Whole brain resting-state analysis reveals decreased functional connectivity in major depression

Ilya M Veer, Christian F Beckmann, Marie-José van Tol, Luca Ferrarini, Julien Milles, Dick J Veltman, André Aleman, Mark A van Buchem, Nic J van der Wee, Serge A R B Rombouts, Ilya M Veer, Christian F Beckmann, Marie-José van Tol, Luca Ferrarini, Julien Milles, Dick J Veltman, André Aleman, Mark A van Buchem, Nic J van der Wee, Serge A R B Rombouts

Abstract

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Keywords: amygdala; functional connectivity; independent component analysis; major depression; resting-state functional magnetic resonance imaging.

Figures

Figure 1
Figure 1
Group ICA functionally relevant resting-state networks. Depicted here are the 13 functionally relevant RSNs resulting from the group PICA step carried out on the concatenated data sets from both patients and controls. Most networks have previously been described (for example in: Beckmann et al., ; Damoiseaux et al., 2006) and show assemblies of regions associated with sensory processing, affective processing, and higher order cognitive processes. Images are z-statistics, ranging from 3 to 8, overlaid on the MNI-152 standard brain. The left hemisphere of the brain corresponds to the right side in this image.
Figure 2
Figure 2
Group main effects and between-group effects. Numbering corresponds to the networks depicted in Figure 1. (A) Depicted here are the group main and between-group effects for three RSNs. Group main effects are corrected for family-wise errors (p < 0.05) and between-group effects are corrected according to a local false discovery rate of 1%. RSN 12 shows an assembly of ventral affective regions, such as temporal poles, insula, medial prefrontal cortex, and amygdala, the latter two regions demonstrating decreased connectivity within the MDD group. RSN 11 shows brain regions linked to attention, of which the left frontal pole shows decreased connectivity in the MDD group. RSN 3 shows MDD-related decreased connectivity of the bilateral lingual gyrus with other medial visual areas. Images are z-statistics, ranging from 2 to 10, overlaid on the MNI-152 standard brain. The left hemisphere of the brain corresponds to the right side in this image. HC, healthy controls; MDD, major depressive disorder. (B) Distribution of the mean individual z-scores within the bilateral amygdala (12), left frontal pole (11), and bilateral lingual gyrus (3). Depicted in red are the controls, in black the MDD group, both sorted from smallest to highest z-value.

References

    1. Abou-Elseoud A., Starck T., Remes J., Nikkinen J., Tervonen O., Kiviniemi V. (2010). The effect of model order selection in group PICA. Hum. Brain Mapp. 31, 1207–1216
    1. American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th Edn.Washington, D.C.: American Psychiatric Association
    1. Anand A., Li Y., Wang Y., Wu J., Gao S., Bukhari L., Mathews V. P., Kalnin A., Lowe M. J. (2005a). Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol. Psychiatry 57, 1079–108810.1016/j.biopsych.2005.02.021
    1. Anand A., Li Y., Wang Y., Wu J., Gao S., Bukhari L., Mathews V. P., Kalnin A., Lowe M. J. (2005b). Antidepressant effect on connectivity of the mood-regulating circuit: an FMRI study. Neuropsychopharmacology 30, 1334–1344
    1. Aron A. R., Robbins T. W., Poldrack R. A. (2004). Inhibition and the right inferior frontal cortex. Trends Cogn. Sci. 8, 170–17710.1016/j.tics.2004.02.010
    1. Ashburner J., Friston K. J. (2000). Voxel-based morphometry – the methods. Neuroimage 11, 805–82110.1006/nimg.2000.0582
    1. Beckmann C. F., DeLuca M., Devlin J. T., Smith S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1001–101310.1098/rstb.2005.1634
    1. Beckmann C. F., Smith S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–15210.1109/TMI.2003.822821
    1. Biswal B., Yetkin F. Z., Haughton V. M., Hyde J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–54110.1002/mrm.1910340409
    1. Burgess P. W., Dumontheil I., Gilbert S. J. (2007a). The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends Cogn. Sci. 11, 290–29810.1016/j.tics.2007.05.004
    1. Burgess P. W., Gilbert S. J., Dumontheil I. (2007b). Function and localization within rostral prefrontal cortex (area 10). Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 887–89910.1098/rstb.2007.2095
    1. Chen C. H., Suckling J., Ooi C., Fu C. H., Williams S. C., Walsh N. D., Mitterschiffthaler M. T., Pich E. M., Bullmore E. (2008). Functional coupling of the amygdala in depressed patients treated with antidepressant medication. Neuropsychopharmacology 33, 1909–191810.1038/sj.npp.1301593
    1. Corbetta M., Shulman G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–21510.1038/nrn755
    1. Cordes D., Haughton V. M., Arfanakis K., Carew J. D., Turski P. A., Moritz C. H., Quigley M. A., Meyerand M. E. (2001). Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am. J. Neuroradiol. 22, 1326–1333
    1. Craddock R. C., Holtzheimer P. E., III, Hu X. P., Mayberg H. S. (2009). Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 62, 1619–162810.1002/mrm.22159
    1. Critchley H. D., Wiens S., Rotshtein P., Ohman A., Dolan R. J. (2004). Neural systems supporting interoceptive awareness. Nat. Neurosci. 7, 189–19510.1038/nn1176
    1. Damoiseaux J. S., Beckmann C. F., Arigita E. J., Barkhof F., Scheltens P., Stam C. J., Smith S. M., Rombouts S. A. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cereb. Cortex 18, 1856–186410.1093/cercor/bhm207
    1. Damoiseaux J. S., Rombouts S. A., Barkhof F., Scheltens P., Stam C. J., Smith S. M., Beckmann C. F. (2006). Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103, 13848–1385310.1073/pnas.0601417103
    1. Davidson R. J., Pizzagalli D., Nitschke J. B., Putnam K. (2002). Depression: perspectives from affective neuroscience. Annu. Rev. Psychol. 53, 545–57410.1146/annurev.psych.53.100901.135148
    1. De Martino F., Gentile F., Esposito F., Balsi M., Di S. F., Goebel R., Formisano E. (2007). Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. Neuroimage 34, 177–19410.1016/j.neuroimage.2006.08.041
    1. Dolcos F., Kragel P., Wang L., McCarthy G. (2006). Role of the inferior frontal cortex in coping with distracting emotions. Neuroreport 17, 1591–159410.1097/
    1. Douaud G., Smith S., Jenkinson M., Behrens T., Johansen-Berg H., Vickers J., James S., Voets N., Watkins K., Matthews P. M., James A. (2007). Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130, 2375–238610.1093/brain/awm184
    1. Drevets W. C., Price J. L., Furey M. L. (2008). Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct. Funct. 213, 93–11810.1007/s00429-008-0189-x
    1. Ebmeier K., Rose E., Steele D. (2006). Cognitive impairment and fMRI in major depression. Neurotox. Res. 10, 87–9210.1007/BF03033237
    1. Efron B. (2004). Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J. Amer. Statistical Assoc. 99, 96–10410.1198/016214504000000089
    1. Filippini N., MacIntosh B. J., Hough M. G., Goodwin G. M., Frisoni G. B., Smith S. M., Matthews P. M., Beckmann C. F., Mackay C. E. (2009). Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc. Natl. Acad. Sci. U. S. A. 106, 7209–721410.1073/pnas.0811879106
    1. Fox M. D., Raichle M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–71110.1038/nrn2201
    1. Fox M. D., Snyder A. Z., Vincent J. L., Corbetta M., Van E., Raichle M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673–967810.1073/pnas.0504136102
    1. Good C. D., Johnsrude I. S., Ashburner J., Henson R. N., Friston K. J., Frackowiak R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21–3610.1006/nimg.2001.0786
    1. Greicius M. D., Flores B. H., Menon V., Glover G. H., Solvason H. B., Kenna H., Reiss A. L., Schatzberg A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–43710.1016/j.biopsych.2006.09.020
    1. 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. Proc. Natl. Acad. Sci. U. S. A. 100, 253–25810.1073/pnas.0135058100
    1. Hyvarinen A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–63410.1109/72.761722
    1. 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, 825–84110.1016/S1053-8119(02)91132-8
    1. Jenkinson M., Smith S. (2001). A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–15610.1016/S1361-8415(01)00036-6
    1. Johnstone T., van Reekum C. M., Urry H. L., Kalin N. H., Davidson R. J. (2007). Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. J. Neurosci. 27, 8877–888410.1523/JNEUROSCI.2063-07.2007
    1. Lorenzetti V., Allen N. B., Fornito A., Yucel M. (2009). Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. J. Affect. Disord. 117, 1–1710.1016/j.jad.2008.11.021
    1. Lowe M. J., Mock B. J., Sorenson J. A. (1998). Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage 7, 119–13210.1006/nimg.1997.0315
    1. Matthews S. C., Strigo I. A., Simmons A. N., Yang T. T., Paulus M. P. (2008). Decreased functional coupling of the amygdala and supragenual cingulate is related to increased depression in unmedicated individuals with current major depressive disorder. J. Affect. Disord. 111, 13–2010.1016/j.jad.2008.05.022
    1. Mayberg H. S. (1997). Limbic-cortical dysregulation: a proposed model of depression. J. Neuropsychiatry Clin. Neurosci. 9, 471–481
    1. Mayberg H. S. (2003). Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. Br. Med. Bull. 65, 193–20710.1093/bmb/65.1.193
    1. Mennes M., Kelly C., Zuo X. N., Di M. A., Biswal B. B., Castellanos F. X., Milham M. P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage 50, 1690–170110.1016/j.neuroimage.2010.01.002
    1. Montgomery S. A., Asberg M. (1979). A new depression scale designed to be sensitive to change. Br. J. Psychiatry 134, 382–38910.1192/bjp.134.4.382
    1. Nichols T. E., Holmes A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–2510.1002/hbm.1058
    1. Penninx B. W., Beekman A. T., Smit J. H., Zitman F. G., Nolen W. A., Spinhoven P., Cuijpers P., De Jong P. J., Van Marwijk H. W., Assendelft W. J., Van Der Meer K., Verhaak P., Wensing M., De Graaf R., Hoogendijk W. J., Ormel J., Van Dyck R. (2008). The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int. J. Methods Psychiatr. Res. 17, 121–14010.1002/mpr.256
    1. Pessoa L. (2008). On the relationship between emotion and cognition. Nat. Rev. Neurosci. 9, 148–15810.1038/nrn2317
    1. Phillips M. L., Drevets W. C., Rauch S. L., Lane R. (2003). Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol. Psychiatry 54, 515–52810.1016/S0006-3223(03)00171-9
    1. 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. Proc. Natl. Acad. Sci. U. S. A. 98, 676–68210.1073/pnas.98.2.676
    1. Rogers M. A., Kasai K., Koji M., Fukuda R., Iwanami A., Nakagome K., Fukuda M., Kato N. (2004). Executive and prefrontal dysfunction in unipolar depression: a review of neuropsychological and imaging evidence. Neurosci. Res. 50, 1–1110.1016/j.neures.2004.05.003
    1. Seeley W. W., Menon V., Schatzberg A. F., Keller J., Glover G. H., Kenna H., Reiss A. L., Greicius M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–235610.1523/JNEUROSCI.5587-06.2007
    1. Shehzad Z., Kelly A. M., Reiss P. T., Gee D. G., Gotimer K., Uddin L. Q., Lee S. H., Margulies D. S., Roy A. K., Biswal B. B., Petkova E., Castellanos F. X., Milham M. P. (2009). The resting brain: unconstrained yet reliable. Cereb. Cortex 19, 2209–222910.1093/cercor/bhn256
    1. Sheline Y. I. (2003). Neuroimaging studies of mood disorder effects on the brain. Biol. Psychiatry 54, 338–35210.1016/S0006-3223(03)00347-0
    1. Smith S. M. (2002). Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–15510.1002/hbm.10062
    1. Smith S. M., Fox P. T., Miller K. L., Glahn D. C., Fox P. M., Mackay C. E., Filippini N., Watkins K. E., Toro R., Laird A. R., Beckmann C. F. (2009). Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106, 13040–1304510.1073/pnas.0905267106
    1. Smith S. M., Jenkinson M., Woolrich M. W., Beckmann C. F., Behrens T. E., Johansen-Berg H., Bannister P. R., De L. M., Drobnjak I., Flitney D. E., Niazy R. K., Saunders J., Vickers J., Zhang Y., De S. N., Brady J. M., Matthews P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl. 1), S208–S21910.1016/j.neuroimage.2004.07.051
    1. Smith S. M., Nichols T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–9810.1016/j.neuroimage.2008.03.061
    1. Stephan K. E., Riera J. J., Deco G., Horwitz B. (2008). The brain connectivity workshops: moving the frontiers of computational systems neuroscience. Neuroimage 42, 1–910.1016/j.neuroimage.2008.04.167
    1. Urry H. L., van Reekum C. M., Johnstone T., Kalin N. H., Thurow M. E., Schaefer H. S., Jackson C. A., Frye C. J., Greischar L. L., Alexander A. L., Davidson R. J. (2006). Amygdala and ventromedial prefrontal cortex are inversely coupled during regulation of negative affect and predict the diurnal pattern of cortisol secretion among older adults. J. Neurosci. 26, 4415–442510.1523/JNEUROSCI.3215-05.2006
    1. Wang L. H., Labar K. S., Smoski M., Rosenthal M. Z., Dolcos F., Lynch T. R., Krishnan R. R., McCarthy G. (2008). Prefrontal mechanisms for executive control over emotional distraction are altered in major depression. Psychiatry Res. 163, 143–15510.1016/j.pscychresns.2007.10.004
    1. Zhou Y., Yu C., Zheng H., Liu Y., Song M., Qin W., Li K., Jiang T. (2009). Increased neural resources recruitment in the intrinsic organization in major depression. J. Affect. Disord. 121, 220–23010.1016/j.jad.2009.05.029

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever