Abstract
Recent studies that examined brain characteristics for major depressive disorder (MDD) have reported deficient neurotransmitter system, altered brain morphology and patterns of white matter-based brain inter-regional connections, and differential brain inter-regional coherence of oscillatory patterns during resting and task performance status compared to healthy controls. For better understanding of the MDD pathophysiology that underlies clinical symptoms, the current review provides practical approaches of multimodal brain imaging encompassing the regional deficiency of neurotransmitter receptors or synaptic density to altered patterns of brain inter-regional connection or communication for MDD. First, to elucidate the deficits of neurotransmitter system in MDD, the current review illustrates how to acquire the brain molecular positron emission tomography (PET) images and estimate the synaptic density in addition to the binding potential (or receptor availabilities) of serotonergic (5-HT transporter and 5-HT1A autoreceptor), glutamatergic (metabotropic glutamate receptor 5), and dopaminergic (D2 receptor) system across the whole brain. Second, the current review demonstrates how to explore the possible associations between the regional deficits of neurotransmitter binding potential and altered resting-state functional connectivity [voxel-to-whole brain (intrinsic functional connectivity) or region-to-region (seed-based functional connectivity)], structural connectivity [brain white matter-based region-to-region structural connectivity, estimated using the probabilistic fiber tracking system], and directed functional connectivity [region-to-region] during task performance in MDD. Third, opened resources of software and pipelines that could be applied in running these analytical procedures are also provided.
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References
Cowen PJ, Browning M (2015) What has serotonin to do with depression? World Psychiatry 14(2):158–160
Maier SF, Watkins LR (2005) Stressor controllability and learned helplessness: the roles of the dorsal raphe nucleus, serotonin, and corticotropin-releasing factor. Neurosci Biobehav Rev 29(4–5):829–841
Ferrés-Coy A, Santana N, Castañé A et al (2013) Acute 5-HT1A autoreceptor knockdown increases antidepressant responses and serotonin release in stressful conditions. Psychopharmacology 225(1):61–74
Savitz J, Lucki I, Drevets WC (2009) 5-HT(1A) receptor function in major depressive disorder. Prog Neurobiol 88(1):17–31
Fisher PM, Meltzer CC, Ziolko SK et al (2006) Capacity for 5-HT1A-mediated autoregulation predicts amygdala reactivity. Nat Neurosci 9(11):1362–1363
Selvaraj S, Mouchlianitis E, Faulkner P et al (2015) Presynaptic serotoninergic regulation of emotional processing: a multimodal brain imaging study. Biol Psychiatry 78(8):563–571
Banerjee P, Mehta M, Kanjilal B (2007) Frontiers in neuroscience the 5-HT(1A) receptor: a signaling hub linked to emotional balance. In: Chattopadhyay A (ed) Serotonin receptors in neurobiology. CRC Press/Taylor & Francis, Boca Raton, FL
Milak MS, DeLorenzo C, Zanderigo F et al (2010) In vivo quantification of human serotonin 1A receptor using 11C-CUMI-101, an agonist PET radiotracer. J Nucl Med 51(12):1892–1900
Krishnan V, Nestler EJ (2008) The molecular neurobiology of depression. Nature 455(7215):894–902
Spies M, Knudsen GM, Lanzenberger R, Kasper S (2015) The serotonin transporter in psychiatric disorders: insights from PET imaging. Lancet Psychiatry 2(8):743–755
Wilson AA, Ginovart N, Hussey D, Meyer J, Houle S (2002) In vitro and in vivo characterisation of [11C]-DASB: a probe for in vivo measurements of the serotonin transporter by positron emission tomography. Nucl Med Biol 29(5):509–515
Sudhof TC (2004) The synaptic vesicle cycle. Annu Rev Neurosci 27:509–547
Holmes SE, Scheinost D, Finnema SJ et al (2019) Lower synaptic density is associated with depression severity and network alterations. Nat Commun 10(1):1529
Finnema SJ, Nabulsi NB, Mercier J et al (2018) Kinetic evaluation and test-retest reproducibility of [(11)C]UCB-J, a novel radioligand for positron emission tomography imaging of synaptic vesicle glycoprotein 2A in humans. J Cereb Blood Flow Metab 38(11):2041–2052
Bajjalieh SM, Frantz GD, Weimann JM, McConnell SK, Scheller RH (1994) Differential expression of synaptic vesicle protein 2 (SV2) isoforms. J Neurosci 14(9):5223–5235
Nabulsi NB, Mercier J, Holden D et al (2016) Synthesis and preclinical evaluation of 11C-UCB-J as a PET tracer for imaging the synaptic vesicle glycoprotein 2A in the brain. J Nucl Med 57(5):777–784
Sanacora G, Treccani G, Popoli M (2012) Towards a glutamate hypothesis of depression: an emerging frontier of neuropsychopharmacology for mood disorders. Neuropharmacology 62(1):63–77
Kokane SS, Armant RJ, Bolaños-Guzmán CA, Perrotti LI (2020) Overlap in the neural circuitry and molecular mechanisms underlying ketamine abuse and its use as an antidepressant. Behav Brain Res 384:112548
Ng TH, Alloy LB, Smith DV (2019) Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit. Transl Psychiatry 9(1):293
Kringelbach ML, Cruzat J, Cabral J et al (2020) Dynamic coupling of whole-brain neuronal and neurotransmitter systems. Proc Natl Acad Sci U S A 117(17):9566–9576
Duman RS, Sanacora G, Krystal JH (2019) Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron 102(1):75–90
Scott J, Hidalgo-Mazzei D, Strawbridge R et al (2019) Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative. Int J Bipolar Disord 7(1):20
Parsey RV, Ogden RT, Miller JM et al (2010) Higher serotonin 1A binding in a second major depression cohort: modeling and reference region considerations. Biol Psychiatry 68(2):170–178
DeLorenzo C, Kumar JS, Mann JJ, Parsey RV (2011) In vivo variation in metabotropic glutamate receptor subtype 5 binding using positron emission tomography and [11C]ABP688. J Cereb Blood Flow Metab 31(11):2169–2180
DeLorenzo C, Gallezot JD, Gardus J et al (2017) In vivo variation in same-day estimates of metabotropic glutamate receptor subtype 5 binding using [(11)C]ABP688 and [(18)F]FPEB. J Cereb Blood Flow Metab 37(8):2716–2727
Schneck N, Tu T, Falcone HR et al (2020) Large-scale network dynamics in neural response to emotionally negative stimuli linked to serotonin 1A binding in major depressive disorder. Mol Psychiatry 26:2393. https://doi.org/10.1038/s41380-020-0733-5
Hamilton JP, Sacchet MD, Hjørnevik T et al (2018) Striatal dopamine deficits predict reductions in striatal functional connectivity in major depression: a concurrent (11)C-raclopride positron emission tomography and functional magnetic resonance imaging investigation. Transl Psychiatry 8(1):264
Kumar JS, Prabhakaran J, Majo VJ et al (2007) Synthesis and in vivo evaluation of a novel 5-HT1A receptor agonist radioligand [O-methyl- 11C]2-(4-(4-(2-methoxyphenyl)piperazin-1-yl)butyl)-4-methyl-1,2,4-triazine-3,5(2H,4H)dione in nonhuman primates. Eur J Nucl Med Mol Imaging 34(7):1050–1060
Ametamey SM, Kessler LJ, Honer M et al (2006) Radiosynthesis and preclinical evaluation of 11C-ABP688 as a probe for imaging the metabotropic glutamate receptor subtype 5. J Nucl Med 47(4):698–705
Langer O, Någren K, Dolle F et al (1999) Precursor synthesis and radiolabelling of the dopamine D2 receptor ligand [11C]raclopride from [11C]methyl triflate. J Label Compd Radiopharm 42(12):1183–1193
Rahmim A, Cheng JC, Blinder S, Camborde ML, Sossi V (2005) Statistical dynamic image reconstruction in state-of-the-art high-resolution PET. Phys Med Biol 50(20):4887–4912
Schneier FR, Slifstein M, Whitton AE et al (2018) Dopamine release in antidepressant-naive major depressive disorder: a multimodal [(11)C]-(+)-PHNO positron emission tomography and functional magnetic resonance imaging study. Biol Psychiatry 84(8):563–573
Pillai RLI, Malhotra A, Rupert DD et al (2018) Relations between cortical thickness, serotonin 1A receptor binding, and structural connectivity: a multimodal imaging study. Hum Brain Mapp 39(2):1043–1055
Smith GS, Kuwabara H, Gould NF et al (2021) Molecular imaging of the serotonin transporter availability and occupancy by antidepressant treatment in late-life depression. Neuropharmacology 194:108447. https://doi.org/10.1016/j.neuropharm.2021.108447
Finnema SJ, Nabulsi NB, Eid T et al (2016) Imaging synaptic density in the living human brain. Sci Transl Med 8(348):348–396
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156
DeLorenzo C, Klein A, Mikhno A et al (2009) A new method for assessing PET-MRI coregistration. Paper presented at the SPIE Medical Imaging, Florida, USA
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155
Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage 26(3):839–851
Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC (1998) Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 22(2):324–333
Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113
Desikan RS, Ségonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968–980
Delorenzo C, Delaparte L, Thapa-Chhetry B, Miller J, Mann J, Parsey RV (2013) Prediction of selective serotonin reuptake inhibitor response using diffusion-weighted MRI. Front Psychiatry 4:5
Hirvonen J, Kajander J, Allonen T, Oikonen V, Någren K, Hietala J (2007) Measurement of serotonin 5-HT1A receptor binding using positron emission tomography and [carbonyl-(11)C]WAY-100635-considerations on the validity of cerebellum as a reference region. J Cereb Blood Flow Metab 27(1):185–195
Parsey RV, Arango V, Olvet DM, Oquendo MA, Van Heertum RL, John Mann J (2005) Regional heterogeneity of 5-HT1A receptors in human cerebellum as assessed by positron emission tomography. J Cereb Blood Flow Metab 25(7):785–793
DeLorenzo C, Sovago J, Gardus J et al (2015) Characterization of brain mGluR5 binding in a pilot study of late-life major depressive disorder using positron emission tomography and [11C]ABP688. Transl Psychiatry 5(12):e693
DuBois JM, Rousset OG, Rowley J et al (2016) Characterization of age/sex and the regional distribution of mGluR5 availability in the healthy human brain measured by high-resolution [(11)C]ABP688 PET. Eur J Nucl Med Mol Imaging 43(1):152–162
Innis RB, Cunningham VJ, Delforge J et al (2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27(9):1533–1539
Lammertsma AA, Hume SP (1996) Simplified reference tissue model for PET receptor studies. NeuroImage 4(3 Pt 1):153–158
Wu Y, Carson RE (2002) Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab 22(12):1440–1452
Parsey RV, Slifstein M, Hwang DR et al (2000) Validation and reproducibility of measurement of 5-HT1A receptor parameters with [carbonyl-11C]WAY-100635 in humans: comparison of arterial and reference tissue input functions. J Cereb Blood Flow Metab 20(7):1111–1133
Alpert NM, Badgaiyan RD, Livni E, Fischman AJ (2003) A novel method for noninvasive detection of neuromodulatory changes in specific neurotransmitter systems. NeuroImage 19(3):1049–1060
Liu Z, Wang Y, Gerig G et al (2010) Quality control of diffusion weighted images. Proc SPIE Int Soc Opt Eng 7628:76280j
Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1):144–155
Drysdale AT, Grosenick L, Downar J et al (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23(1):28–38
Whitfield-Gabrieli S, Nieto-Castanon A (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2(3):125–141
Behzadi Y, Restom K, Liau J, Liu TT (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37(1):90–101
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(2):825–841
Andersson JLR, Jenkinson M, Smith S (2007) Non-linear registration, aka Spatial normalisation. FMRIB technical report TR07JA2. FMRIB Centre, Oxford
Kim JH, Joo YH, Son YD et al (2019) In vivo metabotropic glutamate receptor 5 availability-associated functional connectivity alterations in drug-naïve young adults with major depression. Eur Neuropsychopharmacol 29(2):278–290
Thomas BA, Cuplov V, Bousse A et al (2016) PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. Phys Med Biol 61(22):7975–7993
Greve DN, Salat DH, Bowen SL et al (2016) Different partial volume correction methods lead to different conclusions: an (18)F-FDG-PET study of aging. NeuroImage 132:334–343
Nørgaard M, Ganz M, Svarer C et al (2019) Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: a [(11)C]DASB PET study. NeuroImage 199:466–479
Marcoux A, Burgos N, Bertrand A et al (2018) An automated pipeline for the analysis of PET data on the cortical surface. Front Neuroinform 12:94
Tjerkaski J, Cervenka S, Farde L, Matheson GJ (2020) Kinfitr - an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability. EJNMMI Res 10(1):77
López-González FJ, Paredes-Pacheco J, Thurnhofer-Hemsi K et al (2019) QModeling: a multiplatform, easy-to-use and open-source toolbox for PET kinetic analysis. Neuroinformatics 17(1):103–114
Funck T, Larcher K, Toussaint PJ, Evans AC, Thiel A (2018) APPIAN: automated pipeline for PET image analysis. Front Neuroinform 12:64
Muzic RF Jr, Cornelius S (2001) COMKAT: compartment model kinetic analysis tool. J Nucl Med 42(4):636–645
Acknowledgments
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028464).
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Yun, JY., Kim, YK. (2022). Deficits of Neurotransmitter Systems and Altered Brain Connectivity in Major Depression: A Translational Neuroscience Perspective. 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_14
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DOI: https://doi.org/10.1007/978-1-0716-2083-0_14
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