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Correspondence among gray matter atrophy and atlas-based neurotransmitter maps is clinically relevant in multiple sclerosis

Molecular Psychiatry (2023)Cite this article

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Abstract

In multiple sclerosis (MS), gray matter (GM) atrophy progresses in a non-random manner, possibly in regions with a high distribution of specific neurotransmitters involved in several relevant central nervous system functions. We investigated the associations among regional GM atrophy, atlas-based neurotransmitter distributions and clinical manifestations in a large MS patients’ group. Brain 3 T MRI scans, neurological examinations and neuropsychological evaluations were obtained from 286 MS patients and 172 healthy controls (HC). Spatial correlations among regional GM volume differences and atlas-based nuclear imaging-derived neurotransmitter maps, and their associations with MS clinical features were investigated using voxel-based morphometry and JuSpace toolbox. Compared to HC, MS patients showed widespread GM atrophy being spatially correlated with the majority of neurotransmitter maps (false discovery rate [FDR]-p ≤ 0.004). Patients with a disease duration ≥ 5 vs < 5 years had significant cortical, subcortical and cerebellar atrophy, being spatially correlated with a higher distribution of serotoninergic and dopaminergic receptors (FDR-p ≤ 0.03). Compared to mildly-disabled patients, those with Expanded Disability Status Scale ≥ 3.0 or ≥ 4.0 had significant cortical, subcortical and cerebellar atrophy being associated with serotonergic, dopaminergic, opioid and cholinergic maps (FDR-p ≤ 0.04). Cognitively impaired vs cognitively preserved patients had widespread GM atrophy being spatially associated with serotonergic, dopaminergic, noradrenergic, cholinergic and glutamatergic maps (FDR-p ≤ 0.04). Fatigued vs non-fatigued MS patients had significant cortical, subcortical and cerebellar atrophy, not associated with neurotransmitter maps. No significant association between GM atrophy and neurotransmitter maps was found for depression. Regional GM atrophy with specific neurotransmitter systems may explain part of MS clinical manifestations, including locomotor disability, cognitive impairment and fatigue.

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Fig. 1: Between-group differences in regional GM atrophy and spatial correlations between GM atrophy and neurotransmitter distribution maps in MS patients vs HC and according to their clinical phenotype and disease duration.
Fig. 2: Heatmap.
Fig. 3: Between-group differences in regional GM atrophy and spatial correlations between GM atrophy and neurotransmitter distribution maps in MS patients according their disability.
Fig. 4: Between-group differences in regional GM atrophy and spatial correlations between GM atrophy and neurotransmitter distribution maps in MS patients according the presence of cognitive impairment and fatigue.

Data availability

The dataset used and analyzed during the current study is available from the corresponding author on reasonable request.

References

  1. Filippi M, Bar-Or A, Piehl F, Preziosa P, Solari A, Vukusic S, et al. Multiple sclerosis. Nat Rev Dis Prim. 2018;4:43.

    Article  Google Scholar 

  2. Correale J, Gaitan MI, Ysrraelit MC, Fiol MP. Progressive multiple sclerosis: From pathogenic mechanisms to treatment. Brain 2017;140:527–46.

    Google Scholar 

  3. Friese MA, Schattling B, Fugger L. Mechanisms of neurodegeneration and axonal dysfunction in multiple sclerosis. Nat Rev Neurol. 2014;10:225–38.

    Article  CAS  Google Scholar 

  4. Filippi M, Bruck W, Chard D, Fazekas F, Geurts JJG, Enzinger C, et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 2019;18:198–210.

    Article  Google Scholar 

  5. Vercellino M, Marasciulo S, Grifoni S, Vallino-Costassa E, Bosa C, Pasanisi MB, et al. Acute and chronic synaptic pathology in multiple sclerosis gray matter. Mult Scler J. 2022;28:369–82.

    Article  CAS  Google Scholar 

  6. Filippi M, Preziosa P, Langdon D, Lassmann H, Paul F, Rovira A, et al. Identifying progression in multiple sclerosis: New perspectives. Ann Neurol. 2020;88:438–52.

    Article  Google Scholar 

  7. Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol. 2015;14:183–93.

    Article  CAS  Google Scholar 

  8. Rocca MA, Battaglini M, Benedict RH, De Stefano N, Geurts JJ, Henry RG, et al. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology 2017;88:403–13.

    Article  Google Scholar 

  9. Sastre-Garriga J, Pareto D, Battaglini M, Rocca MA, Ciccarelli O, Enzinger C, et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol. 2020;16:171–82.

    Article  Google Scholar 

  10. Carassiti D, Altmann DR, Petrova N, Pakkenberg B, Scaravilli F, Schmierer K. Neuronal loss, demyelination and volume change in the multiple sclerosis neocortex. Neuropathol Appl Neurobiol. 2018;44:377–90.

    Article  CAS  Google Scholar 

  11. Eshaghi A, Marinescu RV, Young AL, Firth NC, Prados F, Jorge Cardoso M, et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 2018;141:1665–77.

    Article  Google Scholar 

  12. Eshaghi A, Prados F, Brownlee WJ, Altmann DR, Tur C, Cardoso MJ, et al. Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. 2018;83:210–22.

    Article  CAS  Google Scholar 

  13. Eijlers AJC, van Geest Q, Dekker I, Steenwijk MD, Meijer KA, Hulst HE, et al. Predicting cognitive decline in multiple sclerosis: A 5-year follow-up study. Brain 2018;141:2605–18.

    Google Scholar 

  14. Filippi M, Preziosa P, Copetti M, Riccitelli G, Horsfield MA, Martinelli V, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology 2013;81:1759–67.

    Article  Google Scholar 

  15. Rocca MA, Preziosa P, Mesaros S, Pagani E, Dackovic J, Stosic-Opincal T, et al. Clinically isolated syndrome suggestive of multiple sclerosis: Dynamic patterns of gray and white matter changes-A 2-year MR imaging study. Radiology 2016;278:841–53.

    Article  Google Scholar 

  16. Steenwijk MD, Geurts JJ, Daams M, Tijms BM, Wink AM, Balk LJ, et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 2016;139:115–26.

    Article  Google Scholar 

  17. Rocca MA, Valsasina P, Meani A, Gobbi C, Zecca C, Rovira A, et al. Association of gray matter atrophy patterns with clinical phenotype and progression in multiple sclerosis. Neurology 2021;96:e1561–e73.

    Article  CAS  Google Scholar 

  18. Riccitelli G, Rocca MA, Pagani E, Rodegher ME, Rossi P, Falini A, et al. Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp. 2011;32:1535–43.

    Article  Google Scholar 

  19. Filippi M, Preziosa P, Barkhof F, Chard DT, De Stefano N, Fox RJ, et al. Diagnosis of progressive multiple sclerosis from the imaging perspective: A review. JAMA Neurol. 2021;78:351–64.

    Article  Google Scholar 

  20. Pravata E, Rocca MA, Valsasina P, Riccitelli GC, Gobbi C, Comi G, et al. Gray matter trophism, cognitive impairment, and depression in patients with multiple sclerosis. Mult Scler J. 2017;23:1864–74.

    Article  Google Scholar 

  21. Rocca MA, Parisi L, Pagani E, Copetti M, Rodegher M, Colombo B, et al. Regional but not global brain damage contributes to fatigue in multiple sclerosis. Radiology 2014;273:511–20.

    Article  Google Scholar 

  22. Lazo-Gomez R, Velazquez GLL, Mireles-Jacobo D, Sotomayor-Sobrino MA. Mechanisms of neurobehavioral abnormalities in multiple sclerosis: Contributions from neural and immune components. Clin Neurophysiol Pr. 2019;4:39–46.

    Article  Google Scholar 

  23. Klein JC, Eggers C, Kalbe E, Weisenbach S, Hohmann C, Vollmar S, et al. Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology 2010;74:885–92.

    Article  CAS  Google Scholar 

  24. Hampel H, Mesulam MM, Cuello AC, Farlow MR, Giacobini E, Grossberg GT, et al. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain 2018;141:1917–33.

    Article  Google Scholar 

  25. Bhattacharyya PK, Phillips MD, Stone LA, Bermel RA, Lowe MJ. Sensorimotor cortex gamma-aminobutyric acid concentration correlates with impaired performance in patients with MS. Am J Neuroradiol. 2013;34:1733–9.

    Article  CAS  Google Scholar 

  26. Nantes JC, Proulx S, Zhong J, Holmes SA, Narayanan S, Brown RA, et al. GABA and glutamate levels correlate with MTR and clinical disability: Insights from multiple sclerosis. Neuroimage 2017;157:705–15.

    Article  CAS  Google Scholar 

  27. Cawley N, Solanky BS, Muhlert N, Tur C, Edden RA, Wheeler-Kingshott CA, et al. Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis. Brain 2015;138:2584–95.

    Article  Google Scholar 

  28. Muhlert N, Atzori M, De Vita E, Thomas DL, Samson RS, Wheeler-Kingshott CA, et al. Memory in multiple sclerosis is linked to glutamate concentration in grey matter regions. J Neurol Neurosur Ps. 2014;85:833–9.

    Article  Google Scholar 

  29. Carandini T, Mancini M, Bogdan I, Rae CL, Barritt AW, Sethi A, et al. Disruption of brainstem monoaminergic fibre tracts in multiple sclerosis as a putative mechanism for cognitive fatigue: a fixel-based analysis. Neuroimage Clin. 2021;30:102587.

    Article  Google Scholar 

  30. Carotenuto A, Wilson H, Giordano B, Caminiti SP, Chappell Z, Williams SCR, et al. Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications. J Neurol. 2020;267:2042–53.

    Article  Google Scholar 

  31. Hesse S, Moeller F, Petroff D, Lobsien D, Luthardt J, Regenthal R, et al. Altered serotonin transporter availability in patients with multiple sclerosis. Eur J Nucl Med Mol Imaging. 2014;41:827–35.

    Article  CAS  Google Scholar 

  32. Cercignani M, Dipasquale O, Bogdan I, Carandini T, Scott J, Rashid W, et al. Cognitive fatigue in multiple sclerosis is associated with alterations in the functional connectivity of monoamine circuits. Brain Commun. 2021;3:fcab023.

    Article  Google Scholar 

  33. Dobryakova E, Genova HM, DeLuca J, Wylie GR. The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Front Neurol. 2015;6:52.

    Article  Google Scholar 

  34. Carotenuto A, Valsasina P, Preziosa P, Mistri D, Filippi M, Rocca MA. Monoaminergic network abnormalities: a marker for multiple sclerosis-related fatigue and depression. J Neurol Neurosur Ps. 2023;94:94–101.

    Article  Google Scholar 

  35. Dukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PCT, Mehta MA, et al. JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp. 2021;42:555–66.

    Article  Google Scholar 

  36. Hirjak D, Schmitgen MM, Werler F, Wittemann M, Kubera KM, Wolf ND, et al. Multimodal MRI data fusion reveals distinct structural, functional and neurochemical correlates of heavy cannabis use. Addict Biol. 2022;27:e13113.

    Article  CAS  Google Scholar 

  37. Tang C, Ren P, Ma K, Li S, Wang X, Guan Y, et al. The correspondence between morphometric MRI and metabolic profile in Rasmussen’s encephalitis. Neuroimage Clin. 2022;33:102918.

    Article  Google Scholar 

  38. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–73.

    Article  Google Scholar 

  39. Kister I, Chamot E, Cutter G, Bacon TE, Jokubaitis VG, Hughes SE, et al. Increasing age at disability milestones among MS patients in the MSBase Registry. J Neurol Sci. 2012;318:94–9.

    Article  Google Scholar 

  40. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52.

    Article  CAS  Google Scholar 

  41. Rao SM, and the Cognitive Function Study Group of the National Multiple Sclerosis Society. A manual for the brief repeatable battery of neuropsychological test in multiple sclerosis. Milwaukee, WI: Medical College of Wisconsin; 1990.

  42. Ruano L, Portaccio E, Goretti B, Niccolai C, Severo M, Patti F, et al. Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult Scler J. 2017;23:1258–67.

    Article  Google Scholar 

  43. Fisk JD, Ritvo PG, Ross L, Haase DA, Marrie TJ, Schlech WF. Measuring the functional impact of fatigue: Initial validation of the fatigue impact scale. Clin Infect Dis. 1994;18:S79–83.

    Article  Google Scholar 

  44. Flachenecker P, Kumpfel T, Kallmann B, Gottschalk M, Grauer O, Rieckmann P, et al. Fatigue in multiple sclerosis: A comparison of different rating scales and correlation to clinical parameters. Mult Scler J. 2002;8:523–6.

    Article  CAS  Google Scholar 

  45. Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–9.

    Article  CAS  Google Scholar 

  46. Snaith RP, Harrop FM, Newby DA, Teale C. Grade scores of the Montgomery-Asberg depression and the clinical anxiety scales. Br J Psychiatry. 1986;148:599–601.

    Article  CAS  Google Scholar 

  47. Battaglini M, Jenkinson M, De Stefano N. Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp. 2012;33:2062–71.

    Article  Google Scholar 

  48. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479–89.

    Article  Google Scholar 

  49. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007;38:95–113.

    Article  Google Scholar 

  50. Friston KJ, Holmes AP, Poline JB, Grasby PJ, Williams SC, Frackowiak RS, et al. Analysis of fMRI time-series revisited. Neuroimage 1995;2:45–53.

    Article  CAS  Google Scholar 

  51. Bonacchi R, Meani A, Pagani E, Marchesi O, Filippi M, Rocca MA. The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: A multiparametric MRI study. J Neurol. 2022;269:3841–57.

    Article  Google Scholar 

  52. Bonacchi R, Pagani E, Meani A, Cacciaguerra L, Preziosa P, De Meo E, et al. Clinical relevance of multiparametric MRI assessment of cervical cord damage in multiple sclerosis. Radiology 2020;296:605–15.

    Article  Google Scholar 

  53. Arm J, Ribbons K, Lechner-Scott J, Ramadan S. Evaluation of MS related central fatigue using MR neuroimaging methods: Scoping review. J Neurol Sci. 2019;400:52–71.

    Article  Google Scholar 

  54. Bertoli M, Tecchio F. Fatigue in multiple sclerosis: Does the functional or structural damage prevail? Mult Scler J. 2020;26:1809–15.

    Article  Google Scholar 

  55. Filippi M, Preziosa P, Rocca MA. Brain mapping in multiple sclerosis: Lessons learned about the human brain. Neuroimage 2019;190:32–45.

    Article  Google Scholar 

  56. Bakshi R, Czarnecki D, Shaikh ZA, Priore RL, Janardhan V, Kaliszky Z, et al. Brain MRI lesions and atrophy are related to depression in multiple sclerosis. Neuroreport 2000;11:1153–8.

    Article  CAS  Google Scholar 

  57. Feinstein A, Roy P, Lobaugh N, Feinstein K, O’Connor P, Black S. Structural brain abnormalities in multiple sclerosis patients with major depression. Neurology 2004;62:586–90.

    Article  CAS  Google Scholar 

  58. Gobbi C, Rocca MA, Riccitelli G, Pagani E, Messina R, Preziosa P, et al. Influence of the topography of brain damage on depression and fatigue in patients with multiple sclerosis. Mult Scler J. 2014;20:192–201.

    Article  CAS  Google Scholar 

  59. Rocca MA, Schoonheim MM, Valsasina P, Geurts JJG, Filippi M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. Neuroimage Clin. 2022;35:103076.

    Article  Google Scholar 

  60. Filippi M, Preziosa P, Rocca MA. Microstructural MR imaging techniques in multiple sclerosis. Neuroimaging Clin N Am. 2017;27:313–33.

    Article  Google Scholar 

  61. Jurgens T, Jafari M, Kreutzfeldt M, Bahn E, Bruck W, Kerschensteiner M, et al. Reconstruction of single cortical projection neurons reveals primary spine loss in multiple sclerosis. Brain 2016;139:39–46.

    Article  Google Scholar 

  62. Mock EEA, Honkonen E, Airas L. Synaptic loss in multiple sclerosis: A systematic review of human post-mortem studies. Front Neurol. 2021;12:782599.

    Article  Google Scholar 

  63. Kantorova E, Hnilicova P, Bogner W, Grendar M, Cierny D, Heckova E, et al. Positivity of oligoclonal bands in the cerebrospinal fluid predisposed to metabolic changes and rearrangement of inhibitory/excitatory neurotransmitters in subcortical brain structures in multiple sclerosis. Mult Scler Relat Dis. 2021;52:102978.

    Article  CAS  Google Scholar 

  64. Markianos M, Koutsis G, Evangelopoulos ME, Mandellos D, Karahalios G, Sfagos C. Relationship of CSF neurotransmitter metabolite levels to disease severity and disability in multiple sclerosis. J Neurochem. 2009;108:158–64.

    Article  CAS  Google Scholar 

  65. Carandini T, Cercignani M, Galimberti D, Scarpini E, Bozzali M. The distinct roles of monoamines in multiple sclerosis: A bridge between the immune and nervous systems? Brain Behav Immun. 2021;94:381–91.

    Article  CAS  Google Scholar 

  66. Sari Y. Serotonin1B receptors: from protein to physiological function and behavior. Neurosci Biobehav Rev. 2004;28:565–82.

    Article  CAS  Google Scholar 

  67. Savli M, Bauer A, Mitterhauser M, Ding YS, Hahn A, Kroll T, et al. Normative database of the serotonergic system in healthy subjects using multi-tracer PET. Neuroimage 2012;63:447–59.

    Article  CAS  Google Scholar 

  68. Arm J, Oeltzschner G, Al-Iedani O, Lea R, Lechner-Scott J, Ramadan S. Altered in vivo brain GABA and glutamate levels are associated with multiple sclerosis central fatigue. Eur J Radio. 2021;137:109610.

    Article  Google Scholar 

  69. Takahashi H, Yamada M, Suhara T. Functional significance of central D1 receptors in cognition: beyond working memory. J Cereb Blood Flow Metab. 2012;32:1248–58.

    Article  CAS  Google Scholar 

  70. Mehta MA, Sahakian BJ, McKenna PJ, Robbins TW. Systemic sulpiride in young adult volunteers simulates the profile of cognitive deficits in Parkinson’s disease. Psychopharmacol (Berl). 1999;146:162–74.

    Article  CAS  Google Scholar 

  71. Liu KY, Marijatta F, Hammerer D, Acosta-Cabronero J, Duzel E, Howard RJ. Magnetic resonance imaging of the human locus coeruleus: A systematic review. Neurosci Biobehav Rev. 2017;83:325–55.

    Article  Google Scholar 

  72. Crupi R, Impellizzeri D, Cuzzocrea S. Role of metabotropic glutamate receptors in neurological disorders. Front Mol Neurosci. 2019;12:20.

    Article  CAS  Google Scholar 

  73. Donadieu M, Le Fur Y, Lecocq A, Maudsley AA, Gherib S, Soulier E, et al. Metabolic voxel-based analysis of the complete human brain using fast 3D-MRSI: Proof of concept in multiple sclerosis. J Magn Reson Imaging. 2016;44:411–9.

    Article  Google Scholar 

  74. O’Grady KP, Dula AN, Lyttle BD, Thompson LM, Conrad BN, Box BA, et al. Glutamate-sensitive imaging and evaluation of cognitive impairment in multiple sclerosis. Mult Scler J. 2019;25:1580–92.

    Article  Google Scholar 

  75. DeFelipe J. Neocortical neuronal diversity: chemical heterogeneity revealed by colocalization studies of classic neurotransmitters, neuropeptides, calcium-binding proteins, and cell surface molecules. Cereb Cortex. 1993;3:273–89.

    Article  CAS  Google Scholar 

  76. Fichna J, Janecka A, Costentin J, Do Rego JC. The endomorphin system and its evolving neurophysiological role. Pharm Rev. 2007;59:88–123.

    Article  CAS  Google Scholar 

  77. Giboureau N, Som IM, Boucher-Arnold A, Guilloteau D, Kassiou M. PET radioligands for the vesicular acetylcholine transporter (VAChT). Curr Top Med Chem. 2010;10:1569–83.

    Article  CAS  Google Scholar 

  78. Mesulam MM. The cholinergic innervation of the human cerebral cortex. Prog Brain Res. 2004;145:67–78.

    Article  Google Scholar 

  79. Kimura Y, Sato N, Ota M, Maikusa N, Maekawa T, Sone D, et al. A structural MRI study of cholinergic pathways and cognition in multiple sclerosis. eNeurologicalSci. 2017;8:11–6.

    Article  Google Scholar 

  80. Kooi EJ, Prins M, Bajic N, Belien JA, Gerritsen WH, van Horssen J, et al. Cholinergic imbalance in the multiple sclerosis hippocampus. Acta Neuropathol. 2011;122:313–22.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Alessia Fiore, Paolo Preziosa, Nicolò Tedone, Monica Margoni, Carmen Vizzino, Damiano Mistri, Mor Gueye, Maria A. Rocca & Massimo Filippi

  2. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Paolo Preziosa, Monica Margoni, Mor Gueye, Maria A. Rocca & Massimo Filippi

  3. Vita-Salute San Raffaele University, Milan, Italy

    Paolo Preziosa, Maria A. Rocca & Massimo Filippi

  4. Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

  5. Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

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Contributions

AF contributed to analysis and interpretation of clinical and MRI data, drafting and revising the text, and preparing the figures. PP contributed to the conception of the study, acquisition, analysis and interpretation of clinical and MRI data, drafting and revising the text, and preparing the figures. NT, MM, CV, DM, MG contributed to the acquisition, analysis and interpretation of MRI data and revising the manuscript. MAR and MF contributed to the conception of the study, drafting and revising the text, acting as the study supervisors. All the authors gave their approval to the current version of the manuscript.

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Correspondence to Massimo Filippi.

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Competing interests

AF, NT, CV, DM, MG report no competing interests. PP received speaker honoraria from Roche, Biogen, Novartis, Merck Serono, Bristol Myers Squibb and Genzyme. He has received research support from Italian Ministry of Health and Fondazione Italiana Sclerosi Multipla. M. Margoni reports grants and personal fees from Sanofi Genzyme, Merck Serono, Novartis and Almiral. She was awarded a MAGNIMS-ECTRIMS fellowship in 2020. MAR received speaker honoraria from Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva, and receives research support from the MS Society of Canada and Fondazione Italiana Sclerosi Multipla. MF is Editor-in-Chief of the Journal of Neurology and Associate Editor of Radiology, Human Brain Mapping and Neurological Sciences, received compensation for consulting services and/or speaking activities from Almiral, Alexion, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck-Serono, Novartis, Roche, Sanofi, Takeda, and Teva Pharmaceutical Industries, and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi, Almiral, Eli Lilly, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA).

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Fiore, A., Preziosa, P., Tedone, N. et al. Correspondence among gray matter atrophy and atlas-based neurotransmitter maps is clinically relevant in multiple sclerosis. Mol Psychiatry (2023). https://doi.org/10.1038/s41380-023-01943-1

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