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Structural connectome-based prediction of trait anxiety

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Abstract

Neurobiological research on anxiety has shown that trait-anxious individuals may be characterized by weaker structural connectivity of the amygdala-prefrontal circuitry, representing a reduced capacity for efficient communication between the two brain regions. However, comparison of available studies has been inconsistent, possibly related to factors such as aging that influences both trait anxiety and structural connectivity of the brain. To help clarify the nature of brain-anxiety relationship, we applied a connectome-based predictive modeling framework on 148 diffusion-weighted imaging data from the Leipzig Study for Mind-Body Emotion Interactions dataset and identified multivariate patterns of whole-brain structural connectivity that predicted trait anxiety. Results showed that networks predictive of trait anxiety differed across age groups. Specifically, an isolated negative network, which shared overlapping features with the amygdala-prefrontal circuitry, was found in younger adults (20–30 years of age), whereas a widespread positive network highlighted by frontotemporal and frontolimbic connectivity was identified when both younger and older adults (20–80 years of age) were examined. No predictive network was observed when only older adults (30–80 years of age) were considered. Our findings highlight an important age-dependent effect on the structural connectome-based prediction of trait anxiety, supporting ongoing efforts to develop potential neural biomarkers of anxiety.

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Data Availability

The MPI-LEMON dataset (https://www.nitrc.org/projects/mpilmbb)(https://doi.org/10.18112/openneuro.ds000221.v1.0.0) is publicly available.

Code Availability

Neuroimaging data were analyzed using MRtrix3 (https://www.mrtrix.org/).

References

  • Andersson, J. L., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage, 20, 870–888

    Article  PubMed  Google Scholar 

  • Andersson, J. L., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063–1078

    Article  PubMed  Google Scholar 

  • Babayan, A., Erbey, M., Kumral, D., Reinelt, R. D., Reiter, A. M. F., Röbbig, J. … Villringer, A. (2019). A Mind-Brain-Body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Scientific Data, 6, 180308

    Article  PubMed  PubMed Central  Google Scholar 

  • Baur, V., Hänggi, J., Rufer, M., Delsignore, A., Jäncke, L., Herwig, U. … Brühl, A. B. (2011). White matter alterations in social anxiety disorder. Journal of Psychiatric Research, 45, 1366–1372

    Article  PubMed  Google Scholar 

  • Bijsterbosch, J., Smith, S., Forster, S., John, O. P., & Bishop, S. J. (2014). Resting state correlates of subdimensions of anxious affect. Journal of Cognitive Neuroscience, 26, 914–926

    Article  PubMed  Google Scholar 

  • Bishop, S. J. (2007). Neurocognitive mechanisms of anxiety: an integrative account. Trends in Cognitive Sciences, 11, 307–316

    Article  PubMed  Google Scholar 

  • Burghy, C. A., Stodola, D. E., Ruttle, P. L., Molloy, E. K., Armstrong, J. M., Oler, J. A. … Birn, R. M. (2012). Developmental pathways to amygdala-prefrontal function and internalizing symptoms in adolescence. Nature Neuroscience, 15, 1736–1741

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Calhoon, G. G., & Tye, K. M. (2015). Resolving the neural circuits of anxiety. Nature Neuroscience, 18, 1394–1404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Clewett, D., Bachman, S., & Mather, M. (2014). Age-related reduced prefrontal-amygdala structural connectivity is associated with lower trait anxiety. Neuropsychology, 28, 631–642

    Article  PubMed  PubMed Central  Google Scholar 

  • Coombs, G. I. I. I., Loggia, M. L., Greve, D. N., & Holt, D. J. (2014). Amygdala perfusion is predicted by its functional connectivity with the ventromedial prefrontal cortex and negative affect. PLOS One, 9, e97466

    Article  PubMed  Google Scholar 

  • d’Arbeloff, T. C., Kim, M. J., Knodt, A. R., Radtke, S. R., Brigidi, B. D., & Hariri, A. R. (2018). Microstructural integrity of a pathway connecting the prefrontal cortex and amygdala moderates the association between cognitive reappraisal and negative emotions. Emotion, 18, 912–915

    Article  PubMed  PubMed Central  Google Scholar 

  • De Witte, N. A. J., & Mueller, S. C. (2017). 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

    Article  PubMed  Google Scholar 

  • Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D. … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968–980

    Article  PubMed  Google Scholar 

  • Dhollander, T., Tabbara, R., Rosnarho-Tornstrand, J., Tournier, J. D., Raffelt, D., & Connelly, A. (2021). Multi-tissue log-domain intensity and inhomogeneity normalisation for quantitative apparent fibre density. Proceedings of the International Society for Magnetic Resonance in Medicine, 29, 2472

  • Ebeling, U., & von Cramon, D. (1992). Topography of the uncinate fascicle and adjacent temporal fiber tracts. Acta Neurochirurgica (Wien), 115, 143–148

    Article  CAS  Google Scholar 

  • Eden, A. S., Schreiber, J., Anwander, A., Keuper, K., Laeger, I., Zwanzger, P. … Dobel, C. (2015). Emotion regulation and trait anxiety are predicted by the microstructure of fibers between amygdala and prefrontal cortex. Journal of Neuroscience, 35, 6020–6027

    Article  CAS  PubMed  Google Scholar 

  • Feng, C., Wang, L., Li, T., & Xu, P. (2019). Connectome-based individualized prediction of loneliness. Social Cognitive and Affective Neuroscience, 14(4), 353–365

    Article  PubMed  PubMed Central  Google Scholar 

  • Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D. H. … Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22

    Article  PubMed  Google Scholar 

  • Greening, S. G., & Mitchell, D. G. (2015). A network of amygdala connections predict individual differences in trait anxiety. Human Brain Mapping, 36, 4819–4830

    Article  PubMed  PubMed Central  Google Scholar 

  • Hare, T. A., Tottenham, N., Galvan, A., Voss, H. U., Glover, G. H., & Casey, B. J. (2008). Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biological Psychiatry, 63, 927–934

    Article  PubMed  PubMed Central  Google Scholar 

  • Hartley, C. A., & Phelps, E. A. (2009). Changing fear: the neurocircuitry of emotion regulation. Neuropsychopharmacology : Official Publication Of The American College Of Neuropsychopharmacology, 35, 136–146

    Article  Google Scholar 

  • Hasan, K. M., Iftikhar, A., Kamali, A., Kramer, L. A., Ashtari, M., Cirino, P. T. … Ewing-Cobbs, L. (2009). Development and aging of the healthy human brain uncinate fasciculus across the lifespan using diffusion tensor tractography. Brain Research, 1276, 67–76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hettema, J. M., Kettenmann, B., Ahluwalia, V., McCarthy, C., Kates, W. R., Schmitt, J. E. … Fatouros, P. (2012). Pilot multimodal twin imaging study of generalized anxiety disorder. Depression and Anxiety, 29, 202–209

    Article  PubMed  Google Scholar 

  • Jenkinson, M., Bannister, P., Brady, J. M., & Smith, S. M. (2002). Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. Neuroimage, 17, 825–841

    Article  PubMed  Google Scholar 

  • Jeurissen, B., Tournier, J. D., Dhollander, T., Connelly, A., & Sijbers, J. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage, 103, 411–426

    Article  PubMed  Google Scholar 

  • Kim, M. J., & Whalen, P. J. (2009). The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety. Journal of Neuroscience, 29, 11614–11618

    Article  CAS  PubMed  Google Scholar 

  • Kim, M. J., Gee, D. G., Loucks, R. A., Davis, F. C., & Whalen, P. J. (2011a). Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest. Cerebral Cortex, 21, 1667–1673

    Article  PubMed  Google Scholar 

  • Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N. … Whalen, P. J. (2011b). The structural and functional connectivity of the amygdala: From normal emotion to pathological anxiety. Behavioural Brain Research, ​223, 403–410

    Article  PubMed  PubMed Central  Google Scholar 

  • Kim, M. J., Brown, A. C., Mattek, A. M., Chavez, S. J., Taylor, J. M., Palmer, A. L. … Whalen, P. J. (2016). The inverse relationship between the microstructural variability of amygdala-prefrontal pathways and trait anxiety is moderated by sex. Frontiers in Systems Neuroscience, 10, 93

    Article  PubMed  PubMed Central  Google Scholar 

  • Knowles, K. A., & Olatunji, B. O. (2020). Specificity of trait anxiety in anxiety and depression: Meta-analysis of the State-Trait Anxiety Inventory. Clinical Psychology Review, 82, 101928

    Article  PubMed  PubMed Central  Google Scholar 

  • Kochunov, P., Glahn, D. C., Lancaster, J., Thompson, P. M., Kochunov, V., Rogers, B. … Williamson, D. E. (2011). Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan. Neuroimage, 58, 41–49

    Article  CAS  PubMed  Google Scholar 

  • Laux, L., Glanzmann, P., Schaffner, P., & Spielberger, C. D. (1981). Das State-Trait-Angstinventar. Weinheim: Beltz Test GmbH

    Google Scholar 

  • Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008). Microstructural maturation of the human brain from childhood to adulthood. NeuroImage 40, 1044–1055

  • Melhem, E. R., Itoh, R., Jones, L., & Barker, P. B. (2000). Diffusion tensor MR imaging of the brain: effect of diffusion weighting on trace and anisotropy measurements. American Journal of Neuroradiology, 21, 1813–1820

    CAS  PubMed  PubMed Central  Google Scholar 

  • Milad, M. R., & Quirk, G. J. (2012). Fear extinction as a model for translational neuroscience: ten years of progress. Annual Review of Psychology, 63, 129–151

    Article  PubMed  PubMed Central  Google Scholar 

  • Modi, S., Trivedi, R., Singh, K., Kumar, P., Rathore, R. K. S., Tripathi, R. P. … Khushu, S. (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. Behavoural Brain Research, 238, 188–192

    Article  Google Scholar 

  • Montag, C., Reuter, M., Weber, B., Markett, S., & Schoene-Bake, J. C. (2012). Individual differences in trait anxiety are associated with white matter tract integrity in the left temporal lobe in healthy males but not females. Neuroscience, 217, 77–83

    Article  CAS  PubMed  Google Scholar 

  • Pedersen, W. S., Dean, D. C., Adluru, N., Gresham, L. K., Lee, S. D., Kelly, M. P. … Schaefer, S. M. (2022). Individual variation in white matter microstructure is related to better recovery from negative stimuli. Emotion, 22, 244–257

    Article  PubMed  Google Scholar 

  • Phan, K. L., Orlichenko, A., Boyd, E., Angstadt, M., Coccaro, E. F., Liberzon, I., & Konstantinos, A. (2009). Preliminary evidence of white matter abnormality in the uncinate fasciculus in generalized social anxiety disorder. Biological Psychiatry, 66, 691–694

    Article  PubMed  PubMed Central  Google Scholar 

  • Pines, A. R., Sacchet, M. D., Kullar, M., Ma, J., & Williams, L. M. (2018). Multi-unit regions among neural, self-report, and behavioral correlates of emotion regulation in comorbid depression and obesity. Scientific Reports, 8, 1–11

    Article  CAS  Google Scholar 

  • Raffelt, D. A., Tournier, J. D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R. … Connelly, A. (2017). Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage, 144, 58–73

    Article  PubMed  Google Scholar 

  • Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., Jovicich, J., & De Pisapia, N. (2020). Trait and state anxiety are mapped differently in the human brain. Scientific Reports, 10, 11112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M., Chun, M. M., Papademetris, X. … Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12, 506–518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H. … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23 Suppl 1, S208-219

  • Smith, R. E., Tournier, J. D., Calamante, F., & Connelly, A. (2012). Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage, 62, 1924–1938

    Article  PubMed  Google Scholar 

  • Smith, R. E., Tournier, J. D., Calamante, F., & Connelly, A. (2015). The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage, 104, 253–265

    Article  PubMed  Google Scholar 

  • Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). STAI: Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press

    Google Scholar 

  • Tournier, J. D., Calamante, F., & Connelly, A. (2010). Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, 1670

  • Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M. … Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualization. Neuroimage, 202, 116137

    Article  PubMed  Google Scholar 

  • Tromp, D. P., Grupe, D. W., Oathes, D. J., McFarlin, D. R., Hernandez, P. J., Kral, T. R. … Nitschke, J. B. (2012). Reduced structural connectivity of a major frontolimbic pathway in generalized anxiety disorder. JAMA Psychiatry, 69, 925–934

    Google Scholar 

  • Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A. … Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29, 1310–1320

    Article  PubMed  PubMed Central  Google Scholar 

  • Vanderlind, W. M., Everaert, J., Caballero, C., Cohodes, E. M., & Gee, D. G. (2021). Emotion and emotion preferences in daily life: The role of anxiety. Clinical Psychological Science, 10, 109–126

    Article  PubMed  Google Scholar 

  • Von Heide, D., Skipper, R. J., Klobusicky, L. M., E., & Olson, I. R. (2013). Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain, 136, 1692–1707

    Article  PubMed  Google Scholar 

  • Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L. … Mori, S. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage, 36, 630–644

    Article  PubMed  Google Scholar 

  • Wang, Z., Goerlich, K., Ai, H., Aleman, A., Luo, Y., & Xu, P. (2021). Connectome-based predictive modeling of individual anxiety. Cerebral Cortex, 31, 3006–3020

    Article  PubMed  Google Scholar 

  • Westlye, L. T., Bjornebekk, A., Grydeland, H., Fjell, A. M., & Walhovd, K. B. (2011). Linking an anxiety-related personality trait to brain white matter microstructure: Diffusion tensor imaging and harm avoidance. Archives Of General Psychiatry, 68, 369–377

    Article  PubMed  Google Scholar 

  • Yoo, K., Rosenberg, M. D., Hsu, W., Zhang, S., Li, C. R., Scheinost, D. … Chun, M. M. (2018). Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. Neuroimage, 167(15), 11–22

    Article  PubMed  Google Scholar 

  • Zuurbier, L. A., Nikolova, Y. S., Åhs, F., & Hariri, A. R. (2013). Uncinate fasciculus fractional anisotropy correlates with typical use of reappraisal in women but not men. Emotion, 13, 385–390

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the original authors of the MPI-LEMON dataset for their generosity in making it available for use.

Funding

This research was supported by the National Research Foundation of Korea (NRF-2021R1F1A1045988 and NRF-2021S1A5A2A03070229) (Kim).

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    Authors and Affiliations

    1. Department of Psychology, Sungkyunkwan University, Seoul, South Korea

      Chaebin Yoo, Sujin Park & M. Justin Kim

    2. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea

      M. Justin Kim

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    1. Chaebin Yoo
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    CY – Data analysis, interpretation, manuscript writing, editing. SP – Data analysis, interpretation, manuscript writing, editing. MJK – Study design, data interpretation, funding, manuscript writing, editing.

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    Correspondence to M. Justin Kim.

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    The LEMON study was in accordance with the World Medical Association Declaration of Helsinki and was approved by the Ethics Committee of the University of Leipzig (reference number 154/13-ff).

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    Chaebin Yoo and Sujin Park contributed equally and are sharing first authorship.

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    Yoo, C., Park, S. & Kim, M.J. Structural connectome-based prediction of trait anxiety. Brain Imaging and Behavior 16, 2467–2476 (2022). https://doi.org/10.1007/s11682-022-00700-2

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