Brain Imaging and Behavior

, Volume 11, Issue 6, pp 1604–1615 | Cite as

White matter integrity in brain networks relevant to anxiety and depression: evidence from the human connectome project dataset

  • Nele A. J. De Witte
  • Sven C. Mueller
Original Research


Anxiety and depression are associated with altered communication within global brain networks and between these networks and the amygdala. Functional connectivity studies demonstrate an effect of anxiety and depression on four critical brain networks involved in top-down attentional control (fronto-parietal network; FPN), salience detection and error monitoring (cingulo-opercular network; CON), bottom-up stimulus-driven attention (ventral attention network; VAN), and default mode (default mode network; DMN). However, structural evidence on the white matter (WM) connections within these networks and between these networks and the amygdala is lacking. The current study in a large healthy sample (n = 483) observed that higher trait anxiety-depression predicted lower WM integrity in the connections between amygdala and specific regions of the FPN, CON, VAN, and DMN. We discuss the possible consequences of these anatomical alterations for cognitive-affective functioning and underscore the need for further theory-driven research on individual differences in anxiety and depression on brain structure.


Diffusion tensor imaging Structural MRI Anxiety Depression Human connectome project HCP 



Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) and funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University.

The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), and funded by Ghent University, the Hercules Foundation and the Flemish Government – Department EWI.

SCM and NDW are supported by Ghent University (Multidisciplinary Research Partnership “The integrative neuroscience of behavioral control”).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Achenbach, T. M. (2009). The Achenbach system of empirically based Assessement (ASEBA): Development, findings, theory, and applications. Burlington, VT: University of Vermont Research Center for Children, Youth and Families.Google Scholar
  2. Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20(2), 870–888. doi: 10.1016/S1053-8119(03)00336-7.CrossRefPubMedGoogle Scholar
  3. Andersson, J. L. R., Jenkinson, M., & Smith, S. (2007). Non-linear registration aka spatial normalisation. Accessed 14 Mar 2016.
  4. Andersson, J. L. R., Xu, J., Yacoub, E., Auerbach, E., Moeller, S., & Ugurbil, K. (2012). A comprehensive Gaussian process framework for correcting distortions and movements in diffusion images. Proceedings of the 20th Annual Meeting of ISMRM, Melbourne, 2426.Google Scholar
  5. Ayling, E., Aghajani, M., Fouche, J. P., & van der Wee, N. (2012). Diffusion tensor imaging in anxiety disorders. Current Psychiatry Reports, 14(3), 197–202. doi: 10.1007/s11920-012-0273-z.CrossRefPubMedGoogle Scholar
  6. Banich, M. T., Mackiewicz, K. L., Depue, B. E., Whitmer, A. J., Miller, G. A., & Heller, W. (2009). Cognitive control mechanisms, emotion and memory: a neural perspective with implications for psychopathology. Neuroscience and Biobehavioral Reviews, 33(5), 613–630. doi: 10.1016/j.neubiorev.2008.09.010.CrossRefPubMedGoogle Scholar
  7. Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van, I. M. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychological Bulletin, 133(1), 1–24. doi: 10.1037/0033-2909.133.1.1.CrossRefPubMedGoogle Scholar
  8. Beesdo, K., Lau, J. Y., Guyer, A. E., McClure-Tone, E. B., Monk, C. S., Nelson, E. E., et al. (2009). Common and distinct amygdala-function perturbations in depressed vs anxious adolescents. Archives of General Psychiatry, 66(3), 275–285. doi: 10.1001/archgenpsychiatry.2008.545.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Behrens, T. E., Berg, H. J., Jbabdi, S., Rushworth, M. F., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage, 34(1), 144–155. doi: 10.1016/j.neuroimage.2006.09.018.CrossRefPubMedGoogle Scholar
  10. Bennett, I. J., Madden, D. J., Vaidya, C. J., Howard Jr., J. H., & Howard, D. V. (2011). White matter integrity correlates of implicit sequence learning in healthy aging. Neurobiology of Aging, 32(12), 2317.e1–2317.e12. doi: 10.1016/j.neurobiolaging.2010.03.017.CrossRefGoogle Scholar
  11. Bishop, S. J. (2009). Trait anxiety and impoverished prefrontal control of attention. Nature Neuroscience, 12(1), 92–98. doi: 10.1038/nn.2242.CrossRefPubMedGoogle Scholar
  12. Bollen, K. A., & Jackman, R. W. (1990). Regression diagnostics: An expository treatment of outliers and influential cases. In J. Fox & J. S. Long (Eds.), Modern methods of data analysis (pp. 257–291). Newbury Park, CA: Sage.Google Scholar
  13. Budisavljevic, S., Dell'Acqua, F., Zanatto, D., Begliomini, C., Miotto, D., Motta, R., et al. (2016). Asymmetry and structure of the fronto-parietal networks underlie Visuomotor processing in humans. Cerebral Cortex. doi: 10.1093/cercor/bhv348.Google Scholar
  14. Bzdok, D., Laird, A. R., Zilles, K., Fox, P. T., & Eickhoff, S. B. (2013). An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Human Brain Mapping, 34(12), 3247–3266. doi: 10.1002/hbm.22138.CrossRefPubMedGoogle Scholar
  15. Carlson, J. M., Cha, J., & Mujica-Parodi, L. R. (2013). Functional and structural amygdala - anterior cingulate connectivity correlates with attentional bias to masked fearful faces. Cortex, 49(9), 2595–2600. doi: 10.1016/j.cortex.2013.07.008.CrossRefPubMedGoogle Scholar
  16. Carter, R. M., & Huettel, S. A. (2013). A nexus model of the temporal-parietal junction. Trends in Cognitive Sciences, 17(7), 328–336. doi: 10.1016/j.tics.2013.05.007.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Clayden, J. D., Jentschke, S., Munoz, M., Cooper, J. M., Chadwick, M. J., Banks, T., et al. (2012). Normative development of white matter tracts: similarities and differences in relation to age, gender, and intelligence. Cerebral Cortex, 22(8), 1738–1747. doi: 10.1093/cercor/bhr243.CrossRefPubMedGoogle Scholar
  18. Clewett, D., Bachman, S., & Mather, M. (2014). Age-related reduced prefrontal-amygdala structural connectivity is associated with lower trait anxiety. Neuropsychology, 28(4), 631–642. doi: 10.1037/neu0000060.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. doi: 10.1038/nrn755.CrossRefPubMedGoogle Scholar
  20. Cullen, K. R., Westlund, M. K., Klimes-Dougan, B., Mueller, B. A., Houri, A., Eberly, L. E., et al. (2014). Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry, 71(10), 1138–1147. doi: 10.1001/jamapsychiatry.2014.1087.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Davis, M., & Whalen, P. J. (2001). The amygdala: vigilance and emotion. Molecular Psychiatry, 6(1), 13–34.CrossRefPubMedGoogle Scholar
  22. Diez, I., Bonifazi, P., Escudero, I., Mateos, B., Munoz, M. A., Stramaglia, S., et al. (2015). A novel brain partition highlights the modular skeleton shared by structure and function. Scientific Reports, 5, 10532. doi: 10.1038/srep10532.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., et al. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104(26), 11073–11078. doi: 10.1073/pnas.0704320104.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Dosenbach, N. U., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105. doi: 10.1016/j.tics.2008.01.001.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Drevets, W. C., Price, J. L., Simpson Jr., J. R., Todd, R. D., Reich, T., Vannier, M., et al. (1997). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386(6627), 824–827. doi: 10.1038/386824a0.CrossRefPubMedGoogle Scholar
  26. Eden, A. S., Schreiber, J., Anwander, A., Keuper, K., Laeger, I., Zwanzger, P., et al. (2015). Emotion regulation and trait anxiety are predicted by the microstructure of fibers between amygdala and prefrontal cortex. Journal of Neuroscience, 35(15), 6020–6027. doi: 10.1523/JNEUROSCI.3659-14.2015.CrossRefPubMedGoogle Scholar
  27. Etkin, A., Klemenhagen, K. C., Dudman, J. T., Rogan, M. T., Hen, R., Kandel, E. R., et al. (2004). Individual differences in trait anxiety predict the response of the basolateral amygdala to unconsciously processed fearful faces. Neuron, 44(6), 1043–1055. doi: 10.1016/j.neuron.2004.12.006.CrossRefPubMedGoogle Scholar
  28. Etkin, A., Prater, K. E., Schatzberg, A. F., Menon, V., & Greicius, M. D. (2009). Disrupted amygdalar subregion functional connectivity and evidence of a compensatory network in generalized anxiety disorder. Archives of General Psychiatry, 66(12), 1361–1372. doi: 10.1001/archgenpsychiatry.2009.104.CrossRefPubMedGoogle Scholar
  29. Etkin, A., Prater, K. E., Hoeft, F., Menon, V., & Schatzberg, A. F. (2010). Failure of anterior cingulate activation and connectivity with the amygdala during implicit regulation of emotional processing in generalized anxiety disorder. American Journal of Psychiatry, 167(5), 545–554. doi: 10.1176/appi.ajp.2009.09070931.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Fair, D. A., Cohen, A. L., Dosenbach, N. U., Church, J. A., Miezin, F. M., Barch, D. M., et al. (2008). The maturing architecture of the brain's default network. Proceedings of the National Academy of Sciences of the United States of America, 105(10), 4028–4032. doi: 10.1073/pnas.0800376105.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Finner, H. (1990). Some new inequalities for the range distribution, with application to the determination of optimum significance levels of multiple range tests. Journal of the American Statistical Association, 85(409), 191–194. doi: 10.2307/2289544.CrossRefGoogle Scholar
  32. Finner, H. (1993). On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association, 88(423), 920–923. doi: 10.2307/2290782.CrossRefGoogle Scholar
  33. Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. doi: 10.1016/j.neuroimage.2012.01.021.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences of the United States of America, 103(26), 10046–10051. doi: 10.1073/pnas.0604187103.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Friedel, E., Schlagenhauf, F., Sterzer, P., Park, S. Q., Bermpohl, F., Strohle, A., et al. (2009). 5-HTT genotype effect on prefrontal-amygdala coupling differs between major depression and controls. Psychopharmacology, 205(2), 261–271. doi: 10.1007/s00213-009-1536-1.CrossRefPubMedGoogle Scholar
  36. Goldin, P. R., Manber-Ball, T., Werner, K., Heimberg, R., & Gross, J. J. (2009). Neural mechanisms of cognitive reappraisal of negative self-beliefs in social anxiety disorder. Biological Psychiatry, 66(12), 1091–1099. doi: 10.1016/j.biopsych.2009.07.014.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258. doi: 10.1073/pnas.0135058100.CrossRefPubMedGoogle Scholar
  38. Hariri, A. R., & Whalen, P. J. (2011). The amygdala: inside and out. F1000 Biology Reports, 3, 2. doi: 10.3410/B3-2.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040. doi: 10.1073/pnas.0811168106.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Inano, S., Takao, H., Hayashi, N., Abe, O., & Ohtomo, K. (2011). Effects of age and gender on white matter integrity. American Journal of Neuroradiology, 32(11), 2103–2109. doi: 10.3174/ajnr.A2785.CrossRefPubMedGoogle Scholar
  41. Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Grana, M., & Behrens, T. E. (2012). Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magnetic Resonance in Medicine, 68(6), 1846–1855. doi: 10.1002/mrm.24204.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841. doi: 10.1006/nimg.2002.1132.CrossRefPubMedGoogle Scholar
  43. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782–790. doi: 10.1016/j.neuroimage.2011.09.015.CrossRefPubMedGoogle Scholar
  44. Jones, D. K., Knosche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage, 73, 239–254. doi: 10.1016/j.neuroimage.2012.06.081.CrossRefPubMedGoogle Scholar
  45. Kanaan, R. A., Chaddock, C., Allin, M., Picchioni, M. M., Daly, E., Shergill, S. S., et al. (2014). Gender influence on white matter microstructure: a tract-based spatial statistics analysis. PloS One, 9(3), e91109. doi: 10.1371/journal.pone.0091109.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Khalsa, S., Mayhew, S. D., Chechlacz, M., Bagary, M., & Bagshaw, A. P. (2013). The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. NeuroImage, 102, 118–127. doi: 10.1016/j.neuroimage.2013.12.022.CrossRefPubMedGoogle Scholar
  47. Kim, H. (2014). Involvement of the dorsal and ventral attention networks in oddball stimulus processing: a meta-analysis. Human Brain Mapping, 35(5), 2265–2284. doi: 10.1002/hbm.22326.CrossRefPubMedGoogle Scholar
  48. Kim, M. J., & Whalen, P. J. (2009). The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety. Journal of Neuroscience, 29(37), 11614–11618. doi: 10.1523/jneurosci.2335-09.2009.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Kollndorfer, K., Krajnik, J., Woitek, R., Freiherr, J., Prayer, D., & Schopf, V. (2013). Altered likelihood of brain activation in attention and working memory networks in patients with multiple sclerosis: an ALE meta-analysis. Neuroscience and Biobehavioral Reviews, 37(10 Pt 2), 2699–2708. doi: 10.1016/j.neubiorev.2013.09.005.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Korgaonkar, M. S., Fornito, A., Williams, L. M., & Grieve, S. M. (2014). Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biological Psychiatry, 76(7), 567–574. doi: 10.1016/j.biopsych.2014.02.018.CrossRefPubMedGoogle Scholar
  51. Laeger, I., Dobel, C., Radenz, B., Kugel, H., Keuper, K., Eden, A., et al. (2014). Of 'Disgrace' and 'Pain' - Corticolimbic interaction patterns for disorder- relevant and emotional words in social phobia. PloS One, 9(11), e109949. doi: 10.1371/journal.pone.0109949.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Liao, W., Chen, H., Feng, Y., Mantini, D., Gentili, C., Pan, Z., et al. (2010a). Selective aberrant functional connectivity of resting state networks in social anxiety disorder. NeuroImage, 52(4), 1549–1558. doi: 10.1016/j.neuroimage.2010.05.010.CrossRefPubMedGoogle Scholar
  53. Liao, W., Qiu, C., Gentili, C., Walter, M., Pan, Z., Ding, J., et al. (2010b). Altered effective connectivity network of the amygdala in social anxiety disorder: a resting-state FMRI study. PloS One, 5(12), e15238. doi: 10.1371/journal.pone.0015238.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Liao, W., Xu, Q., Mantini, D., Ding, J., Machado-de-Sousa, J. P., Hallak, J. E., et al. (2011). Altered gray matter morphometry and resting-state functional and structural connectivity in social anxiety disorder. Brain Research, 1388, 167–177. doi: 10.1016/j.brainres.2011.03.018.CrossRefPubMedGoogle Scholar
  55. Liu, X., Watanabe, K., Kakeda, S., Yoshimura, R., Abe, O., Hayashi, K., et al. (2016). Relationship between white matter integrity and serum cortisol levels in drug-naive patients with major depressive disorder: diffusion tensor imaging study using tract-based spatial statistics. The British Journal of Psychiatry. doi: 10.1192/bjp.bp.114.155689.Google Scholar
  56. Lu, Q., Li, H. R., Luo, G. P., Wang, Y., Tang, H., Han, L., et al. (2012). Impaired prefrontal-amygdala effective connectivity is responsible for the dysfunction of emotion process in major depressive disorder: a dynamic causal modeling study on MEG. Neuroscience Letters, 523(2), 125–130. doi: 10.1016/j.neulet.2012.06.058.CrossRefPubMedGoogle Scholar
  57. Modi, S., Trivedi, R., Singh, K., Kumar, P., Rathore, R. K., Tripathi, R. P., et al. (2013). Individual differences in trait anxiety are associated with white matter tract integrity in fornix and uncinate fasciculus: preliminary evidence from a DTI based tractography study. Behavioural Brain Research, 238, 188–192. doi: 10.1016/j.bbr.2012.10.007.CrossRefPubMedGoogle Scholar
  58. Monk, C. S., Telzer, E. H., Mogg, K., Bradley, B. P., Mai, X. Q., Louro, H. M. C., et al. (2008). Amygdala and ventrolateral prefrontal cortex activation to masked angry faces in children and adolescents with generalized anxiety disorder. Archives of General Psychiatry, 65(5), 568–576. doi: 10.1001/archpsyc.65.5.568.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Nakamae, T., Sakai, Y., Abe, Y., Nishida, S., Fukui, K., Yamada, K., et al. (2014). Altered fronto-striatal fiber topography and connectivity in obsessive-compulsive disorder. PloS One, 9(11), e112075. doi: 10.1371/journal.pone.0112075.CrossRefPubMedPubMedCentralGoogle Scholar
  60. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9(5), 242–249. doi: 10.1016/j.tics.2005.03.010.CrossRefPubMedGoogle Scholar
  61. Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922. doi: 10.1016/j.neuroimage.2011.02.046.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Peeva, M. G., Tourville, J. A., Agam, Y., Holland, B., Manoach, D. S., & Guenther, F. H. (2013). White matter impairment in the speech network of individuals with autism spectrum disorder. Neuroimage Clinical, 3, 234–241. doi: 10.1016/j.nicl.2013.08.011.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9(2), 148–158. doi: 10.1038/Nrn2317.CrossRefPubMedGoogle Scholar
  64. Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., et al. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678. doi: 10.1016/j.neuron.2011.09.006.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Prater, K. E., Hosanagar, A., Klumpp, H., Angstadt, M., & Phan, K. L. (2013). Aberrant amygdala-frontal cortex connectivity during perception of fearful faces and at rest in generalized social anxiety disorder. Depression and Anxiety, 30(3), 234–241. doi: 10.1002/da.22014.CrossRefPubMedGoogle Scholar
  66. Raichle, M. E. (2011). The restless brain. Brain Connectivity, 1(1), 3–12. doi: 10.1089/brain.2011.0019.CrossRefPubMedPubMedCentralGoogle Scholar
  67. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682. doi: 10.1073/pnas.98.2.676.CrossRefPubMedPubMedCentralGoogle Scholar
  68. Rauch, S. L., Shin, L. M., & Wright, C. I. (2003). Neuroimaging studies of amygdala function in anxiety disorders. Annals of the New York Academy of Sciences, 985, 389–410.CrossRefPubMedGoogle Scholar
  69. Raven, J., Raven, J. C., & Court, J. H. (2003). Manual for Raven's progressive matrices and vocabulary scales. Section 1: General overview. San Antonio, TX: Harcourt Assessment.Google Scholar
  70. Ruigrok, A. N., Salimi-Khorshidi, G., Lai, M. C., Baron-Cohen, S., Lombardo, M. V., Tait, R. J., et al. (2014). A meta-analysis of sex differences in human brain structure. Neuroscience and Biobehavioral Reviews, 39, 34–50. doi: 10.1016/j.neubiorev.2013.12.004.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Sotiropoulos, S. N., Moeller, S., Jbabdi, S., Xu, J., Andersson, J. L., Auerbach, E. J., et al. (2013). Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE. Magnetic Resonance in Medicine, 70(6), 1682–1689. doi: 10.1002/mrm.24623.CrossRefPubMedGoogle Scholar
  72. Sylvester, C. M., Corbetta, M., Raichle, M. E., Rodebaugh, T. L., Schlaggar, B. L., Sheline, Y. I., et al. (2012). Functional network dysfunction in anxiety and anxiety disorders. Trends in Neurosciences, 35(9), 527–535. doi: 10.1016/j.tins.2012.04.012.CrossRefPubMedPubMedCentralGoogle Scholar
  73. Taylor, W. D., MacFall, J. R., Gerig, G., & Krishnan, R. R. (2007). Structural integrity of the uncinate fasciculus in geriatric depression: relationship with age of onset. Neuropsychiatric Disease and Treatment, 3(5), 669–674.PubMedPubMedCentralGoogle Scholar
  74. Teipel, S. J., Bokde, A. L., Meindl, T., Amaro Jr., E., Soldner, J., Reiser, M. F., et al. (2010). White matter microstructure underlying default mode network connectivity in the human brain. NeuroImage, 49(3), 2021–2032. doi: 10.1016/j.neuroimage.2009.10.067.CrossRefPubMedGoogle Scholar
  75. Thayer, J. F., & Brosschot, J. F. (2005). Psychosomatics and psychopathology: looking up and down from the brain. Psychoneuroendocrinology, 30(10), 1050–1058. doi: 10.1016/j.psyneuen.2005.04.014.CrossRefPubMedGoogle Scholar
  76. Tromp, D. P., Grupe, D. W., Oathes, D. J., McFarlin, D. R., Hernandez, P. J., Kral, T. R., et al. (2012). Reduced structural connectivity of a major frontolimbic pathway in generalized anxiety disorder. Archives of General Psychiatry, 69(9), 925–934. doi: 10.1001/archgenpsychiatry.2011.2178.CrossRefPubMedPubMedCentralGoogle Scholar
  77. Walker, L., Chang, L. C., Koay, C. G., Sharma, N., Cohen, L., Verma, R., et al. (2011). Effects of physiological noise in population analysis of diffusion tensor MRI data. NeuroImage, 54(2), 1168–1177. doi: 10.1016/j.neuroimage.2010.08.048.CrossRefPubMedGoogle Scholar
  78. Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., & Fischl, B. (2013). Spurious group differences due to head motion in a diffusion MRI study. NeuroImage, 88, 79–90. doi: 10.1016/j.neuroimage.2013.11.027.CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Experimental Clinical and Health PsychologyGhent UniversityGhentBelgium

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