Reduced fractional anisotropy in the uncinate fasciculus in patients with major depression carrying the met-allele of the Val66Met brain-derived neurotrophic factor genotype

Department of Psychiatry, Institute of Neuroscience, University of Dublin, Trinity College Dublin, Dublin, Ireland.
American Journal of Medical Genetics Part B Neuropsychiatric Genetics (Impact Factor: 3.42). 07/2012; 159B(5):537-48. DOI: 10.1002/ajmg.b.32060
Source: PubMed


Experimental studies support a neurotrophic hypothesis of major depressive disorder (MDD). The aim of this study was to determine the effect of Val66Met brain-derived neurotrophic factor (BDNF) polymorphism on the white matter fiber tracts connecting hippocampus and amygdala with the prefrontal lobe in a sample of patients with MDD and healthy controls. Thirty-seven patients with MDD and 42 healthy volunteers were recruited. Diffusion tensor imaging (DTI) data with 61 diffusion directions were obtained with MRI 3 Tesla scanner. Deterministic tractography was applied with ExploreDTI and Val66Met BDNF SNP (rs6265) was genotyped. Fiber tracts connecting the hippocampus and amygdala with the prefrontal lobe, namely uncinate fasciculus (UF), fornix, and cingulum were analyzed. A significant interaction was found in the UF between BDNF alleles and diagnosis. Patients carrying the BDNF met-allele had smaller fractional anisotropy (FA) in the UF compared to those patients homozygous for val-allele and compared to healthy subjects carrying the met-allele. A significant three-way interaction was detected between region of the cingulum (dorsal, rostral, and parahippocampal regions), brain hemisphere and BDNF genotype. Larger FA was detectable in the left rostral cingulum for met-allele carriers when compared to val/val alelle carriers. We provide evidence for the importance of the neurotrophic involvement in limbic and prefrontal connections. The met-allele of the BDNF polymorphism seems to render subjects more vulnerable for dysfunctions associated with the UF, a tract known to be related to negative emotional-cognitive processing bias, declarative memory problems, and autonoetic self awareness.

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Available from: Alexander Leemans, Sep 15, 2014
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    • "Being one of the preferred methods to investigate brain structure, diffusion MRI is nowadays being used in a myriad of biomedical and neuroscience applications, including diabetes (e.g., Kodl et al., 2008; Hsu et al., 2012; Reijmer et al., 2013), amyotrophic lateral sclerosis (Wang and Melhem, 2005; Sage et al., 2009; Blain et al., 2011), Alzheimer's Disease (Reijmer et al., 2012), neuroplasticity (De Groof et al., 2006, 2009; Zatorre et al., 2012), performance and learning (e.g., Moseley et al., 2002; Caeyenberghs et al., 2010a; Sisti et al., 2012; Gooijers et al., 2013; Zatorre et al., 2012), depressive disorders (e.g., White et al., 2008; Carballedo et al., 2012), stroke (e.g., O'Sullivan, 2010; Van der Aa et al., 2011), and traumatic brain injury (Caeyenberghs et al., 2010b, 2011a,b; Zappalà et al., 2012). In many of these studies, group comparisons were performed, where it is of paramount importance that data quality itself will not affect the final outcome. "
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    ABSTRACT: Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI - i.e., Gibbs ringing (GR) - has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 06/2015; 120. DOI:10.1016/j.neuroimage.2015.06.068 · 6.36 Impact Factor
    • "FA is a widely used quantitative measure of white matter microstructure (Basser et al., 1994; Basser and Pierpaoli, 1996) calculated from the diffusion tensor (DTI) model of water diffusion (Thomason and Thompson, 2011). This is an important biomarker in clinical studies, as it can sensitively track the white matter changes in Alzheimer's disease (AD) (Clerx et al., 2012; Teipel et al., 2012), general cognitive function (Penke et al., 2010a; Penke et al., 2010b), and several neurological and psychiatric disorders (Barysheva et al., 2013; Carballedo et al., 2012; Kochunov et al., 2012; Mandl et al., 2013; Sprooten et al., 2011). "
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    ABSTRACT: The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 03/2015; 111. DOI:10.1016/j.neuroimage.2015.02.050 · 6.36 Impact Factor
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    • "To our knowledge, this study is the first report on BDNF DNA methylation and brain structural changes in depression. Furthermore , the sample size of our study is relatively large compared to recent DTI studies on the BDNF gene and MDD (Carballedo et al., 2012a; Murphy et al., 2012; Cardoner et al., 2013). Although we believe that our study has multiple strengths as compared to other research, there are several limitations to consider. "
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    ABSTRACT: Considerable evidence suggests a crucial role for the epigenetic regulation of brain-derived neurotrophic factor (BDNF) in the pathophysiology of major depressive disorder (MDD). However, the relationship between BDNF DNA methylation and white matter (WM) integrity in MDD has not yet been investigated. In the current study, we examined the association between the DNA methylation status of the BDNF promoter region and WM integrity in MDD. Sixty patients with MDD and 53 healthy controls underwent T1-weighted structural magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), to assess their WM integrity. BDNF DNA methylation at 4 CpG sites of the promoter region was also measured.As compared to healthy controls, the MDD group demonstrated reduced fractional anisotropy (FA) in the bilateral anterior and posterior corona radiata (ACR and PCR), genu of the corpus callosum, and the bilateral posterior thalamic radiations. We observed a significant inverse correlation between the DNA methylation of the BDNF promoter region and the FA of the right ACR in MDD patients.Our findings demonstrate a relationship between methylation of the BDNF promoter region and the integrity of the ACR, a key structural component of the emotional and cognitive control network involved in the pathophysiology of MDD. This correlation suggests that BDNF DNA methylation may contribute to structural WM changes in MDD patients.
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