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Modulatory effects of brain-derived neurotrophic factor Val66Met polymorphism on prefrontal regions in major depressive disorder

Published online by Cambridge University Press:  02 January 2018

Rebecca MacGregor Legge
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, King's College London
Shahbaz Sendi
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, King's College London
James H. Cole
Affiliation:
Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, UK
Sarah Cohen-Woods
Affiliation:
Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, Australia
Sergi G. Costafreda
Affiliation:
Department of Old Age Psychiatry, Institute of Psychiatry, King's College London
Andrew Simmons
Affiliation:
Department of Neuroimaging, Institute of Psychiatry, National Institute of Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Department of Old Age Psychiatry, Institute of Psychiatry, King's College London
Anne E. Farmer
Affiliation:
Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK
Katherine J. Aitchison
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Peter McGuffin
Affiliation:
NIHR Biomedical Research Centre for Mental Health at South London and Department of Old Age Psychiatry, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London
Cynthia H. Y. Fu*
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, King's College London, and School of Psychology, University of East London, London, UK
*
Dr C. H. Y. Fu, School of Psychology, University of East London, Stratford Campus, Water Lane, London E15 4LZ, UK. Email: [email protected].
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Abstract

Background

Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism contributes to the development of depression (major depressive disorder, MDD), but it is unclear whether neural effects observed in healthy individuals are sustained in MDD.

Aims

To investigate BDNF Val66Met effects on key regions in MDD neurocircuitry: amygdala, anterior cingulate, middle frontal and orbitofrontal regions.

Method

Magnetic resonance imaging scans were acquired in 79 persons with MDD (mean age 49 years) and 74 healthy volunteers (mean age 50 years). Effects on surface area and cortical thickness were examined with multiple comparison correction.

Results

People who were Met allele carriers showed reduced caudal middle frontal thickness in both study groups. Significant interaction effects were found in the anterior cingulate and rostral middle frontal regions, in which participants in the MDD group who were Met carriers showed the greatest reduction in surface area.

Conclusions

Modulatory effects of the BDNF Val66Met polymorphism on distinct subregions in the prefrontal cortex in MDD support the neurotrophin model of depression.

Type
Papers
Copyright
Copyright © Royal College of Psychiatrists, 2015 

Heritability estimates in major depressive disorder (MDD) are of the order of 48–75%. Reference McGuffin, Katz, Watkins and Rutherford1 A potential candidate gene is the brain-derived neurotrophic factor (BDNF) polymorphism which has been linked with an increased incidence of MDD. Reference Verhagen, van der Meij, van Deurzen, Janzing, Arias-Vasquez and Buitelaar2 The BDNF protein is the most common of the neurotrophins and has an important role in synaptic plasticity, neurogenesis, neural growth and differentiation. Reference Maisonpierre, Belluscio, Friedman, Alderson, Wiegand and Furth3,Reference McAllister, Katz and Lo4 A common single nucleotide polymorphism (SNP) at codon 66 of the BDNF gene results in a valine to methionine (Val66Met) substitution, which has a functional impact on cellular packaging, transportation and secretion, and the neurotrophic model proposes that decreased BDNF expression contributes to the development of depression. Reference Duman and Monteggia5 Brain-derived neurotrophic factor is widely distributed within key regions in the neural circuitry of affective processing and in major depressive disorder, including in the anterior cingulate, prefrontal regions, hippocampus and amygdala. Reference Conner, Lauterborn, Yan, Gall and Varon6 However, effects of the BDNF Val66Met polymorphism in healthy individuals have been variable. In subcortical limbic regions, reduced volumes of the hippocampus, Reference Pezawas, Verchinski, Mattay, Callicott and Kolachana7,Reference Montag, Weber, Fliessbach, Elger and Reuter8 and amygdala, Reference Montag, Weber, Fliessbach, Elger and Reuter8 as well as no significant difference, Reference Schofield, Williams, Paul, Gatt, Brown and Luty9,Reference Frodl, Schule, Schmitt, Born, Baghai and Zill10 have been reported in a Met carrier group relative to a Val homozygous group. In prefrontal regions, individuals who were Met carriers showed reduced middle and inferior frontal cortical volumes, Reference Pezawas, Verchinski, Mattay, Callicott and Kolachana7,Reference Nemoto, Ohnishi, Mori, Moriguchi, Hashimoto and Asada11 although no significant difference has been found in orbitofrontal volumes. Reference Gerritsen, Tendolkar, Franke, Vasquez, Kooijman and Buitelaar12

Few studies have examined the effect of the BDNF polymorphism in depression; the main region of interest to date has been the hippocampus which has shown mixed findings, with reduced volume in people who are Met carriers, Reference Frodl, Schule, Schmitt, Born, Baghai and Zill10,Reference Cardoner, Soria, Gratacos, Hernández-Ribas, Pujol and Lopez-Sola13 as well as reduced and increased volume in those homozygous for Val, Reference Gonul, Kitis, Eker, Eker, Ozan and Coburn14,Reference Kanellopoulos, Gunning, Morimoto, Hoptman, Murphy and Kelly15 whereas previously we found no difference between genotypes. Reference Cole, Weinberger, Mattay, Cheng, Toga and Thompson16 However, studies in depression have been limited in their sample characteristics: absence of a healthy control group, Reference Cardoner, Soria, Gratacos, Hernández-Ribas, Pujol and Lopez-Sola13 specified regions of interest (namely, hippocampus and amygdala), Reference Frodl, Schule, Schmitt, Born, Baghai and Zill10,Reference Gonul, Kitis, Eker, Eker, Ozan and Coburn14Reference Cole, Weinberger, Mattay, Cheng, Toga and Thompson16 and measures of white-matter tracts only. Reference Montag, Schoene-Bake, Faber, Reuter and Weber17,Reference Carballedo, Amico, Ugwu, Fagan, Fahey and Morris18 Moreover, the majority of studies have measured regional grey-matter, including grey-matter density. The determinants of grey-matter volume of a given cortical region are its surface area and cortical thickness, which have distinct developmental and genetic origins. Reference Panizzon, Fennema-Notestine, Eyler, Jernigan, Prom-Wormley and Neale19 Volumetric alterations are thus a product of independent or concurrent disparities in the two constituents. In a healthy sample Yang et al found a broad distribution of reduced cortical thickness in Chinese adults who were homozygous for Met compared with those homozygous for Val. Reference Yang, Liu, Sun, Wang, Zeng and Yuan20 To our knowledge, BDNF Val66Met modulation of cortical thickness and surface area has not been investigated in MDD.

We sought to examine the effects of the BDNF Val66Met polymorphism on the neurocircuitry of depression, in particular the amygdala and the prefrontal regions of the anterior cingulate cortex (ACC), middle frontal and orbitofrontal cortices. We investigated the modulatory effects on regional brain volume and its components of surface area and cortical thickness. We expected to observe an effect of Met carrier status in reduced cortical volume in the middle frontal cortices, Reference Pezawas, Verchinski, Mattay, Callicott and Kolachana7,Reference Nemoto, Ohnishi, Mori, Moriguchi, Hashimoto and Asada11 and perhaps in the corresponding cortical thickness, Reference Yang, Liu, Sun, Wang, Zeng and Yuan20 in healthy participants, whereas it was uncertain whether the impact of the polymorphism would be sustained in MDD.

Method

The study was conducted in the UK and approved by the ethics research committee of the Institute of Psychiatry, King’s College London; all participants provided written informed consent. All participants had previously participated in genetic association studies Reference Cohen-Woods, Gaysina, Craddock, Farmer, Gray and Gunasinghe21 and were of White European ancestry. A total of 153 persons were included: 79 patients with a diagnosis of recurrent major depressive disorder and a healthy control group (n = 74) matched for age, gender, handedness and IQ score. All participants in the MDD group met criteria for recurrent MDD, as characterised by the DSM-IV-TR using the Schedules for Clinical Assessment in Neuropsychiatry interview, Reference Wing, Babor, Brugha, Burke, Cooper and Giel22 and the control group was screened to ensure that its members had never experienced a depressive episode. All participants were screened for contraindications to magnetic resonance imaging (MRI), as well as any indication of neurological disorder such as head injury leading to loss of consciousness or conditions known to affect brain structure, such as alcohol or drug misuse. Potential participants were excluded if they or a first-degree relative had ever experienced an episode of mania, hypomania, schizophrenia or mood-incongruent psychosis. The IQ score was measured using the Wechsler Abbreviated Scale of Intelligence, Reference Wechsler23 depressive symptoms with the Beck Depression Inventory Reference Beck, Steer and Brown24 and anxiety symptoms with the State-Trait Anxiety Inventory. Reference Spielberger, Gorsuch, Lushene, Vagg and Jacobs25

Genotyping

Genotyping for the Val66Met BDNF polymorphism was performed using the TaqMan 5′ exonuclease assay (www.appliedbiosystems.com). Reference Cole, Weinberger, Mattay, Cheng, Toga and Thompson16 The BDNF genotypes were divided into three groups: Val/Val, Val/Met and Met/Met. For the purposes of this analysis the groups were combined into Val homozygotes (Val/Val) and Met carrier (Met-allele) groups owing to the small number of participants homozygous for Met.

Image acquisition

Magnetisation-prepared rapid gradient echo (MP-RAGE) T 1-weighted scans were acquired at 1.5 T (Signa HDx 1.5 T system, General Electric, Wisconsin, USA) with the following parameters: time to echo 3.8 ms, repetition time 8.59 ms, flip angle 8°, field of view 24 × 24 cm2, slice thickness 1.2 mm, number of slices 180, image matrix 256 × 256. The MP-RAGE volume was acquired using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) custom pulse sequence, with full brain and skull coverage. Reference Jack, Bernstein, Fox, Thompson, Alexander and Harvey26

Statistical analysis

Grey-matter volumes, surface area and average cortical thickness were measured using Freesurfer Pipeline version 5.1.0 (http://surfer.nmr.mgh.harvard.edu). The analysis involved removal of non-brain tissue using a hybrid watershed/surface deformation procedure, automated Talairach transformation, segmentation of the subcortical white-matter and deep grey-matter volumetric structures, intensity normalisation, tessellation of the grey matter-white matter boundary, automated topology correction and surface deformation following intensity gradients to optimally place the grey/white and grey/cerebrospinal fluid borders at the location where the greatest shift in intensity defined the transition to the other tissue class. Once the cortical models were complete, registration to a spherical atlas took place using individual cortical folding patterns to match cortical geometry across individuals. This was followed by parcellation of the cerebral cortex into units based on gyral and sulcal structures (see online Fig. DS1). The pipeline generated 68 cortical thickness, cortical volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures (34 from each hemisphere) and 46 regional subcortical volumes. All volumetric measures from each participant were normalised by the participant’s intracranial volume; cortical thickness measures were not normalised.

As a priori hypotheses, only the following regions were examined: amygdala, anterior cingulate (rostral and caudal ACC), middle frontal cortex (rostral and caudal) and orbitofrontal cortex (medial and lateral) bilaterally. A multivariate analysis of variance (ANOVA) in SPSS version 21 was used to assess differences between the MDD group and the control group and also between those homozygous for Val and those classified as Met carriers (Val/Met and Met/Met genotypes). False discovery rate (FDR) was used to adjust for multiple comparisons resulting from the ANOVA models with α = 0.05. Reference Benjamini and Hochberg27

Results

The demographic characteristics of the sample are given in Table 1, and the clinical details of the participants with depression are summarised in Table 2. Most participants with depression were taking at least one antidepressant medication (n = 58), although some were not taking any medication at the time of the MRI scan (n = 21). The antidepressant medications encompassed selective serotonin reuptake inhibitors, serotonin-noradrenergic reuptake inhibitors, tricyclic antidepressants and other antidepressant classes. In addition, 7 patients were taking additional drugs for augmentation of the antidepressant medication: mood stabilisers, benzodiazepines, antipsychotic medication and thyroxine.

Table 1 Demographic features of the participants

MDD groupFootnote a
(n = 79)
Control group
(n = 74)
Age, years: mean (s.d.) 49.09 (8.96) 50.92 (7.82) F = 1.803, P = 0.181
Gender, n 27 M, 52 F 34 M, 40 F χ2 = 2.207, P = 0.137
Verbal IQ score: mean (s.d.) 117.44 (11.59) 119.04 (8.74) F = 0.917, P = 0.340
Handedness,Footnote b n
 Right 69 63 χ2 = 0.390, P = 0.823
 Left 8 10
BDNF status, n
 Met carrier 33 27 χ2 = 0.448, P = 0.503
 Val/Val 46 47

BDNF, brain-derived neurotrophic factor; F, female; M, male; MDD, major depressive disorder.

a. Data were missing for one person in the MDD group.

b. One person in each group was ambidextrous.

Table 2 Clinical features of the participants with major depressive disorder

Allele statusFootnote a
Met carrier
(n = 33)
Val/Val
(n = 46)
Assessment scores: mean (s.d.)
 BDI 15.6 (11.6) 15.6 (11.1)
 STAI 39.2 (11.1) 38.6 (10.2)
Age at onset, years: mean (s.d.) 20.3 (9.5) 20.3 (9.7)
Number of previous episodes: mean (s.d.) 4.4 (3.2) 4.2 (3.2)
History of suicide attempt, n (%) 14 (42) 19 (41)
History of ECT, n (%) 2 (6) 4 (9)
History of hospital admissions, n (%) 9 (27) 13 (28)
Current antidepressant medication, n (%) 26 (79) 32 (70)

BDI, Beck Depression Inventory; ECT, electroconvulsive therapy; MDD, major depressive disorder; STAI, State Trait Anxiety Inventory.

a. There was no significant difference between the two groups for any of the measures.

A significant effect of genotype that survived FDR correction for multiple comparisons was found in the left caudal middle frontal cortex (Brodmann’s area 6) in cortical thickness, in which the Met carrier subgroup showed greater reduction in cortical thickness compared with the Val homozygote subgroup in both the MDD and control groups: Val/Val MDD group 2.474 mm (s.d. = 0.138); Met-allele MDD group 2.434 mm (s.d. = 0.177); Val/Val control group 2.491 mm (s.d. = 0.158); Met-allele control group 2.36 mm (s.d. = 0.141); F (1,149) = 11.029, P = 0.001 (Fig. 1). Significant interaction effects that survived multiple comparison correction were found in the surface area in three regions:

Fig 1 A significant main effect of BDNF Val66Met polymorphism which survived correction for multiple comparisons was found in the left caudal middle frontal region (Brodmann’s area 6). Participants who carried the Met allele showed the greatest reduction in cortical thickness in both the major depressive disorder (MDD) and control groups. Boxplots indicate interquartile range, median and range.

  1. (a) right caudal anterior cingulate (Fig. 2): Val/Val MDD 782.48 mm2, s.d. = 140.17; Met-allele MDD 711.00 mm2, s.d. = 150.61; Val/Val control 764.02 mm2, s.d. = 153.13; Metallele control 869.26 mm2, s.d. = 186.70; F (1,149) = 11.73, P = 0.001;

    Fig 2 The significant interaction effect in the right caudal anterior cingulate. Participants with major depressive disorder (MDD) who carried the Met allele showed the greatest reduction in surface area compared with those homozygous for Val in both the MDD and control groups as well as with those who were Met carriers in the control group. Boxplots indicate interquartile range, median and range.

  2. (b) right rostral middle frontal cortex: Val/Val MDD 5975.30 mm2, s.d. = 667.01; Met-allele MDD 5592.39 mm2, s.d. = 611.78; Val/Val control 5673.02 mm2, s.d. = 754.37; Met-allele control 6125.70 mm2, s.d. = 671.12; F (1,149) = 13.49, P = 0.0003;

  3. (c) left rostral middle frontal cortex: Val/Val MDD 5723.28 mm2, s.d.633.79; Met-allele MDD 5383.06 mm2, s.d. = 473.55; Val/Val control 5477.19 mm2, s.d. = 677.13; Met-allele control 5890.04 mm2, s.d. = 711.31; F (1,149) = 12.87, P = 0.0005.

In all of these regions reduced surface area was associated with Met carrier status relative to Val homozygosity in MDD, whereas the inverse was observed in the control group. No other main or interaction effect remained significant following multiple comparison correction, including in the amygdala: MDD left amygdala 1385.24 mm3, s.d. = 219.40, MDD right 1482.92 mm3, s.d. = 231.58; control left 1433.81 mm3, s.d. = 257.16, control right 1552.50 mm3, s.d. = 271.31; Val/Val left 1432.30 mm3, s.d. = 249.52, Val/Val right 1537.49 mm3, s.d. = 250.53; Met-allele left 1372.20 mm3, s.d. = 218.36, Met-allele right 1484.20 mm3, s.d. = 255.90 (all P>0.3).

Discussion

Modulatory effects of the BDNF Val66Met polymorphism as well as genotype by diagnosis interactions were revealed in key nodes in the neurocircuitry of MDD. Distinct effects were observed in the anterior cingulate and subregions of the middle frontal cortices implicating converging yet separate influences of the disease process, genetic modulation and their interaction.

Changes in prefrontal cortical regions

Pezawas et al provided the first report of an effect of the BDNF Val66Met polymorphism in the caudal middle frontal cortex, revealing reduced grey-matter volume in healthy individuals who were Met carriers compared with those homozygous for Val. Reference Pezawas, Verchinski, Mattay, Callicott and Kolachana7 The potential contribution of cortical thinning to the volumetric reductions has recently been added, Reference Yang, Liu, Sun, Wang, Zeng and Yuan20 and reduced middle frontal activity has also been observed in a healthy Met carrier group. Reference Schofield, Williams, Paul, Gatt, Brown and Luty9 In our study we found a main effect of Met carrier status on cortical thinning in the caudal middle frontal cortex in both healthy individuals and in people with depression. Our study extends both the observation by Yang et al in healthy Chinese adults, Reference Yang, Liu, Sun, Wang, Zeng and Yuan20 and the original finding, Reference Pezawas, Verchinski, Mattay, Callicott and Kolachana7 by localising the contribution of cortical thinning to the reductions in caudal middle frontal volume. Moreover, this is the first report of the extent that Met carrier status leads to middle frontal cortical thinning, as the effect of the Met allele appears to supersede the disease process effects of MDD on cortical thickness. Grey-matter reductions in the caudal middle frontal region have been frequently observed in MDD, with further reductions in recurrent MDD. Reference Bora, Fornito, Pantelis and Yucel28 Our findings indicate that the impact of the BDNF Val66Met polymorphism on cortical thinning in the caudal middle frontal region is even greater than that of recurrent MDD. It is possible that the influence of the Met allele in this region may not necessarily be causal in itself for MDD, but may be a predisposing, contributory factor in association with other neurogenic effects. Stress is linked to an increase in cortisol, which in turn causes a reduction in BDNF. Reference Castren, Voikar and Rantamaki29 Individuals who are Met carriers are less able to compensate for this BDNF reduction owing to deficient transport of the BDNF preprotein which causes clumping around the nucleus, perhaps leading to neuronal atrophy in response to reduced BDNF levels. Reference McAllister, Katz and Lo4,Reference Duman and Monteggia5

We also observed a significant interaction effect in surface area in the caudal anterior cingulate and rostral middle frontal cortices. The anterior cingulate and middle frontal cortices are key regions in the neurocircuitry of mood disorders, and the anterior cingulate is a well-replicated predictive marker of clinical response in MDD, Reference Fu, Steiner and Costafreda30 which is evident at the individual level. Reference Costafreda, Chu, Ashburner and Fu31 Surface area and cortical thickness have independent genetic and developmental origins. Reference Panizzon, Fennema-Notestine, Eyler, Jernigan, Prom-Wormley and Neale19 The radial unit hypothesis proposes that cortical thickness is determined by the number of cells within a neuronal column, and cortical surface area is determined by the number of neuronal columns. Reference Rakic32 There is support for a general regional expansion of surface area from childhood into adolescence, particularly in boys, Reference Koolschijn and Crone33 followed by subsequent decreases in adulthood with increasing age, Reference Hogstrom, Westlye, Walhovd and Fjell34 whereas cortical thinning is a pronounced feature of adolescence that continues into adulthood. Reference Koolschijn and Crone33 There is, however, some notable variability in the regional changes, Reference Koolschijn and Crone33,Reference Hogstrom, Westlye, Walhovd and Fjell34 for example the surface area of the anterior cingulate cortex may show relatively fewer changes in adulthood. Reference Koolschijn and Crone33 Modulation of grey-matter density by the BDNF Val66Met polymorphism in both the anterior cingulate and middle frontal cortices has been found in bipolar disorder, Reference Matsuo, Walss-Bass, Nery, Nicoletti, Hatch and Frey35 and in the anterior cingulate in healthy individuals with a history of childhood abuse, Reference Gerritsen, Tendolkar, Franke, Vasquez, Kooijman and Buitelaar12 whereas no prefrontal regional effect has been reported in schizophrenia. Reference Ho, Milev, O'Leary, Librant, Andreasen and Wassink36 Our study localises the genetic influence to cortical surface area in its contribution to grey-matter volume, which had not been examined in previous studies in patient populations. Reference Matsuo, Walss-Bass, Nery, Nicoletti, Hatch and Frey35,Reference Ho, Milev, O'Leary, Librant, Andreasen and Wassink36 It is possible that the surface area reduction we observed is an endophenotype for MDD, or more generally for mood disorders including bipolar disorder, or it may also be a feature in schizophrenia that has not been well captured by morphometric studies to date. The lack of an association of BDNF with schizophrenia, Reference Kawashima, Ikeda, Kishi, Kitajima, Yamanouchi and Kinoshita37 however, suggests that the effect may be more strongly expressed in mood disorders.

Effects on amygdala volume

Contrary to our hypothesis, no significant interaction effect was found in the grey-matter volume of the amygdala. Our observations are consistent with findings in healthy individuals carrying the BDNF Met allele with a history of childhood adversity and in depression, Reference Frodl, Schule, Schmitt, Born, Baghai and Zill10,Reference Gerritsen, Tendolkar, Franke, Vasquez, Kooijman and Buitelaar12 but there have also been reports of reduced amygdala volume in healthy people with and without a history of stressful events. Reference Montag, Weber, Fliessbach, Elger and Reuter8,Reference Costafreda, Chu, Ashburner and Fu31 Similarly, studies of amygdala responsivity have been mixed, with reports of significant effects, Reference Montag, Reuter, Newport, Elger and Weber38,Reference Gasic, Smoller, Perlis, Sun, Lee and Kim39 but more frequently of no impact, Reference Schofield, Williams, Paul, Gatt, Brown and Luty9,Reference Hariri, Goldberg, Mattay, Kolachana, Callicott and Egan40,Reference Hashimoto, Moriguchi, Yamashita, Mori, Nemoto and Okada41 of the BDNF polymorphism. The literature on amygdala volume in depression, however, is inconsistent, with some suggestion that amygdala volume is reduced in more chronic forms of depression. Reference Drevets, Price and Furey42 In our study, participants had a history of recurrent depression characterised by discrete acute depressive episodes with periods of euthymia rather than a more chronic, treatment-resistant type of depression. Our findings are most comparable with those of Frodl et al, who similarly did not observe any effect of diagnosis or BDNF genotype on amygdala volume. Reference Frodl, Schule, Schmitt, Born, Baghai and Zill10

Study limitations

We limited our analysis to a priori defined regions in the prefrontal cortex and the amygdala within the neurocircuitry of depression. However, our sample size was relatively modest and replication in an independent sample is required. Cortical thinning in the medial orbitofrontal region has been reported in first-episode depression, Reference Grieve, Korgaonkar, Koslow, Gordon and Williams43 and in a younger cohort of patients with MDD (mean age 34 years) than in our study. Reference Li, Zhang, Wang, Chen, Fan and Guan44 Although there was evidence of left medial orbitofrontal cortical thinning in MDD patients relative to healthy controls in our sample, this difference did not survive correction for multiple comparisons. Furthermore, BDNF Val66Met effects have also been observed in the temporal and parietal cortices in healthy Chinese adults. Reference Yang, Liu, Sun, Wang, Zeng and Yuan20 There may also be a modulatory effect of early life stress, as healthy individuals carrying the Met allele with a history of greater stressful events have shown reduced grey-matter volume in the amygdala and hippocampus, Reference Gatt, Nemeroff, Dobson-Stone, Paul, Bryant and Schofield45 and also in the anterior cingulate cortex, Reference Gerritsen, Tendolkar, Franke, Vasquez, Kooijman and Buitelaar12 compared with a group homozygous for Val. Another limitation of our study is the absence of data on the history of possible childhood trauma in our participants, although it is unclear whether these effects would persist alongside the pathophysiological effects of the illness. Moreover, most of the participants with MDD were taking antidepressant medication, which may have had an effect on BDNF levels, Reference Duman and Monteggia5 and in turn potentially on neural volumes. Reference Schmidt and Duman46 The anterior cingulate region has been consistently identified as a predictive marker of clinical response, Reference Fu, Steiner and Costafreda30 and the BDNF polymorphism has shown an association with treatment response. Reference Licinio, Dong and Wong47 Furthermore, an interaction effect of BDNF and its high-affinity receptor, neurotrophic tyrosine kinase receptor 2 (NTRK2), gene polymorphisms has been associated with the development of treatment-resistant depression. Reference Van Eijndhoven, van Wingen, Katzenbauer, Groen, Tepest and Fernandez48 How these potentially complementary markers may interact at a neural level in predicting clinical response requires further investigation. Neural correlates of BDNF associations with clinical response in a sample of patients who were medication-free and perhaps in their first episode of depression would elucidate the effects of medication, recurrent episodes of depression and prediction of clinical response.

Concluding remarks

In summary, we demonstrated sustained effects of the BDNF Val66Met polymorphism on distinct subregions in the prefrontal cortex in depression. The effects in the caudal middle frontal regions exceeded those of the illness as Met carrier status was associated with greater cortical thinning in both MDD and control groups. Effects in the anterior cingulate and rostral middle frontal regions revealed an interaction with BDNF Val66Met genotype, in which the MDD Met carrier group showed the greatest reduction in surface area. Our findings specify the anterior cingulate and middle frontal regions as key regions within the neurotrophin hypothesis of depression.

Funding

The study was funded in part by GlaxoSmithKline UK, the National Institute of Health Research Biomedical Research Centre for Mental Health at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London, Institute of Psychiatry, and a NARSAD (Brain and Behaviour Research Foundation) Young Investigator Award to C.F. The GENDEP study was funded by the European Commission Framework 6 grant, EC contract reference: LSHB-CT-2003-503428. Funding for the Depression Case Control study was provided by the Medical Research Council (MRC). J.C. was funded by an MRC studentship and a Wellcome Trust Value in People award. K.A. holds an Alberta Centennial Addiction and Mental Health Research Chair funded by the Government of Alberta, Canada.

Footnotes

These authors contributed equally to the paper.

Declaration of interest

None.

References

1 McGuffin, P, Katz, R, Watkins, S, Rutherford, J. A hospital-based twin register of the heritability of DSM-IV unipolar depression. Arch Gen Psychiatry 1996; 53: 129–36.Google Scholar
2 Verhagen, M, van der Meij, A, van Deurzen, PA, Janzing, JG, Arias-Vasquez, A, Buitelaar, JK, et al. Meta-analysis of the BDNF Val66Met polymorphism in major depressive disorder: effects of gender and ethnicity. Mol Psychiatry 2010; 15: 260–71.Google Scholar
3 Maisonpierre, PC, Belluscio, L, Friedman, B, Alderson, RF, Wiegand, SJ, Furth, ME, et al. NT-3, BDNF, and NGF in the developing rat nervous system: parallel as well as reciprocal patterns of expression. Neuron 1990; 5: 501–9.Google Scholar
4 McAllister, AK, Katz, LC, Lo, DC. Neurotrophins and synaptic plasticity. Annu Rev Neurosci 1999; 22: 295318.CrossRefGoogle ScholarPubMed
5 Duman, RS, Monteggia, LM. A neurotrophic model for stress related mood disorders. Biol Psychiatry 2006; 59: 1116–27.CrossRefGoogle ScholarPubMed
6 Conner, JM, Lauterborn, JC, Yan, Q, Gall, CM, Varon, S. Distribution of brain-derived neurotrophic factor (BDNF) protein and mRNA in the normal adult rat CNS: evidence for anterograde axonal transport. J Neurosci 1997; 17: 2295–313.Google Scholar
7 Pezawas, L, Verchinski, BA, Mattay, VS, Callicott, JH, Kolachana, BS. The brain-derived neurotrophic factor Val66Met polymorphism and variants in human cortical morphology. J Neurosci 2004; 24: 10099–102.Google Scholar
8 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: 1831–9.Google Scholar
9 Schofield, PR, Williams, LM, Paul, RH, Gatt, JM, Brown, K, Luty, A, et al. Disturbances in selective information processing associated with the BDNF Val66Met polymorphism: evidence from cognition, the P300 and fronto-hippocampal systems. Biol Psychol 2009; 80: 176–88.Google Scholar
10 Frodl, T, Schule, C, Schmitt, G, Born, C, Baghai, T, Zill, P, et al. Association of the brain-derived neurotrophic factor Val66Met polymorphism with reduced hippocampal volumes in major depression. Arch Gen Psychiatry 2007; 64: 410–6.Google Scholar
11 Nemoto, K, Ohnishi, T, Mori, T, Moriguchi, Y, Hashimoto, R, Asada, T, et al. The Val66Met polymorphism of the brain-derived neurotrophic factor gene affects age-related brain morphology. Neurosci Lett 2006; 397: 25–9.Google Scholar
12 Gerritsen, L, Tendolkar, I, Franke, B, Vasquez, AA, Kooijman, S, Buitelaar, J, et al. BDNF Val66Met genotype modulates the effect of childhood adversity on subgenual anterior cingulate cortex volume in healthy subjects. Mol Psychiatry 2012; 17: 597603.Google Scholar
13 Cardoner, N, Soria, V, Gratacos, M, Hernández-Ribas, R, Pujol, J, Lopez-Sola, M, et al. Val66Met BDNF genotypes in melancholic depression: effects on brain structure and treatment outcome. Depress Anxiety 2013; 30: 225–33.Google Scholar
14 Gonul, AS, Kitis, O, Eker, MC, Eker, OD, Ozan, E, Coburn, K. Association of the brain-derived neurotrophic factor Val66Met polymorphism with hippocampus volumes in drug-free depressed patients. World J Biol Psychiatry 2011; 12: 110–8.Google Scholar
15 Kanellopoulos, D, Gunning, FM, Morimoto, SS, Hoptman, MJ, Murphy, CF, Kelly, RE, et al. Hippocampal volumes and the brain-derived neurotrophic factor val66met polymorphism in geriatric major depression. Am J Geriatr Psychiatry 2011; 19: 1322.CrossRefGoogle ScholarPubMed
16 Cole, J, Weinberger, DR, Mattay, VS, Cheng, X, Toga, AW, Thompson, PM, et al. No effect of 5HTTLPR or BDNF Val66Met polymorphism on hippocampal morphology in major depression. Genes Brain Behav 2011; 10: 756–64.Google Scholar
17 Montag, C, Schoene-Bake, JC, Faber, J, Reuter, M, Weber, B. Genetic variation on the BDNF gene is not associated with differences in white matter tracts in healthy humans measured by tract-based spatial statistics. Genes Brain Behav 2010; 9: 886–91.CrossRefGoogle Scholar
18 Carballedo, A, Amico, F, Ugwu, I, Fagan, AJ, Fahey, C, Morris, D, et al. Reduced fractional anisotropy in the uncinate fasciculus in patients with major depression carrying the met-allele of the Val66Met brain-derived neurotrophic factor genotype. Am J Med Genet B Neuropsychiatr Genet 2012; 159B: 537–48.Google Scholar
19 Panizzon, MS, Fennema-Notestine, C, Eyler, LT, Jernigan, TL, Prom-Wormley, E, Neale, M, et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 2009; 19: 2728–35.Google Scholar
20 Yang, X, Liu, P, Sun, J, Wang, G, Zeng, F, Yuan, K, et al. Impact of brain-derived neurotrophic factor Val66Met polymorphism on cortical thickness and voxel-based morphometry in healthy Chinese young adults. PLoS One 2012; 7: e37777.Google Scholar
21 Cohen-Woods, S, Gaysina, D, Craddock, N, Farmer, A, Gray, J, Gunasinghe, C, et al. Depression case control (DeCC) Study fails to support involvement of the muscarinic acetylcholine receptor M2 (CHRM2) gene in recurrent major depressive disorder. Hum Mol Genet 2009; 18: 1504–9.CrossRefGoogle ScholarPubMed
22 Wing, JK, Babor, T, Brugha, T, Burke, J, Cooper, J, Giel, R, et al. SCAN: schedules for clinical assessment in neuropsychiatry. Arch Gen Psychiatry 1990; 47: 589–93.Google Scholar
23 Wechsler, D. Wechsler Abbreviated Scale of Intelligence. Psychological Corporation, 1999.Google Scholar
24 Beck, AT, Steer, RA, Brown, GK. Beck Depression Inventory. Psychological Corporation, 1993.Google Scholar
25 Spielberger, CD, Gorsuch, RL, Lushene, R, Vagg, PR, Jacobs, GA. Manual for the State-Trait Anxiety Inventory. Consulting Psychologists Press, 1983.Google Scholar
26 Jack, CR, Bernstein, MA, Fox, NC, Thompson, P, Alexander, G, Harvey, D, et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008; 27: 685–91.Google Scholar
27 Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B 1995; 57: 289300.Google Scholar
28 Bora, E, Fornito, A, Pantelis, C, Yucel, M. Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. J Affect Dis 2012; 138: 918.Google Scholar
29 Castren, E, Voikar, V, Rantamaki, T. Role of neurotrophic factors in depression. Curr Opin Pharmacol 2007; 7: 1821.Google Scholar
30 Fu, CHY, Steiner, H, Costafreda, SG. Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 2013; 52: 7583.Google Scholar
31 Costafreda, SG, Chu, C, Ashburner, J, Fu, CHY. Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 2009; 4: e6353.Google Scholar
32 Rakic, P. Specification of cerebral cortical areas. Science 1988; 241: 170–6.Google Scholar
33 Koolschijn, PC, Crone, EA. Sex differences and structural brain maturation from childhood to early adulthood. Dev Cogn Neurosci 2013; 5: 106–18.Google Scholar
34 Hogstrom, LJ, Westlye, LT, Walhovd, KB, Fjell, AM. The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb Cortex 2013; 23: 2521–30.Google Scholar
35 Matsuo, K, Walss-Bass, C, Nery, FG, Nicoletti, MA, Hatch, JP, Frey, BN. Neuronal correlates of brain-derived neurotrophic factor Val66Met polymorphism and morphometric abnormalities in bipolar disorder. Neuropsychopharmol 2009; 34: 1904–13.Google Scholar
36 Ho, BC, Milev, P, O'Leary, DS, Librant, A, Andreasen, NC, Wassink, TH. Cognitive and magnetic resonance imaging brain morphometric correlates of brain-derived neurotrophic factor Val66Met gene polymorphism in patients with schizophrenia and healthy volunteers. Arch Gen Psychiatry 2006; 63: 731–40.Google Scholar
37 Kawashima, K, Ikeda, M, Kishi, T, Kitajima, T, Yamanouchi, Y, Kinoshita, Y, et al. BDNF is not associated with schizophrenia: data from a Japanese population study and meta-analysis. Schizophr Res 2009; 112: 72–9.Google Scholar
38 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; 42:1554–9.Google Scholar
39 Gasic, GP, Smoller, JW, Perlis, RH, Sun, M, Lee, S, Kim, BW. BDNF, relative preference, and reward circuitry responses to emotional communication. Am J Med Genet B Neuropsychiatr Genet 2009; 150B: 762–81.Google Scholar
40 Hariri, AR, Goldberg, TE, Mattay, VS, Kolachana, BS, Callicott, JH, Egan, MF. Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J Neurosci 2003; 23: 6690–4.Google Scholar
41 Hashimoto, R, Moriguchi, Y, Yamashita, F, Mori, T, Nemoto, K, Okada, T. Dose-dependent effect of the Val66Met polymorphism of the brain-derived neurotrophic factor gene on memory-related hippocampal activity. Neurosci Res 2008; 61: 360–7.Google Scholar
42 Drevets, WC, Price, JL, Furey, ML. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct 2008; 213: 93118.Google Scholar
43 Grieve, SM, Korgaonkar, MS, Koslow, SH, Gordon, E, Williams, LM. Widespread reductions in gray matter volume in depression. Neuroimage Clin 2013; 3: 332–9.Google Scholar
44 Li, Z, Zhang, Y, Wang, Z, Chen, J, Fan, J, Guan, Y, et al. The role of BDNF, NTRK2 gene and their interaction in development of treatment-resistant depression: data from multicenter, prospective, longitudinal clinic practice. J Psychiatr Res 2013; 47: 814.Google Scholar
45 Gatt, JM, Nemeroff, CB, Dobson-Stone, C, Paul, RH, Bryant, RA, Schofield, PR, et al. Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety. Mol Psychiatry 2009; 14: 681–95.Google Scholar
46 Schmidt, HD, Duman, DS. Peripheral BDNF produces antidepressant-like effects in cellular and behavioral models. Neuropsychopharmol 2010; 35: 2378–91.Google Scholar
47 Licinio, J, Dong, C, Wong, ML. Novel sequence variations in the brain-derived neurotrophic factor gene and association with major depression and antidepressant treatment response. Arch Gen Psychiatry 2009; 66: 488–97.Google Scholar
48 Van Eijndhoven, P, van Wingen, G, Katzenbauer, M, Groen, W, Tepest, R, Fernandez, G, et al. Paralimbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation. Am J Psychiatry 2013; 170: 1477–86.Google Scholar
Figure 0

Table 1 Demographic features of the participants

Figure 1

Table 2 Clinical features of the participants with major depressive disorder

Figure 2

Fig 1 A significant main effect of BDNF Val66Met polymorphism which survived correction for multiple comparisons was found in the left caudal middle frontal region (Brodmann’s area 6). Participants who carried the Met allele showed the greatest reduction in cortical thickness in both the major depressive disorder (MDD) and control groups. Boxplots indicate interquartile range, median and range.

Figure 3

Fig 2 The significant interaction effect in the right caudal anterior cingulate. Participants with major depressive disorder (MDD) who carried the Met allele showed the greatest reduction in surface area compared with those homozygous for Val in both the MDD and control groups as well as with those who were Met carriers in the control group. Boxplots indicate interquartile range, median and range.

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