Tph2 gene variants modulate response control processes in adult ADHD patients and healthy individuals

Abstract

Although therapeutic interventions in attention-deficit/hyperactivity disorder (ADHD) still focus on the dopaminergic system, recent studies indicate a serotonergic dysfunction in this disease as well. In that respect, several variants of the tryptophan hydroxylase gene (TPH2), which codes for the rate-limiting enzyme in the biosynthesis of serotonin (5-HT), have been associated with ADHD. The rs4570625 G-allele polymorphisms of the TPH2 gene have already been related to altered reactivity of the brain during perception tasks with emotional stimuli in healthy adults. Here we investigated the influence of the ADHD related risk alleles for rs4570625 and for rs11178997 on prefrontal brain function during cognitive response control in large samples of adult ADHD patients (n=124) and healthy controls (n=84). Response control was elicited with a Go-NoGo task (continuous performance test; CPT) performed during recording of an ongoing EEG. From the resulting event-related potentials in the Go- and NoGo conditions of the CPT, the NoGo-anteriorization (NGA) has been calculated as a valid neurophysiological parameter for prefrontal brain function. In the current study, ADHD risk alleles of both polymorphisms were found to be associated with a reduction in the NGA in both healthy controls and ADHD patients. These findings are in line with the notion that genetic variations associated with altered serotonergic neurotransmission are also associated with the function of the prefrontal cortex during response inhibition. This mechanism might also be relevant in the pathophysiology of ADHD.

Introduction

Different genes that are implicated in serotonergic (5-HT) neurotransmission have been suggested to be involved in attention-deficit/hyperactivity disorder (ADHD) pathology, but results are contradictory.1, 2 One of the serotonergic genes of interest is the second isoform of the tryptophan hydroxylase gene (TPH2). The TPH2 gene codes for the rate-limiting enzyme of serotonin synthesis in serotonergic neurons in the raphe nuclei of the brain stem.3, 4 For several single nucleotide polymorphisms (SNPs) of the TPH2 gene, an association with ADHD was found.1, 5, 6 Walitza et al.6 analyzed the transmission disequilibrium of three SNPs of the TPH2 gene in 103 families with 225 affected children. The authors discovered preferential allele transmission for two SNPs in TPH2's regulatory region (rs4570625 and rs11178997) but not for a specific SNP in intron 2 (rs4565946). For these polymorphisms, functionality at the level of gene expression has not yet been demonstrated directly, but indirectly via brain imaging studies.7, 8 For rs4570625 (−703 G/T), the G-allele was transmitted more frequently to offspring with ADHD. For rs11178997 (−473 T/A), the T-allele was identified as the risk allele for ADHD, which was overtransmitted. Brookes et al.1 also investigated rs4570625 but could not replicate the findings of Walitza et al.6

Case-control and family-based association studies are one way of uncovering the impact of genes in the pathogenesis or pathophysiology of a disease. A second approach aims at the identification of biological mechanisms that may contribute to the complex phenotypic symptoms and their correlation with functional genetic polymorphisms. These biological mechanisms are considered as vulnerability factors for clinical phenotypes and are called intermediate phenotypes or endophenotypes. Endophenotypes may be more directly related to genetic variations than the overt phenotypes themselves.9 Certain genetic variants affect neurotransmitter systems, thereby changing the activity of the brain, which may be measurable by neuroimaging methods. The approach of ‘imaging genomics’ or ‘imaging genetics’ is an elegant way to shed light on the relationship between genetic variations and brain function.10 ADHD patients consistently show impairments in response inhibition, that is, in their ability to suppress a response to an external stimulus.11 The prefrontal cortex, which is crucial for the brain processes underlying response inhibition, is also modulated by serotonergic activity.12 Serotonergic influences on response inhibition have already been shown in both healthy controls and patients or persons at risk for diseases that possibly involve the 5-HT system (performance,13, 14, 15, 16, 17, 18 electrophysiology,19, 20 functional magnetic resonance imaging21).

To further explore the putative endophenotype of response inhibition, we administered a continuous performance test (CPT) to adult ADHD patients and healthy controls while an ongoing electroencephalogram (EEG) was recorded. Electrophysiological correlates of response inhibition were then correlated with the two SNPs in TPH2's regulatory region (rs4570625 and rs11178997) from the Walitza study.6 This approach of imaging genetics with event-related potentials (ERPs) has already been successfully demonstrated.8, 19, 22, 23

During the execution (Go condition) and the inhibition (NoGo condition) of an anticipated motor response within the CPT, topographical analyses of ERPs show typical neurophysiological activation patterns. In the P300 time window, the center of gravity of the positive brain electrical field (centroid) is located over parietal brain areas in Go conditions, whereas in NoGo conditions the centroid location is markedly more anterior over fronto-central areas of the brain (NoGo-anteriorization, NGA24). Low-resolution electromagnetic tomography25 indicated that in healthy controls the activation of the medial prefrontal cortex (particularly the anterior cingulate cortex) is increased during response inhibition (NoGo) compared to trials when a correct response is executed (Go).26, 27 Adult patients with a probable ADHD were characterized by a reduced NGA, and boys with ADHD showed a reduced frontalization as well, indicating a dysfunction of the medial prefrontal cortex in this disease.28, 29 The NGA, therefore, seems to be an adequate endophenotypic marker for prefrontal dysfunction during processes of response control in ADHD patients.

Based on these findings, we hypothesized that homozygous G-allele carriers for rs4570625 and homozygous T-allele carriers for rs11178997 (the alleles that were found to be transmitted more frequently to ADHD patients in previous studies) should exhibit impaired prefrontal functioning indicated by reduced NGA values. This should be the case in ADHD patients and healthy controls. Moreover, we expect ADHD patients to have a smaller mean NGA than healthy controls.

Materials and methods

Participants

The investigation was approved by the Ethics Committee of the University of Wuerzburg. A total of 123 adult ADHD (Diagnostic and Statistical Manual of Mental Disorders, 4th edn, DSM-IV) in- and outpatients of the Department of Psychiatry and Psychotherapy of the University of Wuerzburg gave their written informed consent after complete description of the study. Exclusion criteria were age below 18 and above 60 years, current medication with methylphenidate or any other psychotropic compound as well as serious somatic disorders comorbidities. Due to an insufficient number of artifact-free EEG epochs (<20), one patient had to be excluded from further analyses so that the final study sample consisted of 122 ADHD patients. For these patients, the genotype distribution for rs4570625 was in Hardy–Weinberg equilibrium (χ2=0.061, d.f.=1, P=0.80). Of these patients, 76 were homozygous G-allele carriers, 40 had the G/T genotype and 6 were homozygous T-allele carriers. The frequencies of the G and T-allele were therefore 78.7 and 21.3%, respectively. For rs11178997, the genotype distribution in the group of ADHD patients was again in Hardy–Weinberg equilibrium (χ2=0.523, d.f.=1, P=0.469). Out of all, 107 patients were homozygous T-allele carriers and 15 had the T/A genotype, whereas no patient was homozygous for the A-allele. The resulting allele frequency was 93.9% for the T-allele and 6.1% for the A-allele.

Healthy controls (86) without any psychiatric disorder underwent the same procedures as the ADHD group. Two subjects had to be excluded due to artifacts, so 84 healthy controls were eventually included in the analyses. Healthy controls were recruited via newspaper advertisement. The genotype distribution for rs4570625 was in Hardy–Weinberg equilibrium (χ2=0.057, d.f.=1, P=0.811); the allele frequencies were 82.1% for the G-allele and 17.9% for the T-allele. Of the healthy controls, 57 were homozygous for the G-allele, 24 were heterozygous and 3 were homozygous T-allele carriers. The genotype distribution for rs11178997 was in Hardy–Weinberg equilibrium as well (χ2=0.412, d.f.=1, P=0.521); the allele frequencies were 93.5% for the T-allele and 6.5% for the A-allele. Of the healthy controls, 73 were homozygous T-allele carriers, 11 were heterozygous and no homozygous A-allele carrier was found.

All homozygous G-allele carriers of rs4570625 were also homozygous T-allele carriers of rs11178997. The linkage disequilibrium between the two markers was D′=1.00. Because only six ADHD patients and only three persons in the control group were homozygous for the rs4570625 T-allele, we compared the homozygous G-allele carriers with persons carrying at least one T-allele in all subsequent analyses (combined TT and GT genotypes). All patients and healthy controls were interviewed with the Structured Clinical Interview of DSM-IV (SCID-I)30 and the Structured Clinical Interview of DSM-IV personality disorders (SCID-II)31 by an experienced psychiatrist for diagnosis and differential diagnosis of psychiatric disorders. Controls were only accepted without any axis I or II morbidities.

The two diagnostic groups did not differ significantly regarding their distribution of gender (χ2=0.890, d.f.=1, P=0.392), handedness (χ2=1.784, d.f.=1, P=0.213), rs4570625 (χ2=0.673, d.f.=1, P=0.460) or rs11178997 genotypes (χ2=0.029, d.f.=1, P=1.000). Moreover, genotype distribution for rs4570625 and rs11178997 did not significantly differ between males and females, neither for the study sample as a whole (χ2=0.309, d.f.=1, P=0.660 and χ2=0.023, d.f.=1, P=1.000, respectively) nor within each of the diagnostic groups (ADHD: χ2=0.495, d.f.=1, P=0.570 and χ2=0.414, d.f.=1, P=0.586, respectively; healthy controls: χ2=0.004, d.f.=1, P=1.000 and χ2=0.243, d.f.=1, P=0.750, respectively). Age did not differ between diagnostic groups (T=−0.075, d.f.=204, P=0.940) or rs4570625 genotypes (T=0.74, d.f.=204, P=0.941). For rs11178997 genotypes, however, the T/T group was found to be significantly older than the T/A group (T=2.318, d.f.=204, P=0.021). For further details, please refer to Table 1.

Table 1 Sample characteristics

Out of 122 ADHD patients, 66 (54%) had a comorbidity with another axis I disorder as assessed by means of the SCID I interview. Comorbid disorders were mood disorders (n=27), neurotic disorders (n=20), substance addiction/misuse (n=17) and eating disorders (n=2). The most common comorbid personality disorder in this sample was narcissistic personality disorder (n=36), followed by histrionic personality disorder (n=33). Out of all, 32 patients belonged to the inattentive subtype of ADHD, 10 to the hyperactive/impulsive subtype and 80 to the mixed subtype. Subtype composition did not significantly differ between rs4570625 (χ2=5.080, d.f.=2, P=0.079) or rs11178997 genotypes (Fisher's exact test: P=0.562).

TPH2 genotyping

DNA was isolated from EDTA blood using the QIAamp Blood Kit (Qiagen, Hilden, Germany). Two common potentially functional SNPs located in the regulatory region of TPH2 were chosen for association analyses. TPH2 genotyping has previously been described by Gutknecht et al.32 In short, for SNP rs4570625, located at position −703 from the transcription start site, forward primer 5′-IndexTermtttccatgatttccagtagagag-3′ and a modifying reverse primer 5′-IndexTermaagctttttctgacttgacaaat-3′ creating a restriction site dependent on the allele at position −703 were used, and the PCR product was digested with ApoI. The genotyping for SNP rs11178997, located at position −473 from the transcription start site, was carried out using two primer pairs, each one of them containing an allele-specific primer encompassing the polymorphism. The allele specific primers forward 5′-IndexTermtcttgattaccttatttgatcattacacct-3′ and reverse 5′-IndexTermcacatgtgatattttgacacaagcgtacct-3′ were combined with reverse 5′-IndexTermgaaccctggtgctgaagagcaat-3′ and forward 5′-IndexTermcacatttgcatgcacaaaattagaatatgt-3′ primers in a multiplex PCR amplification. PCR products were electrophoretically resolved on agarose gels containing ethidium bromid.

Continuous performance test

The participants sat in an electrically shielded, dimly lit room on a comfortable chair. 400 letters were presented on a computer screen in pseudorandomized order one at a time for 200 ms with an interstimulus interval of 1650 ms. Participants had to press the space bar of a keyboard with their right hand whenever the letter O (primer) was directly followed by the letter X (Go condition). Response speed and accuracy were equally emphasized. The stimulus set consisted of 400 letters (12 different letters: A, B, C, D, E, F, G, H, J, L, O, X). Of those, 114 were primer stimuli (O), followed by 57 Go trials (O followed by X) and 57 NoGo trials (O followed by any letter other than X). The remaining stimuli were 172 distractor letters (other letters, or X without a preceding O). Following the instruction, participants performed a short training session to make sure they understood the task. The above-described CPT version took about 13 min.

Electrophysiological investigation

The EEG was recorded from 21 scalp electrodes placed according to the extended international 10–20 system. For the registration of eye movements (electrooculogram, EOG) three additional electrodes were added. Two were placed at the outer canthi of both eyes (bipolar reference for horizontal eye movements) and one below the right eye (bipolar reference with Fp2 for vertical eye movements). The recording reference was placed between Fz and Cz; the ground electrode was placed between Fpz and Fz. Electrode impedances were constantly kept below 5 kΩ. Recordings were performed with a 32-channel DC BrainAmp amplifier (Brain Products, Munich, Germany) and the software, Brain Vision Recorder (version 1.01 b; Brain Products). The A/D rate was 1000 Hz and the hardware filter was set to 0.1–100 Hz. A notch filter was applied (50 Hz).

Data analysis

Offline data analysis was performed with the software Vision Analyzer (Brain Products). All channels were re-referenced to the average reference. The average reference included all channels except the EOG channels. Data were filtered with a high pass of 0.1 and a low-pass filter of 30 Hz. A correction for ocular artifacts was executed.33 Data were segmented according to the relevant conditions of the CPT (Go, NoGo; length of the segments: −100 to 700 ms after stimulus presentation). Only correct Go- and NoGo epochs were analyzed in this study. Segments were rejected if amplitudes exceeded ±98 μV in any of the EEG channels. The remaining segments were averaged to one Go and one NoGo ERP per subject, if at least a minimal number of 20 epochs was available (with group medians of 54 epochs for both the Go- and the NoGo condition of the CPT, most participants far surpassed this criterion).

The anterior-posterior location of the positive centroids (the amplitude-weighted center of gravity of the positive brain-electrical field)34 was calculated according to the individual P300 latencies of the Go- and NoGo root mean square peaks in a time window of 277–434 ms based on the study of Fallgatter et al.24 This time frame was also adequate for the current data set as confirmed through visual inspection of the grand average curves (Figure 1). The coordinate system used to quantify the centroid locations is defined by a two-dimensional delineation of the electrode array. The digits 1–5 indicate the electrode positions in the anterior-posterior and the left-right direction, respectively. For this study, only the anterior-posterior localization was of interest. Smaller numbers on the anterior-posterior axis represent more anterior locations of the centroids (1; frontal electrode position Fpz; 5, occipital electrode position Oz; locations in between two electrodes are expressed by decimal numbers). For calculating the NGA, the localization of the NoGo centroid was subtracted from the localization of the Go centroid.

Figure 1
figure1

Grand averages of the root mean square (RMS) time course for the Go- (thin line) and the NoGo condition (bold line) of the continuous performance test (CPT). All participants (patients and controls) were included.

Statistical analysis

Separate analyses for rs4570625 and rs11178997 were performed for all dependent variables. For the following performance data, Mann–Whitney U-tests were used to test for differences between diagnostic groups and genotypes, as they were not normally distributed according to Kolmogorov–Smirnov tests: commission errors type 1 (a button press after the primer O or any distractor letter including X without a preceding O), commission errors type 2 (a button press in a NoGo trial, that is, when a letter other than X followed the primer O), omission errors (Go trials not followed by a button press), mean reaction time of correct Go responses. All other variables were normally distributed. Therefore, ANOVAs with the independent variables diagnosis and genotype were performed for the standard deviation of Go reaction times (a measure of response variability) and the NGA. To account for multiple statistical testing, we chose P<0.01 as significance level for performance data.

For the centroids, repeated measurements ANOVAs were performed with condition (Go vs NoGo) as within-subject variable and diagnosis and the respective genotype as between-subject variables. For repeated measurement ANOVAs, violations of sphericity were corrected by the Greenhouse–Geisser procedure, if necessary. For post hoc testing, two-tailed t-tests for matched or independent samples were conducted. For t-tests, Levene tests checking for variance homogeneity were performed and corrections were made whenever necessary. For the electrophysiological analysis, the significance level was set to P<0.05.

Results

Performance

For means and respective standard deviations see Table 2. Only significant results are reported.

Table 2 Performance

ADHD patients made more commission errors type 1 than healthy controls (U=3467.0, P=0.000). This difference between diagnostic groups could be found in the G/G group of rs4570625 (U=1432.5, P=0.000) and in the T/T group of rs11178997 (U=2644.000, P=0.000). Moreover, ADHD patients performed more omission errors than healthy controls (U=3641.0, P=0.000). This effect was only found in the high-risk groups (G/G (U=1461.0, P=0.001) and T/T (U=2600.0, P=0.000)) but not in the low-risk groups (T (U=481.5, P=0.09) and T/A (U=82.0, P=0.98)) of both SNPs.

The individual reaction times were more variable in ADHD patients than in healthy controls in an ANOVA with diagnosis and rs4570625 as independent variables (F=18.260, d.f.=1, 202, P=0.000) and in an ANOVA with diagnosis and rs11178997 as independent variables (F=8.803, d.f.=1, 202, P=0.003).

Electrophysiological results

rs4570625

Participants with the risk constellation of two G-alleles showed a significantly smaller mean NGA than carriers of at least one T-allele (F=9.574; d.f.=1, 202, P=0.002). ADHD patients were characterized by a numerically smaller NGA as compared to controls, which, however, in this sample did not reach statistical significance (F=1.645, d.f.=1, 202, P=0.201). The interaction diagnosis × genotype did not show significant results (F=0.001, d.f.=1, 202, P=0.981; Table 3).

Table 3 NGA and centroids

The centroid analysis indicated that, overall, the NoGo centroid was located significantly more anterior than the Go centroid (F=89.437, d.f.=1, 202, P=0.000). The interactions condition (Go/NoGo) × diagnosis (F=1.645, d.f.=1, 202, P=0.201) and condition × diagnosis × genotype (F=0.001, d.f.=1, 202, P=0.981) were not statistically significant. ADHD patients had significantly more anterior centroids than controls (F=10.184, d.f.=1, 202, P=0.002), irrespective of the CPT condition (Go vs NoGo). Genotype (F=0.481 d.f.=1, 202, P=0.489) and the interaction genotype × diagnosis did not show significant effects (F=0.184, d.f.=1, 202, P=0.668). In line with the above mentioned findings regarding NGA, the interaction genotype × condition revealed significant results (F=9.574, d.f.=1, 202, P=0.002): for the Go condition, the centroid was not different between genotypes (T=−0.785, d.f.=204, P=0.433), whereas the group with the risk alleles (G/G) displayed more posterior centroids than the group with at least one T-allele for the NoGo condition of CPT (T=2.602, d.f.=204, P=0.010). However, both genotype groups displayed a significant anteriorization of the NoGo centroid as compared to the Go centroid (G/G: T=5.558, d.f.=132, P=0.000; T: T=7.299, d.f.=72; P=0.000; Table 3 and Figure 2 ).

Figure 2
figure2

Effect of TPH2 rs4570625 variants on Go- and NoGo centroids in all participants. The unit is electrode positions (y axis); error bars represent the standard error of the mean.

rs11178997

Participants with the two T risk alleles had a significantly smaller NGA than participants with the heterozygous genotype (F=6.007, d.f.=1, 202, P=0.015). The numerically smaller NGA in ADHD patients again did not reach statistical significance (F=1.410, d.f.=1, 202, P=0.236). The interaction diagnosis × genotype did not show significant results either (F=0.344, d.f.=1, 202, P=0.558; Table 3).

As already described above, the NoGo centroid was overall located significantly more anterior than the Go centroid (‘NGA-effect’; F=60.311, d.f.=1, 202, P=0.000) and ADHD patients had more anterior centroids than control participants (F=4.928, d.f.=1, 202, P=0.028). The main effect genotype (F=0.801, d.f.=1, 202, P=0.372) as well as interactions condition (Go/NoGo) × diagnosis (F=1.410, d.f.=1, 202, P=0.236), condition × diagnosis × genotype (F=0.344, d.f.=1, 202, P=0.558) and genotype × diagnosis (F=0.004, d.f.=1, 202, P= 0.982) did not reach statistical significance. The interaction genotype × condition, however, revealed significant results (F=6.007, d.f.=1, 202, P=0.015): the Go centroid was significantly more posterior in the T/A group as compared to the T/T group (T=−1.988, d.f.=204, P=0.048), whereas for the NoGo centroid no significant differences were observed (T=0.412, d.f.=28.709, P=0.683). Once again, the NoGo centroid was located significantly more anterior than the Go centroid in both genotype groups (T/A: T=4.249, d.f.=25, P=0.000; T/T: T=7.760, d.f.=179, P=0.000; Table 3 and Figure 3).

Figure 3
figure3

Effect of TPH2 rs11178997 variants on Go- and NoGo centroids. The unit is electrode positions (y axis); error bars represent the standard error of the mean.

Discussion

The findings show an association of the potentially functional TPH2 variants rs4570625 and rs11178997 with an electrophysiological marker of response control (NGA). For both SNPs, the homozygous carriers of the respective ADHD risk allele (Walitza et al.6) had significantly smaller NGAs, indicating altered prefrontal brain function. For rs4570625, the diminished NGA in G/G-allele carriers was due to more posterior NoGo centroids (see Figure 4 for consistently lower P300 NoGo amplitudes in homozygous G-allele carriers over frontal brain areas), whereas the diminished NGA in T/T-allele carriers of rs11178997 was due to more anterior Go centroids.

Figure 4
figure4

Interpolated head view of t-values of the comparison of the P300 amplitudes at all electrode sites between the G/G- and T variants of rs4570625.

These results indicate that the ADHD risk alleles of rs4570625 and rs11178997 are associated with an alteration of prefrontal brain function during a response control task, for rs450625 clearly due to an altered topography during response inhibition (and not response execution). This effect was equally present in both ADHD patients and healthy controls, indicating that it is largely independent of a specific psychiatric diagnosis. With respect to performance measures, differences between diagnostic groups were only detected in the high-risk genotype groups of both SNPs. For these genetic ‘high-risk groups’, ADHD patients displayed increased error rates and a more variable reaction time than healthy controls. Therefore, the nonrisk alleles of these SNPs might be an index of a more favorable prefrontal functioning in this Go-NoGo task irrespective of the participants’ diagnosis. However, it cannot be excluded that—due to much smaller sample sizes in the ‘nonrisk allele’ groups (Materials and methods section)—nonsignificant differences between ADHD patients and controls in this subgroup might simply be due to insufficient statistical power.

An impact of TPH2 polymorphisms on brain reactivity in healthy controls has already been shown in previous studies. In an fMRI study, healthy T-allele carriers of rs4570625 had a greater amygdala reactivity during the processing of emotional stimuli than carriers of the ADHD risk allele, who were found to show a reduced reactivity to emotional stimuli.7 Moreover, the T-variant of rs4570625 in combination with the short variant of the serotonin transporter promoter polymorphism (SERT) heightened the reactivity to visual emotional stimuli in an ERP study in healthy controls.8 For SERT, the long variant enhances the risk for ADHD.35 This is in line with the results of Canli et al.7 and with our own study, indicating that the carriers of ADHD risk alleles show a different brain reactivity as compared to nonrisk allele carriers. Our study therefore expands the results of the imaging genetics approach from the emotional domain to the cognitive domain.

Obviously, the current study did not replicate the association of the two putative risk alleles with ADHD, as reported by Walitza et al.6 However, it has to be argued that our study was not designed to investigate an association of genetic variants with ADHD in the sense of a preferred transmission of specific alleles to patients as compared to controls (for example, sample size). We therefore did not expect to ‘replicate’ the findings of Walitza et al.,6 but instead intended to examine the functional impact of the putative ‘risk alleles’ on basic neurophysiological mechanisms. The fact that the TPH2 SNPs we examined had an impact on brain activity in a response control task independently of the psychiatric diagnosis may lead to the conclusion that these TPH2 genes are, in this matter, not ADHD specific. The possible impact of these SNPs on the development of ADHD may involve further gene–gene interactions and/or gene–environment interactions. Future research is needed to further clarify the exact mechanisms involved.

In conclusion, this study adds another piece of evidence to the notion that the investigated SNPs are potentially functional, and that genetic variants affecting serotonergic neurotransmission may impact on prefrontal brain function. Further studies based on this imaging genetics approach are necessary to provide a framework for these small gene effects that might help to explain the genetic foundation of complex psychiatric diseases such as ADHD.

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Acknowledgements

This study was supported by the Deutsche Forschungsgemeinschaft (KFO 125/1-1).

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Correspondence to A J Fallgatter.

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Keywords

  • imaging genetics
  • NoGo-anteriorization
  • endophenotypes
  • tryptophan hydroxylase
  • attention-deficit/hyperactivity disorder
  • serotonin

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