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
Belmaker RH, Agam G (2008) Major depressive disorder. N Engl J Med 358(1):55–68. doi:10.1056/NEJMra073096
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
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
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
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
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
Drevets WC (2007) Orbitofrontal cortex function and structure in depression. Ann NY Acad Sci 1121:499–527. doi:10.1196/annals.1401.029
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
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
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
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
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
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
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
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
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
Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Natl Acad Sci USA 107(21):9885–9890. doi:10.1073/pnas.1001414107
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
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
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
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
Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62
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
Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113. doi:10.1016/j.neuroimage.2007.07.007
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
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
Ashburner J, Friston KJ (2000) Voxel-based morphometry: the methods. NeuroImage 11(6 Pt 1):805–821. doi:10.1006/nimg.2000.0582
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
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
Buckner RL (2010) Human functional connectivity: new tools, unresolved questions. Proc Natl Acad Sci USA 107(24):10769–10770. doi:10.1073/pnas.1005987107
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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Bin Zhang and Meng Li have contributed equally to this work.
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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)
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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)
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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)
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Figure S4. Spatial distributions of the salience and visual networks (p < 0.05, FWE correction), identified using individual component analysis (ICA).(TIFF 4766 kb)
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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)
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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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DOI: https://doi.org/10.1007/s00406-015-0614-0