . Gene-gene effects on central processing of aversive stimuli. Mol Psychiatry 2007; 12: 307–317.

    CAS  Article  Google Scholar 

  27. 27

    Yacubian J, Sommer T, Schroeder K, Glaescher J, Kalisch R, Leuenberger B et al. Gene-gene interaction associated with neural reward sensitivity. Proc Natl Acad Sci USA 2007; 104: 8125–8130.

    CAS  Article  Google Scholar 

  28. 28

    Heinz A, Braus DF, Smolka MN, Wrase J, Puls I, Hermann D et al. Amygdala-prefrontal coupling depends on a genetic variation of the serotonin transporter. Nat Neurosci 2005; 8: 20–21.

    CAS  Article  Google Scholar 

  29. 29

    Bookheimer SY, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance MA, Mazziotta JC et al. Patterns of brain activation in people at risk for Alzheimer′s disease. N Engl J Med 2000; 343: 450–456.

    CAS  Article  Google Scholar 

  30. 30

    Puls I, Gallinat J . The concept of endophenotypes in psychiatric diseases. Meeting the expectations? Pharmacopysychiatry 2008; 41 (Suppl 1): S37–S43.

    Google Scholar 

  31. 31

    Green EK, Grozeva D, Jones I, Kirov G, Caesar S, Gordon-Smith K et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol Psychiatry 2009, published online 21 July 2009; doi:10.1038/mp.2009.49.

  32. 32

    Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D et al. Common variants conferring risk of schizophrenia. Nature 2009; 460: 744–747.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Enoch MA, Schuckit MA, Johnson BA, Goldman D . Genetics of alcoholism using intermediate phenotypes. Alcohol Clin Exp Res 2003; 27: 169–176.

    Article  Google Scholar 

  34. 34

    Robinson ES, Eagle DM, Economidou D, Theobald DE, Mar AC, Murphy ER et al. Behavioural characterisation of high impulsivity on the 5-choice serial reaction time task: specific deficits in ‘waiting’ and ‘stopping’. Behav Brain Res 2009; 196: 310–316.

    CAS  Article  Google Scholar 

  35. 35

    Dalley JW, Fryer TD, Brichard L, Robinson ESJ, Theobald DEH, Laane K et al. Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science 2007; 315: 1267–1270.

    CAS  Article  Google Scholar 

  36. 36

    Goodman R . The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 1997; 38: 581–586.

    CAS  Article  Google Scholar 

  37. 37

    Goodman R, Ford T, Richards H, Gatward R, Meltzer H . The development and well-being assessment: description and initial validation of an integrated assessment of child and adolescent psychopathology. J Child Psychol Psychiatry 2000; 41: 645–655.

    CAS  Article  Google Scholar 

  38. 38

    Costa PT, McCrae RR . NEO PI-R. Professional manual. Psychological Assessment Resources: Odessa, FL, 1992.

    Google Scholar 

  39. 39

    Cloninger CR, Przybeck TR, Swrakic DM, Wetzel RD (eds). The Temperament and Character Inventory (TCI): A guide to its development and use. Center for Psychobiology of Personality, Washington University: St Louis, Missouri, 1994.

    Google Scholar 

  40. 40

    Conrod PJ, Woicik P . Validation of a four-factor model of personality risk for substance abuse and examination of a brief instrument of assessing personality risk. Addict Biol 2002; 7: 329–346.

    Article  Google Scholar 

  41. 41

    Woicik P, Stewart SH, Pihl RO, Conrod PJ . The Substance Use Risk Profile Scale: a scale measuring traits linked to reinforcement-specific substance use profiles. Addictive Behav 2009; 34: 1042–1055.

    Article  Google Scholar 

  42. 42

    Kirby KN, Petry NM, Bickel WK . Heroin addicts have higher discount rates for delayed rewards than non-drug using controls. J Exp Psychol Gen 1999; 128: 78–87.

    CAS  Article  Google Scholar 

  43. 43

    Maxwell ME . Manual for the Figures Clinical Neurogenetics Branch, Intramural Research Program. National Institute of Mental Health 1992.

  44. 44

    Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L, Rabbit P . Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia 1994; 5: 266–281.

    CAS  PubMed  Google Scholar 

  45. 45

    Pollak SD, Kistler DJ . Early experience is associated with the development of categorical representations for facial expressions of emotion. Proc Natl Acad Sci 2002; 99: 9072–9076.

    CAS  Article  Google Scholar 

  46. 46

    MacLeod C, Mathews A, Tata P . Attentional bias in emotional disorders. J Abnorm Psychol 1986; 95: 15–20.

    CAS  Article  Google Scholar 

  47. 47

    Arnett PA, Newman JP . Gray′ three-arousal model: an empirical investigation. Personality and Individual Differences 2000; 28: 1171–1189.

    Article  Google Scholar 

  48. 48

    Hibell B, Andersson B, Bkarnason T, Kokkevi A, Morgan M, Narusk A . The 1995 ESPAD resport: alcohol and ohter drug use among students in 26 European countries. Swedish Council for Information on Alcohol and Other Drugs: Stockholm, 1997.

    Google Scholar 

  49. 49

    Gavin DR, Ross HE, Skinner HA . Diagnostic validity of the Drug Abuse Screening Test in the assessment of DSM-III drug disorders. Br J Addict 1989; 84: 301–307.

    CAS  Article  Google Scholar 

  50. 50

    Selzer ML . The Mitchigan alcoholism screening test: the quest for a new diagnostic instrument. Am J Psychiatry 1971; 127: 1653–1658.

    CAS  Article  Google Scholar 

  51. 51

    Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO . The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 1991; 86q: 1119–1127.

    Article  Google Scholar 

  52. 52

    Sobell LC, Sobell MB (eds) Timeline followback user's guide: A calendar method for assessing alcohol and drug use. Addiction Research Foundation: Toronto, Ontario, Canada, 1996.

    Google Scholar 

  53. 53

    Newcomb MD, Huba GJ, Bentler PM . A multidimensional assessment of stressful life events among adolescents: derivation and correlates. J Health Soc Behav 1981; 22: 400–415.

    Article  Google Scholar 

  54. 54

    Straus MA . Measuring intra family conflict and violence: The Conflict Tactics Scale. JMarriage Family 1979; 41: 75–88.

    Article  Google Scholar 

  55. 55

    Pausova Z, Paus T, Abrahamowicz M, Almerigi J, Arbour N, Bernard M et al. Genes, maternal smoking, and the offspring brain and body during adolescence: Design of the Saguenay Youth Study. Hum Brain Mapp 2007; 28: 502–518.

    Article  Google Scholar 

  56. 56

    Petersen A, Crockett L, Richards M, Boxer A . A self-report measure of pubertal status: reliability, validity, and initial norms. J Youth Adoles 1988; 17: 117–133.

    CAS  Article  Google Scholar 

  57. 57

    Tiffin J (ed) Purdue Pegboard examiner manual. Science Research Associate: Chicago, IL, 1968.

    Google Scholar 

  58. 58

    Tofts PS, Lloyd D, Clark CA, Barker GJ, Parker GJ, McConville P et al. Test liquids for quantitative MRI measurements of self-diffusion coefficient in vivo. Mag Res Med 2000; 47: 24–31.

    Google Scholar 

  59. 59

    Jones DK, Williams SCR, Gasston D, Horsfield MA, Simmons A, Howard R . Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time. Hum Brain Mapp 2002; 15: 216–230.

    Article  Google Scholar 

  60. 60

    Rubia K, Smith AB, Taylor E, Brammer MJ . Linear age-correlated functional development of right inferior fronto-stiato-cerebellar networks during response inhibition and anterior cingulate during error-related processes. Hum Brain Mapp 2007; 28: 1163–1177.

    Article  Google Scholar 

  61. 61

    Grosbras MH, Paus T . Brain networks involved in viewing angry hands or faces. Cereb Cortex 2007; 16: 1087–1096.

    Article  Google Scholar 

  62. 62

    Knutson B, Westdorp A, Kaiser E, Hommer D . FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 2000; 12: 20–27.

    CAS  Article  Google Scholar 

  63. 63

    Pinel P, Thirion B, Meriaux S, Jobert A, Serres J, Le Bihan D et al. Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neurosci 2007; 8: 91.

    Article  Google Scholar 

  64. 64

    Robbins TW . The 5-choice serial reaction time task: behavioural pharmacology and functional neurochemistry. Psychopharmacology (Berlin) 2002; 163: 362–380.

    CAS  Article  Google Scholar 

  65. 65

    Dalley JW, Mar AC, Economidou D, Robbins TW . Neurobehavioural mechanisms of impulsivity: fronto-striatal systems and functional neurochemistry. Pharmacol Biochem Behav 2008; 90: 250–260.

    CAS  Article  Google Scholar 

  66. 66

    Hirschhorn JN, Daly MJ . Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005; 6: 95–108.

    CAS  Article  Google Scholar 

  67. 67

    McCarthy MI, Hirschhorn JN . Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet 2008; 17 (R2): R156–R166.

    CAS  Article  Google Scholar 

  68. 68

    Adolphs R . Social cognition and the human brain. Trends Cognitive Sci 1999; 12: 469–479.

    Article  Google Scholar 

  69. 69

    Schultz W . Multiple reward signals in the brain. Nat Rev Neurosci 2000; 1: 199–207.

    CAS  Article  Google Scholar 

  70. 70

    Garavan H, Hester R, Murphy K, Fassbender C, Kelly C . Individual differences in the functional neuroanatomy of inhibitory control. Brain Res 2006; 11015: 130–142.

    Article  Google Scholar 

Download references


IMAGEN study receives research funding from the European Community's Sixth Framework Programme (LSHM-CT-2007-037286). This paper reflects only the author's views, and the Community is not liable for any use that may be made of the information contained therein. The Saguenay Youth Study project was funded by the Canadian Institutes of Health Research, Heart and Stroke Foundation of Quebec, and the Canadian Foundation for Innovation. We thank all families for their help with this study.

Author contributionsDevelopment of the neuropsychological test battery in humans and behavioural test batteries in animals: TW Robbins, D Stephens, H Flor, JW Dalley; recruitment and psychometric standardization: P Conrod, M Struve, H Flor, H Garavan, A Heinz, K Mann, J-L Martinot, T Paus and Partner Delosis; Neuroimaging Standardization: G Barker, L Reed, C Mallik, B Ittermann; Neuroimaging assessment and analyses: C Büchel, T Paus, E Loth, H Flor, H Garavan, J Gallinat, M Smolka, K Mann, T Banaschewski; Data base development, preprocessing and statistical analyses (biostatistics): J-B Poline, A Barbot and Partners Nordic NeuroLab, Pertimm, Scito; Genetic analyses: G Schumann, M Lathrop, R Spanagel; Ethics: M Rietschel; Genetic neuroimaging training: A Ströhle, A Heinz; E Loth and G Schumann wrote this paper.

Author information




Corresponding author

Correspondence to G Schumann.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Molecular Psychiatry website

Supplementary information



IMAGEN consortium

King's College, Institute of Psychiatry, London, UK

G Schumann

P Conrod

L Reed

G Barker

S Williams

E Loth

M Struve

A Lourdusamy

S Costafreda

A Cattrell

C Nymberg

L Topper

L Smith

S Havatzias

K Stueber

C Mallik

T-K Clarke

D Stacey

C Peng Wong

H Werts

S Williams

C Andrew

S Desrivieres

S Zewdie (Coordination office)

Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité – Universitätsmedizin Berlin, Berlin, Germany

A Heinz

J Gallinat

I Häke

N Ivanov

A Klär

J Reuter

C Palafox

C Hohmann

C Schilling

K Lüdemann

A Romanowski

A Ströhle

E Wolff

M Rapp

Physikalisch-Technische Bundesanstalt, Berlin, Germany

B Ittermann

R Brühl

A Ihlenfeld

B Walaszek

F Schubert

Institute of Neuroscience, Trinity College, Dublin, Ireland

H Garavan

C Connolly

J Jones

E Lalor

E McCabe

A Ní Shiothcháin

R Whelan

Department of Psychopharmacology, Central Institute of Mental Health, Mannheim, Germany

R Spanagel,

F Leonardi-Essmann,

W Sommer

Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Mannheim, Germany

H Flor

S Vollstaedt-Klein

F Nees

Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Mannheim, Germany

T Banaschewski

L Poustka

S Steiner

Department of Addictive Behaviour and Addiction Medicine, Mannheim, Germany

K Mann

M Buehler

S Vollstedt-Klein

Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, Germany

M Rietschel

E Stolzenburg

C Schmal

F Schirmbeck

Brain and Body Centre, University of Nottingham, Nottingham, UK

T Paus

P Gowland

N Heym

C Lawrence

C Newman

Z Pausova

Technische Universitaet Dresden, Dresden, Germany

M Smolka

T Huebner

S Ripke

E Mennigen

K Muller

V Ziesch

Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

C Büchel

U Bromberg

T Fadai

L Lueken

J Yacubian

J Finsterbusch

Institut National de la Santé et de la Recherche Médicale, Service Hospitalier Frédéric Joliot, Orsay, France

J-L Martinot

E Artiges

N Bordas

S de Bournonville

Z Bricaud

F Gollier Briand

H Lemaitre

J Massicotte

R Miranda

M-L Paillère Martinot

J Penttilä

Neurospin, Commissariat à l′Energie Atomique, Paris, France

J-B Poline

A Barbot

Y Schwartz

C Lalanne

V Frouin

B Thyreau

Department of Experimental Psychology, Behavioural and Clinical Neurosciences Institute, University of Cambridge, Cambridge, UK

J Dalley

A Mar

T Robbins

N Subramaniam

D Theobald

N Richmond

M de Rover

A Molander

E Jordan

E Robinson

L Hipolata

M Moreno

Mercedes Arroyo

University of Sussex, Brighton, UK

D Stephens

T Ripley

H Crombag

Y Pena

Centre National de Genotypage, Evry, France (CNG)

M Lathrop

D Zelenika

S Heath

German Centre for Ethics in Medicine, Bonn (DZEM), Germany

D Lanzerath

B Heinrichs

T Spranger

Gesellschaft fuer Ablauforganisation m.b.H. (Munich) (GABO), Germany

B Fuchs

C Speiser

Klinik für Kinder- und Jugendpsychiatrie, Zentrum für Psychosoziale Medizin, Universitätsklinikum Heidelberg, Germany

F Resch

J Haffner

P Parzer

R Brunner

Scito, Paris, France

A Klaassen

I Klaassen

PERTIMM, Asnières-Sur-Seine, France

P Constant

X Mignon

NordicNeuroLabs, Bergen, Norway

T Thomsen

S Zysset

A Vestboe

Delosis Ltd, London, UK

J Ireland

J Rogers

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Schumann, G., Loth, E., Banaschewski, T. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry 15, 1128–1139 (2010).

Download citation


  • impulsivity
  • reward
  • emotional reactivity
  • fMRI
  • genome-wide association scan
  • functional genetics

Further reading


Quick links