Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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.
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.
Andersson, J. L. R., Jenkinson, M., & Smith, S. (2007). Non-linear registration aka spatial normalisation. https://www.fmrib.ox.ac.uk/analysis/techrep/tr07ja2/tr07ja2.pdf. Accessed 14 Mar 2016.
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.
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.
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.
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.
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.
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.
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.
Bishop, S. J. (2009). Trait anxiety and impoverished prefrontal control of attention. Nature Neuroscience, 12(1), 92–98. doi:10.1038/nn.2242.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Davis, M., & Whalen, P. J. (2001). The amygdala: vigilance and emotion. Molecular Psychiatry, 6(1), 13–34.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. doi:10.1016/j.neuroimage.2012.01.021.
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.
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.
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.
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.
Hariri, A. R., & Whalen, P. J. (2011). The amygdala: inside and out. F1000 Biology Reports, 3, 2. doi:10.3410/B3-2.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9(2), 148–158. doi:10.1038/Nrn2317.
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.
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.
Raichle, M. E. (2011). The restless brain. Brain Connectivity, 1(1), 3–12. doi:10.1089/brain.2011.0019.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Acknowledgments
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”).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Rights and permissions
About this article
Cite this article
De Witte, N.A.J., Mueller, S.C. White matter integrity in brain networks relevant to anxiety and depression: evidence from the human connectome project dataset. Brain Imaging and Behavior 11, 1604–1615 (2017). https://doi.org/10.1007/s11682-016-9642-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-016-9642-2