Advertisement

Journal of Zhejiang University-SCIENCE B

, Volume 19, Issue 8, pp 643–653 | Cite as

Spontaneous activity in medial orbitofrontal cortex correlates with trait anxiety in healthy male adults

  • Shao-wei Xue
  • Tien-wen Lee
  • Yong-hu Guo
Article
  • 2 Downloads

Abstract

Medial orbitofrontal cortex (mOFC) abnormalities have been observed in various anxiety disorders. However, the relationship between mOFC activity and anxiety among the healthy population has not been fully examined. Here, we conducted a resting state functional magnetic resonance imaging (R-fMRI) study with 56 healthy male adults from the Nathan Kline Institute/Rockland Sample (NKI-RS) to examine the relationship between the fractional amplitude of low-frequency fluctuation (fALFF) signals and trait anxiety across the whole brain. A Louvain method for module detection based on graph theory was further employed in the automated functional subdivision to explore subregional correlates of trait anxiety. The results showed that trait anxiety was related to fALFF in the mOFC. Additionally, the resting-state functional connectivity (RSFC) between the right subregions of the mOFC and the precuneus was correlated with trait anxiety. These findings provided evidence about the involvement of the mOFC in anxiety processing among the healthy population.

Key words

Trait anxiety Fractional amplitude of low-frequency fluctuation (fALFF) Medial orbitofrontal cortex Precuneus Functional connectivity 

成年男性特质型焦虑与内侧眶额的自发活动有关

中文概要

目的

研究内侧眶额与焦虑加工的关系。

创新点

采用Louvain 网络模块检测方法对大脑内侧眶额进行自动化功能亚区分割,并发现内侧眶额与健康成年男性特质型焦虑的关系。

方法

采用静息态低频振幅比率(fALFF)、静息态功能连通性(RSFC)和脑区功能亚区自动化分割方法。

结论

成年男性特质型焦虑分数与内侧眶额的fALFF指标间存在显著相关性,并与右脑内侧眶额和楔前叶之间的功能连通性有关。因此,可以认为内侧眶额涉及特质型焦虑加工。

关键词

特质型焦虑 低频振幅比率 内侧眶额 楔前叶 功能连通性 

CLC number

B845 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Banks SJ, Eddy KT, Angstadt M, et al., 2007. Amygdalafrontal connectivity during emotion regulation. Soc Cogn Affect Neurosci, 2(4):303–312. https://doi.org/10.1093/scan/nsm029 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Biswal B, Yetkin FZ, Haughton VM, et al., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34(4):537–541. https://doi.org/10.1002/mrm.1910340409 CrossRefPubMedGoogle Scholar
  3. Biswal BB,van Kylen J, Hyde JS, 1997. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed, 10(4-5): 165–170. https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5<165::AID-NBM454>3.0.CO;2-7CrossRefPubMedGoogle Scholar
  4. Blair J, Mitchell D, Blair K, 2005. The Psychopath: Emotion and the Brain. Blackwell Publishing, Oxford, p.81–86.Google Scholar
  5. Blair RJR, 2007. Dysfunctions of medial and lateral orbitofrontal cortex in psychopathy. Ann NYA cad Sci, 1121(1):461–479. https://doi.org/10.1196/annals.1401.017 CrossRefGoogle Scholar
  6. Buckner RL, Andrews-Hanna JR, Schacter DL, 2008. The brain’s default network: anatomy, function, and relevance to disease. Ann NYA cad Sci, 1124(1):1–38. https://doi.org/10.1196/annals.1440.011 CrossRefGoogle Scholar
  7. Bystritsky A, Pontillo D, Powers M, et al., 2001. Functional MRI changes during panic anticipation and imagery exposure. Neuroreport, 12(18):3953–3957. https://doi.org/10.1097/00001756-200112210-00020 CrossRefPubMedGoogle Scholar
  8. Chambers JA, Power KG, Durham RC, 2004. The relationship between trait vulnerability and anxiety and depressive diagnoses at long-term follow-up of generalized anxiety disorder. J Anxiety Disord, 18(5):587–607. https://doi.org/10.1016/j.janxdis.2003.09.001 CrossRefPubMedGoogle Scholar
  9. Foti NJ, Hughes JM, Rockmore DN, 2011. Nonparametric sparsification of complex multiscale networks. PLoS ONE, 6(2):e16431. https://doi.org/10.1371/journal.pone.0016431 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fox MD, Raichle ME, 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci, 8(9):700–711. https://doi.org/10.1038/nrn2201 CrossRefPubMedGoogle Scholar
  11. Gawda B, Szepietowska E, 2016. Trait anxiety modulates brain activity during performance of verbal fluency tasks. Front Behav Neurosci, 10:10. https://doi.org/10.3389/fnbeh.2016.00010 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Grachev ID, Apkarian AV, 2000. Anxiety in healthy humans is associated with orbital frontal chemistry. Mol Psychiatry, 5(5):482–488. https://doi.org/10.1038/sj.mp.4000778 CrossRefPubMedGoogle Scholar
  13. Greicius MD, Krasnow B, Reiss AL, et al., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA, 100(1):253–258. https://doi.org/10.1073/pnas.0135058100 CrossRefPubMedGoogle Scholar
  14. Hahn A, Stein P, Windischberger C, et al., 2011. Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder. Neuroimage, 56(3):881–889. https://doi.org/10.1016/j.neuroimage.2011.02.064 CrossRefPubMedGoogle Scholar
  15. Hakamata Y, Matsuoka Y, Inagaki M, et al., 2007. Structure of orbitofrontal cortex and its longitudinal course in cancerrelated post-traumatic stress disorder. Neurosci Res, 59(4):383–389. https://doi.org/10.1016/j.neures.2007.08.012 CrossRefPubMedGoogle Scholar
  16. Hamm LL, Jacobs RH, Johnson MW, et al., 2014. Aberrant amygdala functional connectivity at rest in pediatric anxiety disorders. Biol Mood Anxiety Disord, 4(1):15. https://doi.org/10.1186/s13587-014-0015-4 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Heekeren HR, Wartenburger I, Marschner A, et al., 2007. Role of ventral striatum in reward-based decision making. Neuroreport, 18(10):951–955. https://doi.org/10.1097/WNR.0b013e3281532bd7 CrossRefPubMedGoogle Scholar
  18. Hoptman MJ, Zuo XN, Butler PD, et al., 2010. Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study. Schizophr Res, 117(1):13–20. https://doi.org/10.1016/j.schres.2009.09.030 CrossRefPubMedGoogle Scholar
  19. Ieong HFH, Yuan Z, 2017. Abnormal resting-state functional connectivity in the orbitofrontal cortex of heroin users and its relationship with anxiety: a pilot fNIRS study. Sci Rep, 7:46522. https://doi.org/10.1038/srep46522 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Kahnt T, Chang LJ, Park SQ, et al., 2012. Connectivity-based parcellation of the human orbitofrontal cortex. J Neurosci, 32(18):6240–6250. https://doi.org/10.1523/JNEUROSCI.0257-12.2012 CrossRefPubMedGoogle Scholar
  21. Kent JM, Coplan JD, Mawlawi O, et al., 2005. Prediction of panic response to a respiratory stimulant by reduced orbitofrontal cerebral blood flow in panic disorder. Am J Psychiatry, 162(7):1379–1381. https://doi.org/10.1176/appi.ajp.162.7.1379 CrossRefPubMedGoogle Scholar
  22. Kim MJ, Gee DG, Loucks RA, et al., 2011. Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest. Cereb Cortex, 21(7):1667–1673. https://doi.org/10.1093/cercor/bhq237 CrossRefPubMedGoogle Scholar
  23. Kim MJ, Brown AC, Mattek AM, et al., 2016. The inverse relationship between the microstructural variability of amygdala-prefrontal pathways and trait anxiety is moderated by sex. Front Syst Neurosci, 10:93. https://doi.org/10.3389/fnsys.2016.00093 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Lai CH, Wu YT, 2015. The patterns of fractional amplitude of low-frequency fluctuations in depression patients: the dissociation between temporal regions and fronto-parietal regions. J Affect Disord, 175:441–445. https://doi.org/10.1016/j.jad.2015.01.054 CrossRefPubMedGoogle Scholar
  25. Liao W, Qiu CJ, Gentili C, et al., 2010. Altered effective connectivity network of the amygdala in social anxiety disorder: a resting-state fMRI study. PLoS ONE, 5(12): e15238. https://doi.org/10.1371/journal.pone.0015238 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Liu HG, Qin W, Qi HT, et al., 2015. Parcellation of the human orbitofrontal cortex based on gray matter volume covariance. Hum Brain Mapp, 36(2):538–548. https://doi.org/10.1002/hbm.22645 CrossRefPubMedGoogle Scholar
  27. Maddock RJ, Garrett AS, Buonocore MH, 2003. Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task. Hum Brain Mapp, 18(1):30–41. https://doi.org/10.1002/hbm.10075 CrossRefPubMedGoogle Scholar
  28. Milad MR, Rauch SL, 2007. The role of the orbitofrontal cortex in anxiety disorders. Ann NYA cad Sci, 1121(1):546–561. https://doi.org/10.1196/annals.1401.006 CrossRefGoogle Scholar
  29. Milad MR, Rauch SL, Pitman RK, et al., 2006. Fear extinction in rats: implications for human brain imaging and anxiety disorders. Biol Psychol, 73(1):61–71. https://doi.org/10.1016/j.biopsycho.2006.01.008 CrossRefPubMedGoogle Scholar
  30. Newman MEJ, 2006. Modularity and community structure in networks. Proc Natl Acad Sci USA, 103(23):8577–8582. https://doi.org/10.1073/pnas.0601602103 CrossRefPubMedGoogle Scholar
  31. Nooner KB, Colcombe SJ, Tobe RH, et al., 2012. The NKI-Rockland Sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci, 6:152. https://doi.org/10.3389/fnins.2012.00152 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Power JD, Barnes KA, Snyder AZ, et al., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3):2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018 CrossRefPubMedGoogle Scholar
  33. Qiu CJ, Feng Y, Meng YJ, et al., 2015. Analysis of altered baseline brain activity in drug-naive adult patients with social anxiety disorder using resting-state functional MRI. Psychiatry Investig, 12(3):372–380. https://doi.org/10.4306/pi.2015.12.3.372 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Raichle ME, MacLeod AM, Snyder AZ, et al., 2001. A default mode of brain function. Proc Natl Acad Sci USA, 98(2):676–682. https://doi.org/10.1073/pnas.98.2.676 CrossRefPubMedGoogle Scholar
  35. Raymond JG, Steele JD, Seriès P, 2017. Modeling trait anxiety: from computational processes to personality. Front Psychiatry, 8:1. https://doi.org/10.3389/fpsyt.2017.00001 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Scheinost D, Stoica T, Saksa J, et al., 2013. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl Psychiatry, 3(4):e250. https://doi.org/10.1038/tp.2013.24 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Shiba Y, Santangelo AM, Roberts AC, 2016. Beyond the medial regions of prefrontal cortex in the regulation of fear and anxiety. Front Syst Neurosci, 10:12. https://doi.org/10.3389/fnsys.2016.00012 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Shin LM, Liberzon I, 2010. The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology, 35(1):169–191. https://doi.org/10.1038/npp.2009.83 CrossRefPubMedGoogle Scholar
  39. Spampinato MV, Wood JN, de Simone V, et al., 2009. Neural correlates of anxiety in healthy volunteers: a voxel-based morphometry study. J Neuropsychiatry Clin Neurosci, 21(2):199–205. https://doi.org/10.1176/jnp.2009.21.2.199 CrossRefPubMedGoogle Scholar
  40. Spielberger CD, Gorsuch RL, Lushene RE, 1983. Manual for the State-Trait Anxiety Inventory. Consulting Psychologists Press, Palo Alto, USA.Google Scholar
  41. Tian X, Wei DT, Du X, et al., 2016. Assessment of trait anxiety and prediction of changes in state anxiety using functional brain imaging: a test–retest study. NeuroImage, 133:408–416. https://doi.org/10.1016/j.neuroimage.2016.03.024 CrossRefPubMedGoogle Scholar
  42. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1):273–289. https://doi.org/10.1006/nimg.2001.0978 CrossRefPubMedGoogle Scholar
  43. Wang S, Xu X, Zhou M, et al., 2017. Hope and the brain: trait hope mediates the protective role of medial orbitofrontal cortex spontaneous activity against anxiety. Neuroimage, 157:439–447. https://doi.org/10.1016/j.neuroimage.2017.05.056 CrossRefPubMedGoogle Scholar
  44. Wang ZQ, Xia MR, Dai ZJ, et al., 2015. Differentially disrupted functional connectivity of the subregions of the inferior parietal lobule in Alzheimer’s disease. Brain Struct Funct, 220(2):745–762. https://doi.org/10.1007/s00429-013-0681-9 CrossRefPubMedGoogle Scholar
  45. Worsley K, 2001. Statistical analysis of activation images. In: Jezzard P, Matthews PM, Smith SM (Eds.), Functional MRI: an Introduction to Methods. Oxford University Press, New York, USA, p.251–270.Google Scholar
  46. Xia MR, Wang ZQ, Dai ZJ, et al., 2014. Differentially disrupted functional connectivity in posteromedial cortical subregions in Alzheimer’s disease. J Alzheimers Dis, 39(3):527–543. https://doi.org/10.3233/JAD-131583 CrossRefPubMedGoogle Scholar
  47. Yan CG, Zang YF, 2010. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci, 4:13. https://doi.org/10.3389/fnsys.2010.00013 Google Scholar
  48. Zang YF, He Y, Zhu CZ, et al., 2007. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev, 29(2):83–91. https://doi.org/10.1016/j.braindev.2006.07.002 CrossRefPubMedGoogle Scholar
  49. Zhang DY, Raichle ME, 2010. Disease and the brain’s dark energy. Nat Rev Neurol, 6(1):15–28. https://doi.org/10.1038/nrneurol.2009.198 CrossRefPubMedGoogle Scholar
  50. Zhao XH, Wang PJ, Li CB, et al., 2007. Altered default mode network activity in patient with anxiety disorders: an fMRI study. Eur J Radiol, 63(3):373–378. https://doi.org/10.1016/j.ejrad.2007.02.006 CrossRefPubMedGoogle Scholar
  51. Zou QH, Zhu CZ, Yang YH, et al., 2008. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods, 172(1):137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Zuo XN,di Martino A, Kelly C, et al., 2010. The oscillating brain: complex and reliable. Neuroimage, 49(2):1432–1445. https://doi.org/10.1016/j.neuroimage.2009.09.037 CrossRefPubMedGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institutes of Psychological SciencesHangzhou Normal UniversityHangzhouChina
  2. 2.Center for Cognition and Brain DisordersHangzhou Normal UniversityHangzhouChina
  3. 3.Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
  4. 4.Department of PsychiatryDajia Lee’s General Hospital, Lee’s Medical CorporationTaichungChina

Personalised recommendations