Abstract
Major depressive disorder (MDD, also known as major depression) is a major health issue in the modern society. The translational research in MDD can help us understand the pathophysiology of MDD. In the neuroimaging field, magnetic resonance imaging (MRI) will be a major tool for the translational research. In this chapter, several methods of MRI category will be addressed, such as the task functional MRI (T-FMRI), resting-state functional MRI (Rs-FMRI), diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI), voxel-based morphometry (VBM), and magnetic resonance spectroscopy (MRS). The theory, preparation, and details of these MRI-related methods will be addressed in this review article. Basically, these methods can compensate each other in the representations of biological meaning. The functional characteristics of MRS, T-FMRI, and Rs-FMRI can enrich the functional fundamentals of structural characteristics of DTI and VBM. Therefore, theoretically, the “multimodal MRI” methods will be a future trend of neuroimaging research to help us make a sophisticated differentiation of pathophysiology subtype of MDD.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, Aboyans V, Abraham J, Ackerman I, Aggarwal R, Ahn SY, Ali MK, Alvarado M, Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour LM, Bahalim AN, Barker-Collo S, Barrero LH, Bartels DH, Basanez MG, Baxter A, Bell ML, Benjamin EJ, Bennett D, Bernabe E, Bhalla K, Bhandari B, Bikbov B, Bin Abdulhak A, Birbeck G, Black JA,https://doi.org/10.1016/S0140-6736(12)61689-4
Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC (2015) The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry 76(2):155–162. https://doi.org/10.4088/JCP.14m09298
Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere K, Kasper S, Lecrubier Y, Montgomery S, Serretti A, Zohar J, Mendlewicz J (2007) Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry 68(7):1062–1070
Alexopoulos GS, Hoptman MJ, Kanellopoulos D, Murphy CF, Lim KO, Gunning FM (2012) Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord 139(1):56–65. https://doi.org/10.1016/j.jad.2011.12.002
van Tol MJ, van der Wee NJ, van den Heuvel OA, Nielen MM, Demenescu LR, Aleman A, Renken R, van Buchem MA, Zitman FG, Veltman DJ (2010) Regional brain volume in depression and anxiety disorders. Arch Gen Psychiatry 67(10):1002–1011. https://doi.org/10.1001/archgenpsychiatry.2010.121
Lai CH, Hsu YY, Wu YT (2010) First episode drug-naive major depressive disorder with panic disorder: gray matter deficits in limbic and default network structures. Eur Neuropsychopharmacol 20(10):676–682. https://doi.org/10.1016/j.euroneuro.2010.06.002
de Kwaasteniet B, Ruhe E, Caan M, Rive M, Olabarriaga S, Groefsema M, Heesink L, van Wingen G, Denys D (2013) Relation between structural and functional connectivity in major depressive disorder. Biol Psychiatry 74(1):40–47. https://doi.org/10.1016/j.biopsych.2012.12.024
Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME (2009) The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A 106(6):1942–1947. https://doi.org/10.1073/pnas.0812686106
Gorka SM, Young CB, Klumpp H, Kennedy AE, Francis J, Ajilore O, Langenecker SA, Shankman SA, Craske MG, Stein MB, Phan KL (2019) Emotion-based brain mechanisms and predictors for SSRI and CBT treatment of anxiety and depression: a randomized trial. Neuropsychopharmacology 44(9):1639–1648. https://doi.org/10.1038/s41386-019-0407-7
Connolly CG, Ho TC, Blom EH, LeWinn KZ, Sacchet MD, Tymofiyeva O, Simmons AN, Yang TT (2017) Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression. J Affect Disord 207:86–94. https://doi.org/10.1016/j.jad.2016.09.026
Smoski MJ, Keng SL, Ji JL, Moore T, Minkel J, Dichter GS (2015) Neural indicators of emotion regulation via acceptance vs reappraisal in remitted major depressive disorder. Soc Cogn Affect Neurosci 10(9):1187–1194. https://doi.org/10.1093/scan/nsv003
Groenewold NA, Roest AM, Renken RJ, Opmeer EM, Veltman DJ, van der Wee NJ, de Jonge P, Aleman A, Harmer CJ (2015) Cognitive vulnerability and implicit emotional processing: imbalance in frontolimbic brain areas? Cogn Affect Behav Neurosci 15(1):69–79. https://doi.org/10.3758/s13415-014-0316-5
Allman JM, Hakeem A, Erwin JM, Nimchinsky E, Hof P (2001) The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Ann N Y Acad Sci 935:107–117
Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 4(6):215–222. S1364-6613(00)01483-2 [pii]
Rushworth MF, Behrens TE, Rudebeck PH, Walton ME (2007) Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behaviour. Trends Cogn Sci 11(4):168–176. https://doi.org/10.1016/j.tics.2007.01.004. S1364-6613(07)00053-8 [pii]
Yucel M, Wood SJ, Fornito A, Riffkin J, Velakoulis D, Pantelis C (2003) Anterior cingulate dysfunction: implications for psychiatric disorders? J Psychiatry Neurosci 28(5):350–354
Devinsky O, Morrell MJ, Vogt BA (1995) Contributions of anterior cingulate cortex to behaviour. Brain 118(Pt 1):279–306
Mayberg HS (1997) Limbic-cortical dysregulation: a proposed model of depression. J Neuropsychiatr Clin Neurosci 9(3):471–481
Ressler KJ, Mayberg HS (2007) Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nat Neurosci 10(9):1116–1124. https://doi.org/10.1038/nn1944. nn1944 [pii]
Li M, Demenescu LR, Colic L, Metzger CD, Heinze HJ, Steiner J, Speck O, Fejtova A, Salvadore G, Walter M (2017) Temporal dynamics of antidepressant ketamine effects on glutamine cycling follow regional fingerprints of AMPA and NMDA receptor densities. Neuropsychopharmacology 42(6):1201–1209. https://doi.org/10.1038/npp.2016.184
Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD (2006) Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 60(12):1356–1363. https://doi.org/10.1016/j.biopsych.2006.03.052
Frodl TS, Koutsouleris N, Bottlender R, Born C, Jager M, Scupin I, Reiser M, Moller HJ, Meisenzahl EM (2008) Depression-related variation in brain morphology over 3 years: effects of stress? Arch Gen Psychiatry 65(10):1156–1165. https://doi.org/10.1001/archpsyc.65.10.1156
Li CT, Lin CP, Chou KH, Chen IY, Hsieh JC, Wu CL, Lin WC, Su TP (2010) Structural and cognitive deficits in remitting and non-remitting recurrent depression: a voxel-based morphometric study. NeuroImage 50(1):347–356. https://doi.org/10.1016/j.neuroimage.2009.11.021
Liao C, Feng Z, Zhou D, Dai Q, Xie B, Ji B, Wang X, Wang X (2012) Dysfunction of fronto-limbic brain circuitry in depression. Neuroscience 201:231–238. https://doi.org/10.1016/j.neuroscience.2011.10.053
Zavorotnyy M, Zollner R, Rekate H, Dietsche P, Bopp M, Sommer J, Meller T, Krug A, Nenadic I (2020) Intermittent theta-burst stimulation moderates interaction between increment of N-Acetyl-Aspartate in anterior cingulate and improvement of unipolar depression. Brain Stimul 13(4):943–952. https://doi.org/10.1016/j.brs.2020.03.015
Watters AJ, Carpenter JS, Harris AWF, Korgaonkar MS, Williams LM (2019) Characterizing neurocognitive markers of familial risk for depression using multi-modal imaging, behavioral and self-report measures. J Affect Disord 253:336–342. https://doi.org/10.1016/j.jad.2019.04.078
Tozzi L, Goldstein-Piekarski AN, Korgaonkar MS, Williams LM (2019) Connectivity of the cognitive control network during response inhibition as a predictive and response biomarker in major depression: evidence from a randomized clinical trial. Biol Psychiatry 87:462. https://doi.org/10.1016/j.biopsych.2019.08.005
Sheline YI (2000) 3D MRI studies of neuroanatomic changes in unipolar major depression: the role of stress and medical comorbidity. Biol Psychiatry 48(8):791–800
Egger K, Schocke M, Weiss E, Auffinger S, Esterhammer R, Goebel G, Walch T, Mechtcheriakov S, Marksteiner J (2008) Pattern of brain atrophy in elderly patients with depression revealed by voxel-based morphometry. Psychiatry Res 164(3):237–244. https://doi.org/10.1016/j.pscychresns.2007.12.018
Gatt JM, Nemeroff CB, Dobson-Stone C, Paul RH, Bryant RA, Schofield PR, Gordon E, Kemp AH, Williams LM (2009) Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety. Mol Psychiatry 14(7):681–695. https://doi.org/10.1038/mp.2008.143
van Eijndhoven P, van Wingen G, Fernandez G, Rijpkema M, Verkes RJ, Buitelaar J, Tendolkar I (2011) Amygdala responsivity related to memory of emotionally neutral stimuli constitutes a trait factor for depression. NeuroImage 54(2):1677–1684. https://doi.org/10.1016/j.neuroimage.2010.08.040
van Tol MJ, Demenescu LR, van der Wee NJ, Kortekaas R, Marjan MAN, Boer JA, Renken RJ, van Buchem MA, Zitman FG, Aleman A, Veltman DJ (2012) Functional magnetic resonance imaging correlates of emotional word encoding and recognition in depression and anxiety disorders. Biol Psychiatry 71(7):593–602. https://doi.org/10.1016/j.biopsych.2011.11.016
Herringa RJ, Birn RM, Ruttle PL, Burghy CA, Stodola DE, Davidson RJ, Essex MJ (2013) Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence. Proc Natl Acad Sci U S A 110(47):19119–19124. https://doi.org/10.1073/pnas.1310766110
Leaver AM, Vasavada M, Kubicki A, Wade B, Loureiro J, Hellemann G, Joshi SH, Woods RP, Espinoza R, Narr KL (2020) Hippocampal subregions and networks linked with antidepressant response to electroconvulsive therapy. Mol Psychiatry 26:4288. https://doi.org/10.1038/s41380-020-0666-z
Gray JP, Muller VI, Eickhoff SB, Fox PT (2020) Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am J Psychiatry 177(5):422–434. https://doi.org/10.1176/appi.ajp.2019.19050560
Le Bihan D (1996) Functional MRI of the brain principles, applications and limitations. J Neuroradiol 23(1):1–5
Richardson FM, Price CJ (2009) Structural MRI studies of language function in the undamaged brain. Brain Struct Funct 213(6):511–523. https://doi.org/10.1007/s00429-009-0211-y
Bastiani M, Roebroeck A (2015) Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI. Front Neuroanat 9:77. https://doi.org/10.3389/fnana.2015.00077
Chow MS, Wu SL, Webb SE, Gluskin K, Yew DT (2017) Functional magnetic resonance imaging and the brain: a brief review. World J Radiol 9(1):5–9. https://doi.org/10.4329/wjr.v9.i1.5
Chen W, Liu X, Zhu XH, Zhang N (2009) Functional MRI study of brain function under resting and activated states. Annu Int Conf IEEE Eng Med Biol Soc 2009:4061–4063. https://doi.org/10.1109/IEMBS.2009.5333175
Glover GH (2011) Overview of functional magnetic resonance imaging. Neurosurg Clin N Am 22(2):133–139., vii. https://doi.org/10.1016/j.nec.2010.11.001
Matthews PM, Jezzard P (2004) Functional magnetic resonance imaging. J Neurol Neurosurg Psychiatry 75(1):6–12
Andellini M, Cannata V, Gazzellini S, Bernardi B, Napolitano A (2015) Test-retest reliability of graph metrics of resting state MRI functional brain networks: a review. J Neurosci Methods 253:183–192. https://doi.org/10.1016/j.jneumeth.2015.05.020
Guye M, Bettus G, Bartolomei F, Cozzone PJ (2010) Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA 23(5–6):409–421. https://doi.org/10.1007/s10334-010-0205-z
Joo SH, Lim HK, Lee CU (2016) Three large-scale functional brain networks from resting-state functional MRI in subjects with different levels of cognitive impairment. Psychiatry Investig 13(1):1–7. https://doi.org/10.4306/pi.2016.13.1.1
Smyser CD, Snyder AZ, Neil JJ (2011) Functional connectivity MRI in infants: exploration of the functional organization of the developing brain. NeuroImage 56(3):1437–1452. https://doi.org/10.1016/j.neuroimage.2011.02.073
Thompson GJ (2018) Neural and metabolic basis of dynamic resting state fMRI. NeuroImage 180(Pt B):448–462. https://doi.org/10.1016/j.neuroimage.2017.09.010
Smith SM, Vidaurre D, Beckmann CF, Glasser MF, Jenkinson M, Miller KL, Nichols TE, Robinson EC, Salimi-Khorshidi G, Woolrich MW, Barch DM, Ugurbil K, Van Essen DC (2013) Functional connectomics from resting-state fMRI. Trends Cogn Sci 17(12):666–682. https://doi.org/10.1016/j.tics.2013.09.016
Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316–329. https://doi.org/10.1016/j.nurt.2007.05.011
Lee SK, Kim DI, Kim J, Kim DJ, Kim HD, Kim DS, Mori S (2005) Diffusion-tensor MR imaging and fiber tractography: a new method of describing aberrant fiber connections in developmental CNS anomalies. RadioGraphics 25(1):53–65.; discussion 66–58. https://doi.org/10.1148/rg.251045085
Cascio CJ, Gerig G, Piven J (2007) Diffusion tensor imaging: application to the study of the developing brain. J Am Acad Child Adolesc Psychiatry 46(2):213–223. https://doi.org/10.1097/01.chi.0000246064.93200.e8
Hennig J, Speck O, Koch MA, Weiller C (2003) Functional magnetic resonance imaging: a review of methodological aspects and clinical applications. J Magn Reson Imaging 18(1):1–15. https://doi.org/10.1002/jmri.10330
Meinert S, Leehr EJ, Grotegerd D, Repple J, Forster K, Winter NR, Enneking V, Fingas SM, Lemke H, Waltemate L, Stein F, Brosch K, Schmitt S, Meller T, Linge A, Krug A, Nenadic I, Jansen A, Hahn T, Redlich R, Opel N, Schubotz RI, Baune BT, Kircher T, Dannlowski U (2020) White matter fiber microstructure is associated with prior hospitalizations rather than acute symptomatology in major depressive disorder. Psychol Med:1–9. https://doi.org/10.1017/S0033291720002950
Liu X, He C, Fan D, Zhu Y, Zang F, Wang Q, Zhang H, Zhang Z, Zhang H, Xie C (2020) Disrupted rich-club network organization and individualized identification of patients with major depressive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry 2020:110074. https://doi.org/10.1016/j.pnpbp.2020.110074
van Velzen LS, Kelly S, Isaev D, Aleman A, Aftanas LI, Bauer J, Baune BT, Brak IV, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Danilenko KV, Dannlowski U, Enneking V, Filimonova E, Forster K, Frodl T, Gotlib IH, Groenewold NA, Grotegerd D, Harris MA, Hatton SN, Hawkins EL, Hickie IB, Ho TC, Jansen A, Kircher T, Klimes-Dougan B, Kochunov P, Krug A, Lagopoulos J, Lee R, Lett TA, Li M, MacMaster FP, Martin NG, McIntosh AM, McLellan Q, Meinert S, Nenadic I, Osipov E, Penninx B, Portella MJ, Repple J, Roos A, Sacchet MD, Samann PG, Schnell K, Shen X, Sim K, Stein DJ, van Tol MJ, Tomyshev AS, Tozzi L, Veer IM, Vermeiren R, Vives-Gilabert Y, Walter H, Walter M, van der Wee NJA, van der Werff SJA, Schreiner MW, Whalley HC, Wright MJ, Yang TT, Zhu A, Veltman DJ, Thompson PM, Jahanshad N, Schmaal L (2020) White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol Psychiatry 25(7):1511–1525. https://doi.org/10.1038/s41380-019-0477-2
Lai CH, Wu YT (2014) Alterations in white matter micro-integrity of the superior longitudinal fasciculus and anterior thalamic radiation of young adult patients with depression. Psychol Med 44(13):2825–2832. https://doi.org/10.1017/S0033291714000440
Lai CH, Wu YT (2016) The white matter microintegrity alterations of neocortical and limbic association fibers in major depressive disorder and panic disorder: the comparison. Medicine 95(9):e2982. https://doi.org/10.1097/MD.0000000000002982
Ashburner J, Friston KJ (2000) Voxel-based morphometry--the methods. NeuroImage 11(6 Pt 1):805–821. https://doi.org/10.1006/nimg.2000.0582
Bigler ED (2015) Structural image analysis of the brain in neuropsychology using magnetic resonance imaging (MRI) techniques. Neuropsychol Rev 25(3):224–249. https://doi.org/10.1007/s11065-015-9290-0
Buonocore MH, Maddock RJ (2015) Magnetic resonance spectroscopy of the brain: a review of physical principles and technical methods. Rev Neurosci 26(6):609–632. https://doi.org/10.1515/revneuro-2015-0010
Brady TJ, Wismer GL, Buxton R, Stark DD, Rosen BR (1986) Magnetic resonance chemical shift imaging. In: Magnetic resonance annual. Raven Press, New York, NY, pp 55–80
Klose U (2008) Measurement sequences for single voxel proton MR spectroscopy. Eur J Radiol 67(2):194–201. https://doi.org/10.1016/j.ejrad.2008.03.023
Dreher W, Erhard P, Leibfritz D (2011) Fast three-dimensional proton spectroscopic imaging of the human brain at 3 T by combining spectroscopic missing pulse steady-state free precession and echo planar spectroscopic imaging. Magn Reson Med 66(6):1518–1525. https://doi.org/10.1002/mrm.22963
Posse S, Otazo R, Tsai SY, Yoshimoto AE, Lin FH (2009) Single-shot magnetic resonance spectroscopic imaging with partial parallel imaging. Magn Reson Med 61(3):541–547. https://doi.org/10.1002/mrm.21855
Zang Y, Jiang T, Lu Y, He Y, Tian L (2004) Regional homogeneity approach to fMRI data analysis. NeuroImage 22(1):394–400. https://doi.org/10.1016/j.neuroimage.2003.12.030
Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, Wang YF, Zang YF (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. S0165-0270(08)00245-8 [pii]
Zuo XN, Kelly C, Di Martino A, Mennes M, Margulies DS, Bangaru S, Grzadzinski R, Evans AC, Zang YF, Castellanos FX, Milham MP (2010) Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 30(45):15034–15043. https://doi.org/10.1523/JNEUROSCI.2612-10.2010
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541
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. nrn2201 [pii]
Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. NeuroImage 53(4):1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
Hong SB, Zalesky A, Cocchi L, Fornito A, Choi EJ, Kim HH, Suh JE, Kim CD, Kim JW, Yi SH (2013) Decreased functional brain connectivity in adolescents with internet addiction. PLoS One 8(2):e57831. https://doi.org/10.1371/journal.pone.0057831
Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E (2005) Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 15(9):1332–1342. https://doi.org/10.1093/cercor/bhi016
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(Suppl 1):S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009) Bayesian analysis of neuroimaging data in FSL. NeuroImage 45(1 Suppl):S173–S186. https://doi.org/10.1016/j.neuroimage.2008.10.055. S1053-8119(08)01204-4 [pii]
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156. S1361841501000366 [pii]
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155. https://doi.org/10.1002/hbm.10062
Smith SM, Johansen-Berg H, Jenkinson M, Rueckert D, Nichols TE, Miller KL, Robson MD, Jones DK, Klein JC, Bartsch AJ, Behrens TE (2007) Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc 2(3):499–503. https://doi.org/10.1038/nprot.2007.45
Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25. https://doi.org/10.1002/hbm.1058
Colloby SJ, Firbank MJ, Vasudev A, Parry SW, Thomas AJ, O’Brien JT (2011) Cortical thickness and VBM-DARTEL in late-life depression. J Affect Disord 133(1–2):158–164. https://doi.org/10.1016/j.jad.2011.04.010
Thomas AG, Marrett S, Saad ZS, Ruff DA, Martin A, Bandettini PA (2009) Functional but not structural changes associated with learning: an exploration of longitudinal Voxel-Based Morphometry (VBM). NeuroImage 48:117
Seidman LJ, Biederman J, Liang L, Valera EM, Monuteaux MC, Brown A, Kaiser J, Spencer T, Faraone SV, Makris N (2011) Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biol Psychiatry 69(9):857–866. https://doi.org/10.1016/j.biopsych.2010.09.053. S0006-3223(10)01054-1 [pii]
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Lai, CH. (2022). Magnetic Resonance Imaging as a Translational Research Tool for Major Depression. In: Kim, YK., Amidfar, M. (eds) Translational Research Methods for Major Depressive Disorder. Neuromethods, vol 179. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2083-0_12
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2083-0_12
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2082-3
Online ISBN: 978-1-0716-2083-0
eBook Packages: Springer Protocols