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Neuropsychopharmacology. 2013 February; 38(3): 525–532.
Published online 2012 November 7. doi:  10.1038/npp.2012.214
PMCID: PMC3547204

Effects of the BDNF Val66Met Polymorphism on White Matter Microstructure in Healthy Adults

Abstract

The BDNF Val66Met polymorphism, a possible risk variant for mental disorders, is a potent modulator of neural plasticity in humans and has been linked to deficits in gray matter structure, function, and cognition. The impact of the variant on brain white matter structure, however, is controversial and remains poorly understood. Here, we used diffusion tensor imaging to examine the effects of BDNF Val66Met genotype on white matter microstructure in a sample of 85 healthy Caucasian adults. We demonstrate decreases of fractional anisotropy and widespread increases in radial diffusivity in Val/Val homozygotes compared with Met-allele carriers, particularly in prefrontal and occipital pathways. These data provide an independent confirmation of prior imaging genetics work, are consistent with complex effects of the BDNF Val66Met polymorphism on human brain structure, and may serve to generate hypotheses about variation in white matter microstructure in mental disorders associated with this variant.

Keywords: brain-derived neurotrophic factor, diffusion tensor imaging, radial diffusivity, myelin, working memory

INTRODUCTION

Secretory neurotrophins are abundantly expressed in the mammalian brain and are potent modulators of synaptic plasticity and neuronal survival (Lu et al, 2005). Specifically, brain-derived neurotrophic factor (BDNF) has been identified as a key regulator of synaptogenesis and memory formation, particularly in the medial temporal lobe (Minichiello, 2009). A frequent non-synonymous coding variant in the 5′ proregion of the BDNF gene (Val66Met, rs6265) has been linked to abnormal activity-dependent secretion of BDNF, deficits in neural activation and cognition (Egan et al, 2003), the modulation of other genes (Pezawas et al, 2008; Tan et al, 2011) and the effects of environmental adversity on neural structure and function (Gatt et al, 2009; Gerritsen et al, 2011). Considering its pivotal role in neurodevelopment and adaptation, the variant has also received considerable attention as a candidate gene locus for mental disorder and treatment response (Martinowich et al, 2007). Among others, BDNF Val66Met has previously been implicated as genetic risk factor for bipolar disorder (Sklar et al, 2002), schizophrenia (Neves-Pereira et al, 2005), and depression (Verhagen et al, 2010), although the validity of the disease associations is subject to much debate (Zhang et al, 2006).

On the brain structural level, in vivo neuroimaging has provided fairly consistent evidence for gray matter volume reductions in healthy BDNF Met66-allele carriers, particularly in limbic areas such as hippocampus and amygdala, at least as inferred from measurements on MRI scans (Ho et al, 2006; Montag et al, 2009; Pezawas et al, 2004). Given that BDNF may target non-neuronal cell types (Cui et al, 2010) and modulates myelinogenesis (Du et al, 2003), it appears plausible that the variant may also impact the microstructure of white matter tracts. The in vivo examination of this question in healthy young volunteers has gained increasing impetus in the recent imaging genetics literature, but initial diffusion tensor imaging (DTI) studies have produced conflicting results. Specifically, one study (Soliman et al, 2010) conducted in 82 healthy individuals linked the BDNF Met66 allele to reductions in fractional anisotropy (FA) in the uncinate fasciculus (UF), a frontal-limbic fiber tract. Other investigators such as Montag et al (2010) (sample size: n=99 subjects), however, were unable to detect significant effects of BDNF Val66Met on FA or found, like Voineskos et al (2011) (sample size: n=69 subjects), significant interactions of FA with age in samples including elderly subjects. In the hitherto largest study on this topic (n=455), Chiang et al (2011) reported FA reductions in Val66 allele homozygotes in prefrontal and occipital pathways, and found significant correlations of FA with cognitive performance in Val/Val homozygotes.

Among neuroimaging methods, DTI is the most appropriate for the non-invasive quantification of white matter tract morphology and microstructure in humans. It has been demonstrated that DTI measures reflect heritable components of brain structure (Chiang et al, 2009; Kochunov et al, 2010), and are useful to delineate potential fiber tracts anomalies related to psychiatric genetic risk variants. Specifically, genetic associations of risk variants for mood disorders with FA in frontolimbic tracts relevant for emotion processing have been repeatedly described (Alexopoulos et al, 2009; Pacheco et al, 2009), as well as associations of schizophrenia risk variants linked to altered neurodevelopment with FA in prefrontal cognitive pathways (McIntosh et al, 2008; Zuliani et al, 2011). Moreover, individual differences in prefrontal white matter microstructure, as indexed by DTI, have been shown to be associated with cognitive behavior in healthy humans. This is suggested, for example, by reports on the association of FA in fronto-basal ganglia tracts and response inhibition (King et al, 2012), or tractography-derived estimates of tract strength of PFC-hippocampus pathways and long-term memory performance (Cohen, 2011).

In recent years, imaging genetics has emerged as a popular approach with which to explore the effects of genetic variation on measures of brain structure and function, but initial studies are often followed by inconsistent results. Consequently, and analogous to clinical and molecular genetics studies, independent replication is important to gain confidence in the validity of reported findings. In order to minimize spurious associations, we used an ethnically homogenous sample and outcome measures such as FA and radial diffusivity (RD) that are known to be heritable (Chiang et al, 2009; Kochunov et al, 2010). We also attempted to apply rigorous methodology during diffusion imaging acquisition and processing by using a cardiac gated sequence, acquiring isotropic voxels (Smith et al, 2007), excluding data affected by motion, correcting for image distortions caused by magnetic susceptibility (Embleton et al, 2010) and using methods to enhance accuracy of normalization in group analyses (Smith et al, 2006).

In this work, we used advanced DTI methods to examine the impact of the BDNF Val66Met variant on DTI estimates of white matter microstructure in a carefully screened sample of healthy Caucasian adults. We investigated FA because this is the index that the majority of the prior literature has focused on. In addition, we analyzed RD and axial diffusivity (AD) in order to better interpret findings with FA. Increases in RD have previously been linked to myelin defects in animal models (Song et al, 2002, 2005), while AD has been considered as an index of axonal integrity (Song et al, 2003; Sun et al, 2006). This dichotomy, though useful for interpretation of the underlying biology of changes in diffusion metrics in very specific contexts, should be viewed with caution in healthy brain. To date, little is known about the effects of BDNF on brain white matter per se, and prior DTI work on this variant has yielded conflicting results. Nevertheless, we expected to observe significant decreases of FA in Met-allele carriers given the results of the large sample published by Chiang et al (2011), but we could not clearly predict the directionality of RD and AD because the only study to assess the effect of the BDNF Val66Met variant on these quantities was negative (Montag et al, 2010).

MATERIALS AND METHODS

Participants

Eighty-five healthy volunteers (mean age=33.5±9.6 years, 46 males) were recruited for this research as part of the NIMH Clinical Brain Disorders Branch ‘Sibling Study' (Egan et al, 2001), an ongoing investigation of neurobiological abnormalities related to genetic risk for schizophrenia (protocol 95-M-0150, principal investigator: Daniel R Weinberger). All recruited subjects were unrelated healthy volunteers, 18–55 years of age, above 75 in IQ, and able to give informed consent. No schizophrenia patients or first-degree relatives of patients were included. To minimize the possibility of misleading associations due to ethnic stratification, only Caucasian individuals of self-identified European descent were included in this study. Structured clinical interviews for DSM-IV were acquired by a research psychiatrist to ensure the absence of a lifetime history of mental or neurological disorder. Other specific exclusion criteria included significant medical problems, history of head trauma, and prior alcohol or drug abuse. In addition, to exclude the presence of asymptomatic brain abnormalities such as subtle white matter lesions (WML), all participants were subjected to a clinical magnetic resonance imaging protocol evaluated by a trained neuroradiologist. All participants provided written informed consent for the ‘sibling study' protocol approved by the NIMH Institutional Review Board.

Genotyping

We used standard methods to extract DNA from white blood cells with the Puregene DNA purification kit (Gentra Systems). The Val66Met single-nucleotide polymorphisms (rs6265) in the 5' proregion of BDNF was determined by the Taqman allelic discrimination assay as previously described (Egan et al, 2003). The observed genotype distribution of rs6265 did not deviate from Hardy–Weinberg equilibrium (n=50 Val/Val, n=32 Val/Met, n=3 Met/Met; P>0.05). Notably, the low frequency of the BDNF Met allele (0.18) and the resulting small number of Met/Met homozygotes (n=3) precluded independent statistical analysis of this genotype group. Thus, analogous to previous DTI studies on this variant (Ho et al, 2006; Kennedy et al, 2009; Montag et al, 2010; Soliman et al, 2010), Val/Met and Met/Met individuals were merged in one group for all analyses. Subject demographics stratified by BDNF genotype are reported in Table 1. No significant group differences were observed.

Table 1
Subject Demographic Information Stratified by BDNF Genotype

Diffusion Tensor Imaging

Magnetic resonance imaging was performed on a 1.5-T Signa scanner (General Electric, Waukesha, WI) equipped with an 8-channel radio frequency coil. Head motion was minimized with a deflatable bead filled pillow and a restraining head band. Whole-brain DTI data were acquired with an axial single shot echo planar imaging (EPI) sequence and cardiac gating for each slice (TE=89 ms, 80 × 2 mm2 thick slices, 2 × 2 mm2 in-plane resolution). Diffusion-weighted gradients were applied in 120 non-collinear directions, acquiring 10 images at each b value of 0, 109, 327, and 545 s/mm2, 30 directions with b=873 s/mm2, and 50 directions with b=1100 s/mm2. For the purpose of field mapping and DT image correction for EPI distortions, gradient echo (GRE) images were acquired with identical slice position and orientation as the DT images (TE1=7 ms, TE2=11.5 ms, TR=1300 ms, matrix size=256 × 256 pixels).

Data Processing

DTI data preprocessing was performed using the FMRIB Diffusion Toolbox (http://www.fmrib.ox.ac.uk/fsl/). The following preprocessing steps were performed: (1) correction of the diffusion images for head motion and eddy currents by affine registration to a reference (b0) image, (2) EPI distortion correction (b0 fieldmap unwarping), (3) extraction of non-brain tissues (Smith, 2002), and (4) linear diffusion tensor fitting. The resulting maps contained voxel-wise parameter estimates for RD, FA, and AD in individual space. Prior to further processing, all images were inspected both visually and quantitatively to confirm their quality, and volumes corrupted by head motion during slice acquisition were dropped. Additionally, no subjects with interscan rotation > 5° were included in this sample.

Data Analysis

Statistical analysis was carried out using tract-based spatial statistics, TBSS (Smith et al, 2006). First, all FA images were non-linearly registered into a common space (FMRIB58_FA template) using the FMRIB's Non-linear Registration Tool, FNIRT (Rueckert et al, 1999). The resulting spatial transformation matrix was subsequently applied to the other diffusion images (RD, AD). The mean FA image was calculated to create the mean FA ‘skeleton', an alignment-invariant representation of the center of all tracts common to the group. A threshold of FA [gt-or-equal, slanted] 0.25 was chosen to minimize the effects of incidental tracts and partial voluming. Each subject's aligned FA data were then projected onto this skeleton via perpendicular search for the highest FA value. The estimated non-linear warps and projection vectors were subsequently used to register each individual's diffusion data onto the skeleton. The resulting maps were fed into voxel-wise cross-subject statistics.

The effects of genotype were examined in the context of a random-effects multiple regression model with genotype as covariate of interest, and age and sex as nuisance covariates. Statistical inference was performed using FSL Randomize, a non-parametric permutation procedure suitable for the analysis of data with unknown null distributions (5000 permutations). Threshold-free cluster enhancement (TFCE) was used to circumvent the methodological drawbacks of conventional cluster-based thresholding (Smith and Nichols, 2009). The statistical threshold was set to P< 0.05 family-wise error (FWE) corrected for multiple comparisons. The most probable location of voxel results was determined with the John Hopkins University (JHU) white matter atlas (http://www.dtiatlas.org).

Exploratory ‘VBM-Type' Analysis of Diffusion Data in the UF

Our results (vide infra) were in the opposite direction of those reported by Soliman et al (2010) in the UF, but obtained with different image processing methodology. A critical difference between the two methods was the use of voxels with high FA at the center of the tracts in our study vs the use of the whole FA image in Soliman et al (2010). This raises the possibility that genetic associations in lateral portions of the UF may have gone undetected in our TBSS analysis. To address the potential impact of methodological differences on our results, additional exploratory ‘voxel-based morphometry (VBM)-type' analyses of diffusion data in the UF were performed, analogous to the procedures of Soliman et al (2010).The effects of genotype were examined using the normalized, non-skeletonized images, and a white matter mask, which was created by thresholding the average FA image of our sample at a value >0.25. The statistical testing of these images was carried out analogous to the TBSS analysis described above.

RESULTS

Diffusion Tensor Imaging

Val/Val subjects had lower values of FA compared with Met carriers in white matter tracts such as the corpus callosum and the right posterior corona radiata (P< 0.05, whole-brain corrected for multiple comparisons, Figure 1; Table 2a). These effects were more diffuse and pronounced for RD, with Val/Val having higher RD than Met-allele carriers in several white matter tracts connecting the prefrontal and occipital lobes, particularly in the genu and body of the corpus callosum, bilateral posterior corona radiate (Figure 2), posterior and superior thalamic radiation, and superior longitudinal fasciculus (P< 0.05, whole-brain corrected for multiple comparisons, Table 2b). No effects of genotype on AD were observed.

Figure 1
Effects of BDNF genotype on FA. Significant FA increases in BDNF Met carriers as compared with Val/Val individuals (colored in red, P< 0.05, corrected for multiple comparisons) overlaid on the local white matter skeleton (green) and the FA template ...
Figure 2
Effects of BDNF genotype on RD. Significant RD increases in BDNF Val/Val individuals as compared with Met carriers (colored in blue, P<0.05, corrected for multiple comparisons) overlaid on the local white matter skeleton (green) and the FA template ...
Table 2
Effects of the BDNF Val66Met Polymorphism on DTI Measures of White Matter Tract Microstructure in Healthy Human Adults

Exploratory ‘VBM-Type' Analysis of Diffusion Data in the UF

Our exploratory VBM-type analysis provided evidence for a decrease in RD in the UF in Met carriers (Figure 3: P< 0.05, corrected; tmax=3.67, MNI coordinates: –37, 0, –19). No effects of BDNF genotype on AD or FA emerged.

Figure 3
Exploratory ‘VBM-style' ROI analysis in the UF. (a) Localization of voxels with significant RD increases in Val/Val subjects in the UF (colored in orange, P<0.05, corrected) overlaid on the mean FA map of the sample (gray scale). The yellow ...

DISCUSSION

Using tract-based DTI methods for the in vivo quantification of white matter microstructure in humans, we provide evidence for a significant association of the BDNF Val66Met polymorphism with FA and RD in healthy young Caucasian adults. Significant effects of genotype were seen in several pathways subserving the prefrontal and occipital lobes, particularly the corpus callosum and the posterior corona radiata. The impact of BDNF genotype manifested as robust increases in RD in Val66 homozygotes, and comparatively weaker reductions in FA, in comparison to Met-allele carriers.

In the context of prior DTI work, several aspects of our data deserve comment. Our data provide independent confirmation of the directionality of findings in at least one recent imaging genetics study, indicating reduced FA in BDNF Val66homozygotes as compared with Met carriers in prefrontal and occipital pathways (Chiang et al, 2011). Although our findings overlap to a great extent with those of Chiang et al, including many of the tracts where genotype-related differences in white matter microstructure were found, there are large differences in the population assessed (Chiang et al assessed twins and siblings from 238 unrelated families) and the DTI acquisition and analysis methods between the two studies that prevent us from claiming a full replication of their investigation. Notably, we did not observe the reduction in FA (or an increase in RD) described by Soliman et al (2010) for BDNF Met-allele carriers in the UF; however, it is important to note that while our sample consisted only of Caucasian individuals, theirs was mixed (~40% Asian). Montag et al (2010) and Voineskos et al (2011), however, found no main effect of BDNF Val/Met genotype on white matter microstructure in primarily Caucasian samples. The reason for these discrepancies regarding the main effect of rs6265 genotype in the literature is unclear, but may relate in part to differences in sampling (eg, approximately half the group studied by Voineskos et al were over 50 years old) and methodology. In principle, many of these between-study differences in methods may contribute to the observed heterogeneity in genetic association findings. Specifically, DTI studies differ broadly in terms of acquisition parameters (eg, anisotropic voxels and large slice thickness were used by Chiang et al (2011), the number of independent diffusion directions varies from 6 in Kennedy et al (2009) to 60 in Montag et al (2010)), image processing pipelines (eg, no cardiac gating or correction for susceptibility induced distortion was applied in most prior work) and data analysis (our pipeline is compatible only with Montag et al (2010)). Moreover, prior DTI work on this genotype varies considerably regarding the definition of white matter areas of interest (eg, manually traced white matter ROI's in Kennedy et al (2009), TBSS-based reconstruction of core white matter tracts as used here and in Montag et al (2010), tractography-defined regions as in Voineskos et al (2011), VBM-type voxel-wise analysis of normalized and thresholded FA images as in Chiang et al (2011) and Soliman et al (2010)). Other methodological differences include varying procedures for statistical inference that included parametric (Chiang et al, 2011; Kennedy et al, 2009; Voineskos et al, 2011) and non-parametric approaches (used here and in Montag et al (2010) and Soliman et al (2010)). Another possible explanation for the variability of findings regarding the main effect of BDNF Val66Met on DTI measures is a possible effect of early life experience, which has been reported to interact with BDNF Val66Met to determine other brain and behavioral phenotypes (Gatt et al, 2009; Gerritsen et al, 2011). Data on early adversity were not collected in this or in any of the other studies reporting on diffusion metrics and BDNF. Yet another possibility is that the various samples collected to date differ in their assortment of other genes that have been shown to interact with BDNF in determining some phenotypes of interest (Pezawas et al, 2008; Tan et al, 2011).

Our data indicate that the effects on measures of RD, analyzed only previously in Montag et al (2010) so far, may be even greater than those on FA, and may explain in large part those found for FA. Pronounced increases in RD without concomitant changes in AD are consistent with reduced myelination in several genetic and environmental models of demyelination (Song et al, 2002, 2005; Tyszka et al, 2006). Reduced BDNF signaling has been linked to altered differentiation of human fetal and adult oligodendrocyte progenitor cells (Cui et al, 2010) and to regional reductions of myelin-related proteins (Vondran et al, 2010), but these observations would be very difficult to relate to our associations with the Val allele of BDNF, which is the normal functional BNDF allele and has not been associated with reduced BDNF levels in human brain. The animal model literature is consistent in showing that the BDNF Met allele, which alters BDNF protein trafficking and secretion, is associated with reduced BDNF protein levels (Bath et al, 2012; Qin et al, 2011). In humans, the available post-mortem information involves only mRNA, which is not expected to be affected by the Val/Met polymorphisms (eg, http://braincloud.jhmi.edu/) and the results are consistent with this expectation (Colantuoni et al, 2011). The effect of the Val/Met variant on white matter biology has not been studied in animal models or in post-mortem brain tissue. Conflicting information is also present in regards to the effects of Val66Met on T2 hyperintensity lesion load in multiple sclerosis, an index of prominent dysmyelination. The only investigation to examine a relatively large population (n=209) found that the Met allele was associated with diminished lesion load as compared with Val/Val (Zivadinov et al, 2007) , but this result has not been confirmed in smaller cohorts (Dinacci et al, 2011; Liguori et al, 2007). Nevertheless, T2 hyperintensities are not found in our samples and cannot explain our results.

In summary, reduced myelin in Val homozygotes is unlikely to account for the effect seen here and by others (Chiang et al, 2011). An alternative scenario might be suggested by a study demonstrating increased axonal branching in Met/Met mice as compared with the wild type (Val/Val) (Cao et al, 2007). This phenomenon was demonstrated in gray matter, and thus inferences on how this might impact on white matter are largely speculative. However, increased axonal branching might reduce extracellular space or change the distribution of axonal diameters and diminish RD in Met-allele carriers. Given the fact that our group previously demonstrated an association of the BDNF Val66Met variant with cortical volume in the hippocampus and the prefrontal cortex (Pezawas et al, 2004), it is possible that our DTI measures reflect some degree of difference in intracortical development between the genotypes.

In conclusion, using rigorous DTI procedures, we provide evidence of BDNF Val66Met genotype effects on white matter microstructure in healthy young Caucasian adults manifesting as reductions in measures of coherent spatial orientation of white matter in Val/Val homozygotes. Our results expand on prior research by suggesting that the effects of the BDNF Val66Met variant on human brain structure is complex, and possibly tissue-specific. In addition, our data may serve to generate hypotheses about the changes in white matter structure in disorders that have been associated with the Val allele. Finally, the Supplementary Information presents preliminary findings on the association between white matter diffusion metrics and cognition and on how BDNF Val66Met genotype modulates this association.

Acknowledgments

This research was supported by the Intramural Research Program of the National Institute of Mental Health, NIH, funding of the Weinberger Lab and a DFG-NIH (To539/1) grant to HT. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the NIH.

Notes

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies the paper on the Neuropsychopharmacology website (http://www.nature.com/npp)

Supplementary Material

Supplementary Information

References

  • Alexopoulos GS, Murphy CF, Gunning-Dixon FM, Glatt CE, Latoussakis V, Kelly RE, et al. Serotonin transporter polymorphisms, microstructural white matter abnormalities and remission of geriatric depression. J Affect Disord. 2009;119:132–141. [PMC free article] [PubMed]
  • Bath KG, Jing DQ, Dincheva I, Neeb CC, Pattwell SS, Chao MV, et al. BDNF Val66Met impairs fluoxetine-induced enhancement of adult hippocampus plasticity. Neuropsychopharmacology. 2012;37:1297–1304. [PMC free article] [PubMed]
  • Cao L, Dhilla A, Mukai J, Blazeski R, Lodovichi C, Mason CA, et al. Genetic modulation of BDNF signaling affects the outcome of axonal competition in vivo. Curr Biol. 2007;17:911–921. [PMC free article] [PubMed]
  • Chiang MC, Barysheva M, Shattuck DW, Lee AD, Madsen SK, Avedissian C, et al. Genetics of brain fiber architecture and intellectual performance. J Neurosci. 2009;29:2212–2224. [PMC free article] [PubMed]
  • Chiang MC, Barysheva M, Toga AW, Medland SE, Hansell NK, James MR, et al. BDNF gene effects on brain circuitry replicated in 455 twins. NeuroImage. 2011;55:448–454. [PMC free article] [PubMed]
  • Cohen MX. Hippocampal-prefrontal connectivity predicts midfrontal oscillations and long-term memory performance. Curr Biol. 2011;21:1900–1905. [PubMed]
  • Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature. 2011;478:519–523. [PMC free article] [PubMed]
  • Cui QL, Fragoso G, Miron VE, Darlington PJ, Mushynski WE, Antel J, et al. Response of human oligodendrocyte progenitors to growth factors and axon signals. J Neuropathol Exp Neurol. 2010;69:930–944. [PubMed]
  • Dinacci D, Tessitore A, Russo A, De Bonis ML, Lavorgna L, Picconi O, et al. BDNF Val66Met polymorphism and brain volumes in multiple sclerosis. Neurol Sci. 2011;32:117–123. [PubMed]
  • Du Y, Fischer TZ, Lee LN, Lercher LD, Dreyfus CF. Regionally specific effects of BDNF on oligodendrocytes. Dev Neurosci. 2003;25:116–126. [PubMed]
  • Egan MF, Goldberg TE, Gscheidle T, Weirich M, Rawlings R, Hyde TM, et al. Relative risk for cognitive impairments in siblings of patients with schizophrenia. Biol Psychiatry. 2001;50:98–107. [PubMed]
  • Egan MF, Kojima M, Callicott JH, Goldberg TE, Kolachana BS, Bertolino A, et al. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell. 2003;112:257–269. [PubMed]
  • Embleton KV, Haroon HA, Morris DM, Ralph MA, Parker GJ. Distortion correction for diffusion-weighted MRI tractography and fMRI in the temporal lobes. Hum Brain Mapp. 2010;31:1570–1587. [PubMed]
  • 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;14:681–695. [PubMed]
  • 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. 2011;17:597–603. [PubMed]
  • 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;63:731–740. [PMC free article] [PubMed]
  • Kennedy KM, Rodrigue KM, Land SJ, Raz N. BDNF Val66Met polymorphism influences age differences in microstructure of the Corpus Callosum. Front Hum Neurosci. 2009;3:19. [PMC free article] [PubMed]
  • King AV, Linke J, Gass A, Hennerici MG, Tost H, Poupon C, et al. Microstructure of a three-way anatomical network predicts individual differences in response inhibition: a tractography study. NeuroImage. 2012;59:1949–1959. [PubMed]
  • Kochunov P, Glahn DC, Lancaster JL, Winkler AM, Smith S, Thompson PM, et al. Genetics of microstructure of cerebral white matter using diffusion tensor imaging. NeuroImage. 2010;53:1109–1116. [PMC free article] [PubMed]
  • Liguori M, Fera F, Gioia MC, Valentino P, Manna I, Condino F, et al. Investigating the role of brain-derived neurotrophic factor in relapsing-remitting multiple sclerosis. Genes Brain Behav. 2007;6:177–183. [PubMed]
  • Lu B, Pang PT, Woo NH. The yin and yang of neurotrophin action. Nature Rev. 2005;6:603–614. [PubMed]
  • Martinowich K, Manji H, Lu B. New insights into BDNF function in depression and anxiety. Nat Neurosci. 2007;10:1089–1093. [PubMed]
  • McIntosh AM, Moorhead TW, Job D, Lymer GK, Munoz Maniega S, McKirdy J, et al. The effects of a neuregulin 1 variant on white matter density and integrity. Mol Psychiatry. 2008;13:1054–1059. [PubMed]
  • Minichiello L. TrkB signalling pathways in LTP and learning. Nature Rev. 2009;10:850–860. [PubMed]
  • 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;9:886–891. [PubMed]
  • 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. [PubMed]
  • Neves-Pereira M, Cheung JK, Pasdar A, Zhang F, Breen G, Yates P, et al. BDNF gene is a risk factor for schizophrenia in a Scottish population. Mol Psychiatry. 2005;10:208–212. [PubMed]
  • 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. [PMC free article] [PubMed]
  • Pezawas L, Meyer-Lindenberg A, Goldman AL, Verchinski BA, Chen G, Kolachana BS, et al. Evidence of biologic epistasis between BDNF and SLC6A4 and implications for depression. Mol Psychiatry. 2008;13:709–716. [PubMed]
  • Pezawas L, Verchinski BA, Mattay VS, Callicott JH, Kolachana BS, Straub RE, et al. The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J Neurosci. 2004;24:10099–10102. [PubMed]
  • Qin L, Kim E, Ratan R, Lee FS, Cho S. Genetic variant of BDNF (Val66Met) polymorphism attenuates stroke-induced angiogenic responses by enhancing anti-angiogenic mediator CD36 expression. J Neurosci. 2011;31:775–783. [PMC free article] [PubMed]
  • Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18:712–721. [PubMed]
  • Sklar P, Gabriel SB, McInnis MG, Bennett P, Lim YM, Tsan G, et al. Family-based association study of 76 candidate genes in bipolar disorder: BDNF is a potential risk locus. Brain-derived neutrophic factor. Mol Psychiatry. 2002;7:579–593. [PubMed]
  • Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143–155. [PubMed]
  • Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31:1487–1505. [PubMed]
  • Smith SM, Johansen-Berg H, Jenkinson M, Rueckert D, Nichols TE, Miller KL, et al. Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc. 2007;2:499–503. [PubMed]
  • Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage. 2009;44:83–98. [PubMed]
  • Soliman F, Glatt CE, Bath KG, Levita L, Jones RM, Pattwell SS, et al. A genetic variant BDNF polymorphism alters extinction learning in both mouse and human. Science (New York, NY) 2010;327:863–866. [PMC free article] [PubMed]
  • Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. NeuroImage. 2003;20:1714–1722. [PubMed]
  • Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 2002;17:1429–1436. [PubMed]
  • Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. NeuroImage. 2005;26:132–140. [PubMed]
  • Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK. Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn Reson Med. 2006;55:302–308. [PubMed]
  • Tan HY, Chen AG, Chen Q, Browne LB, Verchinski B, Kolachana B, et al. Epistatic interactions of AKT1 on human medial temporal lobe biology and pharmacogenetic implications. Mol Psychiatry. 2011;17:1007–1016. [PMC free article] [PubMed]
  • Tyszka JM, Readhead C, Bearer EL, Pautler RG, Jacobs RE. Statistical diffusion tensor histology reveals regional dysmyelination effects in the shiverer mouse mutant. NeuroImage. 2006;29:1058–1065. [PMC free article] [PubMed]
  • 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;15:260–271. [PubMed]
  • Voineskos AN, Lerch JP, Felsky D, Shaikh S, Rajji TK, Miranda D, et al. The brain-derived neurotrophic factor Val66Met polymorphism and prediction of neural risk for Alzheimer disease. Arch Gen Psychiatry. 2011;68:198–206. [PubMed]
  • Vondran MW, Clinton-Luke P, Honeywell JZ, Dreyfus CF. BDNF+/− mice exhibit deficits in oligodendrocyte lineage cells of the basal forebrain. Glia. 2010;58:848–856. [PMC free article] [PubMed]
  • Zhang H, Ozbay F, Lappalainen J, Kranzler HR, van Dyck CH, Charney DS, et al. Brain derived neurotrophic factor (BDNF) gene variants and Alzheimer's disease, affective disorders, posttraumatic stress disorder, schizophrenia, and substance dependence. Am J Med Genet B Neuropsychiatr Genet. 2006;141B:387–393. [PMC free article] [PubMed]
  • Zivadinov R, Weinstock-Guttman B, Benedict R, Tamano-Blanco M, Hussein S, Abdelrahman N, et al. Preservation of gray matter volume in multiple sclerosis patients with the Met allele of the rs6265 (Val66Met) SNP of brain-derived neurotrophic factor. Hum Mol Genet. 2007;16:2659–2668. [PubMed]
  • Zuliani R, Moorhead TW, Bastin ME, Johnstone EC, Lawrie SM, Brambilla P, et al. Genetic variants in the ErbB4 gene are associated with white matter integrity. Psychiatry Res. 2011;191:133–137. [PubMed]

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