Use of Resting-state Functional Magnetic Resonance Imaging to Measure Functional Connectivity in Adolescents with Depression

Kathryn R Cullen, Kristin L Ballard, Bonnie Klimes-Dougan, Kelvin O Lim
European Psychiatric Review, 2011;4(2):94-96
pdf icon download pdf ejournal view ejournal

Abstract

Major depressive disorder (MDD) is a serious mental disorder associated with severe outcomes, including disability and suicide. Abnormalities in the neural development during adolescence may be centrally linked with the risk of developing MDD. A growing body of neuroimaging research, in adults and adolescents, supports a model for the pathophysiology of MDD rooted in the fronto-limbic neural circuitry. One promising technique well-suited for examination of this circuitry is resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI has now been used to investigate functional connectivity in normative populations and in neuropsychiatric disorders. We review research in adults with depression that has led us to consider using this methodology to examine the neural circuitry of depression in adolescents. We also review results from our own study examining MDD in adolescents, which demonstrated lower resting-state functional connectivity in the network arising from the subgenual anterior cingulate cortex (ACC) compared with controls. Resting-state neuroimaging holds considerable promise for advancing our conceptualisation of the underlying neurobiology of depression, with the hope of aiding the development of more effective treatments.
Keywords
Depression, resting-state, functional magnetic resonance imaging (fMRI), adolescence
Disclosure The authors have no conflicts of interest to declare.
Received: October 26, 2011 Accepted November 16, 2011
Correspondence: Kathryn R Cullen, Department of Psychiatry, F256/2B West, 2450 Riverside Avenue, Minneapolis, MN 55454, US. E: rega0026@umn.edu

Major depressive disorder (MDD) is a serious mental illness that leads to severe outcomes, including disability1 and suicide.2 MDD occurs frequently in adolescence, with prevalence rates of 15 %.3 Clinically, adolescent depression is continuous with adult depression.4–7 Neurobiological models for the pathophysiology of depression have been developed largely based on research in adults, but questions remain about how this knowledge applies to adolescence. For instance, does the disruption of biological mechanisms:

  • occur prior to the onset of depression (as reflected in a vulnerability that makes individuals less apt to effectively manage stress);
  • develop early on in the disease process (such as a first episode in adolescence); or
  • result from chronic disease?

Further, adolescence is a time associated with significant brain maturation and refinement of neuronal connections, raising the possibility that the neural underpinnings of depression in young people could differ from that in adults. For instance, adolescent brain development changes include linear white matter increase,8–10 non-linear grey matter decrease8,11,12 and maturation of brain networks from diffuse to focal patterns of connectivity.13–15 Ongoing neural development may render an increased neural plasticity inherent to this time period, which could in turn provide an opportune time to apply early interventions.

The application of an array of techniques under the spectrum of magnetic resonance imaging (MRI) technology has facilitated significant advances in our current understanding of the pathophysiology of MDD. MRI research allows the safe examination of the structure and function of neural systems in vivo. A growing body of neuroimaging research, largely from adults, supports a model for the pathophysiology of MDD rooted in fronto-limbic neural circuitry,16,17 a set of brain connections that mediates emotional processing.18 Structural and functional neuroimaging studies in children and adolescents with depression have also revealed abnormalities in the structure as well as the function of key nodes within this circuitry, including the prefrontal cortex,19,20 the anterior cingulate cortex (ACC),21–23 the amygdala24–27 and the hippocampus.28 Increasing attention has recently been given to the subgenual region of the ACC, an area that appears to have central significance in adult MDD29,30 and in adolescent MDD.21,31 Indeed, this region may represent a ‘hub’ for the fronto-limbic circuits that are dysregulated in MDD.30,32 For instance, this region has been specifically targeted in surgical treatments for refractory depression (i.e., deep brain stimulation).29

Advantages of Resting-state Functional Magnetic Resonance Imaging
Recent advances in neuroimaging have permitted moving beyond just identifying brain structures to investigating the nature of connections within circuits. One neuroimaging technique well-suited for this is resting-state functional MRI (fMRI). This method measures the spontaneous slow-wave (0.01–0.1 Hz) fluctuations in blood oxygen level-dependent (BOLD) signal that are observed while subjects are at rest.33 Temporal resting-state patterns are highly correlated within brain regions that are known to be anatomically connected.34 Thus ‘functional connectivity’ is a measurement of the interregional correlations of these BOLD patterns.35 Increasing evidence supports that this approach can provide new knowledge about the make-up of neural networks, and the practical advantages of this imaging approach are numerous. Specifically, this technique provides a safe and non-invasive way to collect important data about neural connections in a relatively short period of time – our laboratory uses a six-minute resting-state MRI scan length – without requiring the design of an additional cognitive task.

References:
  1. Murray CJ, Salomon JA, Mathers C, A critical examination of summary measures of population health, Bull World Health Organ, 2000;78(8):981–94.
  2. Centers for Disease Control and Prevention, Suicide trends among youths and young adults aged 10–24 years–United States, 1990-2004, MMWR Morb Mortal Wkly Rep, 2007;56(35):905–8.
  3. Kessler RC, Walters EE, Epidemiology of DSM-III-R major depression and minor depression among adolescents and young adults in the National Comorbidity Survey, Depress Anxiety, 1998;7(1):3–14.
  4. Harrington R, Fudge H, Rutter M, et al., Adult outcomes of childhood and adolescent depression. I. Psychiatric status, Arch Gen Psychiatry, 1990;47(5):465–73.
  5. Lewinsohn PM, Rohde P, Klein DN, Seeley JR, Natural course of adolescent major depressive disorder: I. Continuity into young adulthood, J Am Acad Child Adolesc Psychiatry, 1999;38(1):56–63.
  6. Rao U, Hammen C, Daley SE, Continuity of depression during the transition to adulthood: a 5-year longitudinal study of young women, J Am Acad Child Adolesc Psychiatry, 1999;38(7):908–15.
  7. Weissman MM, Wolk S, Goldstein RB, et al., Depressed adolescents grown up, JAMA, 1999;281(18):1707–13.
  8. Giedd JN, Snell JW, Lange N, et al., Quantitative magnetic resonance imaging of human brain development: ages 4–18, Cereb Cortex, 1996;6(4):551–60.
  9. Giedd JN, Blumenthal J, Jeffries NO, et al., Brain development during childhood and adolescence: a longitudinal MRI study, Nat Neurosci, 1999;2(10):861–3.
  10. Sowell ER, Thompson PM, Tessner KD, Toga AW, Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation, J Neurosci, 2001;21(22):8819–29.
  11. Sowell ER, Thompson PM, Holmes CJ, et al., In vivo evidence for post-adolescent brain maturation in frontal and striatal regions, Nat Neurosci, 1999;2(10):859–61.
  12. Gogtay N, Giedd JN, Lusk L, et al., Dynamic mapping of human cortical development during childhood through early adulthood, Proc Natl Acad Sci U S A, 2004;101(21):8174–9.
  13. Fair DA, Dosenbach NU, Church JA, et al., Development of distinct control networks through segregation and integration, Proc Natl Acad Sci U S A, 2007;104(33):13507–12.
  14. Casey BJ, Jones RM, Hare TA, The adolescent brain, Ann N Y Acad Sci, 2008;1124:111–26.
  15. Kelly AM, Di Martino A, Uddin LQ, et al., Development of anterior cingulate functional connectivity from late childhood to early adulthood, Cereb Cortex, 2009;19(3):640–57.
  16. Mayberg HS, Limbic-cortical dysregulation: a proposed model of depression, J Neuropsychiatry Clin Neurosci, 1997;9(3):471–81.
  17. Drevets WC, Prefrontal cortical-amygdalar metabolism in major depression, Ann N Y Acad Sci, 1999;877:614–37.
  18. Phillips ML, Drevets WC, Rauch SL, Lane R, Neurobiology of emotion perception I: The neural basis of normal emotion perception, Biol Psychiatry, 2003;54(5):504–14.
  19. Nolan CL, Moore GJ, Madden R, et al., Prefrontal cortical volume in childhood-onset major depression: preliminary findings, Arch Gen Psychiatry, 2002;59(2):173–9.
  20. Steingard RJ, Renshaw PF, Hennen J, et al., Smaller frontal lobe white matter volumes in depressed adolescents, Biol Psychiatry, 2002;52(5):413–7.
  21. Botteron KN, Raichle ME, Drevets WC, et al., Volumetric reduction in left subgenual prefrontal cortex in early onset depression, Biol Psychiatry, 2002;51(4):342–4.
  22. Mirza Y, Tang J, Russell A, et al., Reduced anterior cingulate cortex glutamatergic concentrations in childhood major depression, J Am Acad Child Adolesc Psychiatry, 2004;43(3):341–8.
  23. Rosenberg DR, Macmaster FP, Mirza Y, et al., Reduced anterior cingulate glutamate in pediatric major depression: a magnetic resonance spectroscopy study, Biol Psychiatry, 2005;58(9):700–4.
  24. Thomas KM, Drevets WC, Dahl RE, et al., Amygdala response to fearful faces in anxious and depressed children, Arch Gen Psychiatry, 2001;58(11):1057–63.
  25. MacMillan S, Szeszko PR, Moore GJ, et al., Increased amygdala: hippocampal volume ratios associated with severity of anxiety in pediatric major depression, J Child Adolesc Psychopharmacol, 2003;13(1):65–73.
  26. Rosso IM, Cintron CM, Steingard RJ, et al., Amygdala and hippocampus volumes in pediatric major depression, Biol Psychiatry, 2005;57(1):21–6.
  27. Caetano SC, Fonseca M, Hatch JP, et al., Medial temporal lobe abnormalities in pediatric unipolar depression, Neurosci Lett, 2007;427(3):142–7.
  28. MacMaster FP, Mirza Y, Szeszko PR, et al., Amygdala and Hippocampal Volumes in Familial Early Onset Major Depressive Disorder, Biol Psychiatry, 2008;63(4):385–90.
  29. Mayberg HS, Lozano AM, Voon V, et al., Deep brain stimulation for treatment-resistant depression, Neuron, 2005;45(5):651–60.
  30. Carhart-Harris RL, Mayberg HS, Malizia AL, Nutt D, Mourning and melancholia revisited: correspondences between principles of Freudian metapsychology and empirical findings in neuropsychiatry, Ann Gen Psychiatry, 2008;7:9.
  31. Yang TT, Simmons AN, Matthews SC, et al., Depressed adolescents demonstrate greater subgenual anterior cingulate activity, Neuroreport, 2009;20(4):440–4.
  32. Johansen-Berg H, Gutman DA, Behrens TE, et al., Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression, Cereb Cortex, 2008;18(6):1374–83.
  33. Fox MD, Raichle ME, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging, Nat Rev Neurosci, 2007;8(9):700–11.
  34. Biswal B, Yetkin FZ, Haughton VM, Hyde JS, Functional connectivity in the motor cortex of resting human brain using echo-planar MRI, Magn Reson Med, 1995;34(4):537–41.
  35. Friston KJ, Frith CD, Liddle PF, Frackowiak RS, Functional connectivity: the principal-component analysis of large (PET) data sets, J Cereb Blood Flow Metab, 1993;13(1):5–14.
  36. Achard S, Salvador R, Whitcher B, et al., A resilient, lowfrequency, small-world human brain functional network with highly connected association cortical hubs, J Neurosci, 2006;26(1):63–72.
  37. Bassett DS, Bullmore E, Small-world brain networks, Neuroscientist, 2006;12(6):512–23.
  38. Roy AK, Shehzad Z, Margulies DS, et al., Functional connectivity of the human amygdala using resting state fMRI, Neuroimage, 2009;45(2):614–26.
  39. Margulies DS, Kelly AM, Uddin LQ, et al., Mapping the functional connectivity of anterior cingulate cortex, Neuroimage, 2007;37(2):579–88.
  40. Di Martino A, Scheres A, Margulies DS, et al., Functional connectivity of human striatum: a resting state fMRI study, Cereb Cortex, 2008;18(12):2735–47.
  41. Fair DA, Cohen AL, Dosenbach NU, et al., The maturing architecture of the brain's default network, Proc Natl Acad Sci U S A, 2008;105:4028–32.
  42. Castellanos FX, Margulies DS, Kelly C, et al., Cingulateprecuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder, Biol Psychiatry, 2008;63(3):332–7.
  43. Camchong J, Macdonald AW 3rd, Bell C, et al., Altered Functional and Anatomical Connectivity in Schizophrenia, Schizophr Bull, 2011;37(3):640–50.
  44. Hoptman MJ, D'Angelo D, Catalano D, et al., Amygdalofrontal Functional Disconnectivity and Aggression in Schizophrenia, Schizophr Bull, 2010;36(5):1020–8.
  45. Etkin A, Prater KE, Schatzberg AF, et al., Disrupted amygdalar subregion functional connectivity and evidence of a compensatory network in generalized anxiety disorder, Arch Gen Psychiatry, 2009;66(12):1361–72.
  46. Anand A, Li Y, Wang Y, et al., Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study, Biol Psychiatry, 2005;57(10):1079–88.
  47. Anand A, Li Y, Wang Y, et al., Antidepressant effect on connectivity of the mood-regulating circuit: an FMRI study, Neuropsychopharmacology, 2005;30(7):1334–44.
  48. Anand A, Li Y, Wang Y, et al., Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: an FMRI study, J Neuropsychiatry Clin Neurosci, 2007;19(3):274–82.
  49. Raichle ME, MacLeod AM, Snyder AZ, et al., A default mode of brain function, Proc Natl Acad Sci U S A, 2001;98(2):676–82.
  50. Buckner RL, Andrews-Hanna JR, Schacter DL, The brain's default network: anatomy, function, and relevance to disease, Ann N Y Acad Sci, 2008;1124:1–38.
  51. Greicius MD, Flores BH, Menon V, et al., Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus, Biol Psychiatry, 2007;62(5):429–37.
  52. Bluhm R, Williamson P, Lanius R, et al., Resting state defaultmode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus, Psychiatry Clin Neurosci, 2009;63(6):754–61.
  53. Cullen KR, Gee DG, Klimes-Dougan B, et al., A preliminary study of functional connectivity in comorbid adolescent depression, Neurosci Lett, 2009;460(3):227–31.
  54. Stein JL, Wiedholz LM, Bassett DS, et al., A validated network of effective amygdala connectivity, Neuroimage, 2007;36(3):736–45.
  55. Chen CH, Suckling J, Ooi C, et al., Functional coupling of the amygdala in depressed patients treated with antidepressant medication, Neuropsychopharmacology, 2008;33(8):1909–18.
close

This content is copyright protected

However, if you would like to share the information on this webpage, you may use the headline and link below:

Use of Resting-state Functional Magnetic Resonance Imaging to Measure Functional Connectivity in Adolescents with Depression

Read more: Use of Resting-state Functional Magnetic Resonance Imaging to Measure Functional Connectivity in Adolescents with Depression