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. 2016 Mar 23;37(7):2468–2478. doi: 10.1002/hbm.23187

Interaction of COMT rs4680 and BDNF rs6265 polymorphisms on functional connectivity density of the left frontal eye field in healthy young adults

Wei Li 1, Bing Liu 2, Jiayuan Xu 1, Tianzi Jiang 2, Chunshui Yu 1,✉
PMCID: PMC6867264  PMID: 27004987

Abstract

As modulators of dopamine availability and release in the brain, COMT and BDNF polymorphisms have demonstrated interactions on human cognition; however, the underlying neural mechanisms remain largely unknown. In this study, we aimed to investigate the interactions of COMT rs4680 and BDNF rs6265 on global functional connectivity density (gFCD) of the brain in 265 healthy young subjects. We found a significant COMT × BDNF interaction on the gFCD in the left frontal eye field (FEF), showing an inverted U‐shape modulation by the presumed dopamine signaling. This finding was consistently repeated in the gFCD analyses using other four connection thresholds. Our findings reveal a COMT × BDNF interaction on the FCD in the left FEF, which may be helpful for understanding the neural mechanisms of the COMT × BDNF interactions on the FEF‐related cognitive functions. Hum Brain Mapp 37:2468–2478, 2016. © 2016 Wiley Periodicals, Inc.

Keywords: COMT, BDNF, functional connectivity density, dopamine, attention

Abbreviations

AG

Angular gyrus

ANCOVA

Analysis of covariance

BDNF

Brain‐derived neurotrophic factor

BRAVO

Brain volume

COMT

Catechol‐O‐methyltransferase

FA

Flip angle

FCD

Functional connectivity density

FD

Framewise displacement

FEF

Frontal eye field

FOV

Field of view

MCC

Mid‐cingulate cortex

MNI

Montreal Neurological Institute

MOG

Middle occipital gyrus

PFC

Prefrontal cortex

pSTG

Posterior superior temporal gyrus

INTRODUCTION

Catechol‐O‐methyltransferase (COMT) catalyzes the degradation of synaptic dopamine in the brain. The COMT rs4680 polymorphism reduces enzymatic activity and increases synaptic dopamine concentration in the Met‐carriers [Mannisto and Kaakkola, 1999; Matsumoto et al., 2003; Tunbridge et al., 2006]. This effect is especially prominent in the prefrontal cortex (PFC) [Akil et al., 2003; Mannisto and Kaakkola, 1999; Seamans and Yang, 2004] due to the lack of dopamine transporter in this region [Lewis et al., 2001]. Brain‐derived neurotrophic factor (BDNF) regulates neuronal survival, differentiation and synaptic plasticity [Chao, 2003; Huang and Reichardt, 2001; Poo, 2001] and exhibits the highest expression in the PFC [Pezawas et al., 2004]. The BDNF rs6265 polymorphism affects the trafficking of BDNF and reduces BDNF release [Chen et al., 2004; Chen et al., 2006; Egan et al., 2003; Yeh et al., 2012], resulting reduced activity‐dependent dopamine release in the Met carriers [Pecina et al., 2014].

Both COMT rs4680 and BDNF rs6265 polymorphisms act on the dopamine system [Tunbridge et al., 2006; Pecina et al., 2014], modulate stress reaction and cognitive functions [Papaleo et al., 2008; Ren‐Patterson et al., 2005; Ursini et al., 2011], and involve in several psychiatric disorders [Nolan et al., 2004; Ho et al., 2007; Twamley et al., 2014]; these findings indicate a possible COMT × BDNF interaction on their external phenotypes. Indeed, significant COMT × BDNF interactions have been reported on boredom susceptibility of sensation seeking traits [Kang et al., 2010], implicit grammar learning [Witte et al., 2012], resilience [Kang et al., 2013], and cognitive performance [Das et al., 2014] in healthy subjects, and on symptoms and cognition in schizophrenia [Han et al., 2008], dysfunctional beliefs in obsessive‐compulsive disorder [Alonso et al., 2013], and anxiety sensitivity in panic disorder [Konishi et al., 2014]. To investigate the neural mechanisms underlying the COMT × BDNF interaction on these external phenotypes, a COMT × BDNF interaction has been found in paired associative stimulation‐induced plasticity in the motor cortex [Witte et al., 2012] and resting‐state functional connectivity (rsFC) between the ventral striatum and the anterior cingulate cortex [Wang et al., 2015]. However, these hypothesis‐driven analyses cannot provide us a full picture of the COMT × BDNF interaction on brain properties.

As a data‐driven method, the functional connectivity density (FCD) mapping can voxel‐wisely identify the intergroup differences in FCD [Tomasi and Volkow, 2010, 2011a, 2011b]. In this study, we aimed to identify the COMT × BDNF interaction on the global FCD (gFCD) in healthy young subjects. We predict that the PFC may show a COMT × BDNF interaction on the gFCD because both genetic variations have a large effect on the PFC [Mannisto and Kaakkola, 1999; Pezawas et al., 2004; Seamans and Yang, 2004].

MATERIALS AND METHODS

Subjects

A total of 323 right‐handed healthy young adults were recruited for this study. They were carefully screened to ensure that they had no history of psychiatric or neurological illness, psychiatric treatment, or drug or alcohol abuse and that they had no contraindications to MRI examination. Only Chinese Han populations were included to purify the sample. All subjects were strongly right‐handed according to the Chinese edition of the Edinburgh Handedness Inventory [Oldfield, 1971]. The study was approved by the Medical Research Ethics Committee of Tianjin Medical University, and all participants provided written informed consent. Fifty‐eight subjects were excluded from further analysis because of poor imaging quality (28 subjects) or genotyping failure (30 subjects). The remaining 265 healthy young adults (145 females and 120 males; mean age, 22.8 years; range, 18‐29 years) were ultimately included in the imaging analysis.

Genotyping

For each subject, the COMT rs4680 and BDNF rs6265 were genotyped using the polymerase chain reaction and ligation detection reaction [Thomas et al., 2004; Yi et al., 2009] with technical support from the Shanghai Biowing Applied Biotechnology Company. The detailed procedures for genotyping have been previously described [Wang et al., 2015].

Image Acquisition

MR images were acquired using a Signa HDx 3.0 tesla MR scanner (General Electric) with 8‐channel radio‐frequency coils. Tight but comfortable foam padding was used to minimize head motion, and ear plugs were used to reduce scanner noise. The resting‐state fMRI data were obtained using single‐shot echo planar imaging with the following parameters: repetition time (TR)/echo time (TE) = 2,000/30 ms; field of view (FOV) = 240 × 240 mm; matrix = 64 × 64; flip angle (FA) = 90°; slice thickness = 4 mm; no gap; 40 interleaved transverse slices; 180 volumes. During the fMRI scans, all subjects were instructed to keep their eyes closed, to relax and move as little as possible, to think of nothing in particular, and to not fall asleep. Sagittal 3D T1‐weighted images were acquired using a brain volume (BRAVO) sequence (TR/TE = 8.1/3.1 ms; inversion time = 450 ms; FA = 13°; FOV = 256 × 256 mm; matrix = 256 × 256; slice thickness = 1 mm; no gap; 176 sagittal slices). The same MRI datasets have been used to investigate the COMT × DRD2 interaction on FCD [Tian et al., 2013].

Data Preprocessing

The fMRI data were preprocessed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm). The first 10 volumes for each subject were discarded to allow the signal to reach equilibrium and the participants to adapt to the scanning noise. The remaining 170 volumes were then corrected for the acquisition time delay between slices. The fMRI data from the finally included 265 subjects were within the defined motion thresholds (translational or rotational motion parameters lower than 2mm or 2°). We also calculated framewise displacement (FD), which indexes volume‐to‐volume changes in head position. These changes were obtained from the derivatives of the rigid‐body realignment estimates that are used to realign blood oxygen level‐dependent (BOLD) data during fMRI preprocessing [Power et al., 2012, 2013]. There was no main effect of each SNP and interaction on the FD (P > 0.05). The approach used to normalize these functional images included the following steps: (1) individual structural images were linear coregistered to the mean motion‐corrected functional image; (2) the transformed structural images were segmented into gray matter, white matter, and CSF, and gray matter was nonlinear coregistered to the Montreal Neurological Institute (MNI) space; and (3) the motion‐corrected functional volumes were spatially normalized to the MNI space using the parameters estimated during nonlinear coregistration. The functional images were then resampled into a voxel size of 3 × 3 × 3 mm3. After normalization, several nuisance covariates (six motion parameters and average BOLD signals of the ventricular and white matter) were regressed out from the data and the datasets were band‐pass filtered with frequency from 0.01 to 0.1 Hz.

gFCD Calculation

We calculated the gFCD of each voxel using the inhouse script that was written in the Linux platform according to the method described by Tomasi and Volkow [2011a, b]. The Pearson's linear correlation was used to calculate the rsFCs, and two voxels with a correlation coefficient > 0.6 were considered functionally connected. The calculation of the gFCD was restricted to voxels in the gray matter regions with a signal‐to‐noise ratio > 50% to minimize unwanted effects from susceptibility‐related signal‐loss artifacts [Tomasi and Volkow, 2010]. The gFCD at a given voxel x 0 was computed as the total number of rsFCs between x 0 and all other voxels. This calculation was repeated for all x 0 voxels in the brain. To increase the normality of the distribution, grand mean scaling of the gFCD was performed by dividing by the mean value of the qualified voxels of the whole brain. The gFCD maps were spatially smoothed with an 8 × 8 × 8 mm3 Gaussian kernel.

gFCD Analysis

A two‐way (COMT rs4680 and BDNF rs6265 genotypes) analysis of covariance (ANCOVA) was used to voxel‐wisely identify the main effect of COMT or BDNF and the COMT × BDNF interaction on the gFCD, controlling for the effects of age, gender and years of education. A corrected threshold of P < 0.005 was derived from a combined threshold of P < 0.005 for each voxel and a cluster size > 64 voxels which was determined by the AlphaSim program in the AFNI software (parameters: single voxel P = 0.005, 5,000 simulations, FWHM = 8 mm, cluster connection radius = 5 mm, with gray matter mask, http://afni.nimh.nih.gov/).

Validation Analysis

Because the threshold of Pearson correlation coefficient (r = 0.6) was arbitrarily selected, we also validated the reliability of our results using r = 0.4, 0.5, 0.7 and 0.8. In the validation analyses, we used the same ANCOVA model and corrected methods to identify the effects of the COMT rs4680 and BDNF rs6265 on the gFCD derived from the connection thresholds of r = 0.4, 0.5, 0.6, 0.7 and 0.8.

Connection Probability Maps

All the preprocessing steps were the same as the gFCD calculation. For each significant cluster under a certain connection threshold, we used this connection threshold to generate the rsFC map of this cluster for each subject. Based on the rsFC maps of this cluster of all subjects, we generated a connection probability map of this cluster under the connection threshold. The connection probability map may represent the connection pattern of this cluster at the connection threshold.

RESULTS

Demographic and Genetic Characteristics

The demographic data of these subjects are summarized in Table 1. The distributions of COMT rs4680 (129 Val/Val, 110 Met/Val, and 26 Met/Met) and BDNF rs6265 (83 Val/Val, 134 Met/Val, and 48 Met/Met) were in Hardy–Weinberg equilibrium (P > 0.05). Subjects who were either homozygous or heterozygous for the Met‐allele were merged into a group of Met‐allele carriers of COMT rs4680 or BDNF rs6265, because of the relatively low frequency of the Met homozygotes; this method has been used previously to address skewed genotypic distributions [Aguilera et al., 2008; Ettinger et al., 2008; Li et al., 2009; Taylor et al., 2007].

Table 1.

Demographic data of subjects

Genotypic groups n = 265 Age (years) Years of education Gender (male : female)
COMT Met‐BDNF Met 93 22.2 ± 2.2 15.2 ± 2.1 43:50
COMT Met‐BDNF Val/Val 43 23.6 ± 2.6 15.9 ± 2.2 21:22
COMT Val/Val‐BDNF Met 89 22.9 ± 2.4 15.9 ± 2.1 38:51
COMT Val/Val‐BDNF Val/Val 40 22.9 ± 2.6 15.8 ± 2.0 18:22
P values 0.017 0.119 0.920
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Mean gFCD Maps of Genotypic Subgroups

Using a connection threshold of 0.6, the mean gFCD maps of the four genotypic subgroups are shown in Figure 1. The four subgroups had similar gFCD spatial distribution: the greatest in the posterior cingulate cortex, precuneus, and medial occipital cortex; the medium in the medial and dorsolateral PFC, and lateral parietal cortex; and the lowest in the sensorimotor cortex and anterior temporal cortex.

Figure 1.

Figure 1

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The mean gFCD maps in each of 4 groups with connectivity threshold of 0.6. A: COMT Met‐BDNF Val/Val subgroup; B: COMT Met‐BDNF Met subgroup; C: COMT Val/Val‐BDNF Val/Val subgroup; D: COMT Val/Val‐BDNF Met subgroup. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

gFCD Analysis Based on the Recommended Connection Threshold

Based on the recommended connection threshold of 0.6 to calculate the gFCD map for each subject [Tomasi and Volkow, 2010], the genetic interactive effects on gFCD are shown in Figure 2. Although neither of the COMT rs4680 or the BDNF rs6265 showed a significant main effect on gFCD, we found a significant COMT × BDNF interaction (AlphaSim corrected, P < 0.005) on the gFCD in the left frontal eye field (FEF) (Fig. 2A), the location of which was confirmed by a meta‐analysis (Details see Supplementary Materials). The distribution of the gFCD in the left FEF across these genotypic subgroups was more likely an invert U‐shape according the presumed dopamine signaling from high to low (COMT Met‐BDNF Val/Val > COMT Val/Val‐BDNF Val/Val and COMT Met‐BDNF Met > COMT Val/Val‐BDNF Met). Specifically, the COMT Met‐BDNF Val/Val carriers had significant reduced gFCD than the COMT Val/Val‐BDNF Val/Val (P = 0.009) and COMT Met‐BDNF Met carriers (P = 0.012). The COMT Val/Val‐BDNF Met carriers had a trend towards gFCD reduction than the COMT Val/Val‐BDNF Val/Val (P = 0.091). There were no significant differences in gFCD between any other contrasts (Fig. 2B).

Figure 2.

Figure 2

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The COMT × BDNF interactions on global functional connectivity density (gFCD) with the connection threshold of 0.6. A: Brain region with significant interaction (AlphaSim corrected, P < 0.005). B: The modulation pattern of the FCD of the left FEF by the presumed dopamine signaling from high to low. C: Brain regions with significant interactions (AlphaSim corrected, P < 0.05). D: The modulation pattern of the FCD of the right FEF by the presumed dopamine signaling from high to low. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

We also used a lenient statistical threshold (AlphaSim corrected, P< 0.05) to confirm whether the right FEF showed the similar interaction effect between COMT rs4680 and BDNF rs6265. The right FEF showed a COMT × BDNF interaction (Fig. 2C) and the gFCD distribution of the right FEF among four genotypic subgroups was similar with that of the left FEF that exhibited an invert U‐shape (Fig. 2D). We used a lenient statistical threshold (P < 0.05, uncorrected) to identify potential main effects of COMT and BDNF on gFCD. We found that multiple brain regions show main effects of COMT rs4680 and BDNF rs6265 on gFCD (Supporting Information Fig. S1).

Validation Analysis

To validate our results derived from the connection threshold of 0.6, we also repeated the gFCD analysis (AlphaSim corrected, P < 0.005) using the connection thresholds from 0.4 to 0.8 with a step of 0.1 (Fig. 3, Table 2). The left FEF consistently showed a significant COMT × BDNF interaction at all thresholds; the left angular gyrus (AG) exhibited an interaction effect at r = 0.4, 0.5 and 0.7; the left middle occipital gyrus (MOG) had an interaction at r = 0.4 and 0.5; the left posterior superior temporal gyrus (pSTG) showed an intraction at r = 0.4 and 0.8; and the right mid‐cingulate cortex (MCC) exhibited an interaction at r = 0.4. In these validation analyses, the distribution of the gFCD of these genotypic subgroups showed an invert U‐shape in the left FEF, the left pSTG and the right MCC and a U‐shape in the left AG and MOG (Fig. 3 and Supporting Information Fig. S2).

Figure 3.

Figure 3

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The COMT × BDNF interactions on gFCD with the connection thresholds ranged from 0.4 to 0.8. A: connection threshold = 0.4; B: connection threshold = 0.5; C: connection threshold = 0.6; D: connection threshold = 0.7; E: connection threshold = 0.8. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Table 2.

Brain areas with significant COMT × BDNF interactions on gFCD using different connection thresholds ranged from 0.4 to 0.8

Connection thresholds Brain regions Peak F‐score Cluster size (voxels) MNI coordinates (x, y, z)
r = 0.4 Left frontal eye field 12.51 67 −24, −9, 45
Left angular gyrus 14.30 96 −39, −69, 54
Left middle occipital cortex 15.15 100 −21, −99, 6
Right mid‐cingulate cortex 15.62 122 12, 6, 45
Left posterior superior temporal gyrus 11.67 68 −54, −27, 12
r = 0.5 Left frontal eye field 12.71 87 −24, 0, 63
Left angular cortex 13.90 101 −39, −69, 54
Left middle occipital gyrus 13.52 121 −21, −99 6
r = 0.6 Left frontal eye field 15.00 113 −27, −3,54
r = 0.7 Left frontal eye field 18.41 128 −24, 0, 63
Left angular cortex 13.21 65 −39, −72, 51
r = 0.8 Left frontal eye field 18.12 105 −24, 0, 63
Left posterior superior temporal cortex 11.82 67 −66, −33,12
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Because we used a relatively strict statistical threshold (AlphaSim corrected, P < 0.05), we also re‐performed our analyses using a cluster‐corrected P < 0.05 threshold. Using this threshold, more brain regions showed significant interactions (Supporting Information Fig. S3). From Table 2, it seems that the strength of the interaction on FCD of the left FEF as a function of r values. We also tested correlations between the strengths (F values) of the interaction on gFCD of the left FEF and the connection thresholds of r. We found a significant correlation (r = 0.943, P = 0.016) (Supporting Information Fig. S4).

Connection Probability Maps

The connection probability maps of the significant clusters at different connection thresholds are shown in Figure 4 and Supporting Information Figure S5. At the same connection probability, brain regions exhibiting significant rsFCs with a certain cluster were largely different across connection thresholds. For example, at the same connection probability of 0.05, brain regions exhibiting rsFCs with the left FEF were gradually reduced at connection thresholds from 0.4 to 0.8 (Supporting Information Fig. S5). For several clusters with significant gFCD differences at a certain connection threshold, the connection probability maps may help to determine whether they are located in the same functional network. For example, under the connection threshold of r = 0.4, five clusters showed a significant interaction effect on gFCD (Fig. 3A). The 20% connection probability maps showed that they belong to three distinct functional networks (Fig. 4). Although four clusters mainly connected with the frontal, parietal and temporal cortices, the connection pattern of the left AG was completely different from those of the other three clusters. The left FEF, the right MCC and the left pSTG showed similar connectivity pattern and they were consistently included in the connection probability map of each of the three clusters, suggesting that they belong to the same functional network. The left MOG only connected with the occipital regions.

Figure 4.

Figure 4

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The connection probability maps of each significant cluster at different connection thresholds. Columns 1–5 show connection probability maps of the left FEF, the left pSTG, the right MCC, the left AG and the left MOG. Rows 1–5 are connection probability maps at connection thresholds of 0.4–0.8.The blue areas are clusters with significant FCD differences, which have been used as seed regions for the calculation of functional connectivity. Abbreviation: AG, angular cortex; FEF, frontal eye field; MCC, mid‐cingulate cortex; MOC, middle occipital cortex; pSTG, posterior superior temporal gyrus. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

The rsFC Difference Between Subgroups With High and Low gFCD of the Left FEF

We combined the COMT Met‐BDNF Val/Val and COMT Val/Val‐BDNF Met carriers into a low gFCD group and combined the COMT Val/Val‐BDNF Val/Val and COMT Met‐BDNF Met carriers into a high gFCD group. We then compared the rsFC difference of the left FEF between the two subgroups within a mask of the 20% connection probability map of the left FEF derived from a connection threshold of r = 0.6. Compared with the high gFCD group, the low gFCD group showed significantly lower rsFC between the left FEF (seed region) and the right FEF, the left parietal cortex, precentral gyrus and insular cortex (AlphaSim corrected, P < 0.005) (Fig. 5).

Figure 5.

Figure 5

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Connectivity differences of the left FEF between the high and low FCD subgroups (AlphaSim corrected, P < 0.005). The low FCD subgroup includes the COMT Met‐BDNF Val/Val and COMT Val/Val‐BDNF Met carriers and the high FCD subgroup includes the COMT Val/Val‐BDNF Val/Val and the COMT Met‐BDNF Met carriers. The blue color represents brain regions with significant lower functional connectivity of the left FEF in the low FCD subgroup than in the high FCD subgroup. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

DISCUSSION

In the present study, we investigated the interaction of COMT rs4680 and BDNF rs6265 on the gFCD in healthy young adults. We found a significant COMT × BDNF interaction on the gFCD in the left FEF, suggesting an invert U‐shaped modulation by the presumed dopamine signaling. This finding was repeated in the gFCD analyses using other four connection thresholds.

Using a seed‐based rsFC analysis, one study has investigated COMT × BDNF interaction on the rsFC of the ventral striatum in healthy subjects [Wang et al., 2015]. The authors found a significant COMT × BDNF interaction on the rsFC between the ventral striatum and the PFC [Wang et al., 2015]. However, this hypothesis‐driven method cannot provide a complete picture of the COMT × BDNF interaction on the rsFCs of the whole brain. The FCD mapping is an ultra‐fast voxel‐wise data‐driven method that measures the number of rsFCs of a given voxel with all other voxels in the entire brain [Tomasi and Volkow, 2010, 2011a,b]. Greater gFCD values for particular voxels indicate that those voxels are functionally connected to a greater number of other brain voxels and suggest that those voxels play more important roles in the information processing. However, in the gFCD calculation, the connection threshold is arbitrarily selected by investigators. To validate the reproducibility of our findings, we repeated our gFCD analysis using the connection thresholds from 0.4 to 0.8 with a step of 0.1. Although results derived from different connection thresholds were not totally the same, the left FEF was the only region where the gFCD showed a significant COMT × BDNF interaction in all the five analyses. Moreover, the modulation patterns of the presumed dopamine signaling on the gFCD of the left FEF were very similar across different connection thresholds. These findings suggest that our results are robust to different thresholds.

The COMT is the main modulator of the synaptic dopamine concentration in the PFC due to the lack of dopamine transporter [Mannisto and Kaakkola, 1999; Seamans and Yang, 2004]. The BDNF exhibits the highest expression in the PFC [Pezawas et al., 2004] and involves in modulating activity‐dependent dopamine release [Pecina et al., 2014]. These may explain why we consistently found a significant COMT × BDNF interaction on the FCD in the left FEF, a brain region of the PFC.

Both COMT and BDNF are expected to modulate dopamine on their own and have exhibited their respective effects on rsFCs [Liu et al., 2010; Meyer et al., 2014; Wang et al., 2014]. In this study, the lack of significant main effects of COMT and BDNF on gFCD may be explained by the lack of enough power of the current sample to obtain significant main effects. This is confirmed by the finding of significant main effects at a lenient statistical threshold (P < 0.05, uncorrected). Similarly, the lack of power may also explain for the lack of significant COMT × BDNF interaction on the gFCD of the right FEF at the current statistical threshold. Future studies with large enough samples are needed to clarify these questions.

We found a linear correlation between the strengths of the interaction effect on gFCD of the left FEF and the connection thresholds of r. At the same connection probability, we also found that brain regions exhibiting rsFCs with the left FEF were gradually reduced at connection thresholds from 0.4 to 0.8. These findings suggest that brain regions with stronger rsFCs with the left FEF may make a greater contribution to the interaction effect on the gFCD of the left FEF than brain regions with weaker rsFCs with this region.

Although there is inconsistency across connection thresholds, four other clusters also show significant COMT × BDNF interaction. The connection probability maps showed that they belong to three distinct functional networks. The FEF, MCC and pSTG belong to the dorsal attention network (DAN); the AG belongs to an independent fronto‐parieto‐temporal network; and the MOG belongs to the visual network. Based on COMT Met and BDNF Val/Val carriers having higher synaptic dopamine concentration [Mannisto and Kaakkola, 1999; Pecina et al., 2014], we can generate genotypic subgroups with different levels of the presumed dopamine signaling (COMT Met‐BDNF Val/Val > COMT Val/Val‐BDNF Val/Val and COMT Met‐BDNF Met > COMT Val/Val‐BDNF Met). We found an invert U‐shape modulation of the presumed dopamine signaling on the FCD in the DAN regions and a U‐shape modulation in the other two clusters. These findings provide new evidence for the hypothesis of a network‐dependent modulation of the dopamine signaling on brain functional properties [Tian et al., 2013; Zhao et al., 2015].

Dopamine has been proposed to play an important role in the modulation of attention and has been linked to the integrity of the attention‐related networks, especially the DAN [Corbetta et al., 2008; Dang et al., 2012; Li et al., 2006; Nieoullon, 2002]. The DAN, involved in the endogenous goal‐driven attention orienting (top‐down) process, is hypothesized to modulate externally directed attention by amplifying or attenuating the saliency of relevant and irrelevant cues [Astafiev et al., 2003; Corbetta and Shulman, 2002; Fox et al., 2006; Giesbrecht et al., 2003; Hopfinger et al., 2000; Ptak and Schnider, 2010; Shulman et al., 2003]. For example, the prefrontal dopamine play a dominant role in the modulation of top‐down attention [Noudoost and Moore, 2011], and the top‐down selective visual attention was modulated by gene variants (i.e., COMT Val158Met) of the dopaminergic system [Schneider et al., 2015]. The administration of methylphenidate, a dopamine reuptake blocker, can enhance the activation in the DAN during visual attention and memory tasks [Muller et al., 2005; Tomasi et al., 2011]. In attention deficit hyperactivity disorder, most of available treatments target on the dopamine system [Solanto, 1998]. Destroying dopaminergic terminals in the frontal cortex in primates can impair the ability to acquire attentional sets [Crofts et al., 2001]. Our finding of the interaction of COMT rs4680 and BDNF rs6265 on FCD in the left FEF of the DAN suggests that they jointly modulate the functional connectivity of the DAN by acting on the dopamine system, which is also supported by our finding that the left FEF showed lower rsFC with several other regions of the DAN in the low gFCD group than in the high gFCD group.

There are several limitations in this study. Although this study included 265 subjects, this sample size is not large enough to identify significant main effects of COMT rs4680 and BDNF rs6265. Except for COMT and BDNF, several other genetic or environmental factors may also affect dopamine system and effect on the gFCD of the brain. These factors and their possible interactions should be further investigated. It has been reported that the use of multichannel array head coils may have an effect on the rsFC [Anteraper et al., 2013] and FCD [Tomasi et al., 2015]. In the present study, only an 8‐channel head coil was used, which may lower the sensitivity of our study to identify meaningful findings.

CONCLUSION

In this study, we used a data‐driven method to search brain regions that showed a significant interaction between COMT rs4680 and BDNF rs6265 on the gFCD in healthy young adults. We repeatedly found a significant COMT × BDNF interaction in the left FEF of the DAN. Moreover, we found that the COMT × BDNF interaction can be explained by an invert U‐shape modulation model of the dopamine. These findings may be helpful for understanding the neural mechanisms of the COMT × BDNF interactions on the FEF‐related attention functions.

Supporting information

Supporting Information

Click here for additional data file. (3MB, docx)

Disclosure: The authors have reported no conflicts of interest.

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