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Published in final edited form as: Neuroimage. 2010 Feb 13;53(3):810–821. doi: 10.1016/j.neuroimage.2010.02.019

Imaging genetics of mood disorders

Christian Scharinger a, Ulrich Rabl a, Harald H Sitte b, Lukas Pezawas a,*
PMCID: PMC4502568  EMSID: EMS32456  PMID: 20156570

Abstract

Mood disorders are highly heritable and have been linked to brain regions of emotion processing. Over the past few years, an enormous amount of imaging genetics studies has demonstrated the impact of risk genes on brain regions and systems of emotion processing in vivo in healthy subjects as well as in mood disorder patients. While sufficient evidence already exists for several monaminergic genes as well as for a few nonmonoaminergic genes, such as brain-derived neurotrophic factor (BDNF) in healthy subjects, many others only have been investigated in single studies so far. Apart from these studies, the present review also covers imaging genetics studies applying more complex genetic disease models of mood disorders, such as epistasis and gene–environment interactions, and their impact on brain systems of emotion processing. This review attempts to provide a comprehensive overview of the rapidly growing field of imaging genetics studies in mood disorder research.

Keywords: Imaging genetics, Mood disorders, Major depressive disorder, Emotion, Amygdala, Anterior cingulate cortex, Ventromedial prefrontal cortex, Orbitofrontal cortex, Hippocampus, Functional connectivity, Genetics, Phenotype–genotype

Introduction

Depression is among the four leading causes of disease burden throughout the world and is associated with medical morbidity and mortality across the lifespan (Wong and Licinio, 2001). Since depression is highly heritable (Wong and Licinio, 2001), there has been intense interest in candidate genes related to this behavioral phenotype. As the genetic architecture of depression is complex and genes are not directly encoding for psychiatric diagnoses or psychiatric symptoms, scientific progress was hindered by weak or contradictory results (Meyer-Lindenberg and Weinberger, 2006). The emergence of imaging genetics (Hariri and Weinberger, 2003) as a strategy for mapping neural phenotypes as a function of genotype has fostered new enthusiasm in depression research, because this approach allows for assessing the neural impact of candidate genes in vivo and thus provides for a new level of evidence. Although imaging genetics was initially proposed as a research tool (Hariri and Weinberger, 2003), it has by now evolved into a new field of research, which is reflected in a dramatic increase of publications over the past few years. Efforts thus undertaken address several questions, such as neural effects of single genes, gene–gene (epistasis) and gene–environment interactions as well as the impact of chromosomal aberration disorders and small deletion syndromes on systems neurobiology.

Despite the genetic complexity of mood disorders, imaging genetics studies corroborate the assumption that effects of risk genes are converging at brain systems of emotion processing (Canli et al., 2009) comprising regions involved in the identification of stimulus-related emotional significance as well as the initiation and regulation of affective states, emotional response, and subsequent behavior (Phillips et al., 2003a,b). Furthermore, they inherently point toward the exploitation of those quantitative imaging traits (QTs) that are thought to more directly index biology than behavioral phenotypes of depression, and hence will likely be helpful in shaping mood disorder diagnostics (Meyer-Lindenberg and Weinberger, 2006).

The growing evidence of gene effects on brain systems of emotion processing is accompanied by a more thorough understanding of the anatomical interconnections between regions of emotion processing (Price and Drevets, 2009), highlighting the crucial role of the orbital and medial prefrontal cortex (OMPFC) for mood disorders. The OMPFC comprises two major networks (Price and Drevets, 2009): (1) the orbital network, which is more sensory-related and acts as a system for integrating multi-modal stimuli as well as a system for assessing the value of those stimuli, is therefore connected to olfactory, gustatory, visual, and somatic sensory cortical areas; (2) the medial prefrontal network, which is a more output-related system that can modulate visceral function in relation to emotion as well as several other factors, is connected to important structures in the context of mood disorders, such as the amygdala, cingulate cortex and hippocampus.

Given the emerging genetic and anatomical knowledge of mood disorders, this review attempts to provide a comprehensive summary of available imaging genetics studies including reports on single genes (see Tables 1 and 2, and Fig. 1) as well as gene–gene and gene–environment interactions with regard to key structures being involved in emotion processing within the OMPFC networks. Only studies available in PubMed have been considered and following inclusion criteria have been applied: (1) studies have been conducted in adult samples of healthy subjects or mood disorder patients, (2) genes have been linked to MDD by at least one association study, and (3) only functional and structural MRI imaging genetics studies have been eligible for inclusion. Age and imaging methods restrictions have been chosen, because those topics are extensively elaborated within this special issue elsewhere (Durston, 2010; Willeit and Praschak-Rieder, 2010).

Table 1.

Reported impact of mood disorder risk genes on brain morphology in healthy subjects.

Genes Comparison Amygdala
 ACC
 OFC
 Hippocampus
Nst Nsj Ws (%) Nst Nsj Ws (%) Nst Nsj Ws (%) Nst Nsj Ws (%)
SLC6A4 S>L 1 37 4.5 – – – – – – – – –
S<L 3 302 91.7 3 232 100.0 1 41 n.a. 1 77 27.8
L=S 1 45 3.8 – – – – – – 2 100 72.2
MAOA L>H – – – – – – 2 101+ 78.6 – – –
L<H 1 97 n.a. 1 97 62.2 – – – – – –
L=H – – – 1 59* 37.8 1 55+ 21.4 – – –
HTR2A His>Tyr – – – – – – – – – 1 76 n.a.
COMT Met>Val 3 203 100.0 – – – – – – 4 353 94.9
Met<Val – – – – – – 1 53 n.a. – – –
Met=Val – – – – – – – – – 1 76 5.1
BDNF Met<Val 1 87 2.0 1 42 n.a. – – – 9 649 94.6
Met=Val 5 483 98.0 – – – – – – 2 167 5.4
mtDNA A>G 1 118 n.a. – – – – – – – – –
DISC1 Ser>Cys – – – 1 108 n.a. – – – 1 71 34.0
Ser<Cys – – – – – – – – – 1 157 11.8
Ser=Cys – – – – – – – – – 1 108 54.2
PER3 4/4>5/5 1 29 n.a. 1 29 n.a
VEGF T<C – – – – – – – – – 1 47 n.a.
A<C – – – – – – – – – 1 47 n.a.
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Genotypes of genes can be found in the text of this article. Gene nomenclature corresponds to OMIM (Online Mendelian Inheritance in Man), corresponding RefSNP accession numbers are mentioned in the text. S or L of SLC6A4 comprises the biallelic or triallelic classification of 5-HTTLPR. ACC, anterior cingulate cortex; OFC, orbitofrontal cortex; Nst, number of studies; Nsj, number of subjects; Ws, support index weighted for number of studies and number of subjects (formula available in Supplementary data); n.a., not applicable;

*

, males only;

+

, females only.

Table 2.

Reported impact of mood disorder risk genes on BOLD signal in healthy subjects.

Genes Comparison Amygdala
 ACC
 OFC
 Hippocampus
Nst Nsj Ws (%) Nst Nsj Ws (%) Nst Nsj Ws (%) Nst Nsj Ws (%)
SLC6A4 S>L 13 601 98.5 5 166 94.5 – – – 1 48 n.a.
S<L 1 54+ 1.0 1 48 5.5 – – – – – –
L=S 1 29 0.5 – – – – – – – – –
MAOA L>H 2 189 100.0 1 47+ 11.7 – – – 2 189 100.0
L<H – – – 2 177 88.3 2 177 100.0 – – –
TPH2 T>G 2 51 65.0 – – – – – – – – –
T<G 1 54+ 35.0 – – – – – – – – –
HTR1A C>G 1 89 61.8 – – – – – – – – –
C=G 1 54+ 38.2 – – – – – – – – –
HTR2A T>C 1 54+ n.a. – – – – – – – – –
HTR3A C>T 1 26 n.a. 1 26 n.a. – – – – – –
COMT Met>Val 3 129 67.6 2 94 87.8 1 27 25.2 4 268 100.0
Met<Val 1 74 13.7 1 27 12.2 2 40 74.8 – – –
Met=Val 1 101 18.7 – – – – – – – – –
CREB1 C<T 1 28 n.a. – – – – – – – – –
BDNF Met>Val 1 37+ 5,46 – – – – – – 2 58 38.9
Met<Val 1 29 4,28 – – – 1 29 n.a. 2 95 61.1
Met=Val 4 153 90,27 – – – – – – – – –
FAAH A<C 1 82 n.a. – – – – – – – – –
SLC5A7 G>T 1 32 n.a. 1 32 n.a. – – – – – –
AVPR1A 320<312 bp 1 121 n.a. – – – – – – – – –
NPY Low>High 1 71 n.a. – – – – – – 1 71 n.a.
RGS2 G>C 1 55 n.a. – – – – – – – – –
DISC1 Ser>Cys – – – – – – – – – 2 122 100.0
GRIK4 Del>In – – – – – – – – – 1 42 n.a.
KCNK2 Low>High – – – 1 31 n.a. 1 31 n.a. – – –
ANKK1 A1<A2 – – – – – – 2 42 100.0 – – –
STMN1 T>C – – – 1 42 n.a. – – – – – –
G>T – – – 1 42 n.a. – – – – – –
DAOA A>T – – – – – – – – – 1 14 n.a.
High<Low – – – – – – – – – 1 83 n.a.
NTRK3 G>C – – – – – – – – – 1 36 n.a.
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Genotypes of genes can be found in the text of this article. Gene nomenclature corresponds to OMIM (Online Mendelian Inheritance in Man), corresponding RefSNP accession numbers are mentioned in the text. S or L of SLC6A4 comprises the biallelic or triallelic classification of 5-HTTLPR. BOLD, blood-oxygen level dependent; ACC, anterior cingulate cortex; OFC, orbitofrontal cortex; Nst, number of studies; Nsj, number of subjects; Ws, support index weighted for number of studies and number of subjects (formula available in Supplementary data); n.a., not applicable;

+

, females only.

Fig. 1.

Fig. 1

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Summary of the genetic impact of mood disorder risk genes on volumetric and BOLD measures in healthy subjects and mood disorder patients. BOLD, blood-oxygen level dependent. ○, only studies in mood disorder samples available; ●, only studies in healthy samples available; +, only gene–gene interaction reported; *, gene–environment interaction reported; three classes of color opacity display available evidence (from light to dark): 30% single study or support index <0.5; 60% support index 0.5–0.75; 100% support index N0.75; further details on the calculation of the support index can be found in Supplementary data; ACC, anterior cingulate cortex; OFC, orbitofrontal cortex; AMY–ACC, amygdala–anterior cingulate cortex circuitry; AMY, amygdala; HIPP, hippocampus. Gene nomenclature corresponds to OMIM (Online Mendelian Inheritance in Man).

Key regions of mood disorders

Amygdala

The amygdala is a complex neural hub critically involved in both normal behavior and mental illness (LeDoux, 2007). Its almond-shaped body encompasses several nuclei receiving inputs from cortical and subcortical regions, such as the hippocampus, sensory thalamus, sensory and association cortices, sensory brainstem, olfactory regions as well as medial prefrontal cortex. Two nuclei – the central and the basal nucleus – act as effector organs and project to a variety of brain regions, such as the ventral striatum involved in controlling actions (running to safety) and brainstem areas critical for controlling emotions (freezing) (LeDoux, 2007). In addition, other regions such as prefrontal cortical as well as association cortices are important projection targets. On a behavioral level, the amygdala has been predominantly associated with fear conditioning and reaction, but evidence for other behavioral correlates, such as an important role in reward processing and the generation of emotional states associated with aggressive, maternal, sexual and ingestive behaviors, exists as well (Costafreda et al., 2008; LeDoux, 2007; Sergerie et al., 2008). Although most concepts evolved from animal experiments, human imaging studies support their validity in humans (Stein et al., 2007).

Due to its prominent role in emotion processing, the amygdala has also been implicated in pathological states such as major depressive disorder (MDD) (Phillips et al., 2003b) along with anxiety disorders (Domschke and Dannlowski, 2010). Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies predominantly report increased amygdala activation in patients with acute MDD (Savitz and Drevets, 2009). With regard to volumetric alterations of the amygdala in MDD, studies are contradictory; it would be thus premature to draw final conclusions (Drevets et al., 2008a; Hamilton et al., 2008).

As MDD is frequently treated with drugs selectively inhibiting serotonin (5-HT) uptake (SSRIs), genetic variation of the serotonin transporter (5-HTT) and its coding gene (SLC6A4) has become a focal theme for the neuroscientific community. Since a variable number of tandem repeats (VNTR) polymorphism in the promoter region (5-HTTLPR) of SLC6A4 has been related to neuroticism (Lesch et al., 1996) and is thought to be a risk factor of MDD (Schinka et al., 2004; Sen et al., 2004), a whole area of study of 5-HTTLPR in the context of emotion processing and MDD (Canli et al., 2009) has emerged. Further enthusiasm regarding 5-HTTLPR stems from a study of MDD patients (Caspi et al., 2003). This study was able to demonstrate that the lower-expressing S allele, which is thought to be the risk allele for MDD, becomes disease-relevant in the context of environmental adversity in a manner similar to animal studies (Barr et al., 2004). However, those initially enthusiastic findings linking 5-HTTLPR to neuroticism as well as underlining the importance of environmental interactions have been questioned by recent meta-analyses (Flint and Munafo, 2008; Munafo et al., 2009a,b; Risch et al., 2009). Such contradictory study results highlight the difficulties in unraveling genetically complex disorders that have been attributed to clinical, neurobiological and genetic heterogeneity as well as the problems regarding the sample size necessary to detect such effects (Flint and Munafo, 2008). Specifically, variable penetrance, epistasis, imprinting, epigenetics, pleiotropy (Murphy and Lesch, 2008) as well as further functional genetic variability in SLC6A4, such as a single nucleotide polymorphism (A–G substitution, rs25531) within the L allele (Hu et al., 2006) and a VNTR in the second intron (Hranilovic et al., 2004), are probable other contributors toward this inconsistency.

Despite these divergent findings in genetic studies attempting to link SLC6A4 function to clinical phenotypes, imaging genetics studies investigating the impact of 5-HTTLPR on amygdala reactivity in normal subjects exhibit a more homogeneous picture (Brown and Hariri, 2006; Munafo et al., 2008). With the exception of one study (Surguladze et al., 2008), numerous studies suggest that S allele carriers of European ancestry show exaggerated amygdala activation in the presence of fearful or negatively valenced stimuli as compared with subjects with L/L genotype (Bertolino et al., 2005; Brown and Hariri, 2006; Canli et al., 2005b; Dannlowski et al., 2010; Dannlowski et al., 2008; Friedel et al., 2009; Hariri et al., 2005, 2002; Heinz et al., 2005; Perlis et al., 2008; Pezawas et al., 2005; Smolka et al., 2007; Williams et al., 2009), a finding that does not necessarily apply to other ethnicities (Lee and Ham, 2008b). It is noteworthy that this difference might also exist during resting state (Canli et al., 2006; Rao et al., 2007), and hence baseline periods may have an impact on those results (Canli et al., 2005b; Heinz et al., 2007). Those findings are in accordance with animal studies showing high levels of 5-HT that are accompanied by blood-level oxygen-dependent (BOLD) signal increases (Preece et al., 2009), which is assumed to apply to adult S allele carriers. Only a few studies so far have investigated the effects of 5-HTTLPR on brain development in humans, although 5-HT is one of the best-studied brain developmental factors available today (Gaspar et al., 2003). Three voxel-based morphometry (VBM) studies in large samples of healthy subjects indicate that S allele carrier status implies a smaller relative amygdala volume (Frodl et al., 2008a; Pezawas et al., 2005; Pezawas et al., 2008), whereas two studies investigating smaller samples either reported the opposite (Scherk et al., 2009) or did not detect any genetic impact on amygdala volume (Canli et al., 2005b). With regard to studies of 5-HTTLPR effects on amygdala function or development in acute MDD, results are sparse, and further replications are needed. Authors have reported S-allele-associated increases in amygdala activation (Dannlowski et al., 2008, 2007; Friedel et al., 2009) as well as, counterintuitively, increases of amygdala volume in a small-scale study of bipolar patients (Scherk et al., 2009).

Overwhelming evidence provided by imaging genetics studies today argues in favor of a specific role of SLC6A4 in amygdala regulation and development in healthy subjects. Thus it is not surprising that other genes involved in clearing 5-HT from the synaptic cleft have been identified as significant contributors. One example is the X-chromosome linked monoamine oxidase A gene (MAOA). MAOA is expressed in the outer mitochondrial membrane of monoaminergic neurons and is a key enzyme in the degradation of 5-HT. A functional VNTR polymorphism in the promoter region of the MAOA gene has been found to impact gene expression by genetic variability of its highly active MAOA-H and less active MAOA-L allele (Sabol et al., 1998). Similar to 5-HTTLPR, this polymorphism is also believed to interact with environmental factors in order to contribute to the formation of complex behaviors, such as antisocial behavior, which is less likely to occur in maltreated children with MAOA-H allele status (Caspi et al., 2002). MAOA has frequently been related to aggressive and impulsive behavior; however, studies have also singled out an association with MDD (Schulze et al., 2000; Yu et al., 2005). Regarding amygdala regulation, one study recorded increased amygdala reactivity and decreased amygdala volume in healthy Caucasian MAOA-L carriers (Meyer-Lindenberg et al., 2006), a finding that was replicated in an Asian sample (Lee and Ham, 2008a). Similarly, it has been suggested that the MAOB gene is related to amygdala volume (Good et al., 2003). This assumption has been made in connection with X-monosomal Turner syndrome patients exhibiting functional (Skuse et al., 2005) and volumetric (Cutter et al., 2006; Good et al., 2003; Kesler et al., 2004) amygdala alterations, which can also be found in Turner patients with partial X-chromosome deletions only lacking the genetic locus Xp11.3 containing MAOB (Good et al., 2003). Complementary findings indicating X-chromosomal effects on amygdala volume exist for Klinefelter syndrome (Patwardhan et al., 2002; Shen et al., 2004).

So far we have discussed genes impacting amygdala function and structure that are critically involved in the elimination of 5-HT, such as SLC6A4 and MAOA. In contrast, the tryptophan hydroxylase 2 gene (TPH2) is critical for the synthesis of 5-HT, and several functional variants with an impact on TPH2 expression have been identified (Haghighi et al., 2008). The T allele of G(−703)T TPH2 (rs4570625) has been associated with increased amygdala reactivity in two studies of healthy subjects (Brown et al., 2005; Canli et al., 2005a), whereas the opposite was ascertained in an Asian sample, once more suggesting the significance of ethnic background (Lee and Ham, 2008b).

The importance of the serotonergic system in the context of amygdala activation is further highlighted by imaging genetics studies, suggesting an important role of 5-HT receptor genes, such as the 5-HT1A auto-receptor gene (HTR1A) crucially involved in the regulation of 5-HT signaling (Lemonde et al., 2003). G allele carriers of C(−1019)G HTR1A (rs6295), a functional SNP in the promoter region of HTR1A, have been associated with diminished transcriptional repression, leading to increased 5-HT1A density and decreased amygdala reactivity in healthy subjects as compared with the C/C genotype (Fakra et al., 2009), a finding that has not been replicated in a Korean sample (Lee and Ham, 2008b). Similarly, a PET study reported an inverse correlation between 5-HT1A auto-receptor binding in the dorsal raphe nuclei and amygdala reactivity (Fisher et al., 2006). With regard to MDD, increased amygdala activation has been identified in G allele carriers (Dannlowski et al., 2007). Some evidence exists for other genes regulating postsynaptic 5-HT receptor expression, such as HTR2A and HTR3A. A promoter polymorphism (rs6311) within the 5-HT2A receptor gene (HTR2A) has been linked to aggressive behavior (Giegling et al., 2006) as well as to increased amygdala activity in healthy subjects (Lee and Ham, 2008b). Furthermore, C(178)T HTR3A of the 5-HT3A receptor gene (HTR3A) has been related to harm avoidance and altered amygdala reactivity in healthy subjects (Iidaka et al., 2005).

In addition to 5-HT, dopamine (DA) has been implicated in depression since anhedonia is a prominent clinical symptom of depression, and euphoria can be induced by dopaminergic drugs. Due to a lack of cortical DA transporters, the monoamine-degrading enzyme catechol-O-methyltransferase (COMT) is specifically important for DA catabolism and has been linked to a variety of psychiatric disorders including MDD (Craddock et al., 2006). The Met allele of Val158Met COMT (rs4680) has been associated with significantly lower enzymatic activity than the Val allele (Chen et al., 2004) putatively affecting tonic and phasic dopamine level relations (Bilder et al., 2004). Although several other variants have been shown to profoundly affect COMT expression (Nackley et al., 2006), with respect to imaging genetics studies Val158Met COMT has been shown to be more informative than haplotype analysis likely due to a reduction of complexity (Puls et al., 2009). With regard to Val158Met COMT effects on amygdala volume and function in healthy subjects, results suggest Met-allele-associated volume increase (Cerasa et al., 2008b; Ehrlich et al., 2010; Taylor et al., 2007) and amygdala hyperreactivity (Smolka et al., 2007; Smolka et al., 2005; Williams et al., 2010), while a minority of studies reported contrary (Kempton et al., 2009) or even lacking effects (Drabant et al., 2006), which might in part be explained by sexual dimorphism (Harrison and Tunbridge, 2008).

It is noteworthy that 5-HT might impact BDNF expression via induction of the cyclic adenosine monophosphate response element-binding protein gene (CREB1), which by itself has been related to altered amygdala activation in a single study (Perlis et al., 2008). This relationship between monoamines and neurotrophins has resulted in the controversially discussed (Groves, 2007) neuroplasticity hypothesis of depression (Castren, 2005), which pushed BDNF into the spotlight of mood disorder research (Berton et al., 2006; Krishnan et al., 2007). Cultured neurons transfected with Met BDNF of Val66Met BDNF (rs6265), which has been related to anxious behavior in animal models and antidepressant drug resistance (Chen et al., 2006), show reduced depolarization-induced secretion and fail to localize BDNF to secretory granules and dendritic processes (Egan et al., 2003). In accordance with these molecular mechanisms, the Met allele has been related to a smaller amygdala volume (Montag et al., 2009) possibly dependent on aging effects (Sublette et al., 2008); however, most studies have been unable to detect effects on amygdala volume (Frodl et al., 2007; Matsuo et al., 2009; Nemoto et al., 2006; Pezawas et al., 2004; Schofield et al., 2009). Counterintuitively, the Val allele has mostly been associated with increased trait anxiety (Lang et al., 2005; Sen et al., 2003; Willis-Owen et al., 2005), and similarly to morphological studies, activation studies are inconclusive, with authors reporting increased amygdala reactivity associated with the Val allele (Gasic et al., 2009) or the Met allele (Montag et al., 2008), while most studies have been unable to produce any relevant proof (Egan et al., 2003; Hariri et al., 2003; Hashimoto et al., 2008; Schofield et al., 2009).

Most imaging genetics studies have hitherto been conducted within the framework of “classical” depression concepts, such as the monoamine and neuroplasticity hypothesis; however, many more gene candidates have been suggested as important for MDD and hence likely for amygdala regulation as well (Kato, 2007). Several imaging genetics studies have focused on these candidates, such as C (−385)A FAAH (rs324420) of the fatty acid amid hydrolase gene (FAAH), which plays a role in the catabolism of endogenous ligands of the cannabinoid receptor and has been shown to alter amygdala reactivity in healthy subjects (Hariri et al., 2009). Analogous results were produced by a study investigating the cannabinoid receptor 1 gene (CNR1) in MDD (Domschke et al., 2008). Another candidate, is, rs333229 of the choline transporter gene 1 (SLC5A7), that indicates increased amygdala reactivity and has been associated with depression and autonomic variability in heart rate (Neumann et al., 2006). Furthermore, a microsatellite 312 bp variant (RS1) near the promoter region of the arginine vasopressin receptor 1A gene (AVPR1A) was related to decreased harm avoidance and increased amygdala reactivity in healthy subjects (Meyer-Lindenberg et al., 2008). Another neuropeptide – neuropeptide Y (NPY), which is released by stress – presented robust amygdala activation and increased trait anxiety via lower haplotype-driven NPY expression in healthy subjects (Cotton et al., 2009; Zhou et al., 2008). Moreover, markers of the gene encoding regulator of G protein signaling 2 (RGS2) – linked to anxious behavior in rodents – have been associated with introversion and increased amygdala activation (Smoller et al., 2008). A(10398)G mitochondrial DNA (mtDNA), which is important for calcium regulation in neurons and probably mental illness, has been related to volumetric alterations within the amygdala (Yamasue et al., 2008). Finally, variation in two loci of D-amino acid oxidase activator G72 (DAOA), a gene associated with schizophrenia and bipolar disorder as well as MDD and neuroticism (Rietschel et al., 2008), has been linked to alterations in amygdala gray matter density in patients with bipolar disorder (Zuliani et al., 2009).

Advances in genetics highlight the importance of gene–gene and gene–environment interactions for understanding the complex depression phenotype. Recently, imaging genetics studies have been launched with the aim of applying such complex models derived from in vitro or animal studies to humans. Epistasis – gene–gene interaction – in particular reflects this sort of complex relationship. Animals genetically engineered to be hypomorphic at BDNF and SLC6A4 have shown epistatic effects (Murphy et al., 2003), a finding supported by a genetically humanized mouse model demonstrating that anxiety behavior in animals carrying Met BDNF alleles is unresponsive to SSRIs and can be viewed as a pharmacological analog of 5-HTTLPR S alleles (Krishnan et al., 2007). Recently, an imaging genetics study investigating epistasis between 5-HTTLPR and Val66-Met BDNF in healthy subjects reported that the Met allele reduces the volumetric effect of the S allele on amygdala volume (Pezawas et al., 2008). With regard to amygdala activation, additive effects of the Met allele of Val158Met COMT and S/LG alleles of 5-HTTLPR and rs25531 (Smolka et al., 2007) as well as the T allele of G(−703)T TPH2 and the S allele of 5-HTTLPR (Canli et al., 2008) were identified in healthy subjects, whereas additive effects of C(−1019)G HTR1A, 5-HTTLPR and rs25531 have been reported in a sample of MDD patients (Dannlowski et al., 2007). Furthermore, gene–environment interactions impacting amygdala activity have been demonstrated between 5-HTTLPR (Canli et al., 2006) as well as Val66Met BDNF (Gatt et al., 2009) and life stress.

Cingulate cortex

The cingulate cortex (CC), a belt-shaped cortical area wrapped around the corpus callosum, is of critical importance for both emotion processing and depression models and has become an outstanding example of the powerful convergence of brain imaging and numerous other neuroscientific disciplines (Vogt, 2009). Based on structural, circuitry-related, imaging and pharmacological studies, the CC can be split up into four distinct anatomical regions: anterior cingulate (ACC, primary limbic cortex), midcingulate (MCC, premotor limbic cortex), posterior cingulate (PCC, limbic association cortex) and retrosplenial cortex (RCC, memory access) (Palomero-Gallagher et al., 2009). One of the primary reasons for differentiating between ACC and MCC lies in the profound connections with the amygdala, which is a unique trait of the ACC and highlights the specific importance of this region for emotion processing (Vogt, 2009). Furthermore, these four regions are subdivided into eight subregions encompassing several cytoarchitectonic areas frequently reflected in imaging studies (Vogt, 2009). With regard to emotion processing, both ACC regions – the subgenual ACC (sACC) implicated in autonomic control and the perigenual ACC (pACC) implicated in emotional and autonomic integration – as well as the anterior MCC (aMCC) implicated in approach/avoidance processing are of primary importance (Vogt, 2009). It is noteworthy that the sACC, the rostral parts of the pACC as well as the medial orbitofrontal cortex (mOFC) are sometimes also referred to as the ventromedial prefrontal cortex (vmPFC), a large region encompassing the ventral area of the medial prefrontal cortex primarily associated with decision-making (Koenigs and Grafman, 2009).

Since the first reports of increased metabolism (Drevets et al., 1997) and decreased tissue volume in the sACC of MDD patients (Ongur et al., 1998), a tremendous amount of clinical research has been dedicated to this field (Drevets et al., 2008b). The sACC, which exhibits the highest 5-HTT and 5-HT1A density of the human cortex (Gaspar et al., 2003), has been related to the processing of negative mood, treatment response to SSRIs, deep brain stimulation, electro-convulsive therapy as well as cognitive behavioral therapy (Drevets et al., 2008b) and thus qualifies as a primary research target for MDD (Ressler and Mayberg, 2007).

Abnormalities in the sACC have frequently been reported to be more pronounced in subjects with a family history of mood disorders (Boes et al., 2008; Hajek et al., 2008; McDonald et al., 2004; Ongur et al., 1998), suggesting a profound genetic background for the characteristics thus observed. Given the high expression of 5-HTT within the ACC, studies investigating 5-HTTLPR in healthy controls reported S-allele-associated increases of basal metabolism (Graff-Guerrero et al., 2005) and blood-flow in response to visceral pain (Fukudo et al., 2009) as well as decreases of cerebral blood-flow under resting-state conditions (Rao et al., 2007). Despite the lack of any fMRI paradigm robustly engaging the ACC and specifically the sACC, several studies showed increased activation in S allele carriers of 5-HTTLPR (Dannlowski et al., 2008; Friedel et al., 2009; Passamonti et al., 2008; Roiser et al., 2009; Smolka et al., 2007), whereas only one study reported opposing effects (Shah et al., 2009). Hence, MRI studies primarily focused on the impact of SLC6A4 on ACC anatomy, as suggested by the well-known and important role of 5-HT during brain development (Gaspar et al., 2003). A structural imaging study in a large sample of healthy subjects revealed S-allele-moderated gray matter volume loss in the ACC, exhibiting maximum effects within the sACC (Pezawas et al., 2005), similarly to other human studies recording S-allele-associated volume loss in the ACC or sACC (Canli et al., 2005b; Frodl et al., 2008a) as well as a non-human primate study with a rhesus macaque orthologue of 5-HTTLPR (rh5-HTTLPR) (Jedema et al., 2010). Despite current evidence in healthy humans and non-human primates, patient studies are as yet inconclusive, with authors reporting S-allele-driven increases of ACC activation in mood disorder patients under (Benedetti et al., 2007) and without (Dannlowski et al., 2008) treatment conditions, while others found decreased activation (Shah et al., 2009) or have been unable to detect any difference (Friedel et al., 2009; Frodl et al., 2008a). In addition, other monoaminergic genetic variants, such as the MAOA-L allele of MAOA, have been related to volume loss (Meyer-Lindenberg et al., 2006) as well as decreased activation in fMRI studies (Meyer-Lindenberg et al., 2006; Passamonti et al., 2008) in the ACC, and specifically the sACC in healthy controls. However, negative or opposing volumetric findings exist in underpowered (Cerasa et al., 2008a) or gender-restricted non-Caucasian (Lee and Ham, 2008a) samples. Furthermore, studies suggest monoaminergic genetic impact of Val158Met COMT with decreased activation in Val allele carriers (Smolka et al., 2007; Williams et al., 2010), whereas in a small sample the Val allele has been associated with a failure of deactivation (Pomarol-Clotet et al., 2010). In addition, the C allele of C(178)T HTR3A of the 5-HT3A receptor gene (HTR3A) has been found to increase ACC activation (Iidaka et al., 2005).

In addition, a few other human imaging studies have reported nonmonoaminergic genetic effects on ACC development and function. One study showed a volume decrease of the ACC in Met allele carriers of Val66Met BDNF in a sample of healthy subjects and bipolar patients (Matsuo et al., 2009), while another study reported this type of volume decrease in Japanese Cys allele carriers of Ser704Cys (rs821616) of the disrupted-in-schizophrenia 1 gene (DISC1) (Hashimoto et al., 2006), which has been linked to MDD as well as schizophrenia by various markers (Hashimoto et al., 2006; Schosser et al., 2010). Further genetic variants of genes, such as SLC5A7 (Neumann et al., 2006), TREK1 (KCNK2) (Dillon et al., 2010), and Period 3 (PER3) (Vandewalle et al., 2009), have been found to impact on cingulate activation in healthy controls; the same holds for CLOCK (Benedetti et al., 2008) in patients. Finally, genes of the Wnt signaling pathway, which regulate aspects of neurodevelopment and neuroplasticity, have been shown to be associated with volume alterations in the ACC (Inkster et al., 2010).

Similar to the amygdala, epistatic effects have also been investigated in the ACC. One author reported that Val66Met BDNF interacts epistatically with 5-HTTLPR, presumably affecting the development and integrity of this neural system (Pezawas et al., 2008). Specifically, this study found that the Met BDNF allele protects against the adverse developmental effects of 5-HTTLPR S alleles, which the ancestral Val BDNF allele potentiates. With respect to activation studies, one author reported that MAOA-H leads to an increase of S-allele-associated activity in the ACC (Passamonti et al., 2008), whereas another author detected additive effects of 5-HTTLPR and Val158Met COMT on ACC, MCC and PCC (Smolka et al., 2007). Finally, gene–environment interactions have been identified for Val66Met BDNF, but not for 5-HTTLPR (Gatt et al., 2009).

Orbitofrontal cortex

The OFC, which receives numerous sensory inputs and is heavily interconnected with the amygdala, ventral striatum and ACC, mirrors reward or affective values of primary reinforcers and integrates representations of other stimuli in order to estimate expected reward, therefore acting as a key structure in emotion processing and decision-making (Rolls and Grabenhorst, 2008). Interestingly, the OFC and ACC have a lot of similarities, as both structures are implicated in reinforcement-guided decision-making, emotion and social behavior, share connections to the ventral striatum and the amygdala, and might therefore be involved in similar tasks (Rushworth et al., 2007). Similar to the ACC in mood disorder patients, volume loss and metabolic increases have also been reported for the medial and lateral posterior OFC (Drevets et al., 2008a).

Genetic impact of monoaminergic genes has also been shown for 5-HTTLPR, reporting S-allele-induced increases of OFC perfusion by visceral pain induction (Fukudo et al., 2009) as well as S-allele-induced volume loss (Canli et al., 2005b). Further evidence stems from MAOA, demonstrating an increase of OFC volume in healthy male MAOA-L allele carriers and a decrease of activation of the lateral OFC in both genders (Meyer-Lindenberg et al., 2006), similarly to volumetric (Cerasa et al., 2008a) and functional (Passamonti et al., 2008) replication studies. By the same token, it has been suggested by studies in Turner syndrome patients (Cutter et al., 2006; Good et al., 2003) that the MAOB gene is related to OFC volume. With respect to Val158Met COMT, increased OFC activation (Bishop et al., 2006; Pomarol-Clotet et al., 2010) and volume (Cerasa et al., 2008b) have been reported for the Val allele, whereas one functional study reported the opposite (Dreher et al., 2009). Taq1A (rs1800497), a D2-receptor-linked polymorphism located in ankyrin repeat and kinase domain containing 1 gene (ANKK1), has demonstrated diminished lateral OFC activation in healthy carriers of the A1 allele (Cohen et al., 2005; Jocham et al., 2009), a region associated with increased response to aversive stimuli in the Val/Val genotype of Val66Met BDNF (Gasic et al., 2009). Furthermore, an increased OFC response has been found in healthy carriers of the low risk haplotype of TREK1 (KCNK2) (Dillon et al., 2010).

Epistasis of 5-HTTLPR and Val66Met BDNF has also been shown for the OFC, which was related to a significant BDNF effect in S allele carriers, leading to OFC volume decrease in the presence of a Met allele for a sample of healthy individuals at high risk for alcohol dependence (Hill et al., 2009). Another study investigated interactions between Val158Met COMT and a VNTR polymorphism in the dopamine transporter 1 gene (SLC6A3), which detected maximum OFC activation in healthy subjects with 9-repeat DAT1 allele and COMT Met/Met genotype, a finding putatively related to high dopamine levels (Dillon et al., 2010; Dreher et al., 2009).

Hippocampus

Located in the medial temporal lobe, the ‘seahorse’-shaped hippocampus is a macroscopic structure that is composed of distinct histological subregions and is interconnected with the entorhinal cortex, the parahippocampal cortex and the perirhinal cortex, receiving input from the ends of many cortical processing streams, such as the cerebral association cortex including visual and auditory temporal lobe association cortical areas, the prefrontal cortex and the parietal cortex. Moreover, the hippocampus receives inputs from the amygdala and orbitofrontal cortex, which suggests a specific role in emotion processing (Rolls, 2007). With regard to cognitive functions, the hippocampus has been predominantly associated with explicit memory functions (Burwell, 2000; Rolls and Kesner, 2006) as well as with stress responsivity (Lopez et al., 1999). Similar to the abovementioned regions, the hippocampus is also a key region in mood disorder research (Phillips et al., 2003a,b), since hippocampal volume loss has frequently been reported in MDD and can be reversed with antidepressant drug therapy in animal models (Kasper and McEwen, 2008). As MDD development has been clinically related to stress exposure and the hippocampus is among the brain regions most sensitive to the deleterious effects of stress (McEwen, 2001), extensive work has focused on the interplay between neural stress response and hippocampal function and development (de Kloet et al., 2005).

Although monoamines exert an impact on adult hippocampal neurogenesis (Lledo et al., 2006), only a few authors reported an increase of hippocampal activation (Smolka et al., 2007) or blood-flow (Fukudo et al., 2009) as well as volume loss (Frodl et al., 2008a) associated with the S allele of 5-HTTLPR, while the vast majority of studies investigating 5-HTTLPR neglected alterations in the hippocampus or reported negative results in healthy subjects (Frodl et al., 2004, 2008b). Similarly, little is known about 5-HTTLPR effects in MDD patients, and, counterintuitively, reports showing L-allele-associated hippocampal volume loss exist (Frodl et al., 2008a,b, 2004). However, addressing the likely relationship between 5-HTTLPR and hippocampal aging one study comparing early with late-onset depression found an L-allele-driven volume reduction only in the late-onset group (Taylor et al., 2005), whereas another study reported lacking effects (Hickie et al., 2007). With regard to MAOA, two studies demonstrated increased hippocampal activation in healthy MAOA-L carriers (Lee and Ham, 2008a; Meyer-Lindenberg et al., 2006). Furthermore, studies investigating Val158Met COMT effects in healthy subjects have shown Val-allele-associated decreases in hippocampal volume (Cerasa et al., 2008b; Ehrlich et al., this issue; Honea et al., 2009; Taylor et al., 2007) and activation (Drabant et al., 2006; Krach et al., this issue; Smolka et al., 2007; Smolka et al., 2005); the latter bears similarities to several studies of prefrontal regions supporting the “worrier/warrior” hypothesis of Val158Met COMT (Mier et al., 2009). However, morphological alterations could not be replicated in healthy Japanese (Ohnishi et al., 2006). In addition, one author demonstrated that healthy Tyr carriers of His452Tyr (rs6314) within the 5-HT2A receptor gene (HTR2A) exhibit smaller hippocampal volumes (Filippini et al., 2006).

However, the major bulk of evidence regarding hippocampal development and function in imaging genetics studies is available for BDNF, which was shown to modulate hippocampal plasticity and hippocampal-dependent memory and learning (Egan et al., 2003) and is considered being related to hippocampal stress sensitivity as well as the emergence of mood disorders (Berton et al., 2006). Healthy carriers of the defective Met allele of Val66Met BDNF have been associated with decreased hippocampal volume (Bueller et al., 2006; Chepenik et al., 2009; Frodl et al., 2007; Matsuo et al., 2009; Montag et al., 2009; Nemoto et al., 2006; Pezawas et al., 2004; Schofield et al., 2009; Szeszko et al., 2005) and neuroticism-dependent effects (Joffe et al., 2009), whereas only a minority of studies reported lacking effects (Jessen et al., 2009; Koolschijn et al., in press). Functional studies in healthy subjects demonstrating the impact of Val66Met BDNF on hippocampal activation found an association of the Val allele with increased hippocampal activity (Hariri et al., 2003; Hashimoto et al., 2008), while others presented contrary results (Egan et al., 2003; Schofield et al., 2009). With regard to patient studies, hippocampal volume reduction has also been reported for Met carriers, independent of clinical diagnosis (Chepenik et al., 2009; Frodl et al., 2007), whereas one author was unable to replicate those findings (Jessen et al., 2009).

Besides BDNF, several other genes have been related to hippocampal alterations in the context of mood disorders. Genetic variation (Ser704Cys) in DISC1 exhibited increased hippocampal response in Ser allele carriers (Callicott et al., 2005; Di Giorgio et al., 2008), whereas, with regard to morphology, studies are inconclusive (Callicott et al., 2005; Di Giorgio et al., 2008; Hashimoto et al., 2006). Another genetic variation (rs58575285) in the ionotropic kainate 4 glutamate receptor gene (GRIK4), which has been associated with bipolar disorder, demonstrated increased hippocampal activation in healthy participants carrying the Del allele (Whalley et al., 2009a,b). Similar findings have been reported by studies investigating NPY (Zhou et al., 2008), PER3 (Vandewalle et al., 2009), and the neurotrophin receptor 3 gene (NTRK3) (Otnaess et al., 2009). Researchers have further demonstrated that a genetic variant (rs6438552) in GSK3B (Inkster et al., 2009) as well as other Wnt pathway-related genes (Inkster et al., this issue) impact on hippocampal volume in MDD patients, but not in healthy controls. Furthermore, two genetic variants (rs833070, rs2146323) in the vascular endothelial growth factor gene (VEGF), which is of importance for adult hippocampal neurogenesis, have revealed some morphometric impact (Blumberg et al., 2008). Finally, genetic alteration in DAOA has been linked to altered activation in the parahippocampal gyrus (Jansen et al., 2009) and hippocampus (Goldberg et al., 2006) in healthy subjects.

Reports on hippocampal alterations resulting from epistatic effects of mood disorder genes are limited. Two studies suggest epistatic effects of Val158Met COMT and a VNTR polymorphism of the dopamine transporter (SLC6A3) with hippocampus activation correlating positively with the number of COMT Met alleles and SLC6A3 10-repeat alleles (Bertolino et al., 2008) as well as additive effects of Val158Met COMT and 5-HTTLPR showing the highest response in carriers of both Val/Val and LA/LA genotype (Smolka et al., 2007). Furthermore, reports on gene–environment interactions are available for 5-HTTLPR, showing a modulation of S allele effects on hippocampal connectivity, resting activation as well as hippocampal volume by life stress (Canli et al., 2006), whereas Met carriers of Val66Met BDNF exposed to greater early-life stress exhibited smaller hippocampal volumes (Gatt et al., 2009).

Neural systems of mood disorders

The development of tools to study imaging data on a brain systems level, which has become a standard procedure within the neuroimaging community over the last couple of years (Rubinov and Sporns, in press), triggered a new era in understanding the complexity of brain function and its relationship to human behavior and mental illness (Ramnani et al., 2004). The importance of a brain systems level understanding of brain function is highlighted by the fact that behavior is orchestrated by the engagement of a variety of specialized and interconnected brain regions. Such a systems level view of human behavior is specifically important for the understanding of emotion processing, since the limbic system is among the anatomically most complex brain systems. Due to the large number of interconnections between structures of the limbic system, it has been necessary in imaging studies to dissect this system into smaller neural systems, which has been proven to be a promising approach for imaging genetics studies (Meyer-Lindenberg, 2009).

One of these subsystems is the amygdala–ACC circuitry (Ressler and Mayberg, 2007), which has been postulated to be composed of feedforward projections from the amygdala to the sACC and feedback projections from the pACC/aMCC back to the amygdala (Pezawas et al., 2005). This circuitry being implicated in depressive illness (Wang et al., 2009) likely corresponds to anatomical interconnections such as the uncinatus and cingulum bundle (Pezawas et al., 2005). Since 5-HT probably impacts the formation of growth cones (Gaspar et al., 2003) and cell migration (Riccio et al., 2009) in humans, it has been hypothesized that 5-HTTLPR exerts an impact on the development and function of this neural circuitry (Pezawas et al., 2005). In a large-scale imaging study in healthy subjects, it has been demonstrated that the S allele leads to a decrease in structural covariance and functional connectivity between amygdala and sACC (Pezawas et al., 2005). Since those findings are commonly interpreted as aspects of neural wiring, it has been suggested that the S allele putatively decreases anatomical connections between the amygdala and sACC, leading to a diminished functional coupling between those two structures and hence resulting in a disinhibition of the feedback loop being reflected in increased amygdala activity (Pezawas et al., 2005). These results are in accordance with other functional (Dannlowski et al., 2007; Friedel et al., 2009; Heinz et al., 2005; Roiser et al., 2009) and anatomical studies (Pacheco et al., 2009) of patients or healthy subjects reporting S allele effects on amygdala–mPFC coupling, which has been related to trait anxiety via functional connectivity (Pezawas et al., 2005) and fractional anisotropy (Kim and Whalen, 2009). It is noteworthy that it has been suggested that S-allele-induced amygdala hyperreactivity can be down-regulated by cognitive control mechanisms affecting this circuitry during non-automatic processing of threatening stimuli (Schardt et al., this issue). Notably, the anatomical impact of the S allele has also been reported for orthologous rh5-HTTLPR in rhesus monkeys in the corresponding brain regions (Jedema et al., 2010). Furthermore, effects of epistasis between 5-HTTLPR and Val66Met BDNF have been studied in a large sample of healthy subjects, demonstrating that the Met allele counteracts S-allele-induced structural reductions of amygdala–sACC coupling (Pezawas et al., 2008). Furthermore, some evidence exists for gene–gene interaction between SLC6A4 and HTR1A (Dannlowski et al., 2007). While studies investigating this specific circuitry in patients are still scarce (Houenou et al., 2007), it has been reported by studies of acute MDD that connectivity between amygdala and mPFC varies depending on the genotype and is associated with depressive symptomatology (Friedel et al., 2009) as well as global functioning, duration of episodes and lifetime hospitalization (Dannlowski et al., 2007).

Further studies highlight that this circuitry is affected by functional variation of multiple genes, such as the MAOA-L-allele of MAOA uVNTR, which was shown to be associated with increases in sACC-mediated vmPFC–amygdala connectivity, a finding that correlates positively with harm avoidance and negatively with reward dependence (Buckholtz et al., 2008). Decreases of amygdala–pACC and amygdala–aMCC connectivity have also been reported in a smaller sample of healthy MAOA-H subjects, whereas MAOA-H MDD patients showed the weakest amygdala–prefrontal coupling, indicating a longer and more severe course of disease (Dannlowski et al., 2009). Finally, C(178)T HTR3A of the 5-HT3A receptor gene (HTR3A) (Iidaka et al., 2005) and rs1344706 in ZNF804A that has been related to bipolar disorder, were found to profoundly affect the functional connectivity (Esslinger et al., 2009) of the amygdala–ACC circuitry, whereas DISC1 was reported as being related to reduced white matter tracts comprising this circuitry (Hashimoto et al., 2006).

Knowledge of genetic impact on other mood circuitries is limited, but some evidence exists that the MAOA-L genotype of MAOA uVNTR reduces functional coupling of the amygdala–OFC circuitry in healthy males (Meyer-Lindenberg et al., 2006), which appears to also apply to the Val allele of Val158Met COMT for both genders (Drabant et al., 2006).

Conclusions

Since the introduction of imaging genetics as a research tool (Hariri and Weinberger, 2003), a dramatically increasing number of scientists are applying this approach to mood disorder studies, thereby facilitating the understanding of how genes and interacting factors affect the OMPFC and interconnected structures, such as the amygdala, OFC, ACC and hippocampus (Price and Drevets, 2009).

Imaging genetics studies corroborate the crucial role of monoaminergic genetic variation as functional or developmental regulators of the amygdala (e.g. SLC6A4, TPH2, HTR1A, COMT), OFC (e.g. MAOA, COMT), ACC (e.g. SLC6A4, MAOA, COMT), AMY–mPFC circuitry (e.g. SLC6A4, MAOA) and hippocampus (e.g. MAOA, COMT) in healthy subjects (see Fig. 1). However, the hippocampus appears to be under strong direct genetic control of further and non-monoaminergic genes, such as BDNF and DISC1, likely reflecting its specific role in memory formation (see Fig. 1). As detailed above, many more genes have been reported to affect OMPFC structures and networks in single studies, and doubtlessly more evidence will emerge over the next couple of years. Thus, further replications will be necessary in order to draw final conclusions. In addition, imaging genetics studies have emphasized the validity of complex disease models of depression, including epistasis as well as gene–environment interactions, which are likely more suitable to reflect the highly complex neurobiology of mood disorders. Such models may be considered more appropriate for studies in patient populations, which are currently sparse and inconclusive.

So far, candidate gene selection has been primarily based on a priori assumptions derived from preclinical and association studies, and imaging genetics studies have repeatedly shown small effect sizes, which was frequently attributed to the genetic complexity of mood disorders. This raises the question of whether genes with significantly stronger effects on the OMPFC than the currently known candidates exist, which might be conceivably more important for emotion processing and, consequently, for mood disorders. Recent advances in ‘agnostic’ genome-wide association (GWA) studies (Baum et al., 2008; Ising et al., 2009; The Wellcome Trust Case Control Consortium, 2007) promise such insights, and imaging genetics studies have begun to adopt these new GWA techniques optimized for gene discovery (Potkin et al., 2009a,b,c,d; Stein et al., 2010) and are starting to investigate new candidate genes derived from GWA studies (Esslinger et al., 2009). While GWA imaging genetics studies are still in their infancy, a clear weakness with regard to statistical confirmation exists, and strategies on how to deal with problems of multiple testing in connection with such enormous data sets are still under development, this approach will likely be helpful for drawing final conclusions regarding expected effect sizes in imaging genetics studies and might equally contribute to the discovery of new candidate genes for mood disorders.

Imaging genetics studies have provided a proof of concept in mood disorder research by indicating and detailing how “classical” candidate genes impact brain circuitries of emotion processing in vivo in humans. These results support major hypotheses of depression and moreover suggest alternative mechanisms. Further progress in the field of imaging genetics research is on the horizon given the large number of genes being currently under investigation along with the development of more sophisticated techniques, which will be helpful in unraveling the uncertainties of mood disorder neurobiology.

Supplementary Material

Appendix

Acknowledgments

This review is part of the educational efforts of the Special Research Project SFB-35 (project nr. F3514-B11 and F3506-B11) funded by the Austrian Science Fund (FWF).

Footnotes

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.02.019.

References

  1. Barr CS, Newman TK, Schwandt M, Shannon C, Dvoskin RL, Lindell SG, Taubman J, Thompson B, Champoux M, Lesch KP, Goldman D, Suomi SJ, Higley JD. Sexual dichotomy of an interaction between early adversity and the serotonin transporter gene promoter variant in rhesus macaques. Proc. Natl. Acad. Sci. U. S. A. 2004;101:12358–12363. doi: 10.1073/pnas.0403763101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Baum AE, Hamshere M, Green E, Cichon S, Rietschel M, Noethen MM, Craddock N, McMahon FJ. Meta-analysis of two genome-wide association studies of bipolar disorder reveals important points of agreement. Mol. Psychiatry. 2008;13:466–467. doi: 10.1038/mp.2008.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Benedetti F, Bernasconi A, Blasi V, Cadioli M, Colombo C, Falini A, Lorenzi C, Radaelli D, Scotti G, Smeraldi E. Neural and genetic correlates of antidepressant response to sleep deprivation: a functional magnetic resonance imaging study of moral valence decision in bipolar depression. Arch. Gen. Psychiatry. 2007;64:179–187. doi: 10.1001/archpsyc.64.2.179. [DOI] [PubMed] [Google Scholar]
  4. Benedetti F, Radaelli D, Bernasconi A, Dallaspezia S, Falini A, Scotti G, Lorenzi C, Colombo C, Smeraldi E. Clock genes beyond the clock: CLOCK genotype biases neural correlates of moral valence decision in depressed patients. Genes Brain Behav. 2008;7:20–25. doi: 10.1111/j.1601-183X.2007.00312.x. [DOI] [PubMed] [Google Scholar]
  5. Bertolino A, Arciero G, Rubino V, Latorre V, De Candia M, Mazzola V, Blasi G, Caforio G, Hariri A, Kolachana B, Nardini M, Weinberger DR, Scarabino T. Variation of human amygdala response during threatening stimuli as a function of 5′HTTLPR genotype and personality style. Biol. Psychiatry. 2005;57:1517–1525. doi: 10.1016/j.biopsych.2005.02.031. [DOI] [PubMed] [Google Scholar]
  6. Bertolino A, Di Giorgio A, Blasi G, Sambataro F, Caforio G, Sinibaldi L, Latorre V, Rampino A, Taurisano P, Fazio L, Romano R, Douzgou S, Popolizio T, Kolachana B, Nardini M, Weinberger DR, Dallapiccola B. Epistasis between dopamine regulating genes identifies a nonlinear response of the human hippocampus during memory tasks. Biol. Psychiatry. 2008;64:226–234. doi: 10.1016/j.biopsych.2008.02.001. [DOI] [PubMed] [Google Scholar]
  7. Berton O, McClung CA, Dileone RJ, Krishnan V, Renthal W, Russo SJ, Graham D, Tsankova NM, Bolanos CA, Rios M, Monteggia LM, Self DW, Nestler EJ. Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science. 2006;311:864–868. doi: 10.1126/science.1120972. [DOI] [PubMed] [Google Scholar]
  8. Bilder RM, Volavka J, Lachman HM, Grace AA. The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology. 2004;29:1943–1961. doi: 10.1038/sj.npp.1300542. [DOI] [PubMed] [Google Scholar]
  9. Bishop SJ, Cohen JD, Fossella J, Casey BJ, Farah MJ. COMT genotype influences prefrontal response to emotional distraction. Cogn. Affect. Behav. Neurosci. 2006;6:62–70. doi: 10.3758/cabn.6.1.62. [DOI] [PubMed] [Google Scholar]
  10. Blumberg HP, Wang F, Chepenik LG, Kalmar JH, Edmiston E, Duman RS, Gelernter J. Influence of vascular endothelial growth factor variation on human hippocampus morphology. Biol. Psychiatry. 2008;64:901–903. doi: 10.1016/j.biopsych.2008.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Boes AD, McCormick LM, Coryell WH, Nopoulos P. Rostral anterior cingulate cortex volume correlates with depressed mood in normal healthy children. Biol. Psychiatry. 2008;63:391–397. doi: 10.1016/j.biopsych.2007.07.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Brown SM, Hariri AR. Neuroimaging studies of serotonin gene polymorphisms: exploring the interplay of genes, brain, and behavior. Cogn. Affect. Behav. Neurosci. 2006;6:44–52. doi: 10.3758/cabn.6.1.44. [DOI] [PubMed] [Google Scholar]
  13. Brown SM, Peet E, Manuck SB, Williamson DE, Dahl RE, Ferrell RE, Hariri AR. A regulatory variant of the human tryptophan hydroxylase-2 gene biases amygdala reactivity. Mol. Psychiatry. 2005;10:884–888. 805. doi: 10.1038/sj.mp.4001716. [DOI] [PubMed] [Google Scholar]
  14. Buckholtz JW, Callicott JH, Kolachana B, Hariri AR, Goldberg TE, Genderson M, Egan MF, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Genetic variation in MAOA modulates ventromedial prefrontal circuitry mediating individual differences in human personality. Mol. Psychiatry. 2008;13:313–324. doi: 10.1038/sj.mp.4002020. [DOI] [PubMed] [Google Scholar]
  15. Bueller JA, Aftab M, Sen S, Gomez-Hassan D, Burmeister M, Zubieta JK. BDNF Val66Met allele is associated with reduced hippocampal volume in healthy subjects. Biol. Psychiatry. 2006;59:812–815. doi: 10.1016/j.biopsych.2005.09.022. [DOI] [PubMed] [Google Scholar]
  16. Burwell RD. The parahippocampal region: corticocortical connectivity. Ann. N. Y. Acad. Sci. 2000;911:25–42. doi: 10.1111/j.1749-6632.2000.tb06717.x. [DOI] [PubMed] [Google Scholar]
  17. Callicott JH, Straub RE, Pezawas L, Egan MF, Mattay VS, Hariri AR, Verchinski BA, Meyer-Lindenberg A, Balkissoon R, Kolachana B, Goldberg TE, Weinberger DR. Variation in DISC1 affects hippocampal structure and function and increases risk for schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 2005;102:8627–8632. doi: 10.1073/pnas.0500515102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Canli T, Congdon E, Gutknecht L, Constable RT, Lesch KP. Amygdala responsiveness is modulated by tryptophan hydroxylase-2 gene variation. J. Neural Transm. 2005a;112:1479–1485. doi: 10.1007/s00702-005-0391-4. [DOI] [PubMed] [Google Scholar]
  19. Canli T, Omura K, Haas BW, Fallgatter A, Constable RT, Lesch KP. Beyond affect: a role for genetic variation of the serotonin transporter in neural activation during a cognitive attention task. Proc. Natl. Acad. Sci. U. S. A. 2005b;102:12224–12229. doi: 10.1073/pnas.0503880102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Canli T, Qiu M, Omura K, Congdon E, Haas BW, Amin Z, Herrmann MJ, Constable RT, Lesch KP. Neural correlates of epigenesis. Proc. Natl. Acad. Sci. U. S. A. 2006;103:16033–16038. doi: 10.1073/pnas.0601674103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Canli T, Congdon E, Todd Constable R, Lesch KP. Additive effects of serotonin transporter and tryptophan hydroxylase-2 gene variation on neural correlates of affective processing. Biol. Psychol. 2008;79:118–125. doi: 10.1016/j.biopsycho.2008.01.004. [DOI] [PubMed] [Google Scholar]
  22. Canli T, Ferri J, Duman EA. Genetics of emotion regulation. Neuroscience. 2009;164:43–54. doi: 10.1016/j.neuroscience.2009.06.049. [DOI] [PubMed] [Google Scholar]
  23. Caspi A, McClay J, Moffitt TE, Mill J, Martin J, Craig IW, Taylor A, Poulton R. Role of genotype in the cycle of violence in maltreated children. Science. 2002;297:851–854. doi: 10.1126/science.1072290. [DOI] [PubMed] [Google Scholar]
  24. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, McClay J, Mill J, Martin J, Braithwaite A, Poulton R. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science. 2003;301:386–389. doi: 10.1126/science.1083968. [DOI] [PubMed] [Google Scholar]
  25. Castren E. Is mood chemistry? Nat. Rev. Neurosci. 2005;6:241–246. doi: 10.1038/nrn1629. [DOI] [PubMed] [Google Scholar]
  26. Cerasa A, Gioia MC, Labate A, Lanza P, Magariello A, Muglia M, Quattrone A. MAO A VNTR polymorphism and variation in human morphology: a VBM study. Neuroreport. 2008a;19:1107–1110. doi: 10.1097/WNR.0b013e3283060ab6. [DOI] [PubMed] [Google Scholar]
  27. Cerasa A, Gioia MC, Labate A, Liguori M, Lanza P, Quattrone A. Impact of catechol-O-methyltransferase Val(108/158) Met genotype on hippocampal and prefrontal gray matter volume. Neuroreport. 2008b;19:405–408. doi: 10.1097/WNR.0b013e3282f5f784. [DOI] [PubMed] [Google Scholar]
  28. Chen J, Lipska BK, Halim N, Ma QD, Matsumoto M, Melhem S, Kolachana BS, Hyde TM, Herman MM, Apud J, Egan MF, Kleinman JE, Weinberger DR. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am. J. Hum. Genet. 2004;75:807–821. doi: 10.1086/425589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Chen ZY, Jing D, Bath KG, Ieraci A, Khan T, Siao CJ, Herrera DG, Toth M, Yang C, McEwen BS, Hempstead BL, Lee FS. Genetic variant BDNF (Val66Met) polymorphism alters anxiety-related behavior. Science. 2006;314:140–143. doi: 10.1126/science.1129663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Chepenik LG, Fredericks C, Papademetris X, Spencer L, Lacadie C, Wang F, Pittman B, Duncan JS, Staib LH, Duman RS, Gelernter J, Blumberg HP. Effects of the brain-derived neurotrophic growth factor val66met variation on hippocampus morphology in bipolar disorder. Neuropsychopharmacology. 2009;34:944–951. doi: 10.1038/npp.2008.107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Cohen MX, Young J, Baek JM, Kessler C, Ranganath C. Individual differences in extraversion and dopamine genetics predict neural reward responses. Brain Res. Cogn. Brain Res. 2005;25:851–861. doi: 10.1016/j.cogbrainres.2005.09.018. [DOI] [PubMed] [Google Scholar]
  32. Costafreda SG, Brammer MJ, David AS, Fu CH. Predictors of amygdala activation during the processing of emotional stimuli: a meta-analysis of 385 PET and fMRI studies. Brain Res. Rev. 2008;58:57–70. doi: 10.1016/j.brainresrev.2007.10.012. [DOI] [PubMed] [Google Scholar]
  33. Cotton CH, Flint J, Campbell TG. Is there an association between NPY and neuroticism? Nature. 2009;458 doi: 10.1038/nature07927. E6 discussion E7. [DOI] [PubMed] [Google Scholar]
  34. Craddock N, Owen MJ, O’Donovan MC. The catechol-O-methyl transferase (COMT) gene as a candidate for psychiatric phenotypes: evidence and lessons. Mol. Psychiatry. 2006;11:446–458. doi: 10.1038/sj.mp.4001808. [DOI] [PubMed] [Google Scholar]
  35. Cutter WJ, Daly EM, Robertson DM, Chitnis XA, van Amelsvoort TA, Simmons A, Ng VW, Williams BS, Shaw P, Conway GS, Skuse DH, Collier DA, Craig M, Murphy DG. Influence of X chromosome and hormones on human brain development: a magnetic resonance imaging and proton magnetic resonance spectroscopy study of Turner syndrome. Biol. Psychiatry. 2006;59:273–283. doi: 10.1016/j.biopsych.2005.06.026. [DOI] [PubMed] [Google Scholar]
  36. Dannlowski U, Ohrmann P, Bauer J, Kugel H, Baune BT, Hohoff C, Kersting A, Arolt V, Heindel W, Deckert J, Suslow T. Serotonergic genes modulate amygdala activity in major depression. Genes Brain Behav. 2007;6:672–676. doi: 10.1111/j.1601-183X.2006.00297.x. [DOI] [PubMed] [Google Scholar]
  37. Dannlowski U, Ohrmann P, Bauer J, Deckert J, Hohoff C, Kugel H, Arolt V, Heindel W, Kersting A, Baune BT, Suslow T. 5-HTTLPR biases amygdala activity in response to masked facial expressions in major depression. Neuropsychopharmacology. 2008;33:418–424. doi: 10.1038/sj.npp.1301411. [DOI] [PubMed] [Google Scholar]
  38. Dannlowski U, Ohrmann P, Konrad C, Domschke K, Bauer J, Kugel H, Hohoff C, Schoning S, Kersting A, Baune BT, Mortensen LS, Arolt V, Zwitserlood P, Deckert J, Heindel W, Suslow T. Reduced amygdala–prefrontal coupling in major depression: association with MAOA genotype and illness severity. Int. J. Neuropsychopharmacol. 2009;12:11–22. doi: 10.1017/S1461145708008973. [DOI] [PubMed] [Google Scholar]
  39. Dannlowski U, Konrad C, Kugel H, Zwitserlood P, Domschke K, Schöning S, Ohrmann P, Bauer J, Pyka M, Hohoff C, Zhang W, Baune BT, Heindel W, Arolt V, Suslow S. Emotion specific modulation of automatic amygdala responses by 5-HTTLPR genotype. Neuroimage. 2010;53:891–896. doi: 10.1016/j.neuroimage.2009.11.073. [DOI] [PubMed] [Google Scholar]
  40. de Kloet ER, Joels M, Holsboer F. Stress and the brain: from adaptation to disease. Nat. Rev. Neurosci. 2005;6:463–475. doi: 10.1038/nrn1683. [DOI] [PubMed] [Google Scholar]
  41. Di Giorgio A, Blasi G, Sambataro F, Rampino A, Papazacharias A, Gambi F, Romano R, Caforio G, Rizzo M, Latorre V, Popolizio T, Kolachana B, Callicott JH, Nardini M, Weinberger DR, Bertolino A. Association of the SerCys DISC1 polymorphism with human hippocampal formation gray matter and function during memory encoding. Eur. J. Neurosci. 2008;28:2129–2136. doi: 10.1111/j.1460-9568.2008.06482.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Dillon DG, Bogdan R, Fagerness J, Holmes AJ, Perlis RH, Pizzagalli DA. Variation in TREK1 gene linked to depression-resistant phenotype is associated with potentiated neural responses to rewards in humans. Hum Brain Mapp. 2010;31:210–221. doi: 10.1002/hbm.20858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Domschke K, Dannlowsk U. Imaging genetics of anxiety disorders. Neuroimage. 2010;53:820–829. doi: 10.1016/j.neuroimage.2009.11.042. [DOI] [PubMed] [Google Scholar]
  44. Domschke K, Dannlowski U, Ohrmann P, Lawford B, Bauer J, Kugel H, Heindel W, Young R, Morris P, Arolt V, Deckert J, Suslow T, Baune BT. Cannabinoid receptor 1 (CNR1) gene: impact on antidepressant treatment response and emotion processing in major depression. Eur. Neuropsychopharmacol. 2008;18:751–759. doi: 10.1016/j.euroneuro.2008.05.003. [DOI] [PubMed] [Google Scholar]
  45. Drabant EM, Hariri AR, Meyer-Lindenberg A, Munoz KE, Mattay VS, Kolachana BS, Egan MF, Weinberger DR. Catechol O-methyltransferase val158met genotype and neural mechanisms related to affective arousal and regulation. Arch. Gen. Psychiatry. 2006;63:1396–1406. doi: 10.1001/archpsyc.63.12.1396. [DOI] [PubMed] [Google Scholar]
  46. Dreher JC, Kohn P, Kolachana B, Weinberger DR, Berman KF. Variation in dopamine genes influences responsivity of the human reward system. Proc. Natl. Acad. Sci. U. S. A. 2009;106:617–622. doi: 10.1073/pnas.0805517106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Drevets WC, Price JL, Simpson JR, Jr., Todd RD, Reich T, Vannier M, Raichle ME. Subgenual prefrontal cortex abnormalities in mood disorders. Nature. 1997;386:824–827. doi: 10.1038/386824a0. [DOI] [PubMed] [Google Scholar]
  48. Drevets WC, Price JL, Furey ML. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct. Funct. 2008a;213:93–118. doi: 10.1007/s00429-008-0189-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Drevets WC, Savitz J, Trimble M. The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008b;13:663–681. doi: 10.1017/s1092852900013754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Durston S. Imaging genetics in ADHD. Neuroimage. 2010;53:830–836. doi: 10.1016/j.neuroimage.2010.02.071. [DOI] [PubMed] [Google Scholar]
  51. Egan MF, Kojima M, Callicott JH, Goldberg TE, Kolachana BS, Bertolino A, Zaitsev E, Gold B, Goldman D, Dean M, Lu B, Weinberger DR. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell. 2003;112:257–269. doi: 10.1016/s0092-8674(03)00035-7. [DOI] [PubMed] [Google Scholar]
  52. Ehrlich S, Morrow EM, Roffman JL, Wallace SR, Naylor M, Bockholt J, Lundquist A, Yendiki A, Ho B, White T, Manoach DS, Clark VP, Calhoun VC, Gollub RL, Holt DJ. The COMT Val108/158Met polymorphism and medial temporal lobe volumetry in patients with schizophrenia and healthy adults. Neuroimage. 2010;53:990–998. doi: 10.1016/j.neuroimage.2009.12.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Arnold C, Haddad L, Mier D, Opitz von Boberfeld C, Raab K, Witt SH, Rietschel M, Cichon S, Meyer-Lindenberg A. Neural mechanisms of a genome-wide supported psychosis variant. Science. 2009;324:605. doi: 10.1126/science.1167768. [DOI] [PubMed] [Google Scholar]
  54. Fakra E, Hyde LW, Gorka A, Fisher PM, Munoz KE, Kimak M, Halder I, Ferrell RE, Manuck SB, Hariri AR. Effects of HTR1A C(−1019)G on amygdala reactivity and trait anxiety. Arch. Gen. Psychiatry. 2009;66:33–40. doi: 10.1001/archpsyc.66.1.33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Filippini N, Scassellati C, Boccardi M, Pievani M, Testa C, Bocchio-Chiavetto L, Frisoni GB, Gennarelli M. Influence of serotonin receptor 2A His452Tyr polymorphism on brain temporal structures: a volumetric MR study. Eur. J. Hum. Genet. 2006;14:443–449. doi: 10.1038/sj.ejhg.5201573. [DOI] [PubMed] [Google Scholar]
  56. Fisher PM, Meltzer CC, Ziolko SK, Price JC, Moses-Kolko EL, Berga SL, Hariri AR. Capacity for 5-HT1A-mediated autoregulation predicts amygdala reactivity. Nat. Neurosci. 2006;9:1362–1363. doi: 10.1038/nn1780. [DOI] [PubMed] [Google Scholar]
  57. Flint J, Munafo MR. Forum: interactions between gene and environment. Curr. Opin. Psychiatry. 2008;21:315–317. doi: 10.1097/YCO.0b013e328306a791. [DOI] [PubMed] [Google Scholar]
  58. Friedel E, Schlagenhauf F, Sterzer P, Park SQ, Bermpohl F, Strohle A, Stoy M, Puls I, Hagele C, Wrase J, Buchel C, Heinz A. 5-HTT genotype effect on prefrontal–amygdala coupling differs between major depression and controls. Psychopharmacology (Berl.) 2009;205:261–271. doi: 10.1007/s00213-009-1536-1. [DOI] [PubMed] [Google Scholar]
  59. Frodl T, Meisenzahl EM, Zill P, Baghai T, Rujescu D, Leinsinger G, Bottlender R, Schule C, Zwanzger P, Engel RR, Rupprecht R, Bondy B, Reiser M, Moller HJ. Reduced hippocampal volumes associated with the long variant of the serotonin transporter polymorphism in major depression. Arch. Gen. Psychiatry. 2004;61:177–183. doi: 10.1001/archpsyc.61.2.177. [DOI] [PubMed] [Google Scholar]
  60. Frodl T, Schule C, Schmitt G, Born C, Baghai T, Zill P, Bottlender R, Rupprecht R, Bondy B, Reiser M, Moller HJ, Meisenzahl EM. Association of the brain-derived neurotrophic factor Val66Met polymorphism with reduced hippocampal volumes in major depression. Arch. Gen. Psychiatry. 2007;64:410–416. doi: 10.1001/archpsyc.64.4.410. [DOI] [PubMed] [Google Scholar]
  61. Frodl T, Koutsouleris N, Bottlender R, Born C, Jager M, Morgenthaler M, Scheuerecker J, Zill P, Baghai T, Schule C, Rupprecht R, Bondy B, Reiser M, Moller HJ, Meisenzahl EM. Reduced gray matter brain volumes are associated with variants of the serotonin transporter gene in major depression. Mol. Psychiatry. 2008a;13:1093–1101. doi: 10.1038/mp.2008.62. [DOI] [PubMed] [Google Scholar]
  62. Frodl T, Zill P, Baghai T, Schule C, Rupprecht R, Zetzsche T, Bondy B, Reiser M, Moller HJ, Meisenzahl EM. Reduced hippocampal volumes associated with the long variant of the tri- and diallelic serotonin transporter polymorphism in major depression. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2008b;147B:1003–1007. doi: 10.1002/ajmg.b.30680. [DOI] [PubMed] [Google Scholar]
  63. Fukudo S, Kanazawa M, Mizuno T, Hamaguchi T, Kano M, Watanabe S, Sagami Y, Shoji T, Endo Y, Hongo M, Itoyama Y, Yanai K, Tashiro M, Aoki M. Impact of serotonin transporter gene polymorphism on brain activation by colorectal distention. Neuroimage. 2009;47:946–951. doi: 10.1016/j.neuroimage.2009.04.083. [DOI] [PubMed] [Google Scholar]
  64. Gasic GP, Smoller JW, Perlis RH, Sun M, Lee S, Kim BW, Lee MJ, Holt DJ, Blood AJ, Makris N, Kennedy DK, Hoge RD, Calhoun J, Fava M, Gusella JF, Breiter HC. BDNF, relative preference, and reward circuitry responses to emotional communication. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2009;150B(6):762–781. doi: 10.1002/ajmg.b.30944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Gaspar P, Cases O, Maroteaux L. The developmental role of serotonin: news from mouse molecular genetics. Nat. Rev. Neurosci. 2003;4:1002–1012. doi: 10.1038/nrn1256. [DOI] [PubMed] [Google Scholar]
  66. Gatt JM, Nemeroff CB, Dobson-Stone C, Paul RH, Bryant RA, Schofield PR, Gordon E, Kemp AH, Williams LM. Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety. Mol. Psychiatry. 2009;14:681–695. doi: 10.1038/mp.2008.143. [DOI] [PubMed] [Google Scholar]
  67. Giegling I, Hartmann AM, Moller HJ, Rujescu D. Anger- and aggression-related traits are associated with polymorphisms in the 5-HT-2A gene. J. Affect. Disord. 2006;96:75–81. doi: 10.1016/j.jad.2006.05.016. [DOI] [PubMed] [Google Scholar]
  68. Goldberg TE, Straub RE, Callicott JH, Hariri A, Mattay VS, Bigelow L, Coppola R, Egan MF, Weinberger DR. The G72/G30 gene complex and cognitive abnormalities in schizophrenia. Neuropsychopharmacology. 2006;31:2022–2032. doi: 10.1038/sj.npp.1301049. [DOI] [PubMed] [Google Scholar]
  69. Good CD, Lawrence K, Thomas NS, Price CJ, Ashburner J, Friston KJ, Frackowiak RS, Oreland L, Skuse DH. Dosage-sensitive X-linked locus influences the development of amygdala and orbitofrontal cortex, and fear recognition in humans. Brain. 2003;126:2431–2446. doi: 10.1093/brain/awg242. [DOI] [PubMed] [Google Scholar]
  70. Graff-Guerrero A, De la Fuente-Sandoval C, Camarena B, Gomez-Martin D, Apiquian R, Fresan A, Aguilar A, Mendez-Nunez JC, Escalona-Huerta C, Drucker-Colin R, Nicolini H. Frontal and limbic metabolic differences in subjects selected according to genetic variation of the SLC6A4 gene polymorphism. Neuroimage. 2005;25:1197–1204. doi: 10.1016/j.neuroimage.2004.12.020. [DOI] [PubMed] [Google Scholar]
  71. Groves JO. Is it time to reassess the BDNF hypothesis of depression? Mol. Psychiatry. 2007;12:1079–1088. doi: 10.1038/sj.mp.4002075. [DOI] [PubMed] [Google Scholar]
  72. Haghighi F, Bach-Mizrachi H, Huang YY, Arango V, Shi S, Dwork AJ, Rosoklija G, Sheng HT, Morozova I, Ju J, Russo JJ, Mann JJ. Genetic architecture of the human tryptophan hydroxylase 2 gene: existence of neural isoforms and relevance for major depression. Mol. Psychiatry. 2008;13:813–820. doi: 10.1038/sj.mp.4002127. [DOI] [PubMed] [Google Scholar]
  73. Hajek T, Kozeny J, Kopecek M, Alda M, Hoschl C. Reduced subgenual cingulate volumes in mood disorders: a meta-analysis. J. Psychiatry Neurosci. 2008;33:91–99. [PMC free article] [PubMed] [Google Scholar]
  74. Hamilton JP, Siemer M, Gotlib IH. Amygdala volume in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Mol. Psychiatry. 2008;13:993–1000. doi: 10.1038/mp.2008.57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Hariri AR, Weinberger DR. Imaging genomics. Br. Med. Bull. 2003;65:259–270. doi: 10.1093/bmb/65.1.259. [DOI] [PubMed] [Google Scholar]
  76. Hariri AR, Mattay VS, Tessitore A, Kolachana B, Fera F, Goldman D, Egan MF, Weinberger DR. Serotonin transporter genetic variation and the response of the human amygdala. Science. 2002;297:400–403. doi: 10.1126/science.1071829. [DOI] [PubMed] [Google Scholar]
  77. Hariri AR, Goldberg TE, Mattay VS, Kolachana BS, Callicott JH, Egan MF, Weinberger DR. Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J. Neurosci. 2003;23:6690–6694. doi: 10.1523/JNEUROSCI.23-17-06690.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Hariri AR, Drabant EM, Munoz KE, Kolachana BS, Mattay VS, Egan MF, Weinberger DR. A susceptibility gene for affective disorders and the response of the human amygdala. Arch. Gen. Psychiatry. 2005;62:146–152. doi: 10.1001/archpsyc.62.2.146. [DOI] [PubMed] [Google Scholar]
  79. Hariri AR, Gorka A, Hyde LW, Kimak M, Halder I, Ducci F, Ferrell RE, Goldman D, Manuck SB. Divergent effects of genetic variation in endocannabinoid signaling on human threat- and reward-related brain function. Biol. Psychiatry. 2009;66:9–16. doi: 10.1016/j.biopsych.2008.10.047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Harrison PJ, Tunbridge EM. Catechol-O-methyltransferase (COMT): a gene contributing to sex differences in brain function, and to sexual dimorphism in the predisposition to psychiatric disorders. Neuropsychopharmacology. 2008;33:3037–3045. doi: 10.1038/sj.npp.1301543. [DOI] [PubMed] [Google Scholar]
  81. Hashimoto R, Numakawa T, Ohnishi T, Kumamaru E, Yagasaki Y, Ishimoto T, Mori T, Nemoto K, Adachi N, Izumi A, Chiba S, Noguchi H, Suzuki T, Iwata N, Ozaki N, Taguchi T, Kamiya A, Kosuga A, Tatsumi M, Kamijima K, Weinberger DR, Sawa A, Kunugi H. Impact of the DISC1 Ser704Cys polymorphism on risk for major depression, brain morphology and ERK signaling. Hum. Mol. Genet. 2006;15:3024–3033. doi: 10.1093/hmg/ddl244. [DOI] [PubMed] [Google Scholar]
  82. Hashimoto R, Moriguchi Y, Yamashita F, Mori T, Nemoto K, Okada T, Hori H, Noguchi H, Kunugi H, Ohnishi T. Dose-dependent effect of the Val66Met polymorphism of the brain-derived neurotrophic factor gene on memory-related hippocampal activity. Neurosci. Res. 2008;61:360–367. doi: 10.1016/j.neures.2008.04.003. [DOI] [PubMed] [Google Scholar]
  83. Heinz A, Braus DF, Smolka MN, Wrase J, Puls I, Hermann D, Klein S, Grusser SM, Flor H, Schumann G, Mann K, Buchel C. Amygdala–Prefrontal coupling depends on a genetic variation of the serotonin transporter. Nat. Neurosci. 2005;8:20–21. doi: 10.1038/nn1366. [DOI] [PubMed] [Google Scholar]
  84. Heinz A, Smolka MN, Braus DF, Wrase J, Beck A, Flor H, Mann K, Schumann G, Buchel C, Hariri AR, Weinberger DR. Serotonin transporter genotype (5-HTTLPR): effects of neutral and undefined conditions on amygdala activation. Biol. Psychiatry. 2007;61:1011–1014. doi: 10.1016/j.biopsych.2006.08.019. [DOI] [PubMed] [Google Scholar]
  85. Hickie IB, Naismith SL, Ward PB, Scott EM, Mitchell PB, Schofield PR, Scimone A, Wilhelm K, Parker G. Serotonin transporter gene status predicts caudate nucleus but not amygdala or hippocampal volumes in older persons with major depression. J. Affect. Disord. 2007;98:137–142. doi: 10.1016/j.jad.2006.07.010. [DOI] [PubMed] [Google Scholar]
  86. Hill SY, Wang S, Kostelnik B, Carter H, Holmes B, McDermott M, Zezza N, Stiffler S, Keshavan MS. Disruption of orbitofrontal cortex laterality in offspring from multiplex alcohol dependence families. Biol. Psychiatry. 2009;65:129–136. doi: 10.1016/j.biopsych.2008.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Honea R, Verchinski BA, Pezawas L, Kolachana BS, Callicott JH, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Impact of interacting functional variants in COMT on regional gray matter volume in human brain. Neuroimage. 2009;45:44–51. doi: 10.1016/j.neuroimage.2008.10.064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Houenou J, Wessa M, Douaud G, Leboyer M, Chanraud S, Perrin M, Poupon C, Martinot JL, Paillere-Martinot ML. Increased white matter connectivity in euthymic bipolar patients: diffusion tensor tractography between the subgenual cingulate and the amygdalo-hippocampal complex. Mol. Psychiatry. 2007;12:1001–1010. doi: 10.1038/sj.mp.4002010. [DOI] [PubMed] [Google Scholar]
  89. Hranilovic D, Stefulj J, Schwab S, Borrmann-Hassenbach M, Albus M, Jernej B, Wildenauer D. Serotonin transporter promoter and intron 2 polymorphisms: relationship between allelic variants and gene expression. Biol. Psychiatry. 2004;55:1090–1094. doi: 10.1016/j.biopsych.2004.01.029. [DOI] [PubMed] [Google Scholar]
  90. Hu XZ, Lipsky RH, Zhu G, Akhtar LA, Taubman J, Greenberg BD, Xu K, Arnold PD, Richter MA, Kennedy JL, Murphy DL, Goldman D. Serotonin transporter promoter gain-of-function genotypes are linked to obsessive-compulsive disorder. Am. J. Hum. Genet. 2006;78:815–826. doi: 10.1086/503850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Iidaka T, Ozaki N, Matsumoto A, Nogawa J, Kinoshita Y, Suzuki T, Iwata N, Yamamoto Y, Okada T, Sadato N. A variant C178T in the regulatory region of the serotonin receptor gene HTR3A modulates neural activation in the human amygdala. J. Neurosci. 2005;25:6460–6466. doi: 10.1523/JNEUROSCI.5261-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Inkster B, Nichols TE, Saemann PG, Auer DP, Holsboer F, Muglia P, Matthews PM. Association of GSK3beta polymorphisms with brain structural changes in major depressive disorder. Arch. Gen. Psychiatry. 2009;66:721–728. doi: 10.1001/archgenpsychiatry.2009.70. [DOI] [PubMed] [Google Scholar]
  93. Inkster B, Nichols T, Saemann P, Auer D, Holsboer F, Muglia P, Matthews PM. Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression. Neuroimage. 2010;53:906–915. doi: 10.1016/j.neuroimage.2010.02.065. [DOI] [PubMed] [Google Scholar]
  94. Ising M, Lucae S, Binder EB, Bettecken T, Uhr M, Ripke S, Kohli MA, Hennings JM, Horstmann S, Kloiber S, Menke A, Bondy B, Rupprecht R, Domschke K, Baune BT, Arolt V, Rush AJ, Holsboer F, Muller-Myhsok B. A genome-wide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch. Gen. Psychiatry. 2009;66:966–975. doi: 10.1001/archgenpsychiatry.2009.95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Jansen A, Krach S, Krug A, Markov V, Eggermann T, Zerres K, Stocker T, Shah NJ, Nothen MM, Treutlein J, Rietschel M, Kircher T. A putative high risk diplotype of the G72 gene is in healthy individuals associated with better performance in working memory functions and altered brain activity in the medial temporal lobe. Neuroimage. 2009;45:1002–1008. doi: 10.1016/j.neuroimage.2008.12.054. [DOI] [PubMed] [Google Scholar]
  96. Jedema HP, Gianaros PJ, Greer PJ, Kerr DD, Liu S, Higley JD, Suomi SJ, Olsen AS, Porter JN, Lopresti BJ, Hariri AR, Bradberry CW. Cognitive impact of genetic variation of the serotonin transporter in primates is associated with differences in brain morphology rather than serotonin neurotransmission. Mol. Psychiatry. 2010;52:1059–1069. doi: 10.1038/mp.2009.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Jessen F, Schuhmacher A, von Widdern O, Guttenthaler V, Hofels S, Suliman H, Scheef L, Block W, Urbach H, Maier W, Zobel A. No association of the Val66Met polymorphism of the brain-derived neurotrophic factor with hippocampal volume in major depression. Psychiatr. Genet. 2009;19:99–101. doi: 10.1097/YPG.0b013e32832080ce. [DOI] [PubMed] [Google Scholar]
  98. Jocham G, Klein TA, Neumann J, von Cramon DY, Reuter M, Ullsperger M. Dopamine DRD2 polymorphism alters reversal learning and associated neural activity. J. Neurosci. 2009;29:3695–3704. doi: 10.1523/JNEUROSCI.5195-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Joffe RT, Gatt JM, Kemp AH, Grieve S, Dobson-Stone C, Kuan SA, Schofield PR, Gordon E, Williams LM. Brain derived neurotrophic factor Val66Met polymorphism, the five factor model of personality and hippocampal volume: implications for depressive illness. Hum. Brain Mapp. 2009;30:1246–1256. doi: 10.1002/hbm.20592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Kasper S, McEwen BS. Neurobiological and clinical effects of the antidepressant tianeptine. CNS Drugs. 2008;22:15–26. doi: 10.2165/00023210-200822010-00002. [DOI] [PubMed] [Google Scholar]
  101. Kato T. Molecular genetics of bipolar disorder and depression. Psychiatry Clin. Neurosci. 2007;61:3–19. doi: 10.1111/j.1440-1819.2007.01604.x. [DOI] [PubMed] [Google Scholar]
  102. Kempton MJ, Haldane M, Jogia J, Christodoulou T, Powell J, Collier D, Williams SC, Frangou S. The effects of gender and COMT Val158Met polymorphism on fearful facial affect recognition: a fMRI study. Int. J. Neuropsychopharmacol. 2009;12:371–381. doi: 10.1017/S1461145708009395. [DOI] [PubMed] [Google Scholar]
  103. Kesler SR, Garrett A, Bender B, Yankowitz J, Zeng SM, Reiss AL. Amygdala and hippocampal volumes in Turner syndrome: a high-resolution MRI study of X-monosomy. Neuropsychologia. 2004;42:1971–1978. doi: 10.1016/j.neuropsychologia.2004.04.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Kim MJ, Whalen PJ. The structural integrity of an amygdala–prefrontal pathway predicts trait anxiety. J. Neurosci. 2009;29:11614–11618. doi: 10.1523/JNEUROSCI.2335-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Koenigs M, Grafman J. The functional neuroanatomy of depression: distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav. Brain Res. 2009;201:239–243. doi: 10.1016/j.bbr.2009.03.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Koolschijn PC, van Haren NE, Bakker SC, Hoogendoorn ML, Pol HE, Kahn RS. Effects of brain-derived neurotrophic factor Val66Met polymorphism on hippocampal volume change in schizophrenia. Hippocampus. doi: 10.1002/hipo.20699. in press. [DOI] [PubMed] [Google Scholar]
  107. Krach S, Jansen A, Krug A, Markov V, Thimm M, Sheldrick AJ, Eggermann T, Zerres K, Stöcker T, Shah JN, Kircher T. COMT genotype and its role on hippocampal–prefrontal regions in declarative memory. Neuroimage. 2010;53:596–982. doi: 10.1016/j.neuroimage.2009.12.090. [DOI] [PubMed] [Google Scholar]
  108. Krishnan V, Han M-H, Graham DL, Berton O, Renthal W, Russo SJ, LaPlant Q, Graham A, Lutter M, Lagace DC, Ghose S, Reister R, Tannous P, Green TA, Neve RL, Chakravarty S, Kumar A, Eisch AJ, Self DW, Lee FS, Tamminga CA, Cooper DC, Gershenfeld HK, Nestler EJ. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell. 2007;131:391–404. doi: 10.1016/j.cell.2007.09.018. [DOI] [PubMed] [Google Scholar]
  109. Lang UE, Hellweg R, Kalus P, Bajbouj M, Lenzen KP, Sander T, Kunz D, Gallinat J. Association of a functional BDNF polymorphism and anxiety-related personality traits. Psychopharmacology (Berl.) 2005;180:95–99. doi: 10.1007/s00213-004-2137-7. [DOI] [PubMed] [Google Scholar]
  110. LeDoux J. The amygdala. Curr. Biol. 2007;17:R868–874. doi: 10.1016/j.cub.2007.08.005. [DOI] [PubMed] [Google Scholar]
  111. Lee BT, Ham BJ. Monoamine oxidase A-uVNTR genotype affects limbic brain activity in response to affective facial stimuli. Neuroreport. 2008a;19:515–519. doi: 10.1097/WNR.0b013e3282f94294. [DOI] [PubMed] [Google Scholar]
  112. Lee BT, Ham BJ. Serotonergic genes and amygdala activity in response to negative affective facial stimuli in Korean women. Genes Brain Behav. 2008b;7:899–905. doi: 10.1111/j.1601-183X.2008.00429.x. [DOI] [PubMed] [Google Scholar]
  113. Lemonde S, Turecki G, Bakish D, Du L, Hrdina PD, Bown CD, Sequeira A, Kushwaha N, Morris SJ, Basak A, Ou XM, Albert PR. Impaired repression at a 5-hydroxytryptamine 1A receptor gene polymorphism associated with major depression and suicide. J. Neurosci. 2003;23:8788–8799. doi: 10.1523/JNEUROSCI.23-25-08788.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, Benjamin J, Muller CR, Hamer DH, Murphy DL. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science. 1996;274:1527–1531. doi: 10.1126/science.274.5292.1527. [DOI] [PubMed] [Google Scholar]
  115. Lledo PM, Alonso M, Grubb MS. Adult neurogenesis and functional plasticity in neuronal circuits. Nat. Rev. Neurosci. 2006;7:179–193. doi: 10.1038/nrn1867. [DOI] [PubMed] [Google Scholar]
  116. Lopez JF, Akil H, Watson SJ. Neural circuits mediating stress. Biol. Psychiatry. 1999;46:1461–1471. doi: 10.1016/s0006-3223(99)00266-8. [DOI] [PubMed] [Google Scholar]
  117. Matsuo K, Walss-Bass C, Nery FG, Nicoletti MA, Hatch JP, Frey BN, Monkul ES, Zunta-Soares GB, Bowden CL, Escamilla MA, Soares JC. Neuronal correlates of brain-derived neurotrophic factor Val66Met polymorphism and morphometric abnormalities in bipolar disorder. Neuropsychopharmacology. 2009;34:1904–1913. doi: 10.1038/npp.2009.23. [DOI] [PubMed] [Google Scholar]
  118. McDonald C, Bullmore ET, Sham PC, Chitnis X, Wickham H, Bramon E, Murray RM. Association of genetic risks for schizophrenia and bipolar disorder with specific and generic brain structural endophenotypes. Arch. Gen. Psychiatry. 2004;61:974–984. doi: 10.1001/archpsyc.61.10.974. [DOI] [PubMed] [Google Scholar]
  119. McEwen BS. Plasticity of the hippocampus: adaptation to chronic stress and allostatic load. Ann. N. Y. Acad. Sci. 2001;933:265–277. doi: 10.1111/j.1749-6632.2001.tb05830.x. [DOI] [PubMed] [Google Scholar]
  120. Meyer-Lindenberg A. Neural connectivity as an intermediate phenotype: brain networks under genetic control. Hum. Brain Mapp. 2009;30:1938–1946. doi: 10.1002/hbm.20639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Meyer-Lindenberg A, Weinberger DR. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat. Rev. Neurosci. 2006;7:818–827. doi: 10.1038/nrn1993. [DOI] [PubMed] [Google Scholar]
  122. Meyer-Lindenberg A, Buckholtz JW, Kolachana B, A RH, Pezawas L, Blasi G, Wabnitz A, Honea R, Verchinski B, Callicott JH, Egan M, Mattay V, Weinberger DR. Neural mechanisms of genetic risk for impulsivity and violence in humans. Proc. Natl. Acad. Sci. U. S. A. 2006;103:6269–6274. doi: 10.1073/pnas.0511311103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  123. Meyer-Lindenberg A, Kolachana B, Gold B, Olsh A, Nicodemus KK, Mattay V, Dean M, Weinberger DR. Genetic variants in AVPR1A linked to autism predict amygdala activation and personality traits in healthy humans. Mol. Psychiatry. 2008;14:968–975. doi: 10.1038/mp.2008.54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  124. Mier D, Kirsch P, Meyer-Lindenberg A. Neural substrates of pleiotropic action of genetic variation in COMT: a meta-analysis. Mol. Psychiatry. 2009 doi: 10.1038/mp.2009.36. [DOI] [PubMed] [Google Scholar]
  125. Montag C, Reuter M, Newport B, Elger C, Weber B. The BDNF Val66Met polymorphism affects amygdala activity in response to emotional stimuli: evidence from a genetic imaging study. Neuroimage. 2008;42:1554–1559. doi: 10.1016/j.neuroimage.2008.06.008. [DOI] [PubMed] [Google Scholar]
  126. Montag C, Weber B, Fliessbach K, Elger C, Reuter M. The BDNF Val66Met polymorphism impacts parahippocampal and amygdala volume in healthy humans: incremental support for a genetic risk factor for depression. Psychol. Med. 2009;39:1831–1839. doi: 10.1017/S0033291709005509. [DOI] [PubMed] [Google Scholar]
  127. Munafo MR, Brown SM, Hariri AR. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: a meta-analysis. Biol. Psychiatry. 2008;63:852–857. doi: 10.1016/j.biopsych.2007.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Munafo MR, Durrant C, Lewis G, Flint J. Gene × environment interactions at the serotonin transporter locus. Biol. Psychiatry. 2009a;65:211–219. doi: 10.1016/j.biopsych.2008.06.009. [DOI] [PubMed] [Google Scholar]
  129. Munafo MR, Freimer NB, Ng W, Ophoff R, Veijola J, Miettunen J, Jarvelin MR, Taanila A, Flint J. 5-HTTLPR genotype and anxiety-related personality traits: a meta-analysis and new data. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2009b;150B:271–281. doi: 10.1002/ajmg.b.30808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Murphy DL, Lesch KP. Targeting the murine serotonin transporter: insights into human neurobiology. Nat. Rev. Neurosci. 2008;9:85–96. doi: 10.1038/nrn2284. [DOI] [PubMed] [Google Scholar]
  131. Murphy DL, Uhl GR, Holmes A, Ren-Patterson R, Hall FS, Sora I, Detera-Wadleigh S, Lesch KP. Experimental gene interaction studies with SERT mutant mice as models for human polygenic and epistatic traits and disorders. Genes Brain Behav. 2003;2:350–364. doi: 10.1046/j.1601-1848.2003.00049.x. [DOI] [PubMed] [Google Scholar]
  132. Nackley AG, Shabalina SA, Tchivileva IE, Satterfield K, Korchynskyi O, Makarov SS, Maixner W, Diatchenko L. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science. 2006;314:1930–1933. doi: 10.1126/science.1131262. [DOI] [PubMed] [Google Scholar]
  133. Nemoto K, Ohnishi T, Mori T, Moriguchi Y, Hashimoto R, Asada T, Kunugi H. The Val66Met polymorphism of the brain-derived neurotrophic factor gene affects age-related brain morphology. Neurosci. Lett. 2006;397:25–29. doi: 10.1016/j.neulet.2005.11.067. [DOI] [PubMed] [Google Scholar]
  134. Neumann SA, Brown SM, Ferrell RE, Flory JD, Manuck SB, Hariri AR. Human choline transporter gene variation is associated with cortico-limbic reactivity and autonomic-cholinergic function. Biol. Psychiatry. 2006;60:1155–1162. doi: 10.1016/j.biopsych.2006.03.059. [DOI] [PubMed] [Google Scholar]
  135. Ohnishi T, Hashimoto R, Mori T, Nemoto K, Moriguchi Y, Iida H, Noguchi H, Nakabayashi T, Hori H, Ohmori M, Tsukue R, Anami K, Hirabayashi N, Harada S, Arima K, Saitoh O, Kunugi H. The association between the Val158Met polymorphism of the catechol-O-methyl transferase gene and morphological abnormalities of the brain in chronic schizophrenia. Brain. 2006;129:399–410. doi: 10.1093/brain/awh702. [DOI] [PubMed] [Google Scholar]
  136. Ongur D, Drevets WC, Price JL. Glial reduction in the subgenual prefrontal cortex in mood disorders. Proc. Natl. Acad. Sci. U. S. A. 1998;95:13290–13295. doi: 10.1073/pnas.95.22.13290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  137. Otnaess MK, Djurovic S, Rimol LM, Kulle B, Kahler AK, Jonsson EG, Agartz I, Sundet K, Hall H, Timm S, Hansen T, Callicott JH, Melle I, Werge T, Andreassen OA. Evidence for a possible association of neurotrophin receptor (NTRK-3) gene polymorphisms with hippocampal function and schizophrenia. Neurobiol. Dis. 2009;34:518–524. doi: 10.1016/j.nbd.2009.03.011. [DOI] [PubMed] [Google Scholar]
  138. Pacheco J, Beevers CG, Benavides C, McGeary J, Stice E, Schnyer DM. Frontal-limbic white matter pathway associations with the serotonin transporter gene promoter region (5-HTTLPR) polymorphism. J. Neurosci. 2009;29:6229–6233. doi: 10.1523/JNEUROSCI.0896-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  139. Palomero-Gallagher N, Vogt BA, Schleicher A, Mayberg HS, Zilles K. Receptor architecture of human cingulate cortex: evaluation of the four-region neurobiological model. Hum. Brain Mapp. 2009;30:2336–2355. doi: 10.1002/hbm.20667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  140. Passamonti L, Cerasa A, Gioia MC, Magariello A, Muglia M, Quattrone A, Fera F. Genetically dependent modulation of serotonergic inactivation in the human prefrontal cortex. Neuroimage. 2008;40:1264–1273. doi: 10.1016/j.neuroimage.2007.12.028. [DOI] [PubMed] [Google Scholar]
  141. Patwardhan AJ, Brown WE, Bender BG, Linden MG, Eliez S, Reiss AL. Reduced size of the amygdala in individuals with 47,XXY and 47,XXX karyotypes. Am. J. Med. Genet. 2002;114:93–98. doi: 10.1002/ajmg.10154. [DOI] [PubMed] [Google Scholar]
  142. Perlis RH, Holt DJ, Smoller JW, Blood AJ, Lee S, Kim BW, Lee MJ, Sun M, Makris N, Kennedy DK, Rooney K, Dougherty DD, Hoge R, Rosenbaum JF, Fava M, Gusella J, Gasic GP, Breiter HC. Association of a polymorphism near CREB1 with differential aversion processing in the insula of healthy participants. Arch. Gen. Psychiatry. 2008;65:882–892. doi: 10.1001/archgenpsychiatry.2008.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  143. Pezawas L, Verchinski BA, Mattay VS, Callicott JH, Kolachana BS, Straub RE, Egan MF, Meyer-Lindenberg A, Weinberger DR. The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J. Neurosci. 2004;24:10099–10102. doi: 10.1523/JNEUROSCI.2680-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  144. Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, Egan MF, Mattay VS, Hariri AR, Weinberger DR. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat. Neurosci. 2005;8:828–834. doi: 10.1038/nn1463. [DOI] [PubMed] [Google Scholar]
  145. Pezawas L, Meyer-Lindenberg A, Goldman AL, Verchinski BA, Chen G, Kolachana BS, Egan MF, Mattay VS, Hariri AR, Weinberger DR. Evidence of biologic epistasis between BDNF and SLC6A4 and implications for depression. Mol. Psychiatry. 2008;13:709–716. doi: 10.1038/mp.2008.32. [DOI] [PubMed] [Google Scholar]
  146. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biol. Psychiatry. 2003a;54:504–514. doi: 10.1016/s0006-3223(03)00168-9. [DOI] [PubMed] [Google Scholar]
  147. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol. Psychiatry. 2003b;54:515–528. doi: 10.1016/s0006-3223(03)00171-9. [DOI] [PubMed] [Google Scholar]
  148. Pomarol-Clotet E, Fatjó-Vilas M, McKenna PJ, Monté GC, Sarró S, JOrtiz-Gil J, Aguirre C, Gomar JJ, Guerrero J, Capdevila A, Fañanás L, Salvador R. COMT Val158Met polymorphism affects de-activation in the default mode network: a study in schizophrenic patients and healthy subjects. Neuroimage. 2010;53:897–965. doi: 10.1016/j.neuroimage.2010.04.018. [DOI] [PubMed] [Google Scholar]
  149. Potkin SG, Guffanti G, Lakatos A, Turner JA, Kruggel F, Fallon JH, Saykin AJ, Orro A, Lupoli S, Salvi E, Weiner M, Macciardi F. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS One. 2009a;4(8):e6501. doi: 10.1371/journal.pone.0006501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  150. Potkin SG, Turner JA, Fallon JA, Lakatos A, Keator DB, Guffanti G, Macciardi F. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol. Psychiatry. 2009b;14:416–428. doi: 10.1038/mp.2008.127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  151. Potkin SG, Turner JA, Guffanti G, Lakatos A, Fallon JH, Nguyen DD, Mathalon D, Ford J, Lauriello J, Macciardi F. A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype. Schizophr. Bull. 2009c;35:96–108. doi: 10.1093/schbul/sbn155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  152. Potkin SG, Turner JA, Guffanti G, Lakatos A, Torri F, Keator DB, Macciardi F. Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations. Cogn. Neuropsychiatry. 2009d;14:391–418. doi: 10.1080/13546800903059829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  153. Preece MA, Taylor MJ, Raley J, Blamire A, Sharp T, Sibson NR. Evidence that increased 5-HT release evokes region-specific effects on blood-oxygenation level-dependent functional magnetic resonance imaging responses in the rat brain. Neuroscience. 2009;159:751–759. doi: 10.1016/j.neuroscience.2008.12.032. [DOI] [PubMed] [Google Scholar]
  154. Price JL, Drevets WC. Neurocircuitry of mood disorders. Neuropsychopharmacology. 2009;35:192–216. doi: 10.1038/npp.2009.104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  155. Puls I, Mohr J, Wrase J, Vollstadt-Klein S, Lemenager T, Vollmert C, Rapp M, Obermayer K, Heinz A, Smolka MN. A model comparison of COMT effects on central processing of affective stimuli. Neuroimage. 2009;46:683–691. doi: 10.1016/j.neuroimage.2009.02.033. [DOI] [PubMed] [Google Scholar]
  156. Ramnani N, Behrens TE, Penny W, Matthews PM. New approaches for exploring anatomical and functional connectivity in the human brain. Biol. Psychiatry. 2004;56:613–619. doi: 10.1016/j.biopsych.2004.02.004. [DOI] [PubMed] [Google Scholar]
  157. Rao H, Gillihan SJ, Wang J, Korczykowski M, Sankoorikal GM, Kaercher KA, Brodkin ES, Detre JA, Farah MJ. Genetic variation in serotonin transporter alters resting brain function in healthy individuals. Biol. Psychiatry. 2007;62:600–606. doi: 10.1016/j.biopsych.2006.11.028. [DOI] [PubMed] [Google Scholar]
  158. Ressler KJ, Mayberg HS. Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nat. Neurosci. 2007;10:1116–1124. doi: 10.1038/nn1944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  159. Riccio O, Potter G, Walzer C, Vallet P, Szabo G, Vutskits L, Kiss JZ, Dayer AG. Excess of serotonin affects embryonic interneuron migration through activation of the serotonin receptor 6. Mol. Psychiatry. 2009;14:280–290. doi: 10.1038/mp.2008.89. [DOI] [PubMed] [Google Scholar]
  160. Rietschel M, Beckmann L, Strohmaier J, Georgi A, Karpushova A, Schirmbeck F, Boesshenz KV, Schmal C, Burger C, Jamra RA, Schumacher J, Hofels S, Kumsta R, Entringer S, Krug A, Markov V, Maier W, Propping P, Wust S, Kircher T, Nothen MM, Cichon S, Schulze TG. G72 and its association with major depression and neuroticism in large population-based groups from Germany. Am. J. Psychiatry. 2008;165:753–762. doi: 10.1176/appi.ajp.2008.07060883. [DOI] [PubMed] [Google Scholar]
  161. Risch N, Herrell R, Lehner T, Liang KY, Eaves L, Hoh J, Griem A, Kovacs M, Ott J, Merikangas KR. Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis. JAMA. 2009;301:2462–2471. doi: 10.1001/jama.2009.878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Roiser JP, de Martino B, Tan GC, Kumaran D, Seymour B, Wood NW, Dolan RJ. A genetically mediated bias in decision making driven by failure of amygdala control. J. Neurosci. 2009;29:5985–5991. doi: 10.1523/JNEUROSCI.0407-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  163. Rolls ET. An attractor network in the hippocampus: theory and neurophysiology. Learn. Mem. 2007;14:714–731. doi: 10.1101/lm.631207. [DOI] [PubMed] [Google Scholar]
  164. Rolls ET, Grabenhorst F. The orbitofrontal cortex and beyond: from affect to decision-making. Prog. Neurobiol. 2008;86:216–244. doi: 10.1016/j.pneurobio.2008.09.001. [DOI] [PubMed] [Google Scholar]
  165. Rolls ET, Kesner RP. A computational theory of hippocampal function, and empirical tests of the theory. Prog. Neurobiol. 2006;79:1–48. doi: 10.1016/j.pneurobio.2006.04.005. [DOI] [PubMed] [Google Scholar]
  166. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–1069. doi: 10.1016/j.neuroimage.2009.10.003. [DOI] [PubMed] [Google Scholar]
  167. Rushworth MF, Behrens TE, Rudebeck PH, Walton ME. Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behaviour. Trends Cogn. Sci. 2007;11:168–176. doi: 10.1016/j.tics.2007.01.004. [DOI] [PubMed] [Google Scholar]
  168. Sabol SZ, Hu S, Hamer D. A functional polymorphism in the monoamine oxidase A gene promoter. Hum. Genet. 1998;103:273–279. doi: 10.1007/s004390050816. [DOI] [PubMed] [Google Scholar]
  169. Savitz JB, Drevets WC. Imaging phenotypes of major depressive disorder: genetic correlates. Neuroscience. 2009;164:300–330. doi: 10.1016/j.neuroscience.2009.03.082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  170. Schardt DM, Erk S, Nüsser C, Nöthen MM, Cichon S, Rietschel M, Treutlein J, Goschke T, Walter H. Volition diminishes genetically mediated amygdala hyperreactivity. Neuroimage. 2010;53:941–949. doi: 10.1016/j.neuroimage.2009.11.078. [DOI] [PubMed] [Google Scholar]
  171. Scherk H, Gruber O, Menzel P, Schneider-Axmann T, Kemmer C, Usher J, Reith W, Meyer J, Falkai P. 5-HTTLPR genotype influences amygdala volume. Eur. Arch. Psychiatry Clin. Neurosci. 2009;259:212–217. doi: 10.1007/s00406-008-0853-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  172. Schinka JA, Busch RM, Robichaux-Keene N. A meta-analysis of the association between the serotonin transporter gene polymorphism (5-HTTLPR) and trait anxiety. Mol. Psychiatry. 2004;9:197–202. doi: 10.1038/sj.mp.4001405. [DOI] [PubMed] [Google Scholar]
  173. Schofield PR, Williams LM, Paul RH, Gatt JM, Brown K, Luty A, Cooper N, Grieve S, Dobson-Stone C, Morris C, Kuan SA, Gordon E. Disturbances in selective information processing associated with the BDNF Val66Met polymorphism: evidence from cognition, the P300 and fronto-hippocampal systems. Biol. Psychol. 2009;80:176–188. doi: 10.1016/j.biopsycho.2008.09.001. [DOI] [PubMed] [Google Scholar]
  174. Schosser A, Gaysina D, Cohen-Woods C, Chow PC, Martucci L, Craddock N, Farmer A, Korszun A, Gunasinghe C, Gray J, Jones L, Tozzi F, Perry J, Muglia P, Owen MJ, Craig IW, McGuffin P. Association of DISC1 and TSNAX genes and affective disorders in the depression case-control (DeCC) and bipolar affective case-control (BACCS) studies. Mol. Psychiatry. 2010;15:844–849. doi: 10.1038/mp.2009.21. [DOI] [PubMed] [Google Scholar]
  175. Schulze TG, Muller DJ, Krauss H, Scherk H, Ohlraun S, Syagailo YV, Windemuth C, Neidt H, Grassle M, Papassotiropoulos A, Heun R, Nothen MM, Maier W, Lesch KP, Rietschel M. Association between a functional polymorphism in the monoamine oxidase A gene promoter and major depressive disorder. Am. J. Med. Genet. 2000;96:801–803. [PubMed] [Google Scholar]
  176. Sen S, Nesse RM, Stoltenberg SF, Li S, Gleiberman L, Chakravarti A, Weder AB, Burmeister M. A BDNF coding variant is associated with the NEO personality inventory domain neuroticism, a risk factor for depression. Neuropsychopharmacology. 2003;28:397–401. doi: 10.1038/sj.npp.1300053. [DOI] [PubMed] [Google Scholar]
  177. Sen S, Burmeister M, Ghosh D. Meta-analysis of the association between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety-related personality traits. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2004;127B:85–89. doi: 10.1002/ajmg.b.20158. [DOI] [PubMed] [Google Scholar]
  178. Sergerie K, Chochol C, Armony JL. The role of the amygdala in emotional processing: a quantitative meta-analysis of functional neuroimaging studies. Neurosci. Biobehav. Rev. 2008;32:811–830. doi: 10.1016/j.neubiorev.2007.12.002. [DOI] [PubMed] [Google Scholar]
  179. Shah MP, Wang F, Kalmar JH, Chepenik LG, Tie K, Pittman B, Jones MM, Constable RT, Gelernter J, Blumberg HP. Role of variation in the serotonin transporter protein gene (SLC6A4) in trait disturbances in the ventral anterior cingulate in bipolar disorder. Neuropsychopharmacology. 2009;34:1301–1310. doi: 10.1038/npp.2008.204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  180. Shen D, Liu D, Liu H, Clasen L, Giedd J, Davatzikos C. Automated morphometric study of brain variation in XXY males. Neuroimage. 2004;23:648–653. doi: 10.1016/j.neuroimage.2004.08.018. [DOI] [PubMed] [Google Scholar]
  181. Skuse DH, Morris JS, Dolan RJ. Functional dissociation of amygdala-modulated arousal and cognitive appraisal, in Turner syndrome. Brain. 2005;128:2084–2096. doi: 10.1093/brain/awh562. [DOI] [PubMed] [Google Scholar]
  182. Smolka MN, Schumann G, Wrase J, Grusser SM, Flor H, Mann K, Braus DF, Goldman D, Buchel C, Heinz A. Catechol-O-methyltransferase val158met genotype affects processing of emotional stimuli in the amygdala and prefrontal cortex. J. Neurosci. 2005;25:836–842. doi: 10.1523/JNEUROSCI.1792-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  183. Smolka MN, Buhler M, Schumann G, Klein S, Hu XZ, Moayer M, Zimmer A, Wrase J, Flor H, Mann K, Braus DF, Goldman D, Heinz A. Gene–Gene effects on central processing of aversive stimuli. Mol. Psychiatry. 2007;12:307–317. doi: 10.1038/sj.mp.4001946. [DOI] [PubMed] [Google Scholar]
  184. Smoller JW, Paulus MP, Fagerness JA, Purcell S, Yamaki LH, Hirshfeld-Becker D, Biederman J, Rosenbaum JF, Gelernter J, Stein MB. Influence of RGS2 on anxiety-related temperament, personality, and brain function. Arch. Gen. Psychiatry. 2008;65:298–308. doi: 10.1001/archgenpsychiatry.2007.48. [DOI] [PubMed] [Google Scholar]
  185. Stein JL, Wiedholz LM, Bassett DS, Weinberger DR, Zink CF, Mattay VS, Meyer-Lindenberg A. A validated network of effective amygdala connectivity. Neuroimage. 2007;36:736–745. doi: 10.1016/j.neuroimage.2007.03.022. [DOI] [PubMed] [Google Scholar]
  186. Stein JL, Hua X, Lee S, Ho AJ, Leow AD, Toga AW, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corveneaux JJ, DeChairo BM, Potkin SG, Weiner MW, Thompson P. Voxelwise Genome-Wide Association Study (vGWAS) Neuroimage. 2010;53:1158–1172. doi: 10.1016/j.neuroimage.2010.02.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  187. Sublette ME, Baca-Garcia E, Parsey RV, Oquendo MA, Rodrigues SM, Galfalvy H, Huang YY, Arango V, Mann JJ. Effect of BDNF val66met polymorphism on age-related amygdala volume changes in healthy subjects. Prog. Neuropsychopharmacol. Biol. Psychiatry. 2008;32:1652–1655. doi: 10.1016/j.pnpbp.2008.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  188. Surguladze SA, Elkin A, Ecker C, Kalidindi S, Corsico A, Giampietro V, Lawrence N, Deeley Q, Murphy DG, Kucharska-Pietura K, Russell TA, McGuffin P, Murray R, Phillips ML. Genetic variation in the serotonin transporter modulates neural system-wide response to fearful faces. Genes Brain Behav. 2008;7:543–551. doi: 10.1111/j.1601-183X.2008.00390.x. [DOI] [PubMed] [Google Scholar]
  189. Szeszko PR, Lipsky R, Mentschel C, Robinson D, Gunduz-Bruce H, Sevy S, Ashtari M, Napolitano B, Bilder RM, Kane JM, Goldman D, Malhotra AK. Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Mol. Psychiatry. 2005;10:631–636. doi: 10.1038/sj.mp.4001656. [DOI] [PubMed] [Google Scholar]
  190. Taylor WD, Steffens DC, Payne ME, MacFall JR, Marchuk DA, Svenson IK, Krishnan KR. Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Arch. Gen. Psychiatry. 2005;62:537–544. doi: 10.1001/archpsyc.62.5.537. [DOI] [PubMed] [Google Scholar]
  191. Taylor WD, Zuchner S, Payne ME, Messer DF, Doty TJ, MacFall JR, Beyer JL, Krishnan KR. The COMT Val158Met polymorphism and temporal lobe morphometry in healthy adults. Psychiatry Res. 2007;155:173–177. doi: 10.1016/j.pscychresns.2007.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  192. The Wellcome Trust Case Control Consortium Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–678. doi: 10.1038/nature05911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  193. Vandewalle G, Archer SN, Wuillaume C, Balteau E, Degueldre C, Luxen A, Maquet P, Dijk DJ. Functional magnetic resonance imaging-assessed brain responses during an executive task depend on interaction of sleep homeostasis, circadian phase, and PER3 genotype. J. Neurosci. 2009;29:7948–7956. doi: 10.1523/JNEUROSCI.0229-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  194. Vogt BA. Cingulate Neurobiology and Disease. Oxford University Press; 2009. [Google Scholar]
  195. Wang F, Kalmar JH, He Y, Jackowski M, Chepenik LG, Edmiston EE, Tie K, Gong G, Shah MP, Jones M, Uderman J, Constable RT, Blumberg HP. Functional and structural connectivity between the perigenual anterior cingulate and amygdala in bipolar disorder. Biol. Psychiatry. 2009;66:516–521. doi: 10.1016/j.biopsych.2009.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  196. Whalley HC, Pickard BS, McIntosh AM, Zuliani R, Johnstone EC, Blackwood DH, Lawrie SM, Muir WJ, Hall J. A GRIK4 variant conferring protection against bipolar disorder modulates hippocampal function. Mol. Psychiatry. 2009a;14:467–468. doi: 10.1038/mp.2009.7. [DOI] [PubMed] [Google Scholar]
  197. Whalley HC, Pickard BS, McIntosh AM, Zuliani R, Johnstone EC, Blackwood DH, Lawrie SM, Muir WJ, Hall J. Modulation of hippocampal activation by genetic variation in the GRIK4 gene. Mol. Psychiatry. 2009b;14:465. doi: 10.1038/mp.2009.31. [DOI] [PubMed] [Google Scholar]
  198. Willeit M, Praschak-Rieder N. Imaging the effects of genetic polymorphisms on radioligand binding in the living human brain: a review on genetic neuroreceptor imaging of monoaminergic systems in psychiatry. Neuroimage. 2010;53:878–892. doi: 10.1016/j.neuroimage.2010.04.030. [DOI] [PubMed] [Google Scholar]
  199. Williams LM, Gatt JM, Schofield PR, Olivieri G, Peduto A, Gordon E. Negativity bias’ in risk for depression and anxiety: brain–body fear circuitry correlates, 5-HTTLPR and early life stress. Neuroimage. 2009;47:804–814. doi: 10.1016/j.neuroimage.2009.05.009. [DOI] [PubMed] [Google Scholar]
  200. Williams LM, Gatt JM, Grieve SM, Dobson-Stone C, Paul RH, Gordon E, Schofield PR. COMT Val108/158Met polymorphism effects on emotional brain function and negativity bias. Neuroimage. 2010;53:918–925. doi: 10.1016/j.neuroimage.2010.01.084. [DOI] [PubMed] [Google Scholar]
  201. Willis-Owen SA, Fullerton J, Surtees PG, Wainwright NW, Miller S, Flint J. The Val66Met coding variant of the brain-derived neurotrophic factor (BDNF) gene does not contribute toward variation in the personality trait neuroticism. Biol. Psychiatry. 2005;58:738–742. doi: 10.1016/j.biopsych.2005.05.014. [DOI] [PubMed] [Google Scholar]
  202. Wong ML, Licinio J. Research and treatment approaches to depression. Nat. Rev. Neurosci. 2001;2:343–351. doi: 10.1038/35072566. [DOI] [PubMed] [Google Scholar]
  203. Yamasue H, Kakiuchi C, Tochigi M, Inoue H, Suga M, Abe O, Yamada H, Sasaki T, Rogers MA, Aoki S, Kato T, Kasai K. Association between mitochondrial DNA 10398A>G polymorphism and the volume of amygdala. Genes Brain Behav. 2008;7:698–704. doi: 10.1111/j.1601-183X.2008.00408.x. [DOI] [PubMed] [Google Scholar]
  204. Yu YW, Tsai SJ, Hong CJ, Chen TJ, Chen MC, Yang CW. Association study of a monoamine oxidase a gene promoter polymorphism with major depressive disorder and antidepressant response. Neuropsychopharmacology. 2005;30:1719–1723. doi: 10.1038/sj.npp.1300785. [DOI] [PubMed] [Google Scholar]
  205. Zhou Z, Zhu G, Hariri AR, Enoch MA, Scott D, Sinha R, Virkkunen M, Mash DC, Lipsky RH, Hu XZ, Hodgkinson CA, Xu K, Buzas B, Yuan Q, Shen PH, Ferrell RE, Manuck SB, Brown SM, Hauger RL, Stohler CS, Zubieta JK, Goldman D. Genetic variation in human NPY expression affects stress response and emotion. Nature. 2008;452:997–1001. doi: 10.1038/nature06858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  206. Zuliani R, Moorhead TW, Job D, McKirdy J, Sussmann JE, Johnstone EC, Lawrie SM, Brambilla P, Hall J, McIntosh AM. Genetic variation in the G72 (DAOA) gene affects temporal lobe and amygdala structure in subjects affected by bipolar disorder. Bipolar Disord. 2009;11:621–627. doi: 10.1111/j.1399-5618.2009.00731.x. [DOI] [PubMed] [Google Scholar]

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