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Altered functional connectivity density in major depressive disorder at rest

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Abstract

Major depressive disorder is characterized by abnormal brain connectivity at rest. Currently, most studies investigating resting-state activity rely on a priori restrictions on specific networks or seed regions, which may bias observations. We hence sought to elicit functional alterations in a hypothesis-free approach. We applied functional connectivity density (FCD) to identify abnormal connectivity for each voxel in the whole brain separately. Comparing resting-state fMRI in 21 MDD patients and 23 matched healthy controls, we identified atypical connections for regions exhibiting abnormal FCD and compared our results to those of an independent component analysis (ICA) on networks previously investigated in MDD. Patients showed reduced FCD in mid-cingulate cortex (MCC) and increased FCD in occipital cortex (OCC). These changes in global FCD were driven by abnormal local connectivity changes and reduced functional connectivity (FC) toward the left amygdala for MCC, and increased FC toward the right supplementary motor area for OCC. The altered connectivity was not reflected in ICA comparison of the salience and visual networks. Abnormal FC in MDD is present in cingulate and OCC in terms of global FCD. This converges with previous structural and metabolic findings; however, these particular changes in connectivity would not have been identified using canonical seed regions or networks. This implies the importance of FC measures in the investigation of brain pathophysiology in depression.

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References

  1. Belmaker RH, Agam G (2008) Major depressive disorder. N Engl J Med 358(1):55–68. doi:10.1056/NEJMra073096

    Article  CAS  PubMed  Google Scholar 

  2. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF (2007) Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 62(5):429–437. doi:10.1016/j.biopsych.2006.09.020

    Article  PubMed  PubMed Central  Google Scholar 

  3. Avery JA, Drevets WC, Moseman SE, Bodurka J, Barcalow JC, Simmons WK (2014) Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula. Biol Psychiatry 76(3):258–266. doi:10.1016/j.biopsych.2013.11.027

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cullen KR, Westlund MK, Klimes-Dougan B, Mueller BA, Houri A, Eberly LE, Lim KO (2014) Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry 71(10):1138–1147. doi:10.1001/jamapsychiatry.2014.1087

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kong L, Chen K, Tang Y, Wu F, Driesen N, Womer F, Fan G, Ren L, Jiang W, Cao Y, Blumberg HP, Xu K, Wang F (2013) Functional connectivity between the amygdala and prefrontal cortex in medication-naive individuals with major depressive disorder. JPN 38(6):417–422. doi:10.1503/jpn.120117

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ramasubbu R, Konduru N, Cortese F, Bray S, Gaxiola-Valdez I, Goodyear B (2014) Reduced intrinsic connectivity of amygdala in adults with major depressive disorder. Front Psychiatry 5:17. doi:10.3389/fpsyt.2014.00017

    Article  PubMed  PubMed Central  Google Scholar 

  7. Drevets WC (2007) Orbitofrontal cortex function and structure in depression. Ann NY Acad Sci 1121:499–527. doi:10.1196/annals.1401.029

    Article  PubMed  Google Scholar 

  8. Bremner JD, Vythilingam M, Vermetten E, Nazeer A, Adil J, Khan S, Staib LH, Charney DS (2002) Reduced volume of orbitofrontal cortex in major depression. Biol Psychiatry 51(4):273–279

    Article  PubMed  Google Scholar 

  9. Dutta A, McKie S, Deakin JF (2014) Resting state networks in major depressive disorder. Psychiatry Res 224(3):139–151. doi:10.1016/j.pscychresns.2014.10.003

    Article  PubMed  Google 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. doi:10.1038/nrn2201

    Article  CAS  PubMed  Google Scholar 

  11. Nair A, Keown CL, Datko M, Shih P, Keehn B, Muller RA (2014) Impact of methodological variables on functional connectivity findings in autism spectrum disorders. Hum Brain Mapp 35(8):4035–4048. doi:10.1002/hbm.22456

    Article  PubMed  Google Scholar 

  12. Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619–1628. doi:10.1002/mrm.22159

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lee MH, Smyser CD, Shimony JS (2013) Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 34(10):1866–1872. doi:10.3174/ajnr.A3263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hobson AR, Hillebrand A (2006) Independent component analysis of the EEG: is this the way forward for understanding abnormalities of brain-gut signalling? Gut 55(5):597–600. doi:10.1136/gut.2005.081703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Golestani AM, Goodyear BG (2011) Regions of interest for resting-state fMRI analysis determined by inter-voxel cross-correlation. NeuroImage 56(1):246–251. doi:10.1016/j.neuroimage.2011.02.038

    Article  PubMed  Google Scholar 

  16. Yan FX, Wu CW, Cheng SY, Lim KE, Hsu YY, Liu HL (2013) Resting-state functional magnetic resonance imaging analysis with seed definition constrained by regional homogeneity. Brain Connect 3(4):438–449. doi:10.1089/brain.2013.0164

    Article  PubMed  Google Scholar 

  17. Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Natl Acad Sci USA 107(21):9885–9890. doi:10.1073/pnas.1001414107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Anticevic A, Cole MW, Repovs G, Savic A, Driesen NR, Yang G, Cho YT, Murray JD, Glahn DC, Wang XJ, Krystal JH (2013) Connectivity, pharmacology, and computation: toward a mechanistic understanding of neural system dysfunction in schizophrenia. Front Psychiatry 4:169. doi:10.3389/fpsyt.2013.00169

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tomasi D, Volkow ND (2012) Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biol Psychiatry 71(5):443–450. doi:10.1016/j.biopsych.2011.11.003

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wang T, Li Q, Guo M, Peng Y, Li Q, Qin W, Yu C (2014) Abnormal functional connectivity density in children with anisometropic amblyopia at resting-state. Brain Res 1563:41–51. doi:10.1016/j.brainres.2014.03.015

    Article  CAS  PubMed  Google Scholar 

  21. Qin W, Xuan Y, Liu Y, Jiang T, Yu C (2014) Functional connectivity density in congenitally and late blind subjects. Cereb Cortex. doi:10.1093/cercor/bhu051

    PubMed  Google Scholar 

  22. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2013) Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp. NeuroImage 76:439–441. doi:10.1016/j.neuroimage.2012.03.017

    Article  PubMed  Google Scholar 

  24. Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113. doi:10.1016/j.neuroimage.2007.07.007

    Article  PubMed  Google Scholar 

  25. Li Y, Ma Z, Lu W, Li Y (2006) Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol Meas 27(4):425–436. doi:10.1088/0967-3334/27/4/008

    Article  PubMed  Google Scholar 

  26. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, He Y, Yan CG, Zang YF (2011) REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6(9):e25031. doi:10.1371/journal.pone.0025031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ashburner J, Friston KJ (2000) Voxel-based morphometry: the methods. NeuroImage 11(6 Pt 1):805–821. doi:10.1006/nimg.2000.0582

    Article  CAS  PubMed  Google Scholar 

  28. Woo CW, Krishnan A, Wager TD (2014) Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage 91:412–419. doi:10.1016/j.neuroimage.2013.12.058

    Article  PubMed  PubMed Central  Google Scholar 

  29. Anticevic A, Brumbaugh MS, Winkler AM, Lombardo LE, Barrett J, Corlett PR, Kober H, Gruber J, Repovs G, Cole MW, Krystal JH, Pearlson GD, Glahn DC (2013) Global prefrontal and fronto-amygdala dysconnectivity in bipolar I disorder with psychosis history. Biol Psychiatry 73(6):565–573. doi:10.1016/j.biopsych.2012.07.031

    Article  PubMed  PubMed Central  Google Scholar 

  30. Buckner RL (2010) Human functional connectivity: new tools, unresolved questions. Proc Natl Acad Sci USA 107(24):10769–10770. doi:10.1073/pnas.1005987107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198. doi:10.1038/nrn2575

    Article  CAS  PubMed  Google Scholar 

  32. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, Gong Q (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70(4):334–342. doi:10.1016/j.biopsych.2011.05.018

    Article  PubMed  Google Scholar 

  33. Lord A, Horn D, Breakspear M, Walter M (2012) Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 7(8):e41282. doi:10.1371/journal.pone.0041282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yu C, Zhou Y, Liu Y, Jiang T, Dong H, Zhang Y, Walter M (2011) Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation. NeuroImage 54(4):2571–2581. doi:10.1016/j.neuroimage.2010.11.018

    Article  PubMed  Google Scholar 

  35. Kiviniemi V, Starck T, Remes J, Long X, Nikkinen J, Haapea M, Veijola J, Moilanen I, Isohanni M, Zang YF, Tervonen O (2009) Functional segmentation of the brain cortex using high model order group PICA. Hum Brain Mapp 30(12):3865–3886. doi:10.1002/hbm.20813

    Article  PubMed  Google Scholar 

  36. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 103(37):13848–13853. doi:10.1073/pnas.0601417103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ochsner KN, Gross JJ (2005) The cognitive control of emotion. Trends Cogn Sci 9(5):242–249. doi:10.1016/j.tics.2005.03.010

    Article  PubMed  Google Scholar 

  38. Disner SG, Beevers CG, Haigh EA, Beck AT (2011) Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci 12(8):467–477. doi:10.1038/nrn3027

    Article  CAS  PubMed  Google Scholar 

  39. Mayberg HS, Brannan SK, Mahurin RK, Jerabek PA, Brickman JS, Tekell JL, Silva JA, McGinnis S, Glass TG, Martin CC, Fox PT (1997) Cingulate function in depression: a potential predictor of treatment response. NeuroReport 8(4):1057–1061

    Article  CAS  PubMed  Google Scholar 

  40. Bench CJ, Friston KJ, Brown RG, Frackowiak RS, Dolan RJ (1993) Regional cerebral blood flow in depression measured by positron emission tomography: the relationship with clinical dimensions. Psychol Med 23(3):579–590

    Article  CAS  PubMed  Google Scholar 

  41. Takano H, Kato M, Inagaki A, Watanabe K, Kashima H (2006) Time course of cerebral blood flow changes following electroconvulsive therapy in depressive patients–measured at 3 time points using single photon emission computed tomography. Keio J Med 55(4):153–160

    Article  PubMed  Google Scholar 

  42. Nadeau SE, McCoy KJ, Crucian GP, Greer RA, Rossi F, Bowers D, Goodman WK, Heilman KM, Triggs WJ (2002) Cerebral blood flow changes in depressed patients after treatment with repetitive transcranial magnetic stimulation: evidence of individual variability. Neuropsychiatry Neuropsychol Behav Neurol 15(3):159–175

    PubMed  Google Scholar 

  43. Oda K, Okubo Y, Ishida R, Murata Y, Ohta K, Matsuda T, Matsushima E, Ichimiya T, Suhara T, Shibuya H, Nishikawa T (2003) Regional cerebral blood flow in depressed patients with white matter magnetic resonance hyperintensity. Biol Psychiatry 53(2):150–156

    Article  PubMed  Google Scholar 

  44. Liang X, Zou Q, He Y, Yang Y (2013) Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci USA 110(5):1929–1934. doi:10.1073/pnas.1214900110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, Mathews VP, Kalnin A, Lowe MJ (2005) Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol Psychiatry 57(10):1079–1088. doi:10.1016/j.biopsych.2005.02.021

    Article  PubMed  Google Scholar 

  46. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27(9):2349–2356. doi:10.1523/JNEUROSCI.5587-06.2007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Palomero-Gallagher N, Bidmon HJ, Cremer M, Schleicher A, Kircheis G, Reifenberger G, Kostopoulos G, Haussinger D, Zilles K (2009) Neurotransmitter receptor imbalances in motor cortex and basal ganglia in hepatic encephalopathy. Cell Physiol Biochem 24(3–4):291–306. doi:10.1159/000233254

    Article  CAS  PubMed  Google Scholar 

  48. Walter M, Li S, Demenescu LR (2014) Multistage drug effects of ketamine in the treatment of major depression. Eur Arch Psychiatry Clin Neurosci 264(Suppl 1):S55–S65. doi:10.1007/s00406-014-0535-3

    Article  PubMed  Google Scholar 

  49. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12(5):535–540. doi:10.1038/nn.2303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sanacora G, Gueorguieva R, Epperson CN, Wu YT, Appel M, Rothman DL, Krystal JH, Mason GF (2004) Subtype-specific alterations of gamma-aminobutyric acid and glutamate in patients with major depression. Arch Gen Psychiatry 61(7):705–713. doi:10.1001/archpsyc.61.7.705

    Article  CAS  PubMed  Google Scholar 

  51. Sanacora G, Mason GF, Rothman DL, Behar KL, Hyder F, Petroff OA, Berman RM, Charney DS, Krystal JH (1999) Reduced cortical gamma-aminobutyric acid levels in depressed patients determined by proton magnetic resonance spectroscopy. Arch Gen Psychiatry 56(11):1043–1047

    Article  CAS  PubMed  Google Scholar 

  52. Sanacora G, Mason GF, Rothman DL, Hyder F, Ciarcia JJ, Ostroff RB, Berman RM, Krystal JH (2003) Increased cortical GABA concentrations in depressed patients receiving ECT. Am J Psychiatry 160(3):577–579

    Article  PubMed  Google Scholar 

  53. Northoff G, Walter M, Schulte RF, Beck J, Dydak U, Henning A, Boeker H, Grimm S, Boesiger P (2007) GABA concentrations in the human anterior cingulate cortex predict negative BOLD responses in fMRI. Nat Neurosci 10(12):1515–1517. doi:10.1038/nn2001

    Article  CAS  PubMed  Google Scholar 

  54. Muthukumaraswamy SD, Edden RA, Jones DK, Swettenham JB, Singh KD (2009) Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proc Natl Acad Sci USA 106(20):8356–8361. doi:10.1073/pnas.0900728106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Donahue MJ, Near J, Blicher JU, Jezzard P (2010) Baseline GABA concentration and fMRI response. NeuroImage 53(2):392–398. doi:10.1016/j.neuroimage.2010.07.017

    Article  CAS  PubMed  Google Scholar 

  56. Borchardt V, Krause AL, Starck T, Nissila J, Timonen M, Kiviniemi V, Walter M (2014) Graph theory reveals hyper-functionality in visual cortices of seasonal affective disorder patients. World J Biol Psychiatry 1–12. doi:10.3109/15622975.2014.966144

  57. Guggisberg AG, Honma SM, Findlay AM, Dalal SS, Kirsch HE, Berger MS, Nagarajan SS (2008) Mapping functional connectivity in patients with brain lesions. Annal Neurol 63(2):193–203. doi:10.1002/ana.21224

    Article  PubMed  PubMed Central  Google Scholar 

  58. van Tol MJ, Li M, Metzger CD, Hailla N, Horn DI, Li W, Heinze HJ, Bogerts B, Steiner J, He H, Walter M (2013) Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol Med 1–13. doi:10.1017/S0033291713002742

  59. Hahn A, Wadsak W, Windischberger C, Baldinger P, Hoflich AS, Losak J, Nics L, Philippe C, Kranz GS, Kraus C, Mitterhauser M, Karanikas G, Kasper S, Lanzenberger R (2012) Differential modulation of the default mode network via serotonin-1A receptors. Proc Natl Acad Sci USA 109(7):2619–2624. doi:10.1073/pnas.1117104109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Horn DI, Yu C, Steiner J, Buchmann J, Kaufmann J, Osoba A, Eckert U, Zierhut KC, Schiltz K, He H, Biswal B, Bogerts B, Walter M (2010) Glutamatergic and resting-state functional connectivity correlates of severity in major depression: the role of pregenual anterior cingulate cortex and anterior insula. Front Syst Neurosci. doi:10.3389/fnsys.2010.00033

    PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors thank Anton Lord and Adam Safron for reviewing this manuscript. This work was partially funded by the German Research Foundation Grant SFB/779 awarded to Dr. Martin Walter and Dr. Bernhard Bogerts.

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    Authors and Affiliations

    1. Department of Psychiatry, Otto von Guericke University, Magdeburg, Germany

      Bin Zhang, Coraline Danielle Metzger, Bernhard Bogerts & Martin Walter

    2. Clinical Affective Neuroimaging Laboratory (CANLAB), Otto von Guericke University, Magdeburg, Germany

      Bin Zhang, Meng Li, Liliana Ramona Demenescu & Martin Walter

    3. Department of Neurology, Otto von Guericke University, Magdeburg, Germany

      Meng Li, Liliana Ramona Demenescu & Martin Walter

    4. Department of Radiology and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China

      Wen Qin & Chunshui Yu

    5. Leibniz Institute for Neurobiology, Magdeburg, Germany

      Martin Walter

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    406_2015_614_MOESM1_ESM.tif

    Figure S1. Group difference of functional connectivity density (FCD) map between patient and control groups (correlation coefficient threshold R > 0.4, uncorrected p < 0.001). Hot and cold colors indicate increased and decreased global FCD in depressed patients, respectively. (TIFF 3249 kb)

    406_2015_614_MOESM2_ESM.tif

    Figure S2. Group difference of functional connectivity density (FCD) map between patient and control groups (correlation coefficient threshold R > 0.5, uncorrected p < 0.001). Hot and cold colors indicate increased and decreased global FCD in depressed patients, respectively. (TIFF 3213 kb)

    406_2015_614_MOESM3_ESM.tif

    Figure S3. Group difference of functional connectivity density (FCD) map between patient and control groups (correlation coefficient threshold R > 0.6, small volume corrected p < 0.001), without whole-brain signal regression. Hot and cold colors indicate increased and decreased global FCD in depressed patients, respectively.(TIFF 1075 kb)

    406_2015_614_MOESM4_ESM.tif

    Figure S4. Spatial distributions of the salience and visual networks (p < 0.05, FWE correction), identified using individual component analysis (ICA).(TIFF 4766 kb)

    406_2015_614_MOESM5_ESM.tif

    Figure S5. Group difference of functional connectivity density (FCD) map between patient and control groups, with the gray matter volume (GMV) of mid-cingulate cortex and occipital cortex as covariates, respectively. (TIFF 3204 kb)

    Table S1. Demographic information of depressed patients. (DOCX 14 kb)

    406_2015_614_MOESM7_ESM.docx

    Table S2. Group difference of functional connectivity density (FCD) map between depressed patients and controls with and without the gray matter volume as covariates. To avoid underestimation due to colinearity of GMV in OCC and MCC, two models were tested for MCC and OCC, respectively. (DOCX 12 kb)

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    Zhang, B., Li, M., Qin, W. et al. Altered functional connectivity density in major depressive disorder at rest. Eur Arch Psychiatry Clin Neurosci 266, 239–248 (2016). https://doi.org/10.1007/s00406-015-0614-0

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